2026 June Market Study CX in 2030

MARKET STUDY

CX in 2030

Introduction

Omnichannel engagement. AI transformation. Seamlessness as a standard. Empathy as a differentiator.

These trends and philosophies have dominated the customer experience (CX) conversation over the past decade. They offer the promise of real transformation and signal an opportunity to finally turn the contact center into a value center.

Unfortunately, the more things change, the more they stay the same.

Over the past several years, CMP research has consistently revealed a decline in CX quality. Experiences were continuing to miss the mark, and new digital and AI technologies were actually introducing new pain points rather than creating new value.

Will the tide ever turn? Will the promise of customer centricity ever become reality?

The 2026 CMP Consumer Preferences Survey reveals cause for optimism. For the first time in years, more consumers felt experiences improved than regressed. They also began to witness some upside to AI-powered interactions.

Unfortunately, this optimism is of the cautious variety.

Despite incremental improvements in consumer sentiment, the typical experience is still falling short on key criteria. It may be better than it was in 2025, but it is still inconvenient, impersonal, and inconsistent. Despite gaining more ubiquity, AI-powered environments are still failing to generate widespread confidence.

With organizations passionately articulating their desire for transformation, why are these pain points still lingering? More importantly, how can organizations remedy them and realize their lofty ambitions for 2030?

This Market Study reveals the answers, diving deep into how customers interact, how they feel about those interactions, and what they want to see moving forward.

Methodology & Demographics

CMP conducted its annual Consumer Preferences Survey in May 2026. The in-depth study asked 1004 consumers to assess their recent experiences, articulate their preferences, and surface their future concerns.

Balanced to US census data, the consumer sample encompassed all demographics and income levels. The results thus provide a true window into the voice of today’s customer base – and a North Star for brands looking to elevate their customer contact operations.

About the Author

Brian Cantor Managing Director CMP

In his role as managing director for CMP, Brian Cantor serves as principal analyst for CCW Digital, the global online community and research hub for customer contact professionals. Notably, Brian leads all customer experience, contact center, technology, and employee engagement research initiatives.

CCW Digital’s articles, special reports, commentaries, infographics, executive interviews, webinars, and online events reach a community of over 180,000. A passionate advocate for customer centricity, Brian regularly speaks on major CX conference agendas. He also advises organizations on customer experience and business development strategies.

Key Findings

• For the first time since the COVID-19 pandemic, more consumers feel experiences are improving (45%) than regressing (37%).

• The year-over-year improvement is not tantamount to dramatic transformation. 60% of consumers still feel the typical experience is inconvenient, and even greater percentages cite insufficient levels of personalization (75%), channel autonomy (75%), and consistency (82%).

• Echoing consumer research from the past few decades, pain points like long wait times and repetitive questioning frequently impact many customers.

• “Difficulty reaching a live agent,” a concern amplified by the rise of AI, also remains prevalent. 41% of consumers routinely face this challenge.

• Although AI-powered experiences are excelling in some areas, overall confidence in self-service remains low. Whereas 75% are confident in the support they receive from human phone agents, less than 20% say the same of IVRs and chatbots.

• Despite the lack of consumer confidence, organizations are not relenting in their quest to increase self-service adoption and resolution ahead of 2030. More than 89% of consumers say brands were more likely to require them to start or stay in self-service this year than in the previous year.

• The comparative confidence in live phone support should not be mistaken as a wholesale endorsement of frontline employees. The vast majority of consumers feel these live agents lack sufficient expertise, focus, commitment to resolve, and excitement in their work. Many do not even believe they are polite. Addressing these challenges will be critical if brands expect their employees to function as “consultative support specialists” in the CX of 2030.

• It is equally misguided to equate confidence in phone support with preference for the channel. Just 27% of consumers default to phone support when needing help, with many instead starting with options like web search, official website exploration, and even generative AI inquiries.

• Since some customers are defaulting to AI, and 87% say they would be willing to use selfservice more frequently, brands clearly have an opportunity to change the conversation.

• Consumers say the best ways to increase selfservice adoption would be to provide assurance of a seamless escalation to a human agent, fully resolve issues in the environment, and eliminate repetitive questioning.

• Amid the rise of AI and digital communication, 76% of consumers say they are increasingly concerned about data security and privacy.

• They are also developing an expectation for more transparency. 64% expect brands to disclose the likely wait time, and 55% require clarity on whether they are speaking to a human or AI agent.

• In reflecting on this year’s interactions, consumers did not simply identify issues with how brands are designing their experiences. They also uncovered gaps in how brands respond to misfires. An alarming 29% say brands took no action to address a poor interaction.

Reversing Course: Are Customer Experiences Finally Improving?

CCW Digital’s annual inquiry into consumer sentiment begins with a simple question: over the past year, did experiences generally get better or worse?

In each of the past five years, the response was resoundingly negative. More than half cited regression, and just a small percentage celebrated improvement.

This year, the dynamic finally flips. For the first time since the COVID-19 pandemic, more consumers say experience quality increased (45%) than decreased (37%).

Granted, only 45% actually cited improvement – and only 21% reported significant improvement. This means that the majority of consumers are at least witnessing stagnation, if not regression.

To win over these customers, brands will have to demonstrate an even greater commitment to improvement. They will have to take bigger, clearer, more urgent steps to remedy the pain points, inefficiencies, and inadequacies that have become hallmarks of the customer experience.

Industry Lowlights and Highlights

This year’s finding may be more optimistic than that of years’ past, but it is not exceptional. Nearly 37% of consumers still feel experiences are headed in the wrong direction; 17% worry they are getting significantly worse.

In general, do you feel businesses have gotten better or worse at delivering customer service over the past year?

Regression is most evident in the retail space, where 42% of consumers witnessed a decline in experience quality. Pessimism around experiences in services (41%), healthcare and medical (37%), government and public services (36%), and fast food (34%) is nearly as palpable.

As these industries address dramatically different types of inquiries and deal in vastly different sensibilities, there is no obvious, common thread. Potential fail points across this collective include illogical purchasing and return policies, convoluted processes and journeys, inefficient support frameworks, inadequate displays of empathy and humanity, and insufficient use of customer feedback.

By comparison, consumers were less likely to report declines within the education (10%), entertainment (15%), health and wellness (18%), and sit-down restaurant (18%) industries.

In which of the following contexts have you experienced a decline in customer service quality?

In this case, there is a common thread – the experiential element. Each of these involve a real-world, in-person experience. Success on that front, be it in the form of an engaging concert, a delicious dish, a productive academic or workout class, or human-centric service around such experiences, appears to be fairly widespread.

Progress, Not Perfection: Experiences Remain Underwhelming, Pain Points Remain Unavoidable

There is a stark difference between getting better and getting good. That distinction is on full display when it comes to consumer sentiment.

Many brand experiences have gotten better over the past year. Consumers expressed optimism they are moving in the right direction, and they witnessed progression in areas like personalization and convenience.

Marginal improvement is unfortunately not the same as legitimate excellence. The reality is that brands continue to miss the mark on crucial tenets of the customer experience. They also exhibit many of the same “pain points” that have frustrated consumers for decades.

For example, 40% of consumers feel the typical experience is friendly and conversational, and 38% view it as convenient.

These numbers may reflect substantial year-overyear gains (only 21% felt 2025’s typical experience was friendly, and just 26% cited convenience), but they still reflect an underwhelming overall landscape. The average customer still does not believe brands are succeeding in these areas.

The story is similar for qualities like personalization (25% in 2026 vs. 18% in 2025) and channel autonomy (25% in 2026 vs. 22% in 2025). Brands are demonstrating

progress, but the rate of improvement is too minimal to radically change the landscape. It is too minimal to change the narrative that brands, at the end of the day, are failing to deliver frictionless, personalized, predictive, and proactive experiences.

And it is not as if brands are even marginally improving across all dimensions. Only 18% of consumers, for example, felt the typical experience was consistent across touchpoints in 2026. The exact same percentage recognized consistency in 2025.

Considering the enduring push for omnichannel engagement, the availability of integrated contact center platforms, and the emphasis on data unification, the failure to even slightly move the needle is noteworthy. It curbs at least some of the enthusiasm around the aforementioned improvement.

In a finding that further curbs that enthusiasm: 19% of consumers feel that the typical experience excels in none of these areas.

Customer Experiences are Still Painful

Experiences are not simply disappointing consumers in a holistic sense. They are also subjecting them to the exact same frustrations, inefficiencies, and inadequacies that have existed for years.

A whopping 43% of consumers frequently experience long wait or hold times. Difficulty reaching live agents (41%), the need to re-share information (36%), and the need to answer repetitive questions (35%) are also common frustration drivers in today’s interactions.

These “pain points” were nearly as common in 2025, when 50% reported frequent difficulty reaching a live agent, 47% complained of long wait times, and 40% cited a need for repetition.

It is not that the marginal improvement should be totally swept under the rug: the progress on “difficulty reaching a live agent” is particularly worth monitoring, as it suggests brands are getting better at managing the AI-human balance—or at least better at facilitating escalations.

Which of the following would you say are true of customer service in the last year?

Customer service was very friendly and conversational

Customer service was quick, convenient, and easy

I was able to get immediate help from a real person whenever I wanted

Customer service was highly personalized to my specific needs and expectations

I was able to choose exactly where and how to interact with the business

The technology used (such as chatbots, mobile apps, screen sharing, etc.) made the experience much better

None of the above

The experience felt consistent across the business’ different touch points and channels

Customer service went “off script” / “above and beyond” to give me a special resolution

But there is no denying the reality: today’s experiences are still far from perfect. And no matter how much brands subscribe to philosophies and invest in technologies that emphasize “seamless experiences,” many still impose friction on their customers. They still ask them to wait. They still ask them to restate information. They still place some of the service burden on the consumers they are supposed to be empowering.

Will this change in the build-up to 2030? Will companies successfully leverage new contact channels, new analytics technology, new workforce management initiatives, and new design frameworks to finally eliminate these longstanding pain points?

Or will the customer contact community punt transformation to 2035?

Underwhelming Things Come to Those Who Wait

When one is confident in finding a pot of gold at the end of the rainbow, they may be inclined to tolerate waiting. They may accept that patience is a virtue.

But for many of today’s customers, the convoluted journey of long wait times and repetitive questioning does not lead to an exciting reward. Rather, it leads to more frustration.

Consumers remain disappointed in the experience they receive from live human agents. Only 33% feel the typical employee seriously cares about solving their problem, and the numbers for expertise (31%), focus (30%), emotional intelligence (21%), excitement about their job (17%), and in-depth customer knowledge (16%) are similarly sobering. Less than half of consumers even feel like the typical employee – someone who is literally paid to be cordial – is polite.

As was the trend throughout the survey, some of these numbers do reflect improvements from 2025. Last year, consumers were even less impressed with employees’ ability to demonstrate politeness (39%), focus (22%), expertise (22%), and commitment to resolution (20%).

As was the trend throughout the survey, these marginal improvements may not be sufficient cause for celebration, especially given the ongoing AI transformation.

Which of the following challenges/issues do you often encounter when interacting with businesses for customer service?

Long wait/hold times

Difficulty getting in touch with a live person

Needing to repeat information

Needing to answer repetitive questions

Frequent transfers

Requirement to start in automated channels

Lengthy identification/verification questions (mother’s maiden name, etc)

Inability to get my desired resolution due to “policy”

No option to interact in the channel I want (I want phone, they require email, etc)

Adoption is not yet universal, but the trend is for consumers to leverage AI-powered self-service for simple and moderately complex transactions and inquiries. Human agents will represent the escalation layer: the force that handles complex inquiries, edge cases, and emotionally significant concerns.

If consumers do not feel employees are universally demonstrating expertise, emotional intelligence, focus, or enthusiasm for their brand, are they really ready to embrace them as legitimate consultants? Will they really perceive them as their enthusiastic advocates for superior service outcomes?

For as much as customer contact leaders are trying to perfect technology, it is clear that the human element is also lacking. Today’s employees are not imminently ready to deliver highly creative, consultative, personalized, and empathetic service. If that is the intention for the customer experience of 2030, contact centers must urgently rethink their hiring, recruiting, training, and workforce optimization strategies.

Consider the customer service employees with whom you typically interact. Which of the following do you feel is true for most of them?

They are very nice, friendly, and polite

They seriously care about solving my problems

They are extremely knowledgeable about the company and products they support

They are 100% focused on me and don’t seem distracted or disengaged during the interaction

They are eager to listen to my feedback and share it with their company

They detect when I’m angry or upset and try to calm me down

They know a lot about me and can personalize the conversation without asking too many questions

They are excited about their job and the culture of the company they support

They think “outside the box” and provide resolutions that go off script/policy

Channel Preference: The “Call Center”

Endures, The Human Agent Prevails

Average consumers may not see the typical live human agent as polite, let alone highly knowledgeable, passionate, and emotionally intelligent.

But at the end of the day, they still see calling these agents as their best available service option.

In fact, they are more certain of that perspective this year than they were last year. Three-quarters of consumers express confidence that they could get their problem solved when speaking to a live agent over the phone. Last year, 66% expressed equivalent confidence in traditional phone support.

Despite all the talk about pivoting from a “call center” to a “contact center,” the vote of confidence solidifies live phone support as the most trusted service option – by far. Email support and human-led live chat, the nextmost trusted options, instill confidence in just 38% and 37% of consumers, respectively.

The data also confirms that consumers are far more confident in human-led support than they are selfservice. Whereas 75% are confident in agent-led phone support, just 14% express the same trust in IVR interactions. Viewed as a trusted service option by only 18% of consumers, chatbots are also struggling to win customer confidence.

This finding is not surprising; confidence in chatbots has landed within the 15-20% threshold for the past several years.

It does, however, question the efficacy of AI investment strategy. Despite all the hype and investment, the typical customer remains hesitant about the value of AIpowered service.

A concern now, this reality has significant ramifications for the customer experience of 2030. According to CMP’s most recent Executive Priorities Report, increasing self-service adoption ranks as the #2 objective for customer contact leaders. CCW Digital’s recent inquiry into the state of AI amplified that finding, with 58% of leaders projecting that most interactions will be fully handled by AI agents in 2030.

Presently, that projection feels wildly incompatible with consumer sentiment. The imperative, then, is for brands to swiftly and significantly elevate their self-service experiences. If they do not address the factors that cause customers to distrust AI-based care, and subsequently turn their self-service options into valuable, customer-centric options, they will not succeed in their ambition.

They will either have to settle for far less automation (and thus the resulting inefficiency) or choose to impose more self-service anyway (and risk customer dissatisfaction).

Promising more conversational interactions, personalized communication, and active resolutions, modern agentic AI solutions may represent a viable solution. They may be the key to growing customer confidence.

With organizations of all industries beginning to deploy this technology, future versions of this report will provide a definitive answer on whether modern solutions are closing the trust gap – or simply validating the enduring preference for human-led support.

Suppose you engaged with a company to address a customer service issue. In which of the following channels would you feel confident the company would solve your problem?

Phone (live human employee)

chat, such as Zoom

Chatbot

Social media Phone (automated menu or voice response)

Reason for Optimism: Phone is Not Always the Preference

When thinking about channel mix, it is important to understand the distinction between confidence and preference

Confidence is based on what the consumer has actually experienced in the real-world. Preference is based on what the customer would like to experience in an ideal one.

It is not, therefore, accurate to say that customers fundamentally prefer live phone support to options like email, chat, messaging, and web self-service. Behavior in their personal lives suggests it may actually be wildly inaccurate to make that assumption.

The above section only confirms that customers, knowing what they know now, believe live phone support represents their best shot at a favorable customer service outcome.

Brands can overcome this presupposition by proving the other channels worthwhile. And there is reason to believe customers are very open to receiving that proof.

Upon determining they have a need for support, only 27% of consumers first seek out a traditional phone option. While establishing traditional phone assistance as the most comparatively popular choice, the statistic confirms that most customers instinctively go elsewhere.

Eighteen percent (18%) turn to traditional search engines like Google, while 16% navigate the company website. And in an optimistic sign for AI, 14% are leveraging AI-based platforms like ChatGPT.

Clearly, customers are comfortable using digital and AI-based channels for customer support. They are hoping these channels will work. The issue is that these channels have not yet delivered as consistently as more traditional options.

This lack of consistent delivery explains why confidence in alternative options remains low. It explains why only 42% believe AI has thus far added value to their experiences. It explains why phone remains the most trusted channel – and why increasing self-service adoption remains a focus of aspiration rather than a point of celebration.

When you encounter an issue with a product or experience, what is typically the first thing you would do to get more help?

Contact the company via phone call (or in-person if applicable)

Search a web engine like Google

Search the company’s website, mobile app, or official knowledge base

Search an AI platform like ChatGPT

Use the company’s chatbot, support portal, or other web self-service tool

Contact the company via text-based digital channels (messaging, email, social, chat, etc)

Search social media (including video sites)

Do you think the use of AI is helping to improve customer experiences?

Automating Improvement: Will Customers Ever Embrace AI?

Reservations about self-service aside, organizations remain all-in on autonomous support. Numerous CCW Digital studies and benchmarks identify selfservice adoption and resolution as critical priorities. Organizations, moreover, have not relented in their forecast that AI agents will handle the majority of frontline interactions by 2030.

This commitment to AI-powered self-service is very visible to customers. A whopping 89% say brands grew more likely to require them to use a self-service option before, or in lieu of, interacting with a human agent over the past year. That almost precisely matches the finding from 2025, revealing the dramatic extent to which brands are emphasizing self-service.

Granted, the research indicates that brands have made their self-service push more palatable. Consumers have become less likely to identify “difficulty reaching a live agent” or “requirement to use automation” as a frustration driver in 2026. They also report marginal improvements in the friction – long wait times, repetitive questions – often associated with AI-human escalations.

Still, it is impossible to ignore the disconnect between brand enthusiasm and customer confidence. At the end of the day, brands are still making the least-trusted support options the centerpiece of their experience. They are still emphasizing a class of technology that less than half of customers feel is adding value.

Over the past year, do you feel like companies have been more likely to require you to use a self-service tool (chatbot, voice menu, etc) before, or instead of, speaking to a live representative?

From Aversion to Adoption: Winning Customer Confidence in AI Self-Service

Consumer resistance to AI self-service is significant, but it is not unsolvable. By addressing certain experiential weaknesses and pursuing certain opportunities, organizations can cultivate more trust in their AI-based platforms. In turn, they can make the push toward a more agentic frontline both more feasible and more valuable.

Mirroring the finding from 2025, the most effective effort involves removing walls. A healthy 47% of consumers say they would be more willing to use self-service options if they had assurance of an easy escalation to human agents.

Admittedly, this is not the most ringing endorsement of AI technology. It effectively means that many consumers are only willing to use self-service if they can stop using self-service at any time.

A progressive customer contact leader, however, will look past the skepticism. They will recognize the reality of the situation (and of the opportunity).

The reality is that self-service experiences have been inferior. Customers are not confident in them, and to pretend otherwise is to engage in fantasy. Expecting customers to suddenly say they love self-service is an example of that fantasy.

The goal, then, is to convince them to abandon that stigma. It is to show them that self-service can be an equivalent, if not superior, option for acquiring customer service.

If promising they will have an option to easily escalate is what it takes to get them a captive audience for proving the value of their solution, why would they not take it? Why would they not seize the opportunity to “flex” that while human agents are always there as a backup option, the self-service experience is so good that customers will not need to exercise it?

To ensure the showcase is effective, brands must ensure their self-service experience is both effective and efficient. Thirty percent (30%) of consumers say they would be more open to self-service if assured they could get a full resolution, while 29% say they would gravitate toward platforms that did not require them to repeat information or answers.

Neither of these is an unreasonable request. In the digital age, customers have infinite options to access standard answers and policies. If they choose to exert effort actively conversing with a business, whether via an AI or human agent, it is because they want to know the agent can take real action. This sets the stage for the rise of agentic AI, which goes beyond deterministic responses and focuses on autonomous action.

And if the self-service environment involves something as inefficient as repetition, it loses the inherent “convenience” advantage it has over human-led channels.

The extent to which an organization can accommodate these preferences will vary by organization and industry. If an organization is relying on AI agents because it does not have a 24/7/365 staff capable of jumping it, it obviously cannot make good on the “seamless escalation” in all cases. Industry and brand standards, moreover, may limit the caliber of resolution or extent of data storage.

The ultimate takeaway for 2030, though, is that only 13% of consumers say they are categorically unwilling to embrace AI-based self-service. This means businesses

have a real opportunity to change the narrative. They have a real opportunity to ensure next year’s CCW Digital Market Study, let alone the 2030 iteration, confirms higher levels of consumer trust and adoption.

Which of the following would make you more likely to use a self-service tool (chatbot, voice menu, etc) to solve an issue?

I can quickly escalate to a live representative whenever I want

I know I can get a full resolution to my issue in the self-service tool

I will not have to repeat information if I do escalate to a live representative

I can communicate in natural language

I know there will be a long wait if I instead wanted to talk to a live representative

I can share screenshots, videos, and other media to help explain my issue

I will receive an offer, coupon, or other promotion to use the self-service tool

Does the Rise of AI Raise New Concerns?

When it comes to the customer experience, there are different flavors of trust and confidence.

One aspect concerns experience quality; can the customer trust the brand to deliver an exceptional experience? Another concerns personalization; can the customer trust the brand to know who they are serving –and tailor the experience accordingly?

Finally, there is the more traditional, security-minded vision for trust: does the business protect the privacy and safety of its customers?

With so much effort going into elevating the experience of AI-based channels, it can be easy for brands to overlook the other elements.

Customers are most certainly not overlooking them, however.

Amid the rise of digital- and AI-based communication, 76% of consumers say they are increasingly concerned about data privacy and security. One third say they are very concerned.

On the one hand, this validates a recent CCW Digital finding that security is the greatest differentiation opportunity in the AI age. It proves that with AI technology commodifying and genericizing elements of the care experience, brands that can prove they are operating in customers’ best interests will thrive.

On the other hand, it raises a concern about practical action. Although they philosophically recognize security as a chief differentiator, businesses are not necessarily prioritizing associated initiatives in practice. Security and fraud prevention are middle-of-the-pack focuses in CMP’s annual Executive Priorities report.

If brands are truly all-in on self-service adoption, and truly aiming for an autonomous frontline by 2030, they must take emerging security concerns very seriously.

As AI becomes more robust and conversational, transparency is also becoming a bigger concern. More than half of customers (55%) say it is important for brands to disclose whether the interaction is being handled by an AI or human agent.

Whereas this type of transparency may seem straightforward, nuance may emerge in the coming years. As organizations consider hybrid service models (such as having humans in the loop to provide behind-the-scenes guidance, using AI to validate the data with which the human agent is working, or enabling truly concurrent care where humans and AI each contribute to a real-time interaction), the appropriate way to disclose resourcing will evolve.

Transparency is, of course, valuable outside of the AI-human dynamic. Still adamant that their time is money, 64% of consumers expect disclosures about the expected wait time.

MARKET STUDY

How concerned are you about data security and privacy when using AI and self-service channels when seeking customer service?

Which of the following disclosures do you expect in a customer service experience?

Estimated wait time

Whether you’re communicating with AI or a human

Whether the human representative you’re speaking with is outside the country you’re in

Whether the human representative you’re speaking with works at an outsourcing company

Disclosures related to factors like whether the agent is outsourced (35%) or off-shore (37%) are on the radar, but they are not seen as critical by the majority. As customer contact leaders and legislators debate the role of international outsourcing, this finding suggests that factors like convenience and AI-human interplay matter more than agent location.

The Great Winback: How Can Brands Restore Customer Loyalty?

By elevating support across channels, more effectively using AI, better personalizing interactions, and increasing transparency, brands will build on this year’s momentum. They will ensure that the customer experience of 2030 is better than that of 2026.

But while marked improvement is very much attainable, perfection may not be. Products will break. Service interactions will go off the rails. Mistakes will be made.

The inevitability of imperfection is not, however, tantamount to the inevitability of dissatisfaction. Brands that respond properly to misfires stand to not only minimize the customer’s disappointment but even cultivate more trust.

The key is to consistently align the response with the gravity of the issue and the voice of the customer.

Why It Matters: How Customers React to Poor Experiences

Not all customers make good on the threat of “taking their business elsewhere” in the face of a failed interaction, but most do take some action.

Following their most recent bad experience, only 18% did nothing. The remaining took one or more actions, many of which have consequences for profitability and brand reputation.

Taken by 37% of consumers, the most popular action was to request a supervisor. Comparatively significant percentages also requested a refund (20%), shared their experience on a review site (19%), gave a negative survey response (19%), or filed a formal complaint (17%).

Just shy of 1-in-6 customers did, meanwhile, look to switch to a competitive brand.

Consider your last poor customer service experience. Which of the following actions did you take in response?

Asked to speak to a supervisor/leader

Requested a refund

Gave a negative survey response

Shared the experience on a review site (e.g., Google Reviews, Yelp)

Not applicable: I did not take any action

Shared a formal complaint

Sought a competitor or alternative

Indifferent and Misaligned: Analyzing Today’s Recovery Efforts

Whether the customer’s action creates inefficiency (escalating to a supervisor takes more time, and requires more staff), cost (a refund), or negative marketplace sentiment (public comments and reviews), it has ramifications for the organization. These ramifications, of course, say nothing of any “silent stewing” the customer may be doing – internal animosity that could adversely affect long-term loyalty or lifetime value.

Clearly, organizations have an incentive to take action.

According to consumers, not all are actually doing so. Inaction was actually the most common response to a service misfire.

An alarming 29% of consumers say the brand did nothing to remedy their most recent poor experience.

Other comparatively widespread responses included issuing a formal apology (27%), letting the customer speak to a supervisor (27%), and explaining how they will address the issue moving forward (22%). Just 15% issued a full refund, and 12% provided a partial refund.

Context obviously matters when formulating a response, and it is certainly possible that a refund was not appropriate in many cases. That only 15% of consumers got a full refund in this case does not mean only 15% of consumers ever get refunds. It certainly does not mean that only about 1 in 7 brands provide refunds.

Still, there is no denying the disconnect between what happened, what the customer requested, and what the brand actually delivered. And there is no ignoring that many customers are not even receiving apologies, let alone substantive make-goods.

In this same poor service experience, what if any actions did the company take in response?

Nothing

me discuss the issue with a supervisor or leader

Fixing Inadequacy: The Responses Consumers Prefer

Requesting access to a supervisor may be the most common action, but it is not necessarily the goal. In reflecting upon their last interaction, only 18% would have liked to see the brand honor that request.

A more significant 23% would have preferred a full refund, and an equivalent 18% would have valued proof of the brand’s commitment to fixing the issue.

Belief that leadership is better capable of delivering these outcomes may explain why consumers are so inclined to make that request. If they grew confidence in getting the best possible resolution on the frontlines, they could stop making that demand and stop subjecting themselves to the inefficiency of another escalation.

This further underscores the importance of better empowering AI and human agents. If these agents can offer real empathy, demonstrate real accountability, and issue real resolutions, the customer experience of 2030 will be far more effective.

Instead of demanding escalations, consumers will be celebrating the newfound autonomy and efficiency. Frustration will become loyalty.

In this same poor experience, which of the following actions would you have most preferred the company take?

Issued a full refund

Let me discuss the issue with a supervisor or leader

Explained how they’ll prevent the issue moving forward

or

First-Contact Resolution Has Changed. Your Roadmap Should Too.

By Merlin Bise, CTO, Inbenta AI

The hesitation I see most often when I talk to enterprise CX leaders is not whether AI works. It’s a quiet assumption that what’s possible today is roughly what was possible yesterday.

It isn’t.

The front end and the back end both moved at once, and the most important thing that moved with them is what “first-contact resolution” actually means.

For the last decade, first-contact resolution meant one thing: did the customer get an answer the first time they called. Sometimes it was a real answer from a live agent. Sometimes it was a half-answer from an IVR that bounced them through three menus before connecting them to a person.

Either way, the bar was just a question answered or a simple task completed.

The bar has moved.

MARKET STUDY

In 2026 — and even more so by 2030 and beyond — first-contact resolution means the customer’s task is done on the first call. Not “here’s a link, follow these steps.” Not “we’ll email you a confirmation, then call us back.” Done, meaning that the claim is filed and routed. The address is changed across every system it lives in. The escalation is initiated with the third party. The AI handles the customer’s intent, executes the steps across systems, and confirms the outcome before the call ends. This is the part of the 2030 conversation most CX, IT, and ops leaders haven’t internalized yet.

The rest of this guide makes three things clear:

• First-contact resolution is becoming first-contact completion. If your KPI still measures “question answered,” you’re measuring 2022.

• Most of the perceived risk in moving forward has already been engineered out. Modern platforms ingest content in under an hour, deploy in days, and trace every answer back to source. The fear-of-failure math from two years ago no longer applies.

• The contact center is becoming a revenue center. Autonomous, governed agents don’t just deflect calls. They complete transactions, surface relevant offers in the same conversation, and create use cases that didn’t exist when contact centers were cost centers.

Why voice deployments stall, and what to do about it

When I talk to CX leaders whose voice AI hasn’t delivered, the instinct is to blame the model or the vendor. In most cases that’s the wrong diagnosis. What we notice — across briefings with analysts and conversations with enterprise CX, IT, and ops teams — is that the system was built to deflect calls rather than resolve them. Those are different design goals and they produce different outcomes.

Deflection keeps the caller in the automated channel and out of the live queue. Resolution actually handles the caller’s issue. A deflection system handles the easy questions fine, then escalates the moment the caller goes off-script with a follow-up question, a reference to a previous interaction, anything that doesn’t match a defined intent. Because the AI didn’t capture context, the human agent starts over. Handle time goes up. The cost savings the business case promised disappear. The pilot stalls.

Three patterns show up in nearly every stalled deployment. None of them are vendor-specific. All of them are fixable.

• The system was built for the happy path. It handles password resets and order status. It collapses the moment a caller asks something compound, conditional, or emotional. To fix it, you need an architecture that can hold context across a multi-turn conversation and orchestrate the systems behind it.

• Voice and chat answer the same question differently. Many platforms keep one knowledge store for digital channels and a separate one for voice — sometimes a third for the generative layer on top. Customers notice within two exchanges and trust collapses. The fix here is a single governed knowledge source feeding every channel.

• Nobody is learning from the escalations. Every time a caller bails out to a human, the system just produced a labeled training signal — what it couldn’t handle, what the human had to do, what was missing from the knowledge base. Most platforms don’t capture this. The answer is autonomous maintenance: the system surfaces missing intents, undefined terms, and emerging escalation patterns on its own, and an administrator approves the updates in a few clicks.

“Every escalation is a signal your system missed. Most organizations are not capturing that signal, which means they are making the same mistakes on repeat.”

— Merlin Bise, CTO, Inbenta AI

The contact center doesn’t need to be a cost center

What is possible today is a front end with voice AI that understands a caller in 90+ languages, often more reliably than the human agent it’s replacing, and a back end that can be used as a powerful agentic engine with a governed, integrated knowledge layer that can ingest a company’s CRM, ERP, documents, and recorded calls and structure them into something the AI can retrieve in real time.

This simultaneous leap forward in front end and back end is what makes autonomous, multi-step resolution possible now, and it’s what makes a redefined FCR metric the right thing to build toward.

The reason this conversation matters more than it used to is simple: when an autonomous, governed AI agent can complete a customer’s task on the first interaction, the contact center stops being purely defensive. It becomes a place where retention is earned, where upsell conversations happen naturally inside service interactions, and where new use cases — proactive outreach, in-conversation account upgrades, crosschannel continuity — start generating revenue that wasn’t possible before.

That’s the shift the leaders I talk to are quietly building toward. Not because they’re worried about being left behind, but because the math on AI in CX has changed and the risk of moving has been engineered down to a level that didn’t exist two years ago.

Where I’d start

These are the conversations I would start with my own team if I were sitting in a CX, IT, or ops seat in an enterprise contact center right now.

Do we have an AI perception gap? Are we challenging old assumptions about what is possible, given how fast the technology is moving? If the loudest opinions in the room are based on a vendor evaluation from 18 months ago, your roadmap is being built on stale information.

Have we thought about both sides of the equation? Most teams I talk to are deep on front-end voice vendors and shallow on the answer engine behind them. Expressive voice on a weak knowledge layer just produces more articulate disappointment. Evaluate both with the same rigor.

How will we measure first-contact resolution as agentic capability grows? Containment isn’t resolution. “Question answered” isn’t resolution either, not for much longer. If your team’s definition of FCR doesn’t anticipate multi-step task completion, you’ll be optimizing for the wrong number for the next three years.

What I’d look for, having evaluated a lot of platforms

I have sat across the table from a lot of vendors over the last three years. Here is what separates a platform that ships and improves from one that demos well and stalls in production.

“The organizations that will lead in 2030 are not waiting for the technology to mature. They are making specific architectural decisions today, while their competitors are still pitching pilots.”

— Merlin Bise, CTO, Inbenta AI

Knowledge that’s structured for you, not handed to you. A lot of vendors will say they can ingest content quickly. Ingestion is becoming commoditized. The real question is whether the platform builds a structured, low-latency knowledge map from your content automatically — generating intents, tagging context, and resolving customer-specific terminology — or whether you’re back to paying a professional services team to do it by hand over the next two quarters. If a vendor’s deployment timeline is measured in months, you are looking at the old architecture in a new wrapper.

A glass box, not a black box. In financial services, insurance, healthcare, and any regulated CX environment, the CISO and Chief Risk Officer hold informal veto power over technology decisions. Deals die in fifteen-minute meetings when no one can defend the architecture. The platforms that get deployed in regulated industries trace every response back to its source, pre-validate answers against approved content, and produce an audit trail a regulator can read.

One knowledge base across every channel. If voice draws from one source and chat draws from another, your customer will know. Insist on a single governed knowledge layer that powers voice, chat, agent assist, and any agentic workflows. This is the single most reliable predictor I have seen of whether a deployment delivers a consistent customer experience.

Autonomous improvement, not autonomous wishful thinking. The market is awash in vendors calling trigger-based automation “agentic.” If a human has to script every step the AI takes, that’s not agentic — it’s automation. Sure, it’s useful, but it’s not the thing that will let you redefine FCR. True agentic AI is goaldriven: you specify the outcome, the AI determines the path, and it operates within governed guardrails on your approved content. Ask for the audit log of an autonomous decision. If they can’t show you one, they don’t have one.

Maintenance that doesn’t eat your budget. A platform that doesn’t improve itself becomes a recurring professional services line item. Ask how the system handles model drift when the underlying LLM provider releases a new version. Ask how it surfaces content gaps. Ask what percentage of customers run on autopilot versus human-in-the-loop and why.

How I’d phase the rollout so the first move is the safe one

The fastest way to remove fear of failure is to make the first move small, scoped, and reversible. The phased approach outlined here is the one I’d recommend to any CX leader who wants to move now without betting the contact center on it.

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Phase 1 — Pick one high-volume, low-complexity use case and ship it in 30 days. Password resets, order status, appointment scheduling, account balance inquiries. Something where the existing process is welldocumented, the success criteria are obvious, and the risk of a wrong answer is contained. Run it in parallel with your existing process. Compare resolution rates, customer satisfaction, and handle time side by side for two weeks before you cut over.

Phase 2 — Add multi-step resolution and a second channel. Once the first use case is delivering, extend the same governed knowledge base to chat (or voice, whichever you didn’t start with), and introduce a use case that requires two or three steps — initiating a return and routing it to the carrier, filing a basic claim, scheduling a follow-up across two systems. This is the phase where you start to feel the FCR redefinition in your numbers.

Phase 3 — Move toward agentic workflows in regulated or revenue-adjacent processes. Now you’re letting the AI complete transactions across third-party systems on the customer’s behalf, surface relevant offers in-conversation, and initiate proactive followups. By the time you get here, you have an audit trail, a maintenance pattern, and a team that knows the platform. The “agentic” decision isn’t a leap.

This sequencing is how you take the risk off the table. Each phase has a measurable outcome, each phase de-risks the next, and at no point are you betting the business case on a single launch.

Final thought

AI technology will keep advancing. The question is whether your business is ready to take advantage of it, and whether the first move you make is small enough to be safe and significant enough to learn from. Both can be true at the same time.

Merlin Bise is CTO of Inbenta AI and brings over 20 years of experience in enterprise technology leadership. He recently led the build of Inbenta Encore, the company’s agentic AI orchestration platform, and was previously co-founder of GIACT Systems (acquired by LSEG).

More on Encore at inbenta.com/platform.

2030 Is Closer Than It Looks

By 2030, the most important capabilities in customer experience will no longer be differentiators. They will be expectations.

That is the real challenge facing contact center leaders today. The market is still talking about connected journeys, AI, predictive insights, and personalization as emerging ambitions. Customers are starting to treat them as overdue basics. The gap between what organizations are piloting and what customers will soon expect is narrowing quickly.

The CX of 2030 will not be defined by who adopts the most tools. It will be defined by who connects intelligence, execution, and outcomes most effectively.

For many leaders, the issue is not a lack of investment but a lack of operational translation. More channels do not automatically create continuity. More AI does not automatically improve decisions. More data does not automatically produce action. The winners will not be the organizations that adopt the most tools or chase the most trends; they will be the ones that operationalize those investments with discipline, turning technology into better journeys, better decisions, and better outcomes.

The Contact Center Is Moving from Reactive Support to Connected Growth Engine

The omnichannel contact center of 2030 is not just a support environment. It is a connected growth, retention, and optimization engine.

That shift matters because omnichannel is no longer just about being present across channels. AI is no longer just about automation. CX is no longer just about service recovery. The contact center is becoming a place where brands acquire, engage, retain, and optimize customer value across the full lifecycle.

That broader view reflects a meaningful shift in how the contact center is understood — not as a cost center organized around isolated service tasks but as a business system that simultaneously supports growth, optimization, and transformation. The practical implication is that leaders need two things working together: connected execution on the front end and connected intelligence informing every decision.

That is the operating model iQor has built toward its growth, customer experience, and analytics capabilities, where front-end revenue execution and back-end intelligence are designed to inform each other, not operate in silos.

Four Practical Imperatives for the Omnichannel Contact Center of 2030

1. Connected omnichannel must replace channel expansion

Today, many brands still operate in parallel channels rather than unified journeys. They may offer voice, chat, messaging, email, and social support, but the customer experience across those touchpoints often remains fragmented.

By 2030, customers will expect context to follow them across voice, chat, messaging, email, social, and back-office handoffs. They will expect organizations to recognize not just who they are but where they are in the journey, what has already happened, and what needs to happen next.

The practical implication is that leaders need architecture, data flow, and orchestration, not just channel coverage. The distinction matters. Multichannel presence means offering access in several places, and omnichannel continuity means reducing friction between those places. Orchestration means connecting the customer lifecycle and the internal workflow around it.

The future is not more channels. It is connected journeys across channels.

2. AI must be operationalized intentionally, not layered on

The winners in 2030 will not be those who implemented AI first; they will be the ones who embedded it with purpose.

Today, AI is often fragmented into chatbots, summaries, copilots, or isolated pilots. Those use cases may create value, but they do not, on their own, create a stronger operating model. The 2030 expectation is broader: AI should support decisioning, coaching, personalization, workflow optimization, and business intelligence across the contact center.

That is why the practical challenge is not simply technology adoption. It is use-case clarity, governance, workflow design, and integration. This aligns directly with the principle of intentional support: Technology must be paired with strategy to deliver results.

Insights iQ™, iQor’s proprietary intelligence platform that analyzes 100% of customer interactions and turns them into real-time, actionable intelligence, reflects that approach: AI applied not as a standalone feature but across QA, coaching, churn detection, workforce enablement, and operational analytics, consistently tied to measurable outcomes.

“By 2030, AI will not differentiate contact centers simply because it exists in the tech stack. Differentiation will come from how intentionally it is operationalized across the CX enterprise: connecting customer context, guiding frontline and leadership actions, surfacing predictive insights, and improving outcomes at scale. The organizations that lead will be those that stop treating AI as a feature and start embedding it into the operating model.”

— Joe Przybylowski, SVP, AI, Data Science, & iQor Labs at iQor

3. Predictive intelligence must replace lagging insight

By 2030, reactive CX will feel outdated.

Many organizations still rely on surveys, sample-based QA, and post-fact reporting to understand customer experience. Those approaches can be useful, but they are limited by delay, bias, and incomplete visibility. They often tell leaders what happened only after the opportunity to shape the outcome has passed.

The next standard will be different. Organizations will increasingly identify unmet needs, churn risk, loyalty drivers, sentiment shifts, and friction patterns before they fully surface.

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That means the future-state contact center needs real-time or near-real-time signal capture, integrated VOC and operational analytics, and actionability tied to workflow, coaching, routing, or outreach.

This is where the shift from traditional VOC to nextgeneration customer intelligence becomes especially important, a move from listening to understanding, and from sampled observation to intelligence that is comprehensive, predictive, and operational. The value is not just in seeing more. It is in using those signals to improve outcomes earlier. Insights iQ is built for that model, analyzing 100% of interactions rather than sampled surveys, with predictive churn, NPS forecasting, sentiment detection, and near-real-time visibility designed to drive action, not just reporting.

4. Revenue, retention, and experience must be managed together

In 2030, customer experience leaders will be expected to show business impact, not just service quality.

Today, CX is still often measured separately from commercial performance. By 2030, organizations will connect customer interactions more directly to conversion, retention, loyalty, and operational efficiency. Experience quality will still matter, but it will not be enough to discuss it in isolation from business outcomes.

That is why leaders need operating models that connect customer engagement to measurable results. Front-end engagement, integrated sales and marketing execution, pipeline generation, and revenue impact can and should be operationalized at scale — not treated as separate disciplines from service and retention. iQor Grow is the answer to that challenge: an integrated sales and marketing engine that connects pipeline generation, outbound execution, and account management into a single full-funnel model, built to generate pipeline rapidly and scale on demand.

The larger point is that the future contact center will be expected to influence acquisition, retention, and optimization together. That demands a connected view across growth, customer experience, and performance, not a collection of individually managed metrics.

What the 2030 Toolkit Actually Requires

The toolkit for the next era of omnichannel CX is not a list of point technologies. It is a connected capability stack.

At a minimum, leaders should be building toward five requirements:

• A connected data foundation that unifies customer, operational, and market signals enough to support decisioning

• AI embedded in workflows rather than sitting outside operations as a separate layer

• Journey orchestration that carries context and intent across channels and internal teams

• Workforce intelligence across recruiting, training, coaching, performance, and retention

• Measurement tied to business outcomes such as retention, revenue, loyalty, speed-to-proficiency, and efficiency

Leaders should not ask what tools they still need to buy. They should ask what capabilities they still need to connect.

From Vision to Execution: What Leaders Should Be Looking for Now

This is where the discussion becomes most practical.

Leaders preparing for 2030 should ask:

• Does this solution connect channels or just add another one?

• Does this AI capability improve a workflow or just demonstrate a feature?

• Can this partner support both execution and insight?

• How quickly can this move from pilot to operational value?

• Are outcomes tied to growth, retention, customer experience, and efficiency?

• Does the model scale across the customer lifecycle?

These questions matter because the market is full of point solutions that optimize one interaction but do not improve the broader operating model. The future will belong to integrated partners, not fragmented point solutions.

That distinction is already consistent with where the most capable organizations are heading: toward integrated lifecycle support, one playbook, one dataset, one funnel, and solutions that connect people, process, technology, and measurable outcomes. The point is not to add more innovation theater. It is to close the gap between strategy and execution.

Stop Playing Catch-Up

2030 readiness is not about predicting the future perfectly. It is about building the capabilities that will matter regardless of how the market evolves: connected journeys, intentional AI, predictive insight, and measurable execution.

The organizations that define CX in 2030 will not be the ones still experimenting at the edges. They will be the ones already operationalizing what customers will soon expect everywhere.

That is the challenge in front of today’s CX leaders: to not simply invest in the next wave of technology but to connect intelligence, execution, and outcomes.

The next era of CX will not reward those who keep playing catch-up. It will reward those who start building now.

About iQor

iQor is a trusted partner in customer experience solutions for global brands and a portfolio company of Mill Point Capital. With 45,000 employees across 10 countries, iQor combines three decades of expertise with AI-driven innovation to optimize performance across the entire customer lifecycle. Through its three delivery pillars — Grow, CXBPO, and infinityAiQ — iQor delivers scalable solutions that drive acquisition, engagement, and retention. Powered by advanced analytics and a people-first culture, iQor transforms customer interactions into measurable growth. Recognized as a Great Place to Work® and a leader in CX excellence, iQor empowers brands to grow smarter. Learn more at iQor.com.

The Trust Imperative: Why Testing Matters for Agentic AI

The AI journey has accelerated dramatically. Over the past decade, customer experience organizations have progressed from traditional automation to generative AI and now to agentic AI. As demand grows for more intelligent, efficient customer experiences, AI is becoming a foundational element of CX strategy.

In 2026, CCW Digital research found that 99% of organizations are maintaining or increasing their AI investments. The hesitation that once surrounded AI adoption has largely disappeared. Organizations that initially approached AI cautiously are now pursuing broader transformation initiatives.

Agentic AI represents the next phase of that evolution. Unlike previous AI models, autonomous agents can reason, make decisions, and take action independently. This creates significant opportunities to improve customer support, increase efficiency, and streamline operations. It also introduces a new challenge: ensuring organizations can trust systems that operate with greater autonomy.

The challenge facing CX leaders today is no longer whether to adopt AI. It is how to deploy AI agents responsibly, with the visibility, governance, and testing required to ensure consistent outcomes.

As organizations look toward the contact center of 2030, success will depend on more than speed and innovation. Reliability, accuracy, and accountability will become equally important. This report examines the current state of agentic AI in CX and explores how continuous testing and assurance can help organizations scale AI confidently while protecting customer trust.

The State of Agentic AI in 2026

Before looking ahead to 2030, it is important to understand how organizations are using agentic AI today. Organizations across industries are leaning into the benefits of agentic AI and AI agents, as they offer the ability to reason and act in real-time.

Seth Johnson, Chief Technology Officer at Cyara shares, “Today, organizations are using AI agents primarily for customer support automation, routing, billing inquiries, troubleshooting, and improving agent productivity. But we’re already seeing a shift from scripted bots to agentic AI systems that can reason, adapt, and take action across workflows.”

Early research suggests this shift is resonating with customers. CCW Digital research confirms that 67% of leaders believe AI agents are performing better with customers than traditional chatbots. As customer frustration with rule-based chatbots continues to grow, agentic AI offers a path toward more effective selfservice experiences.

Expectations for these systems are also increasing. 73% of leaders believe AI agents should be capable of resolving issues and making decisions independently, provided appropriate safeguards are in place.

Looking ahead, Johnson predicts that AI agents will eventually orchestrate entire customer journeys, proactively identifying and resolving issues before escalation is necessary. Achieving that vision, however, will require organizations to establish mechanisms that ensure those decisions are accurate, reliable, and aligned with customer expectations.

The Preparedness Gap

While enthusiasm for agentic AI is growing, many organizations are not fully prepared for its operational impact.

CCW Digital research found that 51% of companies introduced two to three new technologies into their stack over the past year. Yet, 46% report that their technology performs well in some areas while underperforming in others. This highlights a growing disconnect between technology adoption and operational readiness.

According to Johnson, many organizations underestimate the complexity of deploying AI within environments affected by fragmented workflows, poor data quality, and inconsistent processes. As a result, implementation efforts often move slower than anticipated.

The organizations making the most progress are taking a different approach. Rather than pursuing broad deployments immediately, they are focusing on targeted, high-impact use cases while establishing governance, testing, and monitoring practices from the outset.

AI adoption is increasingly proving to be a transformation initiative rather than a technology project. Long-term success depends on balancing experimentation with accountability and ensuring operational processes evolve alongside technological capabilities.

The New Agent Workflow

The rise of agentic AI is also reshaping the role of human agents.

For years, organizations have promoted a vision in which AI handles routine interactions while human agents focus on complex and emotionally sensitive issues. However, many contact centers have struggled to realize that vision due to technology limitations, process inefficiencies, and inconsistent training programs.

CCW Digital research reveals that 73% of leaders believe friction across systems, tools, and processes continues to limit agent effectiveness. As a result, many agents remain focused on tasks that should already be automated.

Despite these challenges, leaders increasingly see AI creating opportunities for agents to take on highervalue responsibilities. 67% of leaders believe AI will enable agents to take on supervisory roles, monitor AI interactions and intervene when necessary.

Johnson confirms, “Organizations need to focus on designing AI around human expectations, not just operational efficiency. Customers still want empathy, accountability, transparency, and easy escalation paths. The future of CX will be adaptive, giving customers the right level of automation or human support depending on the situation, like billing disputes, fraud concerns, healthcare questions, or emotionally charged service issues where reassurance matters as much as speed. Leaders will need to balance automation with trust by building hybrid support models and continuously validating customer journeys to ensure experiences remain reliable at scale.”

This evolution reflects a broader shift toward hybrid support models. While AI agents manage a growing share of frontline interactions, human agents will continue to provide empathy, judgment, and accountability in situations where automation alone is insufficient.

The most effective customer experiences will not be fully automated or fully human. They will combine the efficiency of AI with the adaptability and emotional intelligence of human support.

Testing: The Foundation of Agentic AI Trust

As agentic AI comes to the forefront, testing and assurance becomes critical. Unlike traditional automation, agentic systems continuously adapt, learn, and make decisions in real time. That flexibility creates value, but it also increases the risk of inconsistent performance.

For CX leaders, the question is no longer can we deploy AI agents — it’s how do we know they’re working as intended, every single time?

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“As AI becomes more embedded in customer interactions, organizations will need continuous testing, governance, and monitoring to ensure experiences remain accurate, reliable, and aligned with customer expectations,” Johnson states.

Continuous, AI-native testing and assurance can ensure that agentic AI is performing properly while securing customer trust.

By combining journey visibility, governance, compliance validation, and real-time monitoring, organizations can continuously evaluate AI performance before issues affect customers. Rather than treating testing as a pre-deployment activity, leading organizations are integrating assurance throughout the AI lifecycle.

Cyara advocates for an AI-to-AI testing model, in which AI agents are continuously evaluated by specialized AI testing systems capable of identifying failures, deviations, and risks in real time. As agentic systems become more sophisticated, assurance capabilities must evolve at the same pace.

The goal is not simply to detect problems. It is to create an environment where organizations can innovate faster because they have confidence in the systems powering customer interactions.

Earning Trust Through Visibility

As organizations expand their use of agentic AI, trust will increasingly be earned through visibility rather than assumptions.

Continuous testing allows organizations to validate customer journeys, identify issues before they escalate, and maintain confidence in AI-driven interactions at scale. This transforms assurance from a compliance requirement into a competitive advantage.

The organizations that lead the next era of customer experience will not necessarily be those that deploy AI the fastest. They will be the organizations that combine innovation with accountability, ensuring every AI-powered interaction is accurate, reliable, and worthy of customer trust.

The Contextual Intelligence Imperative: Building the Contact Center of 2030

CCW Digital’s Contact Center of 2030 Market Study set out to capture where customer experience actually stands today — what customers expect, where the industry is falling short, and what it will take to close the gap. The findings are hard to ignore: 56% of customers say experiences have not improved, or have gotten worse, over the past year.

The failure points are, unfortunately, familiar. Customers are repeating themselves across channels, utilizing automated tools that don’t help, and waiting longer than they should. These are not new problems, they are the same ones leaders have been working to solve for years.

With so many conversations about the future of experiences, investments in increasingly sophisticated CX technology, and a dedication to improvement, it seems organizations have gotten off track. The challenge today is not a lack of trying, but inefficiencies that seep down to the foundational level. New technology gets layered onto fragmented journeys, and the result is more complexity, not less friction. What’s currently missing is not effort, it’s contextual intelligence: the ability to carry insight across interactions and deliver experiences that feel consistent.

As organizations look toward 2030, the path forward starts with fixing what is broken now. By alleviating friction, securing connection and leading with contextual intelligence, organizations can finally optimize experiences.

What’s wrong with experiences today?

The source of customer frustration is becoming increasingly evident. According to CCW Digital research, 55% of customers say their typical interaction feels generic, and only 25% describe their experiences as highly personalized to their needs and expectations.

“The reality behind this decline in customer satisfaction is that while enterprises have invested billions into customer experience, much of that capital has gone into disconnected digital silos rather than a cohesive journey,” shares Tom Eggemeier, CEO of Zendesk . “The disappointment is not driven by isolated failures, but by a persistent pattern of inconsistency, friction, and the feeling of being unseen.”

The data reflects a consistent pattern of friction across the customer journey. Among the most commonly cited pain points: long wait times, difficulty reaching a live person, needing to repeat information, and being asked repetitive questions across interactions.

These challenges extend to human-led support as well. Only 17% of customers say agents know enough about them to personalize the conversation without extensive prompting, and just 16% say agents regularly go beyond scripted responses to find the right resolution.

What customers are asking for is not complicated, yet organizations consistently fail to recognize the importance of these details. The data makes it clear that customers want experiences that reflect their history, anticipate their needs, and feel tailored rather than transactional. As leaders continue to prioritize operational efficiency, it becomes more and more difficult to meet this level of personalization, with many falling short of expectations.

So, what do customers actually want?

Beyond key pain points, it is essential to nail down what customers actually want out of experiences. The answer, at its core, is resolution powered by continuity. Customers do not want to re-introduce themselves every time they reach out. They do not want to repeat a problem they already explained, or start over because they switched channels. They want to be recognized as someone with a history, a context, and a prior relationship with the brand.

The data makes clear how far organizations are from delivering that right now. Only 17% of customers say experiences felt consistent across touchpoints, and just 18% strongly agree they could switch channels without having to repeat information or start over. Most strikingly, 70% of customers report regularly having to repeat information or re-explain their situation, becoming a friction point so widespread it is now more of a default expectation rather than exception.

This lack of personalization ultimately carries more weight than it once did. As AI absorbs routine tasks, the interactions reaching human agents are increasingly complex, emotionally charged, and high-stakes. Customers arriving at that point have already cleared a higher bar, and their tolerance for disjointed handoffs is lower than ever before. They expect the agent to know who they are and where the conversation left off.

Currently, 75% of customers say a phone interaction with a live human is the only channel where they feel confident their problem will actually be solved. It is becoming increasingly clear that human judgment remains irreplaceable.

“True personalization is not a superficial marketing technique; it requires an architectural baseline of continuous context. If a customer uploads a video detailing a product defect at midnight and calls support the following morning, they expect a seamless continuation of that exact thought. When an enterprise possesses and leverages a unified data memory, personalization ceases to be a reactive tactic and builds a stronger relationship in realtime. Without this continuous context, every interaction is just an expensive, frustrating first date,” shares Eggemeier.

What he is describing is not a feature request or a ‘nice to have’, it should be the baseline. Meeting this standard requires contextual intelligence: a unified data memory that travels with the customer across every channel and every interaction. Without it, consistency is impossible, and the friction customers experience today will persist regardless of how much the surrounding technology evolves.

What Does the Customer of 2030 Want?

Beyond what is happening today, organizations must also prepare for the customer of 2030 — one that might look vastly different to the individuals brands are engaging with right now. By 2030, many consumers will deploy their own personal AI agents to manage interactions on their behalf; disputing bills, adjusting subscriptions, resolving service issues, operating at a heightened speed and expecting the same in return.

This is not a distant hypothetical, it is the logical endpoint of the autonomous shift that is already underway. As AI absorbs more of the routine interaction layer, the contact center of 2030 will need to be built not just for human customers, but for autonomous consumers, each carrying full context and expecting a frictionless resolution.

The implications for human agents are just as significant. The generalist model, where agents bounce between departments, handling whatever comes in, is not built for this environment.

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According to Zendesk Leader’s Guide to the Agentic Contact Center, 90% of contact center leaders agree that human agents must evolve into highly specialized, consultative experts. The agent of 2030 is a specialist, brought in to leverage judgment, nuance, and empathy that automation cannot replicate.

“To keep operations grounded in human value, organizations must focus on context continuity. Humans are fundamentally wired to seek direct connection when an experience goes wrong or when a situation is highly emotional or time-sensitive. We must remain true to what customers want by ensuring that when an issue escalates from an AI Agent to a live human expert, the transition is instantaneous and seamless, passing a full intelligence summary of the interaction history straight into the workspace,” Jonathan Barouch, VP & GM, Contact Center, Zendesk states.

The contact center of 2030 is not simply a faster or more automated version of what exists today. It is fundamentally different; it will be built around specialization and contextual continuity. To prepare, leaders must ensure technology and strategy is built around both of these pillars.

The Architecture of Contextual Intelligence

Closing the gap between where customer experiences stand today and what the customer of 2030 will demand is not a matter of choosing between automation and human connection. The organizations that will succeed are those that design ecosystems where autonomous intelligence and human expertise reinforce one another, each adding value where the other cannot.

The foundation of that ecosystem is real-time, contextual intelligence. Technology must function as an active companion to the human agent, analyzing interactions as they unfold, surfacing relevant knowledge, drafting nuanced responses, and flagging patterns without requiring the agent to search for them.

Baruch shares, “We must design systems that actively reduce cognitive fatigue for employees. By natively embedding global cloud communications and voice infrastructure directly into a single, unified agent workspace, we free human teams from manual data entry and “swivel-chair” tool switching. This allows them to focus 100% of their energy on what customers actually want out of a human interaction: emotional intelligence, active empathy, and authentic relationship building.”

For 87% of the executives expecting agents to shift into a supervisory capacity, overseeing automated customer conversations simultaneously, this integration is critical. By designing systems to actively reduce cognitive fatigue and freeing human teams from manual processes, they can begin the journey of shifting into this supervisor role.

The Unified Data Loop

The value of an intelligence layer extends beyond speed and efficiency, it offers the ability to continuously improve. Every time a human agent applies judgment to solve a complex concern, the system captures that resolution and feeds it back into the knowledge base. With this, individual expertise eventually becomes institutional intelligence.

Each interaction, even those that fail - the system learns from, makes the next one easier. This drives something more structural: the contact center transitioning into a unified data loop; one where every customer signal continuously feeds and improves the capacity for autonomous resolution.

By 2030, that loop will be pivotal. As mentioned earlier, the rise of custombots, or personal AI agents deployed by consumers to manage their own service interactions, will fundamentally change the nature of incoming demand.

According to research from the Leader’s Guide to the Agentic Contact Center, 92% of customer service executives already recognize this shift, agreeing that their departments are evolving from reactive help desks into vital revenue engines. A contact center built on isolated digital silos will have no foundation to meet that moment, its systems will simply be unable to operate at the speed and scale customers will require.

The opportunity, however, runs deeper than just keeping pace. By consolidating infrastructure into a singular data loop, organizations can audit every interaction in real time, transforming everyday customer conversations into strategic intelligence for product design, marketing, and retention.

Conclusion

What makes all of this possible, and what makes the entire architecture work, is seamlessness at the human layer. When that infrastructure is in place, agents can do what no automation will replicate: bring genuine judgment, emotional intelligence, and human connection to the moments that matter most.

With this, customers will begin to see the level of personalization they are looking for. With humans in the loop, both in the background and on the frontlines, customers will be met with the level of care and context that powers personalized interactions. That is the contact center of 2030 — not a system built for efficiency at the expense of humanity, but one architectured to make humanity accessible at every touchpoint.

Whether it is self-service or a human-led interaction, contextual intelligence feeds a tailored, humancentered interaction. In 2030, this dedication to contextual insight and personalization will be more than just a mechanism for relationship building — it will transform experiences for the better.

The State of the Frontline

Discussions about what the frontline should look like have been endless, and largely inconclusive. The prevailing narrative has stayed consistent: AI handles simple issues, humans handle complex ones. But today’s technology is revealing just how much nuance that framework leaves on the table.

The old approach reserved basic, routine-based tasks like password resets and order status requests for chatbots and IVR systems while humans took on higher-complexity issues. It was an intuitive split that many leaders have been preaching for years, but also a limited one.

This may no longer be the reality. According to CCW Digital research, only 28% of leaders believe this clean division will hold; rather, they report that certain issues will remain fully AI-handled while others stay human. Another 30% envision something different: AI taking on the majority of frontline communication, with humans available to guide and intervene when needed.

Cresta’s CX Workforce Report paints an even clearer picture. 76% of customer conversations are already handled by AI and humans together, whether collaboratively or with AI assisting human agents behind the scenes. As AI absorbs more volume, human work isn’t disappearing.

Matter of fact, more than 9 in 10 CX and IT leaders say the issues reaching human agents are becoming more complex. And when it comes to the skills that matter most in an AI-enabled workforce, leaders point to emotional intelligence, judgment, communication, and problem-solving, the capabilities that remain distinctly human.

As AI’s capabilities expand, the real question becomes: if AI agents can handle more than simple tasks, what does the next-generation frontline actually look like?

It’s Not Simple vs. Complex. It’s Transactional vs. Emotional.

The reason so many leaders hesitate to define the future frontline is that they’re still stuck on the wrong distinction.

Devon Mychal, Vice President of Product Marketing at Cresta , puts it directly: “The first thing we’d correct in how most organizations think about this is the assumption that AI agents are only suited for simple, predictable, highly structured interactions.”

That assumption, Mychal argues, is outdated. AI can now handle complex technical troubleshooting, plan changes, billing disputes, and nuanced product questions. “The bar for what AI can handle well is much higher than most people assume,” he confirms.

This reframes the real dividing line: it is no longer simple vs. complex, but emotional vs. transactional.

“AI agents should lead when the conversation, regardless of complexity, doesn’t require the emotional presence of another human or the exercise of judgment that goes beyond policy. A customer walking through advanced technical troubleshooting, navigating a complex account change, or resolving a multi-step billing issue can all be handled exceptionally well by AI, as long as the customer isn’t in a highlycharged emotional state and the situation doesn’t call for discretionary human judgment. Brinks Home is a great example. Their AI agent handles complex, multi-step troubleshooting conversations that most would assume require a human, with customers describing it as the best virtual assistant they’ve ever experienced.”

The implication is significant: what remains distinctly human is less about capability, and more about the ability to truly connect with customers. High-stakes interactions, emotionally-charged conversations, and moments requiring nuanced judgment are where human agents add the most value moving forward.

Automation in Isolation Is the Wrong Starting Point

Before organizations decide how to handle customer conversations, they should ask whether certain conversations need to happen at all. Conversation data, when analyzed at scale, reveals patterns of systemic friction that no amount of automation actually fixes. Repeat contacts driven by unclear billing statements, failed self-service flows, or avoidable policy confusion are symptoms of upstream problems.

Eliminating that volume is the highest-leverage move, and not just for cost reasons alone. Automating a conversation still carries cost, but more importantly, the customer still had a problem that could’ve been avoided. Whether they waited on hold or got an instant AI response, having to reach out at all is the failure. Fixing the upstream issue and preventing the conversation entirely is a better outcome than just handling the same avoidable interaction more efficiently.

Once that work is done, the remaining conversations fall into two categories: those where AI agents can lead the full interaction end-to-end, and those where human agents need AI working alongside them to show up effectively. That’s the right framework: start with what shouldn’t exist, then optimize what remains.

Operational Challenges Limiting Human Agent Performance

As the frontline shifts, human agents will take on fewer transactional interactions and more that are emotionally complex and high-stakes. That shift only works if agents are actually equipped to handle it.

The data suggests most aren’t, yet. Too often, agents who try to slow down, connect, and be proactive get penalized for it through AHT and efficiency metrics that reward speed over quality. Only 15% of customers say agents go above and beyond in a typical interaction. Only 19% say agents can detect when they’re frustrated or angry, let alone de-escalate effectively. Even when agents want to show up differently, the operational environment often won’t let them. An agent who slows down to genuinely connect or handle a sensitive moment with care gets flagged for it. The system optimizes against the very behaviors the nextgeneration frontline demands.

Closing that gap requires two things: clearing agents of unnecessary work, and giving them the context to connect meaningfully with customers. As AI absorbs transactional volume and reduces the overall cost to serve, organizations gain real room to change this. Human agents can be measured differently, hired differently, and paid to reflect a new definition of what great looks like.

50% of contact center leaders report their teams have limited or no access to detailed customer data, profiles, history, and preferences. Even worse, 56% say agents lack real-time visibility into customer intent and sentiment. If agents are expected to lead complex, emotionally-charged conversations, entering those interactions without context is concerning.

Cresta’s CX Workforce Report reinforces how deep this problem runs. Integration complexity (81%) and poor data quality (53%) are the top barriers to AI adoption, and only 7% of leaders say conversation data is easily accessible across the business. The data gap isn’t just a frontline problem; it’s an organizational one, and it’s slowing down the very AI investments meant to solve it.

“Customers arrive with history — past purchases, prior complaints, known preferences — yet most customer experiences treat every interaction like the first one. Agents are working from incomplete snapshots and AI systems are often no better. The result is generic responses that don’t reflect what the customer actually needs, which signals to them that the company doesn’t really know or value them,” Mychal states.

Unfortunately, this generic experience is far too common today. Only 26% of customers say their experiences feel personalized to their specific needs. Until that changes, the promise of the next-generation frontline will remain unrealized.

Experiences Need to Actually Improve

Reflecting on what the new frontline might look like, it is clear that technology must work to actually see change.

According to CCW Digital research, only 27% of customers say the technology used in a typical interaction made the experience better, a troubling data point as we see so many organizations investing in CX. The issue isn’t the technology itself, it’s the fact that too often it has been deployed with cost reduction as the primary objective, not customer experience.

“Customers aren’t necessarily anti-AI. They’re anti-bad -AI. When an interaction works — when it’s fast, accurate, and actually resolves the issue — customers embrace it. What they reject is AI that wastes their time, makes them repeat themselves, or leaves them feeling like they’ve been passed off rather than helped,” Mychal states.

Earning back customer trust means deploying technology that serves the customer and adds value to their experience.

Getting Customers Onboard

To establish a new frontline, customers must be onboard with the changes being made. The path there requires more than effective technology, it requires deliberate design. The Cresta team identifies four pillars that organizations should build toward:

Truly personalized interactions. AI agents can process and act on more customer context than any human agent realistically could in the moment. For a telco, that means when a customer calls about roaming charges, the AI already knows they just traveled internationally, sees their current plan, and can offer the right add-on or credit in seconds. This is not only an efficient experience, but one that builds true loyalty.

Proactively helpful, not just reactive. Most service is still reactive; customers call when something goes wrong. AI can change that by continuously monitoring signals and triggering the right action before customers even know they have a problem. For a health insurer, that means proactively reaching out when a claim has been in review too long, flagging missing documentation, guiding next steps, before frustration ever prompts a call.

Always available. AI agents don’t have shift changes or capacity limits. When a regional outage drives overnight call volume to five times normal, every customer still gets immediate support. No hold times, no trade-offs between service levels and cost control.

Connected journeys that actually build on each other: AI can close one of the longeststanding failures in CX: every interaction starting from zero. By tying together past AI conversations, human agent interactions, and full channel history, each interaction builds on the last. Customers stop feeling like strangers every time they reach out.

When organizations lead with experiences like these, customer perception of AI takes care of itself.

Conclusion

The frontline is being redefined, whether we like it or not. As agentic AI enters the contact center, organizations must rethink the order and flow of every customer journey.

In sticking with traditional CX narratives, too many leaders remain anchored to the ‘AI for simple, humans for complex’ ideal. This ultimately leaves little room to move past an increasingly outdated model.

The true next-generation frontline isn’t AI replacing humans or humans solely managing AI. It’s both, deployed where each creates the most value. As agentic AI absorbs the majority of frontline conversations, human agents are freed to do what only they can: show up with empathy, judgment, and presence in the moments that demand it.

The real next step is building the structures that make that possible; clear orchestration, rich customer context, and workflows designed around emotional and high-stakes interactions rather than repetitive ones.

As customer experience of 2030 comes into focus, this is the strategy that will separate brands that simply meet customer expectations, from those that genuinely earn their loyalty over the long term.

Ready to unlock a new era of customer experience? See how AI and human agents work together on a single, unified platform. Request time with the Cresta team here.

Why AI Projects Fail in 2026

For CX leaders, artificial intelligence has taken on an unprecedented role in both supporting and eroding customer contact operations. Throughout this Market Study, we examined how customers are responding to the interactions and journeys designed for them in 2026, with AI at the center of the vast majority. Customers have become more receptive to AI overall, with 42% of the mind that it has already had a positive effect on customer service, and more expect that it will in the coming years. But another dominant theme in this data is the dissatisfaction, frustration, and ambiguity it creates inside experiences where it does not quite

fit. Acknowledging that many AI initiatives don’t work— due to subpar implementations, misunderstanding of capabilities, false promises, or many other variables—is an essential step for leaders. AI poses one of the most significant sunk cost fallacies facing businesses in the twenty-first century: poor performing AI is not just a waste of capital, it can destroy a brand’s reputation and disappoint customers en masse overnight.

Halfway through 2026, there is plenty to be learned from failed AI projects in contact centers and across the CX world. At a high level, AI failure has shown up in the following ways:

The cost of these integration services, not to mention the cost of system downtime or executive and consultancy costs associated with these projects, are often not factored into the ROI calculation that AI systems promise. Thus, even “successful” projects result in less value than anticipated, and may continue to incur costs as business practices and regulations evolve.

Broken systems are friction generators

The great disconnect of this era of customer service is that most of the technology implemented to improve customer service turns into a source of friction. Take one of the top sources of frustration for customers according to CCW Digital, the need to repeat oneself or repeat information throughout an interaction. More often than not, this is a structural problem: chat history does not transfer to voice, IVR intent does not appear on the agent screen, and more. “Customers do not experience your org chart or your tech stack separately. They experience the handoffs between them. That is where friction lives” says Bill.

AI should make agents better, not just busier

Agents are at the heart of customer contact, and targeted AI solutions that effectively improve the agent’s experience can have a resonant effect on the customers. However, poorly implemented AI becomes another tool agents must manage. The result is an issue plaguing contact centers of all sizes and verticals: tool overload. Tool overload is what happens when employees are asked to deliver a simple customer experience through a complicated internal experience. “Tool overload is the silent killer of customer experience,” says Bill. “The customer hears the delay, the confusion, and the lack of confidence, even if they never see the ten systems behind the agent.”

Strategic partnerships ensure AI is used to its full potential

CX leaders have a lot on their plate. Managing the agent experience, and its downstream effect on customers, throughout this season of change is a sizable responsibility, and learning the nuances of the ever-growing variety of solution providers during this explosion of innovation across customer contact is no small task. Developing a CX strategy that encompasses these technology decisions is a full-time job in itself. Rather than dedicating the majority of their time to understanding the technology landscape, CX leaders are increasingly turning to strategic partnerships to take on the brunt of this work so they can focus on developing strategies and customer journeys.

CX leaders need not worry about the disconnect within systems because they have been tested, trialed, and approved at scale. In response, organizations like DATAMARK, who have already built a legacy as an outsourcing partner, fully embedded within the technology landscape, as strategic intelligence partners. Today’s contact centers function as a business within a business, and vertically integrating these functions within one partnership has long been a value-add for organizations who cannot dedicate an entire IT department to support customer contact.

“By 2030, AI will not be the differentiator by itself,” says Bill. “The differentiator will be operational maturity. The companies that win will be the ones that use AI to simplify the customer journey, support the agent, and continuously improve the business behind the interaction.”

The Path To Autonomous Resolution

It is likely that the contact center of 2030 will be unrecognizable next to the contact center of 2010, or even 2020. We’ve witnessed customer preferences evolve, from resisting AI-powered customer service, to embracing it when it can prove itself worthy, to voluntarily using AI functions across their personal and professional lives. Though the mechanisms and channels have changed, the service customers are looking for has changed very little: customers still seek out the most direct route to a resolution, have a penchant for more intelligent experiences that allow them to communicate in natural language, and a strong distaste for waiting, ambiguous systems, and having to repeat themselves. More than ever before, contact center leaders are primed to eliminate these sources of frustration for customers.

Still, the technology that leaders expect to bridge this gap often disappoints, with 67% of contact center leaders sunsetting one or more CX technology in 2025. Bob Sacunas, CX Industry Executive at UiPath breaks down why even the technology that backs customerapproved experiences still falls flat, “The biggest gap we see in the market is not a shortage of conversational AI. It is a shortage of resolution infrastructure, the ability to take a customer’s intent and execute the multi-system, multi-step actions needed to actually solve the problem. Many organizations deploy a sophisticated bot only to discover that resolution requires touching five legacy systems or orchestrating a process that spans days.

The bot ends up handing off to a human who does manually what the AI was supposed to do, and the economics of the investment collapse.”

Deploying experiences that capture customers’ fickle attention and execute on the actions they’re designed to achieve requires a two-pronged approach.

The Customer Perspective

In the survey associated with this Market Study, we found that customers were most likely to abandon a customer service interaction right away if basic voice recognition was used, more so than any other firstline technology. Examining why this might be, basic voice recognition tends to be a letdown on the two key aptitudes customers are looking for: expediency and ability to communicate in natural language. Basic voice moves slower than both a conversation and customerdirected digital interaction, and requires the customer to interface with an unfamiliar menu.

As has been the case since CCW Digital began collecting data on this topic, the biggest driver in inspiring customers to use a self-service tool is access to a live representative at any time. And yet, 41% of customers find getting in touch with a live person to be challenging, and 43% of customers felt the brand they reached out to was purposefully preventing them from interacting with a live agent. Even worse, in the past year, 89% of customers felt like companies were more likely to require them to use a self-service tool before or instead of speaking to a live representative. Without realizing it, many brands are directly creating customer frustration through the structural nudging of customers towards self-service without incentivizing and supporting those channels.

For this reason, containment as a success metric is being increasingly phased out. “Organizations recognize that deflecting customers without resolving their issue creates frustration, repeat contacts, and poorer overall experiences,” says Bob Sacunas. Customers have learned that interacting with a bot often means a longer path to the same human they wanted to reach in the first place. The bot can converse, but it often cannot resolve, so the default is to route them to an agent who restarts the conversation from scratch. Until customers consistently experience AI that resolves issues end-toend, trust will not follow.

Designing Systems that are Built to Capitalize on AI

What differentiates effective self-service experiences from superficial ones is seamlessness. Access to customer information and knowledgebase data makes or breaks a self-service interaction, but the sources of this intelligence are well beyond the technology accountable for the interaction itself. There are a few different dimensions to explore before you can ensure the self-service will create real value for customers.

1. Treat automation as a digital abstraction layer over your existing tech stack. Most organizations are in a perpetual state of legacy system replacement. This does not need to inhibit progress. Automation can connect to your current systems the way a human agent does and orchestrate work across them today, while modernization continues in the background.

2. Match the technology to the task. Use deterministic automation for repeatable, precision-driven work. They are faster, cheaper, more auditable, and easier to govern. Use AI agents for non-deterministic work that requires reasoning, fuzzy matching, or interpretation of unstructured data.

3. Invest in orchestration. Real CX work happens across systems, interactions, workgroups, and time. An orchestration layer that coordinates AI agents, robots, human agents, and core systems is what turns isolated capabilities into autonomous resolutions.

4 . Accelerate with agentic testing. Testing agentic AI manually creates a bottleneck and limits how extensively you can evaluate performance. Agents being tested by other agents are the only scalable way to conceive of every possible path.

The biggest opportunity is not using AI to make contact center agents type faster or shave a few seconds off handle time. The larger opportunity is to automate the work around the agent: escalations, back-office follow-up, case triage, documentation, status checks, billing adjustments, order changes, and other multistep workflows that often sit between the customer’s request and the actual resolution. Customers no longer accept “this will be resolved in three to five business days” when the work itself may only require minutes of execution, and as more brands demonstrate that they’re able to provide truly immediate resolutions, customer expectations will follow suit.

Taking the Next Step Towards Autonomous Resolution

The truth is that most organizations are not ready for the self-service experiences that they roll out. Failing to consider any of the above dimensions of CX infrastructure can result in significant gaps that customers feel, and those gaps will only become more pronounced as more organizations get it right. Across industries, three emergent trends offer insight into why things aren’t working.

• Resolution at the center: from the customer’s perspective, the unit of work is moving from “the call” to “the issue.” Customers, armed with the experience of resolving their issues in a single interaction in a previous experience, will be fully expecting that level of service everywhere they go. In turn, CX leaders will increasingly be measured on issues resolved per attempt and time-to-resolution across channels, not on average handle time. Many CX operations are in this transitional period, wherein their metrics, especially AHT, are not adequately capturing the level of dissatisfaction customers are facing.

• Channel balance is recalibrating: voice and chat will remain critical, but email and back-office workflows, long underinvested in, will emerge as significant areas of automation value. “Don’t ignore email,” says Bob Sacunas. “It remains one of the highest-volume channels, it is already digital, and it gives the AI the time it needs to do real analysis and produce a thoughtful response.”

• The contact center will sit at the center of the organization: contact centers will operate as a hub of orchestrated work that spans systems, teams, and time, rather than a synchronous-only environment. The barriers created by siloed teams and systems are only going to become more apparent, and failing to dismantle them will quickly become a source of regret for leaders.

This is where UiPath can create meaningful efficiency and performance gains. AI agents can interpret customer intent, extract context from calls, chats, emails, documents, or case notes, and determine the next best action. Robots can then execute the predictable system steps across CRM, billing, claims, ERP, and order management systems. Orchestration coordinates the full process across people, systems, and time, including exceptions, approvals, and humanin-the-loop checkpoints. The result is not just a faster conversation; it is a faster resolution.

UiPath success stories follow:

COMPANY: WEX

CHALLENGE:

WEX needed to reduce manual work across fragmented contact center and back-office systems while improving issue resolution speed, email automation, and customer satisfaction. Agents were spending too much time navigating systems, handling repetitive requests, and processing routine claims manually.

SOLUTION & RESULT:

UiPath Autopilot and agentic automation streamlined customer support workflows, automated card order fulfillment, and improved claims adjudication. WEX also deployed Communications Mining and Agent Builder to classify emails, detect customer intent, and recommend responses directly in Salesforce. Results included $2.7M in savings, 70,000 labor hours saved annually, a 90%+ email classification and recommendation success rate, and faster claims processing with agents focused on higher-value customer interactions.

MARKET STUDY

COMPANY: Flutter

CHALLENGE:

Flutter sought to improve customer experience and modernize customer service operations amid rising inquiry volumes, low customer satisfaction scores, and growing operational costs. The company needed faster, more accurate issue resolution while reducing unnecessary escalations and friction during peak demand periods.

SOLUTION & RESULT:

Flutter partnered with UiPath to automate support journeys using conversational AI, backend automation, and human-in-the-loop workflows. Automation resolved customer issues end-to-end across chat, payments, fraud prevention, and KYC processes. Flutter automated 80% of support journeys, increased customer activity by 140%, improved NPS from -10 to +40, reduced transfers from 20% to 6%, cut KYC response times from 24 hours to 15 minutes, and avoided £12M in projected cost increases.

COMPANY: Encova Insurance

CHALLENGE:

Encova wanted to improve customer retention, reduce manual processing across claims and underwriting workflows, and give agents more time to focus on customer relationships rather than repetitive administrative work.

SOLUTION & RESULT:

UiPath automation streamlined customer retention outreach, policy intake, claims invoice processing, and commercial lines endorsements. AI-powered Document Understanding improved claims processing accuracy and reduced manual effort, while automated email notifications proactively alerted agents to at-risk customers. Results included 99% document processing accuracy, a 98% reduction in policy intake processing time, and a 95% reduction in annual commercial endorsement processing time, enabling faster service and more personalized customer engagement.

The Role of Voice AI in Next-Generation Banking + Financial Services

Delivering exceptional customer service experiences at scale has long been an endeavor of probability. Progress in CX is often secured through initiatives that have the greatest impact on as many customers as possible. Relatively recently, advancements in analytical capabilities have allowed platform providers and enterprise clients to meet and exceed customer expectations. These enhanced insights into customer behavior coupled with the power of novel technologies and AI makes it seem as though success is inevitable for today’s contact centers.

Customers, on the whole, have long been vocal about their desire for immediate, consultative, and personalized customer service. That is why, year over year, CCW Digital data reveals the top customer service pain points are long hold times and difficulties getting in touch with a live agent. But this preference has always been more about the expediency of service. “Consumers expect fast, personalized, and frictionless interactions that feel natural and human,” says Dan Kutchel, CEO of Overtime. “In collections and billing, they also expect proactive communication such as payment reminders, account updates, and self service options before issues

escalate.” These straightforward, thorough engagements with customers are far more effective than the passive, unintuitive interactions that characterize much of the self-service experiences today.

In too many cases, AI investments are driven by pressure to adopt new technology rather than a clear operational strategy, leading to projects that fail to deliver measurable business outcomes. The critical mass of ineffectual AI-forward customer service has soured the public perception of AI-led interactions. Though the inefficiency and frustration of some of these experiences are undeniable, ascribing their failure to the fact that they are AI is misguided, “Today, many organizations already automate a significant portion of routine outbound communications such as payment reminders, account updates, and early stage collections outreach,” says Dan Kutchel. “In some environments we’ve seen, automation rates can reach up to 90% for these repetitive interactions.”

To identify where AI can deliver outsized value for banks and other regulated spaces, we can look to the early wins and losses of AI-led customer service across industries.

Customer standards and sensitivity are higher than ever

Many customers’ introduction to AI customer service skewed negatively. Overzealous leadership that rolled out AI features before they were ready and a lack of adequate communication around the shift to AI were hallmarks of the last three years of customer service, and customers are justifiably fed up with having to waste their time acting as test subjects for new technology. This isn’t an accident, according to CCW Digital’s 2026 Market Study on Banking, Financial Services, and Insurance (BFSI), 85% of BFSI leaders admit that customer-facing AI tools are increasing customer effort. At a time when all signs clearly point towards winning experiences being synonymous with quick and easy experiences, this strategy is counterintuitive.

What brands fail to recognize is that they are now building confidence in AI customer service from the ground up. A stark 41% of customers who recently engaged with a human-like AI voice bot ultimately abandoned the interaction after a failed attempt at receiving service, and customer persistence in seeking resolutions through any channel that is not staffed by a live agent is markedly lower.

Compliance and customer privacy share the same goals

Today’s leaders are tasked with balancing the benefits of AI with transparency, consistency, and auditability. These key tenets that protect customer privacy and build trust also make up the foundation for compliance in technology-driven experience. According to CCW Digital data, 76% of customers are concerned about data security and privacy when using AI and self-service channels, with 33% of customers stating that they are very concerned. Could you afford to lose any of these customers? In this day and age, all it takes is news of a breach, leak, or scam, to reach your customers and their confidence in your brand can be significantly diminished. Even worse, the widespread belief that AI is a black box means even just confusing or unclear experiences may trigger customer concern that their data is not secure.

Banks and financial institutions face unique challenges due to the high stakes nature of their work and the amount of personal information and data required from customers. This data is what shapes experiences today: 49% of BFSI leaders are using historical info to make recommendations, and 51% are making personalized information about customers easily accessible to their agents. Clear insight into how this data is stored, secured, and acted upon is non-negotiable.

Customer centricity is a matter of common sense

Above all, customers want to be treated like people. Again, customers’ persistence in seeking out a live agent is not always driven by a stubborn belief that AI agents are incapable or technology is untrustworthy, it is more that the effort of waiting for and demanding a representative is still less effort and has more reliable outcomes than trying to figure out a digital self-service interaction. Beliefs like this are based on customers’ lived experiences using AI, and cannot be changed by force. Acknowledging this speaks to a different kind of trust: the trust that the experience is worth the customer’s time, effort and energy.

What sets the phone channel apart from the rest is the intuitive ease of communication. Communicating with natural language requires the least amount of effort on behalf of the customer and presents the fewest barriers to accessibility. As part of this Market Study, CCW Digital found that being able to communicate in natural

language is a primary incentive for customers to be more likely to use a self-service tool. When we also consider that one of the top challenges for customers is needing to repeat information, it becomes clearer that customers’ gripes are not with the channel, but are actually a lot simpler: they just want to be understood.

Voice AI: the best of both worlds

Voice AI currently sits at the sweet spot between customer palatability and enterprise-grade capacity to deliver and personalize experiences at scale. Dan Kutchel emphasizes how voice AI agents capture the most value from the technology, “AI delivers the greatest impact in high volume, repeatable customer interactions that are typically handled over the phone. These are often simple but time consuming conversations where customers face long hold times, frustrating self service experiences, or inconsistent support.”

Recognizing that there are abundant voice AI offerings, banking leaders are tasked with choosing a platform that is built specifically for regulated customer operations. Let’s examine the factors that make voice AI agents a good fit for banks:

1. Domain expertise

There are many different ways to measure the accuracy of voice AI, but not all voice AI is graded on its ability to fluently communicate about financial topics and comprehend with near-perfect accuracy. While mass market voice AI agents may be more astute at understanding a wide variety of topics, situations, and customer queries, voice AI for banking need only understand a narrow window. This principle has long applied to human agent talent, wherein a customer service agent from another discipline would never be expected, untrained, to serve banking customers; the same is true of AI agents.

MARKET STUDY

2. Strategy that goes beyond the conversation

The voice experience the customer sees is just the tip of the iceberg. “The real value of Agentic AI is broader than conversation quality alone,” explains Dan Kutchel. As AI scales customer engagement and automation, organizations are increasingly focused on business outcomes and operational impact. In collections, billing, and regulated customer engagement, success is measured by metrics such as recovery performance, promises to pay, operational capacity unlocked, cost reduction, revenue generated, and overall efficiency improvements.

The focus shifts from simply handling conversations to delivering measurable business results. This idea is at the forefront of a major shift taking place across customer contact: use of AI for repeatable tasks is opening up the time, attention, and resources for high-performing agents to deliver more personalized, concierge-style service. This restructuring is changing where value is derived from the contact center, and leaders that can make the most of this transformation will find improved customer service is directly connected to elevated customer lifetime value. Organizations using the Overtime AI CX platform in banking and financial services have already experienced up to 10x higher collections efficiency compared to traditional human-only teams, demonstrating what is possible when AI and human strengths are maximized together.

3. Sovereignty is paramount

Compliance requirements are often a non-starter for AI in CX of regulated industries, as most AI is built to be used within a variety of industries and systems. Where most AI providers have a more horizontal approach, Overtime is vertical: with in house text-to-speech, a customized LLM, and their own transcription model, organizations that use Acclaim’s voice AI need not worry about their customers’ data being sent to third parties. The Overtime platform, which can be deployed on-site or within the cloud, contains everything needed to train and integrate voice AI agents within one system, making auditability more streamlined and guardrails far more straightforward to set up and rely on.

Conclusion

With AI dominating our news and culture, it has been challenging to get accurate and comprehensive insights into how to make it work best for customers. While early iterations did not impress customers much in practice, CCW Digital finds that in 2026, 67% of customers believe in the potential for AI to improve customer experiences, 40% of which have already experienced its benefits. A distinct difference between AI agents that work for customers and AI agents that work for businesses has emerged, and it’s simpler than one would expect.

Most customers’ opposition to AI and newness in general is driven by the confusion and wasted time that often accompanies unfamiliarity, but that unfamiliarity can be quickly and effectively remediated with the right balance of communication and transparency. Voice AI agents like Overtime’s help organizations capitalize on this interaction twofold: customers are pleasantly surprised by how effective it is at reaching their resolution, and organizations reap the benefits of scalable front-line experiences without compromising the integrity of their brand. Success stories highlighting Overtime’s role in this voice AI revolution follow:

COMPANY: TBC Group

CHALLENGE:

Rapid expansion of its loan portfolio put growing pressure on its debt collection operations.

SOLUTION & RESULT:

90% of early-stage delinquency calls and 100K monthly sales interactions go through AI agents.

Delivered $2.3M in operational savings in a single year.

COMPANY: TBC Group

CHALLENGE:

Deliver multilingual solutions and efficiency gains in regions with varied, complex dialects.

SOLUTION & RESULT:

“Overtime’s work assisted in building our Uzbek-language LLMs and Speech Tech, in what is one of Central Asia’s most ambitious AI projects. This technology powers AI agents across sales and collections that have driven efficiency gains and delivered an improved customer experience, and also underpins Lola, our proprietary AI banking assistant.” – Nika Kurdiani, CEO of TBC Uzbekistan

COMPANY: Banco Plata

CHALLENGE:

Partner with fintech innovators to enhance their products

SOLUTION & RESULT:

“Engaging with Overtime’s engineers has given us access to unique, deep knowledge in real-time voice processing and voice agent development that’s hard to find elsewhere.”

– Andrey Shelekhin, CTO, Banco Plata

The issue is not that the technology lacks capability.

The issue is that workflows were never redesigned around it.

True transformation requires CX orchestration.

Orchestration provides the connective tissue that brings customer service operations together.

It enables organizations to:

• Route work to the right resource at the right moment

• Connect data across channels and systems

• Coordinate AI and human decision-making

• Apply governance consistently

• Monitor outcomes in real time

• Create continuous feedback loops that drive improvement

Without orchestration, AI often remains fragmented.

With orchestration, AI becomes part of a larger system designed to deliver measurable business outcomes.

The difference is significant.

Organizations move from:

Fragmented tools → Connected ecosystems

Static reporting → Real-time intelligence

Manual QA → Continuous performance optimization

Isolated automation → End-to-end workflow integration

Technology deployments → Business transformation

AI does not fail because the technology is immature.

AI fails because organizations attempt to scale it without redesigning the workflows surrounding it.

Why Human Expertise Becomes More Important, Not Less

One of the most common misconceptions surrounding AI and innovation is that human involvement will diminish over time.

The opposite is happening.

As AI assumes responsibility for routine and transactional interactions, the remaining customer conversations become increasingly valuable.

They’re often:

• More complex

• More specialized

• More emotionally sensitive

• More regulated

• Higher impact for customers and brands

These interactions require expertise.

Yet traditional staffing models create significant challenges.

Organizations often struggle with:

• Fixed staffing capacity

• Geographic limitations

• Long hiring cycles

• Specialized skill shortages

• Demand volatility

The future of customer service requires a different approach.

Organizations need access to expertise when they need it, not simply where they can hire it.

The most successful customer service organizations will leverage flexible access to specialized talent.

Organizations that can quickly access the right expertise at the right moment will be better positioned to deliver exceptional customer experiences while maintaining operational flexibility.

This shift aligns directly with the evolving role of human expertise within AI-enabled environments.

As automation expands, human contributions become more strategic, specialized, and valuable.

The future belongs to organizations that know how to orchestrate both.

The New CX Operating Model: Trust, Governance, and Adaptability

As organizations accelerate AI adoption, new challenges emerge alongside new opportunities.

Customers are increasingly concerned about privacy, transparency, and accountability.

Regulators are increasing scrutiny.

Business leaders face growing pressure to demonstrate measurable outcomes while managing risk.

As a result, scaling AI successfully requires more than technology deployment.

It requires governance.

Before organizations can scale AI effectively, several foundational elements must be in place:

Connected Data

AI systems are only as effective as the information available to them. Data must be accessible, accurate, and connected across the customer journey.

Workflow Integration

Technology must operate within existing business processes rather than outside them.

Governance and Compliance

Organizations need clear accountability, oversight, security controls, and risk management frameworks.

Continuous Feedback Loops

Customer interactions should generate insights that improve both AI performance and operational decisionmaking over time.

Outcome-Based Measurement

Success should be measured by customer outcomes, business outcomes, and operational effectiveness, not simply activity metrics.

The organizations that lead the next era of customer experience will embrace a fundamentally different operating model.

They will:

• Design around outcomes rather than cost and tools

• Orchestrate AI and human expertise together

• Build trust before pursuing scale

• Embed AI directly into operational workflows

• Leverage flexible access to specialized expertise

• Govern responsibly and transparently

• Continuously adapt as customer expectations evolve

The future of customer service won’t be won by organizations that deploy the most AI.

It will be won by organizations that successfully orchestrate AI, human expertise, workflows, and governance to deliver better outcomes for customers and the business.

That is the orchestration imperative.

And it’s rapidly becoming the defining competitive advantage of modern customer experience.

1+1 = 3: Redefine the AI-Human CX Balance to Regain Customer Trust

In theory, AI represents a vehicle for transforming customer experiences. It represents a way to eliminate long-standing pain points, foster more meaningful connections, and achieve lasting customer trust.

In practice, it has not only fallen short of that promise but, in some cases, actually weakened business-customer relationships.

The root of the issue was the philosophy organizations held regarding the AI-human balance. Organizations have long approached this incorrectly, minimizing the impact of AI investments and preventing humans from doing what they do best.

Initially, AI emerged as an “extra credit” option. It offered slight optimizations to employee workflows or slight enhancements to customer self-service, but it did not elevate core experiences. Broken processes stayed broken, limited channels stayed limited, and inadequate resolutions stayed inadequate. Customers had no reason to trust AI as a pathway to better outcomes.

Then, leaders began to consider a hardline dichotomy between AI and human agents; some issues were exclusively the former, others were reserved for the latter. This seemed appealing on paper, but it ultimately added fragmentation to the customer journey, while also creating unwanted trade-offs. Customers generally either had to accept generic, “policy” answers with AI, or endure slow processes when engaging with humans. They, again, had no reason to trust AI as a pathway to better outcomes.

To overcome these limitations and finally capitalize on the AI transformation, it is imperative to embrace a new vision. Fueling the idea of an “agentic enterprise,” this vision involves leveraging AI as the foundational engine and humans as the way to take experiences to the next level.

“The key for CX leaders is to adopt a new way of thinking. AI as an operating system rather than a new feature. AI in the hands of every employee is a superpower rather than a strategy to reduce headcount. The equation is 1+1=3. AI plus Human will deliver disproportionate benefits to the companies who make the most of this opportunity.” - Chris Arnold, ASAPP

any reason to doubt the information they are receiving is accurate, or that the information they are sharing is protected, they have every reason to question quality. And they have no reason to continue engaging.

Previous approaches to AI fell short of these critical tenets.

AI-Powered Experiences Were Ineffective

CCW Digital’s Consumer Preferences Survey reveals why only 18% of consumers are confident they can solve their problems in chatbots, and why only 13% have that level of confidence in IVRs.

Past AI-powered experiences were inadequate for all but the most basic, routine issues. They felt like efforts to deflect customers from gaining access to the support they wanted, rather than platforms for making valuable resolutions more accessible.

After detailing where AI has missed the mark, this briefing will provide clear action steps for reformulating the AI-human balance and reigniting customer trust and confidence.

Why the Previous AI Model Weakened Customer

Trust

There are three facets to building customer trust.

First, brands must consistently deliver a fundamentally strong customer experience. If customers lack confidence that the interaction will be convenient or produce an adequate resolution, they have no reason to trust the brand. They will not see the organization as one that can stand behind what it is selling – and thus not see any reason to commit their future spend.

Second, brands must demonstrate personalization within that experience. If the interaction lacks appreciation for issue context, customer sentiment, and future intentions, it will fail to ignite a perception of recognition and appreciation. As a result, it will fail to foster a meaningful connection with the customer. Customers may buy from the brand, but they certainly will not feel any unique sense of loyalty.

Third, brands must operate with an unwavering attention to reliability, consistency, and security. If customers have

“Resistance to autonomous solutions in the past has largely been rooted in those solutions being largely ineffective. Not so much the easy interactions, but the more complex interactions. Behind this unfortunate result was the company’s relentless focus on deflecting customers for the sake of cost reductions. We deployed automation for automation’s sake rather than fully committing to great customer experiences.” - Chris Arnold, ASAPP

These automated experiences reduced interactions to fact-based inquiries for which a single, policy-driven answer could always apply. They failed to appreciate the nuance with which customers would express their issue, let alone the variance in the way they would expect their issue to be resolved. Outcomes were not only impersonal but downright inadequate.

“The binary mindset of the past, which was automation or agent, is not the right question to be asking. The right question needs to be grounded in the customer’s perspective. What is required to create the optimal customer experience while also delivering optimal business value?” - Chris Arnold, ASAPP

As a result, customers not only found the resolutions unsatisfactory but the experiences wholly inconvenient, unintuitive, and frustrating. “Difficulty explaining one’s issue” ranked as a common pain point, and the end result is that only 25% feel that technology adds value to the experience.

Handoffs Added More Friction, and Less Value

To make matters worse, the inevitable escalation to a human agent only added more friction. Operating in isolation from the AI-powered experience, the human agent would have little-to-no context on what the customer already shared, and would thus subject the customer to repetitive questions – and undue waiting as they pored through the relevant information.

This is why 61% of customers felt experiences were largely inconvenient, why “repetitive questioning” became more widespread in 2026, and why human agents continued to come across as distracted and unfocused.

As customer frustration mounted, so too did employee dissatisfaction. Employees were all-at-once dealing with the hassle of navigating tedious workflows, complicated lookups, and hostility from customers who felt their time was being wasted. This all combined to adversely affect sentiment, weakening their productivity and incall demeanor, and ultimately inhibiting their ability to cultivate customer trust.

Why a Reformulated AI-Human Balance Reignites Customer Trust and Loyalty

Viewing AI and humans as isolated entities, each suitable for discrete tasks, failed to create the unified, end-toend experience that is critical to customer relationships. Individual touch points were inadequate, journeys were inefficient, and conversations were impersonal.

By instead viewing AI and humanity as part of a spectrum, in which the two work in concert to apply their unique strengths as required, organizations overcome this pitfall. They optimize each moment of truth, and in turn, prompt customers to trust the brand enough to create many future interactions – and significantly more revenue.

This reframing of the AI-human balance hinges on three critical steps.

Step One: Foster Cooperation Between AI and Human Agents

Past models focused on assigning AI or human agents to distinct tasks and issues. A more effective approach involves leveraging them at appropriate phases of the journey. It involves treating them as complementary forces, rather than alternative options.

There is no one-size-fits-all template for how to allocate AI and humans throughout the journey – that is the point of this reformulation – but the general rule will involve relying on agentic AI as the foundational support layer. AI agents will handle the entry point; they will initiate the interaction, qualify the customer, and begin to formulate the support process.

Constantly in the loop, humans will monitor the way the AI agents are operating, and then jump in to either take corrective actions or evaluate exceptions, demonstrate vital emotion and empathy, or make unorthodox decisions.

“This is not simply the ability to escalate from the bot to a human. This is intelligently—and precisely—involving the human agent when human assistance improves customer outcomes. With light human guidance, the AI agent can continue the interaction and achieve first contact resolution. The agentic platform that includes a human-in-the-loop agent (HILA) can provide critical support to the AI without requiring the full escalation. Rather than taking a 10-minute call, the human agent can provide guidance in 30 seconds. This is a massive shift from the either/or mindset of the past to a both/and mindset that delivers an order of magnitude improvement in outcomes. While understanding individual customer journeys by intent can involve more work on the frontend, the payoff is massive in the agentic world. Humans and AI working together on behalf of the customer and the business will be the difference between the winners and losers.”Chris Arnold, ASAPP

Operating in this manner fuels a duality of customer experience “wins.” First, it negates the “deflection” perception, while also eliminating any guesswork or buyers’ remorse associated with channel selection.

Customers will no longer have to assess whether their issue is better-suited for self-service or agent assistance. And they will not have to fear the inefficiency associated with choosing (or being forced to choose) incorrectly.

Instead, they will know with certainty that the AI entry point is their best option, because it will keep them in a single flow that is constantly leveraging the right mix of technological convenience and human ingenuity. Speed and satisfaction will no longer be trade-offs; they will be standards.

Step Two: Make Technological Unity a Non-Negotiable

Delivering personalized, contextual support is vital to achieving customer trust.

Unifying the customer contact framework, and achieving a single source of truth, is vital to meeting that personalization standard.

In today’s landscape, effective AI-powered experiences do more than “answer questions.” They provide contextually relevant resolutions, or at least make it simple for human agents to jump in and introduce the necessary nuance and specificity.

They account for the reality that a customer complaining about a late gift delivery on December 24 may have different sentiments and expectations than one asking for help on April 4. They account for the reality that an errant food order at 11:45PM may have different consequences than one at 3:15PM. They account for the reality that a customer who missed a flight for a lifealtering job interview may be operating with different urgency than one coming home from a tropical vacation.

This level of contextual understanding, and what it may mean for the level of personalization demonstrated, the kind of approval needed, and the type of human employee to get involved, hinges on robust access to customer data and company knowledge. Knowing who the customer is, how they are feeling, and how they are served – or underserved – by existing policies and knowledge entries is the difference between answering a question and providing a resolution.

MARKET STUDY

“Enterprise technology stacks are complex and very diverse. Many were built for a different time and purpose. Most are not optimized for the agentic era. It’s critical to deploy an agentic platform that elevates the value of those historical investments. CRM integration enables a “single pane of glass” where human agents will guide the AI agents. CCaaS integration adds AI-driven intelligence to basic routing and queuing, resulting in substantially higher levels of automated resolution. Data integration might be the most critical. The ability to leverage existing sources of knowledge in an agentic format is unique. The ability to leverage existing APIs by transforming requests and responses for agentic purposes is how workflows are successfully automated. All of this should be intrinsic to the agentic platform as this is what delivers real business value.” - Chris Arnold, ASAPP

Not simply about empowering an effective AI frontline, this level of unity is crucial to human agent performance. When humans have instant access to this “single pane of glass,” they are able to operate with the speed, focus, and confidence expected of “consultative” representatives. They can demonstrate empathy, make authoritative decisions, and secure trust that they are acting in the best interest of the customer.

Step Three: Guarantee Platform Credibility

Even as they embrace digital and AI engagement in many facets of their lives, consumers question the credibility of these systems in a customer service context. The Consumer Preferences Survey reveals that 76% of customers are concerned about data security and privacy.

This means that it is not enough to simply introduce AI with an intentional, customer-centric mindset. The AI an organization introduces must meet rigid standards for accuracy, consistency, compliance, and security. If it communicates false or unclear information or opens the door to fraud or privacy worries, it will eliminate any semblance of trust.

“Trust in the AI’s reliability is hard to measure, but plays a major role in the success of the program. The winning platform will be the one that provides transparency and control over outcomes with minimal effort required to observe and optimize safely over time.” - Chris Arnold, ASAPP

Along with selecting a technology partner that values these elements, successful organizations will make observability and supervision a foundational part of their AI-human balance.

Through real-time human oversight, they will ensure the AI agents are performing in the best interest of the customer, the organization, and the compliance landscape. Through extensive simulations and real-time monitoring, they will ensure that the AI agents are consistently behaving in a predictable, customer-centric manner.

They will leverage consistent training to ensure the AI experience continuously improves in response to customer behavior and sentiment, while carefully assessing workflow impacts to ensure human agents are prepared for the evolution of their role.

Finally, they will develop and enforce guardrails not strictly to cap the potential value of resolutions, but rather as a way to prevent their AI-based tools from accessing, analyzing, or communicating data in a way that would create security or privacy risks.

Success in

Practice:

Optimizing the AI-Human Balance Maximizes Results

A longtime believer in the agentic enterprise, ASAPP pairs its innovative technology with its customer- and employeecentric philosophy. It empowers partners to simultaneously eliminate inefficiencies and elevate care quality.

The process fuels legitimate transformation, with enterprise users citing real change to their operational framework – not just fun “diversions” or “enhancements” to a flawed existing strategy. The results, accordingly, show up across multiple metrics: happier customers, more productive employees, and healthier businesses.

“For example, one of our major airline customers looks to ASAPP as a strategic partner helping them hit ambitious 4-year financial targets by turning AI into a reliable workforce. Another common theme we see is going slow to go fast, where customers take a crawl/walk/ run approach to pace their AI roadmap. But however they choose to reach their goals, the most appropriate metrics for assessing the quality of AI deployments and impact on the overall CX or operation are very familiar: reduced operating costs without degrading CSAT, stronger resolution and containment rates, reduced escalations, and faster time to value.” - Chris Arnold, ASAPP

“The companies who deploy an agentic platform that enables continuous learning, test environment simulations, precise human guidance and decisioning, and transparent observability will be the winners between now and 2030. The agentic platform that has these capabilities built in, and has access to the right proprietary data will be the difference between AI being a cool new feature and AI being the enabler of a new intelligent operating model.”Chris Arnold, ASAPP

Specific success stories follow.

COMPANY: Leading Airline

CHALLENGE:

Provide reliable and caring service that maintains the brand’s human-centric identity while meeting aggressive financial targets. This included modernizing the voice experience.

SOLUTION & RESULT: Agentic solutions for high-volume customer support on Voice & Chat: CXP’s GenerativeAgent® with Human-in-the-Loop Agent (HILA™).

Requests range from simple FAQs to complex airline logistics that traditional linear bots cannot handle.

• Core Trip Logistics

• Complex Baggage Calculations

• TSA & Policy FAQ

HILA roadmap will shift customer service from cost center to growth engine with support for high-stakes, complex transactions

• Proactive Upselling

• HILA as the Closer

• Disruption Management.

Results:

• 92% CSAT

• 25-Point Jump in FCR

• Nearly 20% of Digital Volume

MARKET STUDY

COMPANY:

F500 Media & Telecom company

CHALLENGE:

Scaling volume across channels with generative AI to deliver a superior customer experience

SOLUTION & RESULT: Leading with CXP

CXP’s GenerativeAgent® improves CX by removing the friction of rigid menus and “dead-end” routing that once forced customers into the wrong agent queues.

Intent Precision customers now get an open-ended generative greeting, allowing them to express complex, multi-part needs in natural language. By capturing this specific intent upfront, CXP ensures every customer is instantly routed to the optimal resolution path—whether that is the correct skilled agent or a highly effective, verified self-service flow. This precision eliminates the manual tax of re-triaging incorrectly routed calls.

Use Case Evolution: Five use cases deployed in six months, from simple FAQs (e.g. Bill Details and Orders) to complex, authenticated transactions (e.g. Global Roaming and Payment Extensions) across two consumer brands.

Enterprise Orchestration: CXP is the enterprise “super agent” engine to orchestrate the entire customer conversation—leveraging knowledge and subagents to resolve any need. This approach maintains GenerativeAgent at the top of digital chat (and soon, to the top of the IVR) to capture 100% of customer signals. It also leans into a self-service model, empowering the team to build independently at a pace of 5 new use cases per month.

CXP at the Top of the IVR (Future State): plans to launch GenerativeAgent at the top of the IVR to replace traditional automation menus. Capturing 100% of customer inquiries across brands positions ASAPP as the single primary entry point for all customer service

Results:

• 25% Lift in containment for self-service use cases

• 50% Reduction in immediate agent escalations via initial voice optimization

• Reduction in Unclassified Intent: GenerativeAgent’s advanced natural language reasoning reduced unclassified intents from 8.1% to 4.7%

• 10x Deployment Velocity: From 5 use cases in Year 1 to a self-service model of 5 new use cases every month

• $40 Million+ in targeted cost reduction through 2026

Are You Ready For The Agentic CX? 5 Non-Negotiable Rules of Engagement

What was once a distant possibility is now a certainty. The agentic frontline is coming, and it will define the future of customer engagement.

Not the product of wishful thinking or marketing hype, this optimism is driven by real-world experience. Thanks to promising results from their technology pilots, 58% of leaders believe the vast majority of interactions will be fully handled by AI agents in 2030.

But when pursuing this reality, it is imperative to refrain from putting the cart before the horse. It is essential to avoid letting the theoretical upside of an agentic frontline – more convenient, accessible, and scalable service – overshadow the practical importance of customer centricity.

The goal is not to impose a tolerable AI-powered experience on customers. Rather, it is to design one that they will wholeheartedly embrace.

“Success to us is when an AI agent becomes a customer’s preferred option for service. It’s a pathway that is faster, more convenient, and more personalized than anything previously possible with legacy technology (such as IVRs, basic chatbots, or even human representatives whose time is naturally limited).” -

James Wood, SVP, Engineering, SoundHound

Today, 62% of consumers condemn their typical customer service experience as “inconvenient,” and as many as 75% say it lacks personalization. The market is clearly eager for a change, and with 87% of consumers willing to use self-service, AI represents a pathway to transformation.

What will it take to seize the opportunity? What will it take to develop an agentic frontline that represents a source of empowerment rather than deflection? How can you ensure the rise of AI coincides with the growth of customer centricity?

This briefing provides the answers, revealing 5 guiding principles for the agentic experience.

Rule #1: Emphasize Resolutions, Not Deflections

In theory, self-service is a way to scale customer centricity. It is a way to give customers instant access to support when and where they want it.

In practice, it has historically represented a lesser support option. It has functioned as a way to democratize generic knowledge content and basic, policy-driven answers. It has not been a pathway to achieving meaningful, let alone personalized, resolutions.

Because the emphasis was on sharing information rather than taking action, it never felt like a credible alternative to human-led support. It was about deflection rather than empowerment, and it is why customer confidence in IVR (14%) and chatbots (18%) so greatly trails that for traditional, employee-led phone support (75%).

If brands aim to make self-service their default service option by 2030, they must break from this trend. They must ensure their autonomous technology can take real action, adapt to unique customer context, and deliver compelling resolutions.

Agentic AI technology opens the door to this shift.

“Historical automation models were engineered more for customer deflection than full resolution. For years this was the most viable model, since the market was limited to rigid legacy tools like IVR or scripted chatbots. It was never the best experience. Now, as agentic AI matures, forward-thinking brands are deploying AI agents to create powerful, reliable, and economical self-service experiences that flip the cost equation while improving sentiment. Our recent research evidences the shift: among organizations that have deployed agentic AI, more than half report customers are now actively choosing self-service over human escalation.” - James Wood, SVP, Engineering, SoundHound

Will AI agents fully handle every customer interaction? No.

But in cases where an autonomous resolution is impractical, inefficient, or inadequate, the emphasis needs to be on seamless handoffs that continue the conversation. Asking customers to undergo a traditional escalation process, where they wait on hold and then restart their inquiry from scratch, is unacceptable in the agentic world.

“In an agentic world, we designed our platform so human teams can easily redirect or help the AI if it’s stuck in real time without ever disrupting the customer’s conversation flow. We call this Human-Assisted Resolution. We believe this is necessary to provide a reliable AI journey from end-to-end, even in edge cases. Think of it like how Waymo allows its autonomous cars to be remotely controlled by a human if they get stuck to ensure passengers reach their destination safely. It’s a similar approach, but applied to enterprise customer experience. You can’t deliver a perfectly rounded experience without this capability. It’s how we make sure brands have the forwardthinking tools to optimize the journey from A to B, so customers can trust it every single time.”James Wood, SVP, Engineering, SoundHound

To fully embrace AI agents, customers require assurance that the effort they exert brings them closer to resolution. If they sense they are going to reach a dead end with self-service they are likely to abort the process entirely, at which point they will need to scramble to find another option, endure more waiting, face repetitive questioning, and further lose faith in the process. They will see AI agents as an obstacle rather than an opportunity, and the business’ claim of customer-centric design will fall flat.

Rule #2: Account for Real-World Voice and Conversation Dynamics

According to CCW Digital research, “difficulty explaining one’s issue” has long ranked as the #1 reason customers reject self-service. If customers cannot clearly and naturally express their intentions and receive actionable, relevant, and coherent responses, the idea of self-service representing a more convenient, accessible option goes out the window.

Modern generative and agentic AI solutions directly account for this gap. They bring an organic, conversational element to the self-service experience, breaking from the stilted, unclear language of the FAQ age.

There is a significant difference, however, between having the technological ability to understand language and having the real-world ability to engage in meaningful conversation.

This difference is particularly stark on the voice front. If an AI agent cannot account for the realities and nuances of a voice conversation – different speech volumes, dialects, background noises, tonal expressions – it may struggle to transcribe the conversation, let alone deliver what the customer truly wants.

“For customer service, you cannot rely on generic voice plug-ins that treat voice as a simple building block. You need a foundational voice AI model that is acoustically engineered for the real world, meaning it can process audio and intent simultaneously in real time, block out heavy background noise, and handle instant interruptions when a customer cuts in midsentence.” - James Wood, SVP, Engineering, SoundHound

Along with improving quality within the interaction, an AI agent’s ability to reconcile any discrepancy between what it hears, what the customer explicitly says, and what the customer actually means unlocks actionable intelligence that can elevate the entire customer contact operation.

Indeed, every facet of the experience – from future AI deployment and training, to journey orchestration, to human employee training – hinges on complete conversational understanding.

Rule #3: Fuel AI Agent Memory and Personalization with Robust Data and Channel Integration

Customers already have passive tools like instruction manuals, company websites, and web search engines for finding standard resolutions to their inquiries. When they choose to engage with an agent, AI or human, they are looking for something more.

They are looking for a personalized experience that appreciates their unique interaction history, understands the context and urgency of their inquiry, and frames the answer or resolution in a way best-suited for them.

Not wanting to exert undue effort, they also desire assurance that the information they share will follow them throughout the journey. And in an era where only 18% of consumers feel experiences are consistent, they are craving a support experience that does not share conflicting information or deliver different outcomes based on where they engage.

Collectively, these demands underscore the importance of robust data and channel integration. If they are to rise above a static FAQ page, AI agents must be able to access a single source of truth about the customer, their issue, and the associated company knowledge.

And if they are to deliver a conversational experience, they must be able to access and interpret this information instantly.

“First is internal data maturity. AI agents can’t execute tasks if business data is trapped in slow, disconnected silos. To actually solve a customer’s problem, like tracking a lost shipment or processing a refund, the AI needs real-time, low-latency access to core databases (e.g., ERP, CRM, inventory systems). Your data maturity has to be at a point where APIs can fetch and update clean customer data instantly mid-call, without lagging or breaking the conversation.” - James Wood, SVP, Engineering, SoundHound

Essential for elevating the frontline experience, this level of data access and integration is also critical for risk management. Committing to agentic resolutions requires a high degree of trust in the accuracy of the AI platform; knowing that the solution is pulling from an integrated data and knowledge framework goes a long way in building that trust.

Rule #4: Implement Effective Guardrails

Answer accuracy is critical, but it is only one piece of the trust puzzle.

If an organization’s AI agents are positioned as resolution engines rather than deflection tools, they will be dealing with personally identifiable information, crafting binding messages and resolutions, and taking actions.

Each of these comes with theoretical risk, and it takes the right combination of effective operational governance, high-quality technologies, and well-devised guardrails to mitigate the concerns of internal stakeholders and external users.

“Long-term success with AI agents for CX is going to depend on earning trust with consumers. That requires you to be strategic in your technology choice, ensuring it is not only strong in its native customer service features, but also in what type of controls, guardrails, privacy, and security are provided.” - James Wood, SVP, Engineering, SoundHound

Amid the rise of digital and AI communication, 76% of consumers say they are increasingly worried about privacy and security. Effective guardrails enable organizations to alleviate this concern without restricting the personal, resolution-oriented service that makes agentic AI worthwhile in the first place.

While accounting for traditional risk factors like privacy, security, and compliance, savvy organizations will also consider behavioral risk. If AI agents become the new frontline, they will effectively become the face of the business. Their ability to respond appropriately, consistently, and correctly, while respecting individual customer needs and honoring brand values, is as nonnegotiable as it is for human employees.

Ensuring one’s technology platform accounts for this reality, while keeping humans in the loop for ongoing oversight and exception handling, is obligatory.

“The platform needs to have strict, native guardrail solutions that keep autonomous agents operating safely within your business rules, ensuring they only execute verified actions and navigate unpredictable customer behavior reliably.” - James Wood, SVP, Engineering, SoundHound

Rule #5:

Let AI Fuel Continuous Improvement

When it comes to elevating frontline customer experiences, technology is no longer the bottleneck. Modern AI technology is capable of engaging in meaningful conversations, adapting to personal context, and navigating through even complex support processes.

But it is not perfect. AI agents are still moving through a maturity curve, not only in terms of capability but in terms of practical success. Being able to unequivocally understand actual customer phrasing, uncover actual customer intentions, appreciate actual customer urgency, and deliver actual resolutions is a process that will take time and continuous improvement.

The beauty is that the best AI platforms can facilitate this improvement. By capturing actionable intelligence from the frontline, and then re-training based on the findings, it turns technology that is promising in philosophy into AI agents that are exceptional in reality. It makes the experience more accessible, more conversational, and more effective for a brand’s real-life consumers.

“The most advanced conversational AI platforms support AI agents that are actually generated by the AI system itself, based on your real customer interaction data. On SoundHoundAI’s OASYS (Orchestrated Agent System), this is quickly becoming the preferred, most effective development path. It completely removes the guesswork from design, and it drastically reduces your time to deployment and cost of development. In the best cases, you’re taking that timeline down from weeks to hours.” -

James Wood, SVP, Engineering, SoundHound

Even as “AI builds AI,” humans remain critical to the improvement process. Beyond manually overseeing interactions and assessing performance, they are also responsible for setting the appropriate benchmarks and metrics. Their insight into performance dictates what good looks like, and thus determines whether AI is evolving in the appropriate direction.

Organizations that celebrate containment without thinking about the impact on customer satisfaction risk deploying AI in a way that alienates customer trust. On the other hand, those who only focus on lofty, big picture outcomes will lose sight of incremental performance changes – and thus find themselves unable to adapt as swiftly as needed.

Balancing short-term operational priorities and longterm customer experience values is important, and that synergy must be the driving force behind AI improvement strategy.

When Agentic Gets CustomerCentric: A Portrait of Success

The idea of a predominantly AI-powered frontline may be a few years away, but meaningful progress is already attainable.

Leading organizations from all industries have begun to deploy agentic AI solutions for meaningful work, including frontline resolution handling. Their efforts are already changing the customer experience landscape.

AI agents are shifting customer behavior, with many saying their customers are actually gravitating to selfservice not only as an entry point but as a one-stopshop for their resolution. They are also driving legitimate business outcomes, with organizations reporting higher frontline resolution rates, improved operational efficiency, and superior employee experiences.

“As we saw in our study this year, market momentum has significantly accelerated, and we are not too far away from that future. At SoundHound, we’re doing everything we can to be the definitive solution that brings this vision to life for any brand in the world looking to deliver that next-generation customer experience.” - James Wood, SVP, Engineering, SoundHound

MARKET STUDY

Example success stories follow.

COMPANY: Qualitas

CHALLENGE:

High-stress car assistance calls (theft, lockouts, breakdowns) surging across the Americas, with pressure to control costs without losing the human touch.

SOLUTION & RESULT:

Agentic AI agents that handle natural, out-ofsequence conversation and regional dialects. Partial-theft containment tripled (3x), roadsideassistance containment up 30%, and 100,000+ calls/month now handled by AI—freeing agents for complex claims.

COMPANY: Resorts World

CHALLENGE:

Deliver a modern, seamless luxury guest experience at a 3,500-room resort from opening day, without letting automation undermine hightouch hospitality.

SOLUTION & RESULT:

“RED,” an AI agent on the platform, serves as virtual front desk, 24/7 room service, and employee help desk across phone, chat, and social. Handled 59% of all call volume without escalation (223,000 interactions in 2024), ~610 interactions/day, freeing staff for high-touch moments.

COMPANY: Apivia Courtage

CHALLENGE:

Policyholders hitting rigid IVR trees and long hold times in stressful moments; insurer needed end-to-end resolution while preserving service quality and its Duty to Advise.

SOLUTION & RESULT:

AI agents resolve inquiries end-to-end (identity verification, coverage questions, quotes, appointments), integrated with core systems. ~20% of inbound calls handled by AI, a 20% productivity gain for the customer relations team, supporting 4,000+ brokers.

Appendix

White paper: 6 Key Enterprise AI Trends for 2026

The Complete Guide to AI Chatbots and How They Evolve Into AI Agents

Insights iQ™: CX Analytics That Tells You Why, Not Just What

Omnichannel Support Outsourcing - Voice, Email, Live Chat, SMS

LiveNexus by Liveops: Turn Bold AI Ideas Into Business Outcomes

The Resolution Gap Report: Why Faster CX Doesn’t Always Mean Better CX

From AI Deployment Case Study

How Datascribe Reduces Agent Workload And Improves EX

The Hybrid CX Workforce Report

eBook: Why VoC Programs Miss the Moments That Matter

2026 Editorial Calendar

Market Studies

CX in 2030: Future of the Omnichannel Contact Center

Seminar Series

Customer Contact Industry Forecast

SEPTEMBER

Modernizing Service Experiences With AI & Digital

DECEMBER

Redefining Customer Contact: Trends, Challenges & Opportunities

AUGUST

Modernizing Service Experiences With AI & Digital

OCTOBER

State of Enterprise AI

NOVEMBER

Redefining Customer Contact: Trends, Challenges, Opportunities

Meet the Team

Brian Cantor

Managing Director

CCW Digital

EMAIL BRIAN

Andy Kuang

Senior Marketing Manager

CCW Digital

EMAIL ANDY

Melinda Acuna

Marketing Manager

CCW Digital

EMAIL MELINDA

Audrey Steeves

Content Analyst

CCW Digital

EMAIL AUDREY

Simon Copcutt

Head of Strategic Accounts

CCW Digital

EMAIL SIMON

Brooke Lynch Divisional Director

CCW Digital

EMAIL BROOKE

Ben McClymontt

Divisional Sales Director

CCW Digital

EMAIL BEN

Emily Curran

Customer Success Coordinator

CCW Digital

EMAIL EMILY