MARKET STUDY
Modernizing Service Experiences With AI & Digital
MARKET STUDY
Table of Contents METHODOLOGY & DEMOGRAPHICS...........3
THE SELF-IMPROVING SERVICE EXPERIENCE. 26
INTRODUCTION..........................3
BEYOND SIMPLE VS. COMPLEX: REDEFINING CUSTOMER SUPPORT IN THE AGE OF AI .. 32
THE NEW CHANNEL MIX...................4 SELF-SERVICE IN 2030.................. 7 CHANNEL STRATEGY DRIVERS..............9 MODERNIZING THE SELF-SERVICE EXPERIENCE.......................... 11 WHERE DOES THE HUMAN FIT IN?........ 13 MODERNIZING THE OPERATION............ 16 SO, HOW WILL THIS ALL COME TOGETHER?.18 PRACTICALITY GUIDE..................20
BEYOND CONTAINMENT: DESIGNING CUSTOMER-CENTRIC SERVICE IN 2026.....40 WHERE TRUST COMES FROM: DESIGNING HUMAN EXPERIENCES IN THE AGE OF AI..44 BECOMING RESOLUTION-FIRST: REORIENTING CX AROUND WHAT MATTERS TO CUSTOMERS................50 APPENDIX.............................54 2026 EDITORIAL CALENDAR.............. 55 MEET THE TEAM.......................56
BEYOND THE AI PILOT: A PRACTICAL OPERATING MODEL FOR MODERN CUSTOMER SERVICE ................... 21
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Methodology & Demographics To investigate efforts to modernize the service experience with AI and digital, CCW Digital conducted a survey in July and August 2026. Completed by those responsible for contact centers, customer experience, information technology, operations, and marketing, the survey reached an audience covering most major industries and all company size ranges.
Example respondent job titles included senior vice president of customer experience, senior customer service manager, head of contact center, director of customer development, senior director of support and service operations, chief information officer, head of operations, chief operations officer, and vice president of customer care.
Introduction Once again, customer service is entering a new phase of transformation. As organizations look to 2030, they expect self-service to play a key role in the modern customer experience. Not only is it going to be positioned as the first point of contact, but a channel capable of resolving even complex needs. The findings from this study demonstrate a shift in how customer service leaders envision the future of support. While they are certain that humans will play a role, it is increasingly clear that self-service is expected to handle a dominant share of interactions. Leaders anticipate the percentage of contacts that begin in self-service to rise from 56% today to 77% by 2030. At the same time, they predict that self-service will become significantly more effective at resolving end-toend customer needs. They expect containment rates to increase from 56% today to 74%. While the data points to the fact that self-service will play a pivotal role in the future of support, the goal is not just to automate every interaction away. Speed was named the most important characteristic of the ideal customer service, but empathy, personalization, customized resolutions and accessible support were also highlighted as key values. The channel mix also continues to expand, with organizations stating an increase in availability of almost all channels by 2030. Most organizations, however, are still forcing customers into their preferred journey — only
26% of leaders say they give customers access to all channels. While they are expanding operationally, most organizations are still placing parameters around the customer journey. Together, these findings paint a more nuanced picture of the future contact center. The goal is not necessarily to deflect or shift customers away from human support, but create a model where AI and self-service can handle most of the frontline work. With this, agents can shift their time to leverage judgement, empathy and expertise. Organizations are now placing greater emphasis on technology, data and relationship building, giving their employees more freedom to exercise their own judgment, make exceptions when needed and move beyond their past of rigid scripts and robotic conversations. Because 70% of customers still prefer live service, organizations must continue to ground interactions with human-led support. When considering the findings holistically, it is evident that self-service will become a larger piece of the customer journey. The important distinction, however, is whether organizations can build a selfservice experience that consistently meets customer expectations. If it is effective enough to earn customer trust, this scenario will likely play out by 2030. If not, leaders will have to adjust their reliance on self-service and continually work toward customer adoption.
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The New Channel Mix Organizations are expanding their channel portfolios with planned growth across nearly every channel as they prepare for a more diverse, AI-powered service environment. Contact center leaders are particularly optimistic about the growth of chatbots. The biggest predicted growth channels are social media (50%) and SMS chatbots (47%). Traditional channels like phone and email still represent the greatest share of current offerings, but are expected to see diminished growth for 2030. Greater channel availability, however, does not equate to greater customer choice in this case. While more channels are being added, only 26% of organizations offer customers access to all available channels. This is becoming increasingly clear to customers — 89% shared that over the past year companies have been more likely to require them to use a self-service tool before, or instead of, speaking with a live agent. It is clear that companies are still carefully controlling the customer journey, but trust will ultimately be the indicator of success. When asked about which channels customers are the most confident in resolving their issue, the phone channel was ranked number one. Chatbots, on the other hand, only secured 18% of customers’ trust. This dynamic demonstrates the need for greater investment, education and attention on self-service channels. If organizations see the largest increase in self-service by 2030, they must work to meet customer expectations and bolster trust today.
CMP Analyst Roundtable As organizations expand their channel offerings, how can they balance giving customers a genuine choice while still guiding them towards channels that are most efficient/effective for the organization?
BRIAN: Year after year, the traditional phone call becomes less important in our personal lives. Yet year after year, adult consumers resoundingly rank telephony as the top channel for customer service. Gen Z included.
The explanation for this apparent contradiction is simple: telephony is the only channel in which the majority of consumers are confident they can solve their problem. It may not be perfect, but it is where customers feel they have their best chance of reaching an accountable employee capable of understanding their needs and acting accordingly. The takeaway here is that the question of exactly where to offer service is secondary to the matter of providing exceptional service wherever it is available. This means that a brand will not receive bonus points just for providing access to every conceivable channel. If customers cannot actually get great service in every environment, they will ultimately gravitate to the “good” channels, and the illusion of autonomy will fall apart. It also means that a brand is not necessarily evil for right-channeling. It becomes a problem, however, when consumers feel they are being routed to a lesser channel. What does this mean for brands looking to modernize their service experience?
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It means they have to deeply analyze the channel preferences of their existing and prospective customers. They have to understand exactly why customers select the channels they do, and exactly which of those drivers can be replicated (or even betteraddressed) in another environment. It means they have to act on that analysis, doing everything in their power to optimize the experience in all environments. This does not mean every channel should be identical – phone calls allow for certain things that are impossible in digital channels, and vice versa – but it does not mean that customers should be assured of access to consistent messaging, efficient support, and real resolutions regardless of where they connect. It means they have to be transparent about their channel mix: which channels are actually available, and what are the pros and cons of each option? If a channel is listed on the website, providing insight into the likely wait time and the issues for which it is comparatively well (or poorly) suited will help set the appropriate expectations. If the customer is forced into a particular channel, make a clear effort to educate them on why that was the best starting point. Finally, it means they have to create a seamless ecosystem for transfers and escalations. Customers are more inclined to start in the brand’s preferred channel if they know they will not be “trapped” there if things do not work out.
BROOKE: Continuing with Brian’s point, this data highlights the growing disconnect between what brands want and what customers expect. In CCW Digital’s recent consumer facing study, the difference in preference data was stark: 70% of customers shared a willingness to engage with live humans over 30% on self-service. Even worse, only 6% of customers noted a preference for engaging with chatbots.
This paints a picture of a customer base that is completely averse to engaging with self-service. Yet, leaders are confidently working toward a future where self-service takes on more than ever before. So, how can the customer and the brand get on the same page when neither seems to budge? Brands, ultimately, need to put their money where their mouth is when it comes to self-service optimization. Hopefully, this study will share some insight on some of the steps organizations are taking to modernize experiences, but these steps must be linked to outcomes. If organizations are consistently relying on a hope and a prayer that customers will adopt the technology, they will continue to see these results. They must take the time to understand the ways in which AI and self-service can in fact cater to customer needs, and make a point to prove that to customers. The more they can deliver for customers, the greater results they will see.
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How much choice do your customers have in how/where they are served? Customers can select from most or all of
Do you provide customer service in the following channels? Will you by 2030? Now
By 2030
our channels, but only certain ones are prominently displayed
Chat via website/mobile app 50%
All channels are prominently displayed and
90%
+39 pts
Self-service web portals
available to customers 26%
+44 pts
46%
47%
86%
+39 pts
Chatbots on website/mobile apps 45%
Customers are limited to specified channels
84%
when they begin their journeys 24%
+23 pts
Email 61%
84%
+50 pts
Chatbots on social media 33%
83%
+40 pts
SMS/text/messaging 43%
83%
+33 pts
Social media 50%
83%
+47 pts
Automated email responses 35%
82%
+27 pts
Phone 55%
82%
+46 pts
Voicebots/conversational IVR 32%
78%
+47 pts
Chatbots on SMS/messaging 30%
77%
+40 pts
Video chat/conferencing 34%
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74%
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Self-Service in 2030 The role of self-service is expected to dramatically expand over the next few years. Today, 56% of organizations state that contacts begin in self-service. By the end of 2030, that number is expected to jump to 77%. Self-service is no longer just acting as the channel for simple questions, it is now being positioned as the starting point for just about every customer journey. This is aligned with the fact that organizations expect self-service to become more equipped to actually resolve customer needs. Currently, 56% of contacts are being fully contained or resolved by self-service. By 2030, that figure is expected to reach 77%. On top of that, self-service is now the third highest ranking success metrics for contact center executives, behind CSAT and FCR. This suggests that the goal is to build self-service experiences that are capable of handling end-to-end resolutions. This will take a more sophisticated, intelligent and intuitive self-service function, which is not necessarily something most organizations offer today.
What percent of the total customer contacts started in self-service and how do you expect that to change by the end of 2030?
56%
Currently
77%
By the end of 2030
CMP Analyst Roundtable Are most self-service strategies designed to deflect customers or actually resolve issues? What improvements need to be made to achieve these resolution rates moving forward?
BRIAN: Show me a brand that doesn’t view their self-service investments as a way to reduce inbound contact volume, and I’ll show you a liar. Cost reduction and self-service resolution rate rank as top performance goals, so of course deflection is an objective.
This does not automatically make call centers “evil” or dismissive toward their customers. There is nothing inherently wrong with wanting to contain more issues in self-service, especially since 71% of contact centers feel the majority of issues their employees address do not actually require human involvement. Where it becomes a problem is when the selfservice experience feels markedly lesser or more restrictive than an agent-assisted one. There are three ways this tends to happen: • High-friction. The “selling point” for selfservice is that you can get access to service on your own terms: in your preferred channel, at your desired time, and without waiting on hold for a live representative. If, however, the platform is difficult to navigate or lacks natural language capabilities, this illusion falls flat. The customer now feels they have to endure an extra burden so that the company can cut costs.
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• Impersonal. Customers may not expect over-the-top conversations in self-service environments, but they do expect tailored, relevant ones. When the platform fails to even remotely adapt to the customer’s history and issue context, it inspires no confidence in the eventual resolution quality. It also sends the message that the customer is seen in terms of an issue to address with knowledge base content rather than a person to support with empathy. • Inadequate resolutions. The hazard of the “self-service for simple issues, humans for complex ones” cliché is that it presumes that straightforward questions only require basic answers. The reality is that even customers dealing with technically simple matters (like a late package) may still expect substantive resolutions (an instant reshipment and credit for the delay). If all the self-service tool can do is regurgitate the “policy,” it falls short of this expectation and thus feels fundamentally inferior to employee-led support. What this means is that brands have to invest in agentic AI technology that engages in natural language, leverages data to personalize interactions in real-time, and takes real action to support customers. It also means they have to want to meet this standard. If a brand is just hoping to get away with delivering static, scripted, knowledge base answers at scale, then it is succumbing to the ugly version of deflection. It is not simply looking to become less reliant on human employees; it is looking to deliver lesser service.
BROOKE: Completely agree with Brian, deflection is often a necessary strategy for contact centers overwhelmed with volume, forced to navigate budget cuts, looking to do more with less. While it may be an unfortunate reality, it does not necessarily have to be seen as a bad thing.
For customers looking to avoid a 2 hour wait to speak with a representative, self-service offers a timely reprieve. And when implemented effectively, customers can receive an adequate resolution. More than 59% of leaders share that their self-service tools can make exceptions. This suggests that AI-powered experiences are leaning more toward judgement-based resolutions over the scripted, rigid interactions customers are working to avoid. On the other hand, many organizations are strategically relying on self-service to diminish the burden they are currently placing on human agents. Customers don’t want to speak with an agent who seems distracted, frantic and disengaged. Yet, that is the reality when they are navigating everything from password resets to complex concerns. To shift the perception and lean into the positives of deflection, organizations must change customers’ minds. As I mentioned, the more companies can exceed customer expectations and deliver those exceptions they say self-service can deliver, the greater the results will be. Brian, Audrey and I often talk about the fact that customers just need agency. They want the ability to choose. When brands can prove that a phone conversation is just as good as a chat, they will see the customers’ walls break down and adoption increase. Giving them every chance to choose will make that decision to try self-service that much more positive.
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Channel Strategy Drivers CMP Analyst Roundtable Which of the following variables most impact the customer service channels you choose to offer? Ranked #1
Top 2
Direct customer feedback 31% 56%
56% of leaders share that direct customer feedback seeking these channels was a top driver of their channel strategy. Why do you think customers are directly requesting channels they may not be inclined to use? Is there a missing piece in how organizations are interpreting this feedback?
Changing customer behavior / analytics 29%
47%
Keep up with competitors 17% 31%
Technology marketplace innovations 7% 30%
Need for cost savings 10% 18%
Industry benchmarks 6% 18%
Customer feedback is the leading driver shaping organizations’ channel strategies. This is positive as customers often assume that AI and self-service are being implemented as a cost savings mechanism. But, these findings also highlight an underlying tension. While organizations continue to expand self-service, customer preferences still lean heavily toward live support. In CCW Digital’s most recent market study, 70% of customers stated a preference for live support. Live support also outperformed self-service across key metrics like CSAT and FCR. If leaders believe that they are making strategy decisions that are responding directly to customer feedback, are they asking the right questions? Or misinterpreting customers’ demand for faster, easier, more streamlined support as a cry for more automation?
BROOKE: I don’t think customers are ever going to say they don’t want access to any channel. They want an abundance of choice, they want to engage freely and without boundaries. Just because customers don’t name self-service as their number one choice, does not mean that they will never use it. It serves a purpose: it gives quick, seamless, low-lift responses.
The challenge is, organizations see this request and use it as justification for going all-in on self-service. I think customers are increasingly requesting digital options, they engage digitally in so many aspects of their lives, they utilize LLM’s in their work and free time, and they are willing to leverage technology. But, they still don’t see it as the most valuable player. It is still an add-on, a last resort or a quick alternative when they don’t have time to speak with a person. And the data does point to the fact that customers are utilizing self-service — the second biggest driver in the planned channel mix is changing customer behavior based on internal customer analytics. The missing piece I see here is the other key drivers: keeping up with competitors, industry benchmarks and technology marketplace innovations. These point to the external pressures driving this shift. Emerging technology, competitive innovation and growing demand for more sophisticated, AI-powered support, all factor into the 2030 channel mix.
It is clear that there is a growing disconnect between what organizations are working to deliver and what customers actually want out of experiences. Modernizing Service Experiences with AI and Digital | www.customercontactweekdigital.com
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AUDREY: One of the key themes across this data is emphasis on the concept that customers don’t think in terms of channels. It is challenging for customers to know what they want, much less how and where they want to interact with a brand, when so many of these applications are new to them. As Brooke said, some customers are digitally fluent and enthusiastically use technology in all aspects of their lives, while others are steadfastly opposed to anything unfamiliar. This range of opinion is unlikely to ever change, so a successful approach to shifting contacts from one channel to another will rely on demonstrating clear value and trustworthiness to the customer before expecting them to navigate a new and unfamiliar process.
Though it may seem paradoxical, it makes sense that customer sentiment is more in favor of the idea of self-service than the experiences in practice. At its best, when it’s proven to be reliable and trustworthy, self-service offers customers more direct and frictionless access to the information and assistance they’re looking for. Unfortunately, so many customers’ perception of self-service has been darkened by the suboptimal experiences they’ve had with chatbots in recent years.
BRIAN: At face value, the claim that brands are calibrating their channel mix to “direct customer feedback” actually seems dishonest. CMP research confirms that customers prefer highertouch channels like phone and video to lowertouch, automated options.
Yet brands have declared a resounding commitment to increasing self-service adoption and resolution. And more plan to offer lower-touch options like live chat, self-service portals, and chatbots than they do traditional phone calls by 2030. What explains this apparent contradiction? The harsher take is that brands are conflating “marketplace trends” with “customer preference.” They see the popularity of digital engagement, the obsession over chatbots, and the general enthusiasm over AI as proof that customers are clamoring for non-traditional support options. The less cynical take is that brands are increasingly recognizing channel preference for what it is: a statement not about where customers really want to engage but what they want to get out of that engagement. With AI technology at digital fluency simultaneously improving, companies may legitimately see lower-touch channels as most aligned with what customers want: service that is all-atonce fast, accessible, and engaging. But to Audrey’s point, this only works if brands leverage these channels to their full potential. If chatbots only regurgitate standard knowledge base content, customers are never going to see them as superior to phone calls, even if they may be more readily accessible. If live chat representatives have limited freedom to deliver make-goods and policy exceptions, customers are never going to see them as superior to phone calls, even if the channel is easier to use on the commute home from work. The takeaway here is simple: it does not matter whether brands feel their channel mix is aligned with the channels customers say they want to use. What really matters is whether the experience within those channels aligns with what customers actually value.
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Modernizing the Self-Service Experience Which of the following aspects of self-service are you focused on improving? Simulating human tone and empathy
All signs point to the fact that organizations are working to utilize self-service as more than just a deflection tool, which should be good news for customers. Achieving these goals, however, does not erase the need for continuous human-led support.
56%
Capturing customer insights 52%
Detecting/mitigating security and fraud risks 50%
CMP Analyst Roundtable Which capabilities/improvements will have the biggest impact on the growing challenge of customer adoption?
Recognizing when escalation is needed 45%
Delivering customized resolutions/exceptions 45%
Multilingual engagement 45%
Straightforward transactions/policy-based resolutions 43%
There is work to be done to deliver self-service experiences that feel intelligent and capable. Organizations are aware of the increasingly negative perception customers have of self-service, yet they struggle to break the cycle. Agentic AI seems to be one solution; 74% shared that they believe agentic AI will make customers more likely to adopt self-service. The intuitive nature and potential for more comprehensive resolutions makes it a positive contender. When asked about improvement areas for self-service, leaders seemed to reach a consensus that almost all areas are open to change. The top priorities include personalization (93%), intelligent routing and capturing customer insights (92%) and delivering customized resolutions or exceptions (90%).
AUDREY: The most important thing leaders can do today is build trust and goodwill in what self-service is capable of. I believe brands that excel in processing straightforward transactions and policy-based resolutions will have the most success in converting their customers from skeptics to fans of self-service.
Taking the example of banking and financial services, it was not long ago that many customers were insistent on interacting with their bank solely through in-person channels. This insistence was supported structurally through bank drive-thrus, which subverted the convention of how and where service was offered, but was particularly effective in transforming the customer’s sense of what is possible in terms of convenience and ease. The next stage was the ATM, which again forced customers to adjust their expectations, but was ultimately successful because the ATM is very limited in what it can accomplish, focusing on quality rather than breadth of interaction types. Both of these inventions set the stage for customers to enthusiastically adopt mobile banking today. We cannot change customer perception overnight, but consistent, demonstrable positive outcomes do more to shift customer perception than anything impressive but theoretical.
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BRIAN: The biggest reason customers reject self-service is not because it feels less human. After all, “less human” sort of goes with the territory.
Rather, they are rejecting it because it is unexpectedly unintuitive and difficult. They spend too much time explaining who they are and providing context on their issues, negating any theoretical convenience advantage. And so if they have to devote the same (if not more) time and effort, only to ultimately receive a less “human” experience, they have absolutely zero incentive to embrace self-service – and every reason to demand a “representative.”
The widespread efforts to make self-service more personalized and data-driven directly address this challenge. Customers will endure less frustration en route to resolution, and thus finally experience the benefit of self-service. The knowledge that they can simultaneously save time and receive their desired outcome could be what finally moves the needle on adoption. Will the AI improvement priorities make the self-service experience more humanlike? Absolutely. But the real value is that it finally lets self-service do what it was supposed to do: allow customers to quickly get help on their own terms.
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Where does the human fit in? How has the time you’ve spent training live representatives on the following changed in the past five years?
Compared to five years ago, how much freedom do your frontline representatives have on the following?
Increased (%)
More freedom (%)
Stayed the same (%)
No change (%)
Decreased (%)
Less freedom (%)
Data and feedback management and analysis
Exceed expected handle time for a given interaction
53%
46%
37%
10%
Tools and systems
39%
15%
Engage in small talk/friendly conversation with customer
49%
31%
20% 45%
Sales and relationship-building
43%
12%
Make exceptions to standard procedures or 48%
42%
10%
Products and policies 47%
45% 43%
10%
Communications and empathy 41%
47%
49%
35%
12%
20%
Deviate from standard service scripts and prompts
41%
43%
16%
Deliver make-goods, promotional credits, or other compensatory gestures beyond the scope of
Company culture and brand values 36%
policies when devising resolutions
the resolution 15%
41%
44%
15%
Apologize or express remorse when things go wrong
39%
45%
16%
Bypass certain disclosures or authentication steps
33%
41%
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26%
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MARKET STUDY With the channel structure leaders just laid out, the role of the human agent obviously has to shift. As AI takes on more routine, and potentially even somewhat complex interactions, agents will certainly move away from script reading and begin exercising judgement. The data already suggests that organizations are working to give frontline employees more freedom to exceed expected handle times (45%), make exceptions to policies (45%) and deviate from standard scripts (41%). The role of the human will go beyond just completing work unassigned to AI agents, it will give them the opportunity to engage with customers in a new way. Human agents will be responsible for moments that require empathy, flexibility and human judgement. This increased freedom shows one step toward the ‘AI for simple, humans for complex’ reality leaders have been discussing for the last decade. But, this will certainly take work. 62% of leaders share that their current CX teams are suited for complex/emotional work, but not necessarily tangential tasks like sales, data analytics, or bot training. Training is a priority for getting them up to speed. In terms of time spent, the majority of leaders are focused on training agents on data and feedback management (53%), tools and systems (49%), sales and relationship building (48%), products and policies (47%), and communication and empathy (41%). One of the obstacles, however, is the fact that escalations have not necessarily been smooth. The transition between channels still presents a key challenge: while 64% of leaders say that there is some level of continuity, more than a third share that there is consistently some form of restart, wait time or repetition in the interaction. If agents are expected to step-in as the expert, they must have all of the resources, context and data at their disposal to effectively personalize the interaction.
CMP Analyst Roundtable What are the most critical next-steps to adequately prepare agents for this new role? Are organizations in the position to train/develop their current teams? BRIAN: Embodied by statements like “AI for simple, humans for complex,” we see the human agent of the future handling the unpredictable edge cases. The issues that require creativity, empathy, and adaptability. The situations that require employees to assess urgency and weigh the pros and cons of making exceptions.
These happen to be fundamentally human qualities. Most humans do not automatically know the intricacies of a brand’s products or policies. They do, however, know how to relate to their fellow humans. They know how to be empathetic and accountable. They know how to engage in candid conversations. And so when study after study confirms that most leaders do not think their agents are ready for complex work, we have to recognize the statistic for what it is. It is not a pure condemnation of agent competency; it is not a sign that contact centers, for whatever reason, have only been hiring the small percentage of humans who do not have any ability to show empathy or think critically. Conscious or not, it is a criticism of the environments we have built. We have not been providing employees with a single source of truth about customers. We have not been surfacing the right knowledge at the right time. Perhaps most importantly, we have not been rewiring our processes, metrics, and performance management strategies to reward human-centric support. Given the dramatic transformation we are forecasting for our contact centers, it is concerning that only 42% have increased their emphasis on training over the past five years. And it is concerning that only 45% are prioritizing updates to their recruiting strategy (not to mention the fact that only 35% are improving compensation, thwarting our ability to attract good talent even if we were optimizing recruiting).
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But the truth is that even if we exclusively hired the best people and always trained them to perfection, their impact would be limited if they did not have the tools and freedom to succeed.
BROOKE: That is why I was very excited to see the boost in organizations giving frontline employees more freedom to make exceptions and deviate from scripts. In the past, this seemed to be a major hole in contact center leaders’ plan to establish a ‘next-generation frontline agent’. Without the freedom to actually do more for the customer, agents are simply regurgitating the same information they could get from a chatbot.
With this burgeoning freedom, leaders must ensure their teams are able to leverage their expertise, emotional intelligence and judgement to make experiences more valuable. By 2030, organizations expect most interactions to be resolved in self-service. This places much more pressure on human agents to do more, to take on different interactions and to offer a last line of defense before a customer moves on. This will take quite the skillset. It will also take quite the mental fortitude. Ensuring agents have the correct training, resources and support is essential.
It is definitely nice to see a non-trivial percentage of organizations giving human employees more freedom to actually be human during interactions. BRIAN:
But the devil’s advocate in me cannot help but be underwhelmed by the extent of the progress. The majority of organizations are actually maintaining or even retracting the “freedoms” they give their teams. And insofar as most consumers do not feel today’s agents are sufficiently polite or empathetic, and 60% feel it has become harder than ever to get a refund or make-good, this is not a status quo worth protecting. To make matters worse, the “freedom” with which organizations are most comfortable is relaxed average handle time. I would argue that this is the least consequential one; average handle time would not even be an issue if agents were just able to quickly give customers what they wanted without performative debate or convoluted approval processes. And for as bad as restrictive agent autonomy is now, it will only get worse moving forward. As customers increasingly adopt self-service, they will only rely on humans for exceptions and highly unique matters. Any roadblocks will directly prevent humans from performing their most important role in the contact center of the future.
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Modernizing the Operation What steps have you taken to build a framework for integrating new channels and/or AI options into your customer journey? Initiated a data hygiene project to update and/or unify existing customer records and databases 71%
Introduced or solidified a governance committee responsible for digital and/or AI engagement 61%
Initiated a knowledge hygiene project to update and/or unify existing knowledge content 56%
Conducted a readiness assessment to evaluate existing tech and data framework
As organizations expand their channel offerings and continue to invest in AI, the groundwork becomes critically important. Here are the steps leaders have taken to build a framework for AI implementation: initiating a data hygiene project to update and unify customer records (71%), introducing or solidifying a governance committee for digital and AI engagement (61%), mapping out technology and data readiness assessments (55%), and prioritizing data and knowledge hygiene to update and unify knowledge content (56%). These foundational steps are essential as the technology stack becomes more complex. When it comes to actually improving interactions, organizations are leaning into customer-insights. To build a more intuitive, personalized and contextual interaction, 95% of leaders are moving beyond reactive strategies and leveraging AI to predict and identify customer intent. By understanding customer needs on a granular level, brands will be able to tailor interactions in real-time. They are also focused on leveraging AI to predict sentiment (90%), creating insights for use outside of customer service (91%) and diving into root-cause analysis (91%).
55%
Invested or increased investment into a unified contact center platform 51%
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MARKET STUDY CMP Analyst Roundtable Consider the AI solutions you have been implementing for customer insights and analytics purposes. How important are the following capabilities or outcomes?
Organizations consistently complain about their data foundation and very few admit to having a single source of truth. What is the most important step toward actually achieving this level of preparedness?
Very important (%) Somewhat Important (%) Unimportant (%) Identify and/or predict customer intent
53%
42%
5%
Create insights for consumption outside the customer service function
52%
39%
9%
Connect employee training to customer data
50%
39%
11%
Identify and/or predict customer sentiment 49%
41%
10%
Identify “root causes” of customer service issues 45%
46%
AUDREY: For many organizations, a disjointed data foundation is more due to a lack of coordination between departments and leaders than true disorganization. Unifying this data is undoubtedly the future of customer service, but also effective marketing, sales, and more, as experiences with the brand become more integral to retaining customers. Customer contact leaders have seen the writing on the wall for years, but as other dimensions of the business shift their focus to the value that they can derive from a single source of customer truth, there is renewed opportunity to work on this project at the highest level of authority within the organization. To that end, building an internal coalition of leaders across different departments who are invested in, and see the value of, a single source of truth is a critical first step towards building it.
9%
Capture direct voice of the customer insights 43%
46%
11%
Retrain AI tools based on actual customer data 40%
42%
18%
Improve survey quality or conversion rate 38%
47%
15%
Improve/deepen quality scoring and assessments
37%
48%
15%
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So, how will this all come together? When thinking about your vision for the ideal service experience, how important are the following on a scale of 0–5 (0 = not at all important, 5 = very important)? Speed of interaction 3.97
Ability to engage with preferred type of agent (AI or human) 3.83
Empathy throughout the journey 3.76
Accommodation of preferred language and conversation style 3.73
Access to customized resolutions and/or policy exceptions 3.73
Ability to easily move between channels and/ or agents 3.71
Consistency of messaging and support outcomes regardless of channel or agent 3.69
Personalization throughout the journey 3.64
Seamless routing to an agent (AI or human) suited to the specific need 3.60
Predictive and proactive engagement about service needs
All of this preparation leads us to what’s next: the modern customer experience. The ideal service experience today is not just fast — leaders are prioritizing the preferred type of agent (68%), empathy throughout the interaction (65%), customized resolutions (64%) and greater personalization (62%). It is these principles that will support a more dynamic and fulfilling experience for customers. With more emphasis on self-service and AI-powered support, leaders cannot forget the key pillars that ensure value for customers. As we look ahead, it is understanding the balance between a streamlined, simple experience and a uniquely human touchpoint. When organizations are in the position to do both, they will consistently meet customer expectations.
CMP Analyst Roundtable Looking ahead to 2030, what is one thing organizations need to get right today to ensure their AI and technology investments actually create a better experience for customers?
BRIAN: For all the talk about pivoting from a cost center to a value center, most brands still adhere to the former model. They restrict access to support. They enforce antiquated policies. They reject requests for exceptions and make-goods. They say “no” when a “yes” is technically possible.
These behaviors suggest that the service experience is seen as something to tolerate rather than celebrate. As long as that is the prevailing view, the contact center will be held accountable for short-term efficiency and cost containment. It will have little opportunity to treat exceptional service as long-term investment.
3.45
Ability to engage in channel of choice 3.40
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This environment does not support a meaningful commitment to agent-customer persona matching, customized resolutions, and personalization. Yes, maybe brands will let their AI deliver tailored marketing emails or inject some extra context into a conversation. Maybe they will enhance workflow, allowing employees to be a bit more attentive during interactions. But will they cultivate a team of human employees capable of uniquely connecting with all types of customers? Will they let agents freely “go off script” today hoping that the customers they serve might spend more down the road? Will they deliver substantive care on a proactive basis? Will they continuously reconfigure policies based on customer and employee feedback?
A contact center that sincerely treats customer lifetime value as its North Star is one that can likely answer “yes” to those questions. Those that still prioritize average handle time, or even customer satisfaction, are less likely to take the leap. When modernizing the service experience with AI and digital, the former’s goal is to make every experience measurably better. The latter’s objective is to avoid making it markedly worse.
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Practicality Guide
MODERNIZING SERVICE EXPERIENCES WITH AI AND DIGITAL
MARKET STUDY
Beyond the AI Pilot: A Practical Operating Model for Modern Customer Service AI Investment Is Rising, but Customer Value Is Not Customer service organizations have never put more money behind AI, and by nearly every account, that spending continues into 2026.
Yet the customers on the receiving end tell a more complicated story: a majority say their customer service experiences are getting worse, and only a minority believe AI-powered tools are adding real value to their interactions.
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MARKET STUDY More AI does not automatically mean more resolution. More automation does not automatically mean less effort. More spending on the latest tools and technology does not automatically mean customers have greater trust in a brand. Each of those gaps is closing far more slowly than the dollars behind them are growing, and that gap, not the technology itself, is the actual story of 2026. This is not an argument against AI. It is evidence that adoption and value are two different things and that closing the distance between them is the real work in front of customer service leaders this year. In conversations captured across the industry, prospects have largely moved past asking whether AI works. They are asking how to implement it successfully, how to move it beyond a pilot, and how to prove that it is paying off. That shift matters. The organizations that pull ahead this year will not be the ones with the most AI features live in production. They will be the ones designing for what happens next: journeys built to anticipate how a customer is likely to respond at each step, ready to handle that response directly, or to move the customer to a live agent without friction when it can’t. That kind of design only comes from treating AI, digital channels, human agents, workflows, data, and governance as one coordinated system, not a growing pile of separate initiatives layered onto an operating model that never changed underneath them. The issue isn’t whether AI belongs in customer service. It’s whether it’s been built into an operating model that actually benefits customers.
What a Modern Service Experience Actually Looks Like in 2026 A modern service experience isn’t defined by whether a channel is labeled “AI-powered.” It’s defined by whether a customer can move through it easily, with context intact, toward an actual resolution. That means an experience that’s connected across channels, aware of what the customer has already tried, able to resolve routine needs on its own, and able to hand off an issue or concern to a person without making the customer start over. There’s a distinction worth holding onto here: Digitization adds technology to an existing process; modernization redesigns the process around the outcome. A chatbot layered onto an already-broken journey is digitization.
A system that recognizes intent, resolves what it reasonably can, and routes what it can’t to the right person with full context is modernization. The difference isn’t the technology. It’s whether the journey underneath it was actually rebuilt. Independent research backs up how much this distinction matters to customers. Zendesk’s 2026 CX Trends report, based on surveys of more than 11,000 consumers and CX leaders across 22 countries, found that a large majority of consumers believe the customer journey should be significantly better than it is today, even as AI investment climbs. The same research found that most consumers want a conversation to continue without backtracking, and a majority say they’re frustrated when asked to repeat information they’ve already given. Customers, in short, are not grading the sophistication of the technology behind an interaction. They’re grading how much friction stood between them and a resolution.
Modernization should be judged by what the customer experiences, not by what’s listed in the tech stack.
Four Practical Priorities for Closing the Value Gap The market is full of AI capabilities, not operating models that connect them to customer value. Four priorities separate the organizations closing that gap from the ones widening it. 1. D iagnose the value gap before adding more technology AI creates value when it removes friction or improves resolution, not simply when it increases automation or containment. Where AI initiatives disappoint customers, the pattern is familiar: a use case selected because it was easy to build rather than because it addressed a meaningful problem; technology added without redesigning the journey around it; customer data left fragmented across systems; containment treated as success even when the customer never actually got what they needed.
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MARKET STUDY Measurement is often part of the problem. Teams frequently track deployment activity instead of whether the customer’s problem was actually solved, and that gap between activity and outcome is exactly where trust erodes fastest. It’s visible in a widely reported industry example: In 2024, a well-known financial technology company scaled an AI customer service assistant aggressively, cutting hundreds of human support roles in the process on the belief that AI could handle the work end to end. The company reported strong early efficiency gains. By the following year, however, its own CEO acknowledged that a cost-first approach had produced lower service quality, and the company began re-recruiting human agents. The metrics used to declare early success measured how fast interactions moved, not whether complex problems were actually resolved. Before funding any new AI use case, leaders should be able to answer seven questions: • What customer or operational problem does this solve? • Where should it sit in the workflow? • What customer intent does it support? What human role does it affect? • What data does it require? • What risks and escalation paths must be managed? • What measurable outcomes should improve? A use case that can’t answer all seven isn’t ready for funding, no matter how promising the demo looked. 2. B uild AI and human agents as one operating model, not a binary choice The real design question isn’t AI versus humans. It’s which work belongs to each and how context moves seamlessly between them when a handoff happens. Automate the high-volume, low-ambiguity work where speed matters more than judgment. Augment the interactions where AI can surface context or nextbest-action while a person still drives the conversation. Leave the rest — emotion, complex problem-solving, negotiation, high-value retention, trust repair — to human judgment, fully supported rather than left to work around the technology. “AI and human” working together has been the single most consistently resonant theme in conversations with customer service prospects and leaders.
This is not a hypothetical framework but what leaders are actively asking for. Two design principles matter most in practice: Context has to travel with the customer at every handoff, and agents need real access to knowledge and coaching intelligence, not just AI monitoring layered on top of an already full workload. The best AI-enabled service experiences don’t hide the human behind the technology. They use the technology to enable the human to be faster, better informed, and more effective exactly when it counts. 3. M ove deliberately from pilot to operational scale Across client conversations this year, a consistent pattern emerged: Their organizations are still piloting AI, not scaling it. That’s the market’s real maturity level right now, not a stage most have already passed through. Pilots typically stall for reasons that have little to do with whether the technology works: no clear business owner, incomplete or inaccessible data, a workflow that was never redesigned around the new capability, governance introduced after deployment instead of before it, or a vendor that demos well but doesn’t support live operational deployment. Leaders reported some of these barriers directly and consistently: limited internal manpower to implement AI and vendors that couldn’t be trusted to operate transparently. Moving past pilot stage means working through a deliberate sequence: diagnose pain points, prioritize high-value use cases, establish a baseline, design the AI-human workflow, test under controlled conditions, validate outcomes, deploy with governance and ownership, monitor adoption and model performance, and improve continuously. Two actions make that sequence real rather than aspirational: build a vendor-transparency scorecard, and assign one accountable executive owner to each initiative, not a shared committee that quietly diffuses responsibility.
“Customers don’t experience AI in the abstract. They experience whether their problem was solved without hassle. Turning AI pilots into real operational value requires combining the technology with human expertise, maintaining visibility into every customer interaction, and measuring success by outcomes—not simply by whether something was deployed.” — Martin Lehtio, Chief Operating Officer, iQor
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MARKET STUDY 4. Create visibility across every interaction, not a sample of them Leaders can’t modernize what they can’t see. Small quality samples and delayed reports mean problems typically surface only after they’ve already cost the organization something, such as a wave of repeat contacts, a quarter of declining sentiment, or a churn spike that shows up weeks after the interactions that caused it. Recurring barriers reported this year include limited reporting, incomplete analytics, and an inability to proactively identify where operations are breaking down before customers or agents feel it. Real visibility should surface something specific: why customers are actually contacting the organization, where sentiment is shifting, which processes are generating escalations, where agents need coaching, and where digital friction is quietly pushing customers into the contact center when they never should have needed to call in the first place. One recent account described clients gaining insight into friction points and missed opportunities specifically through broader interaction analysis, not sampled reporting that catches only a fraction of what’s actually happening. Turning that kind of interaction data into something usable at scale is exactly where most organizations get stuck. It takes technology built specifically to listen across every conversation, not just a handful of them. Insights iQ™ is iQor’s proprietary platform for analyzing customer interactions at scale, across every channel, rather than relying on sampled surveys or spot-checked calls. It connects what it finds to coaching, quality, and operational decisions rather than producing another dashboard nobody acts on. The differentiator isn’t analyzing more data for its own sake. It is creating a closed loop where interaction intelligence identifies what is breaking, operations teams know what to change, supervisors know who and how to coach, and leaders can measure whether the change improved customer, employee, operational, and business outcomes.
Visibility is the foundation of responsible automation. See where the experience is actually breaking before deciding what fixes it.
Measure Outcomes, Not AI Activity The problem starts with what gets measured. Usage metrics (containment, deflection, tickets automated) tell you how much automation ran. Value metrics (resolution, effort, trust) tell you whether the customer actually got what they needed. These are two different questions, and answering the first one well says nothing about the second. A high containment rate isn’t evidence of customer value; for instance, it can just as easily be evidence that a customer gave up. Treating usage as if it were proof of value is exactly how a value gap forms in the first place. Handle time can drop while repeat contact quietly rises. Containment can climb while high-value customers grow frustrated and start looking elsewhere. This isn’t an isolated concern. A 2026 study by Roland Berger, conducted with Potloc across more than 550 senior decision-makers in five industries and three global regions, found that balancing cost and quality has become the top concern for more than half of respondents, a sign that this tension is an industrywide problem, not a one-off measurement mistake. Separately, Zendesk’s 2026 research found that the overwhelming majority of consumers now expect clear explanations for AI-made decisions, and most CX leaders say transparency will soon be non-negotiable for any customer-facing AI. Increasingly, how a resolution happened is becoming just as measurable (and just as important to customers) as whether it happened at all. Leaders should track four categories of outcomes together, not in isolation: • Customer outcomes like effort, resolution quality, and sentiment • Employee outcomes like adoption and speed to proficiency • Operational outcomes like cost-to-serve and cycle time • Business outcomes like retention and revenue protection Every AI initiative should report against a pre-defined outcome from Day One, not just how heavily it was used. This is exactly what closes the loop on where this paper started: a widening value gap is what happens when activity gets measured instead of impact.
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MARKET STUDY Turn AI Investment Into Operational Discipline Is the goal to deploy more AI or to close the gap customers are already telling you about? The differentiator in 2026 isn’t who has AI. It’s who has operationalized it around outcomes customers actually feel. The most important modernization decisions are being made right now, in how leaders diagnose, prioritize, and govern, not in some future transformation initiative still waiting for the right moment to start. Leaders who wait for AI maturity to fully arrive will simply be reacting to expectations that have already hardened around them. None of this requires transforming the entire operation at once. It starts with a single quarter of disciplined focus. Within 90 days, leaders can: • Identify one customer journey with measurable friction • Establish a baseline before introducing new automation • Define which work should be automated, augmented, or human-led • Widen interaction analysis beyond small samples • Assign cross-functional ownership with a governance scorecard
The future of AI in customer service isn’t more tools. It is a better operating model: full visibility into what customers need, disciplined decisions about what should be automated and what should stay human-led, skilled agents equipped for complex moments, and measurement tied to outcomes customers and businesses can actually feel.
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 path forward isn’t more experimentation at the edges. It’s disciplined operationalization at the core.
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The Self-Improving Service Experience Modernizing the contact center is a constant endeavor. For years, contact center modernization took a similar, straightforward approach: adding channels, increasing self-service opportunities, automating repetitive tasks and working towards a more efficient, streamlined service experience. In so many ways, this transformation has been quite successful. Customers have the opportunity to engage across more channels than ever before, automation is taking on many of the routine interactions and AI is empowering a more dynamic and intelligent frontline.
The challenge, however, is that this level of support has become tablestakes. It is now an expectation that brands can offer support everywhere. Success, in the eyes of the customer, is receiving a resolution within these channels. The next phase of transformation will be defined by the businesses that can build systems capable of understanding customer needs, orchestrating the steps required to secure a resolution and consistently improving over time. The future of modernizing experiences is about adaptive service.
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MARKET STUDY In this exclusive interview, Nick Delis, Chief Revenue Officer of Omilia shares insight on the self-learning capabilities that will power the next-generation of support. As continuous improvement becomes the baseline, organizations must differentiate on their ability to compound the capability and impact of AI. As we look to 2030, these are the organizations that will lead the next era of CX: ones that ensure experiences are becoming more capable, effective and valuable with every interaction. Our research aims to uncover what the ‘modernized experience’ truly looks like today. As organizations attempt to align experiences with customer expectations, we must understand what customers actually expect. From your perspective, what do customers expect out of modern experiences? The bar for a modern experience has shifted from availability to outcome (proactive 360 degree communication vs reactive). Customers assume a company can be reached by phone, chat or app; what they judge now is whether the interaction actually resolves their issue without repeating themselves, without being routed through menus, and without waiting on hold simply to complete a routine task. In addition, the experience from before transaction to during transaction to post transaction needs to be seamless for the customer, the agent, and the business. Three trends stand out. 1. First-contact resolution has replaced speed as the primary measure of a good experience. Customers will tolerate a slightly longer interaction if it ends with their problem solved; they won’t tolerate a fast interaction that ends in a transfer. Yet, if it does end with a transfer, the seamless hand off between a virtual and physical agent needs to apply all the key conversational recap to the agent with potential next best actions to reduce poor experience. 2. Consistency across channels is now expected, not aspirational. The customer wants their channel of choice not the businesses channel of choice. A customer who verified their identity on a call expects the chat channel to already recognize them. Context and accuracy need to travel with the customer, not reset at every touchpoint
3. Natural conversation is replacing structured selfservice. Virtual agents are balancing deterministic needs versus agentic conversations. Customers expect to describe a problem in their own words, including multipart alpha-numeric requests, and be understood, rather than navigating a menu built around what the system happens to recognize. The Result: Higher NPS and CSAT, Ability to use the conversation as a revenue generator (cross sell, Upsell), create a continuous communication loop. Underneath all three is a harder expectation: that the experience gets better over time. Customers increasingly notice when a company’s automation hasn’t changed in years, and read that as evidence the company isn’t listening. Agentic AI is now at the forefront of innovation. When we think about the ‘next-generation experience’ — agentic AI is powering it. How does agentic AI fit into the modernized service experience? In what ways is it enhancing and optimizing CX today? Traditional self-service, IVR menus, first-generation chatbots are built around anticipated paths: if the customer says X, do Y. It breaks down the moment a request doesn’t fit the path, which is often, because real customer requests are rarely single-intent. Agentic AI changes the underlying model: instead of following a fixed script, the system identifies the goal behind a request, works out the steps needed to resolve it, and calls on the systems and information required, adapting in real time if the customer changes their mind, provides information out of order, or raises a second issue partway through. In the modernized service experience, agentic AI plans and orchestrates the resolution, deciding what needs to happen and in what order while execution in the live conversation stays consistent and fully auditable. That combination is what separates real automation from a smarter menu: the system can handle a genuinely different request every time without becoming unpredictable in how it responds. In practice, that shows up as multi-step requests resolved fully in a single conversation, without escalation, authentication and personalization happening in the flow of conversation rather than as a separate friction-heavy step. Handoffs to a human agent, when they do happen, arrive with full context already assembled instead of asking the customer to start over.
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MARKET STUDY There’s a further level to this that most organizations haven’t reached yet: agentic AI that doesn’t just resolve the conversation, it builds the next agent. Our self-learning capability studies thousands of the best human-agent interactions, learns the pattern that makes them successful, and proposes a new AI agent that replicates that behavior, ready for a human to review and approve before it goes live. Building the success criteria blueprint. That same shift changes who can build automation in the first place. Developer CoPilot guides agent creation through natural-language instructions and a no-code, drag-and-drop interface; someone without an engineering background, a business analyst, a CX lead can configure and launch a new agent directly, describing what they want rather than writing the logic themselves. That’s the real next step for agentic AI: it doesn’t just answer the customer, it continuously creates the next version of itself, and puts that capability in the hands of the people who understand the customer best, not only the people who can code.
“Agentic AI is what finally lets automation handle a real conversation instead of a rigid script. It reasons through what the customer actually needs and works through the steps to resolve it, instead of stopping at the first fork the script didn’t anticipate.”
Omilia’s self-learning AI offers another path toward the modern experience. Can you share more about how self-learning AI works? What are the benefits of a self-learning agentic AI platform? Omilia’s Agentic Self-Learning capability treats every customer interaction, successful or not, as a signal. The platform continuously reviews conversations to identify where automation is succeeding, where it’s stalling, and where an opportunity for self-service exists that didn’t before. It then builds and tests an improved response against that signal, new intents, better phrasing, corrected logic before anything reaches a live customer.
The distinction that matters for enterprises is where the learning happens versus where the conversation happens. Pattern discovery and improvement-building draws on broad, offline analysis; the agent that actually talks to the customer in production runs on purpose-built models that behave consistently and are fully auditable. That separation is what makes continuous self-learning safe to run in regulated, high-volume environments: the system keeps improving without ever putting an unpredictable model in front of a live customer. The benefit compounds over time. Static automation plateaus early, gains flatten out, and every subsequent improvement requires a new vendor project and months of calendar time. Self-learning automation isn’t waiting for the next release cycle to improve; it improves continuously, with every change reviewed and approved by the business before it deploys.
“Most AI in a contact centre is frozen the day it goes live. Ours is designed to keep getting better, on its own, from every conversation it has.”
Price was ranked the #1 unexpected challenge associated with AI implementation. You share that the total cost of ownership / LLM cost can be inconsistent. Why is this such a challenge? How can organizations navigate the cost and prepare accordingly? Total cost of ownership becomes unpredictable when a platform’s core reasoning depends on third-party, usage-metered models. Pricing looks simple in a pilot, when volume is low and the use case is narrow. It stops looking simple once an organization scales a successful automation to more intents, more channels and more volume, because every one of those interactions can trigger variable, per-use costs that the vendor doesn’t fully control either they’re passing through a model they don’t own. Add in the compute required to run larger models at enterprise concurrency, and the gap between a pilot’s cost and year-two’s actual bill can be substantial.
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MARKET STUDY Organizations can protect themselves by asking three questions before signing, not after. • Does the vendor own its own models and infrastructure completely, or are core interactions dependent on a third-party model whose pricing it doesn’t control? • Is pricing tied to a fixed, predictable unit, such as an outcome or a resolved interaction, or to variable, usage-based consumption that scales unpredictably with growth? • Will the vendor commit, contractually, to a transparent cost structure with no hidden compute or pass-through fees as usage grows, not just at initial signature? A platform that owns its full technology stack can price predictably because it isn’t reselling someone else’s metered model. That’s the difference between a cost that scales with the value delivered and a cost that scales with usage nobody forecast. In addition, to those key questions: Know your SLA and ultimately where it is coming from.
“The organizations that get surprised by AI cost are almost always the ones that never asked what happens to the bill once usage and every model call behind the scenes scales up. Bring your own is almost always synonymous with cost overrun”
The last step of the ‘closed loop learning cycle’ you outline is continuous human-AI collaboration. The new or optimized agents are deployed to production under human supervision and every interaction drives improvement. How will this dynamic work? What role does the human play in this shifting workflow?
The learning cycle runs in five stages:
1
Monitor: The platform monitors live interactions across voice and digital channels, capturing what happened and how it was resolved.
2
Analyze: It analyzes that signal to identify specific opportunities, a gap in coverage, a phrasing that’s confusing customers, an intent nobody built for.
3
Build and Test: It builds and tests a proposed improvement against historical and simulated conversations before it ever reaches a real customer. Every proposed change is then surfaced for human review and business approval before deployment nothing selfdeploys.
4
Production: Once approved, the improved agent goes into production, and the cycle immediately starts monitoring its performance again, closing the loop.
The human’s role shifts as a result. Instead of manually rebuilding flows or writing new scripts from scratch, the person in the loop reviews and approves AI-generated recommendations closer to an editor than a builder. That changes the skill the role demands: less rote configuration work, more judgment about what’s right for the customer and the business. It also means the pace of improvement is no longer bottlenecked by how fast a development team can ship a change; it’s bottlenecked by how fast a qualified person can review one, which is a materially faster cycle.
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“Human in the loop. Nothing our AI learns goes live without a person signing off on it first. The system proposes the improvement, a human approves it, and only then does it reach a customer.”
As we look ahead, what predictions do you have for the future of AI-driven support? Where are we now vs. where are we going? Most organizations are past the question of whether to adopt AI and into the harder question of why it isn’t moving the numbers they expected. The typical pattern is a fast initial gain from IVR modernization or a first chatbot, followed by a plateau: call resolution that stops improving, savings that stop growing, and every further change requiring a new vendor engagement. Where this is going is automation that treats every interaction as an opportunity to improve, without waiting for a scheduled release. Expect three shifts over the next few years. Self-service will stop being judged on whether it worked and start being judged on whether it got better this quarter. Continuous improvement becomes the baseline expectation, not a differentiator. The line between an AI agent and a human agent’s toolkit will blur, as the same intelligence that resolves a request automatically also surfaces the right information to a human agent when one is needed. And cost predictability will become a selection criterion on par with accuracy, as more organizations have been burned by AI programs whose cost scaled faster than their savings. The organizations that get ahead of this won’t be the ones who deployed AI first; they’ll be the ones whose AI compounds instead of plateaus.
“The next few years won’t be about whether contact centres have AI they all will. The winners will be the ones whose AI is still improving in year three, not the ones who peaked in month one.”
COMPANY
Discover Financial Services
CHALLENGE
Discover’s legacy IVR a mix of touch-tone menus and directed-dialogue speech menus had no natural language understanding, limited flexibility to add or modify self-service options, and a high rate of calls transferring from one agent to another. RESULT
Discover selected Omilia’s conversational AI platform after a rigorous evaluation and pilot, replacing menu-driven navigation with a natural ‘how may I help you?’ experience and fully migrating more than 40 self-services off the legacy IVR onto Omilia’s single-tenant cloud platform. The result: improved selfservice rates, reduced average handling time, fewer agent-to-agent transfers, and the flexibility for Discover’s own team to build new self-service options independently.
COMPANY
Purolator
CHALLENGE
Purolator Canada’s largest integrated parcel and freight network, moving over 1.3 million packages a day was running on stagnant, on-premise contact centre technology that could no longer keep pace with rising customer expectations for speed, transparency and proactive updates. RESULT
Purolator deployed Omilia’s conversational AI platform across voice and chat. Within weeks of launch, voice accuracy reached 98%, voice containment reached 40%, and chat containment exceeded 95% with the chat solution handling roughly four times the volume Purolator’s contact centre agents previously managed.
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MARKET STUDY
COMPANY
PSEG
CHALLENGE
PSEG New Jersey’s largest electric and gas utility, handling around 7 million inbound calls a year relied on a complex, on-premise, multivendor IVR that could not scale during severeweather call spikes, offered no full visibility into customer journeys, and had a high rate of misrouted calls. RESULT
PSEG deployed Omilia’s conversational AI with advanced intent recognition, real-time interaction analytics and 11 new self-service options (billing, payment plans, repair scheduling and more). Misrouted calls fell from 40% to 15%, containment improved, the system held 100% uptime through severeweather call surges, and PSEG identified more than 50 areas for further improvement in the first year alone.
COMPANY
Taco Bell CHALLENGE
Taco Bell wanted an AI-driven voice ordering solution that could reduce friction in drive-thru interactions and free team members to focus on hospitality rather than repetitive ordertaking. RESULT
Taco Bell piloted Omilia’s Voice AI solution then expanded to four more pilot locations before scaling further. As of April 2026, the solution is live in 890+ Taco Bell U.S. stores. Drive-thru transaction times are on par with or faster than traditional order-taking, sales and transactions at Voice AI stores are on par with or better than non-Voice AI stores, and early indicators point to reduced team member turnover at corporate locations using the solution freeing team members from repetitive order-taking to focus on hospitality and guest experience.
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Beyond Simple vs. Complex: Redefining Customer Support in the Age of AI The modern customer experience will look different, and that’s a good thing. Customer expectations have fundamentally changed. Experiences used to exist on opposite sides of the spectrum: fast and streamlined but straightforward or friendly and empathetic but leisurely. Customers were accepting of these minor barriers if it meant they got the resolution they needed.
Today, customers want both. Customers want support with minimal effort, 24/7 access, instant responses and a contextual conversation. CCW Digital confirms that priorities for the ideal interaction includes: speed of interaction, preferred type of agent (AI or human), empathy throughout the journey, and customized resolutions.
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MARKET STUDY This is possible, but it will take a shift in how organizations deliver support. CX leaders believe that up to 79% of contacts will be fully resolved in self-service by 2030. Achieving this level of resolution requires heightened adoption, satisfaction and trust in AIpowered support.
2
Support is hyper personalised. It’s important that a customer’s specific situation and relationship with an organisation are very evident through the support experience. The frustration of having to constantly provide information that an organisation should know about you already as a customer is becoming unacceptable. This sense of personalisation is evolving too, where there is a growing expectation that if support is engaged, there is already awareness of the situation that brought you to seek support, i.e. what steps or activities you had undertaken prior to seeking support. Over time, there is an added dimension to this – that an organisation will proactively reach out to you if you are having any challenge or issue in using a service or a product.
3
That work will be completed on your behalf. There is a growing frustration amongst customers at being advised of the task or activities that they should undertake to resolve an issue when many of these tasks could be automatically completed on their behalf. The offer should be made to complete work for a customer where it is practical and feasible to do it.
Currently, 70% of customers state that they prefer live support over self-service, meaning organizations have work to do to bolster this trust. Agentic AI, however, offers a powerful engine for establishing a self-service experience that exceeds customer expectations. As high as 74% of leaders believe that agentic AI will make customers more likely to adopt self-service. CCW Digital is thrilled to share an exclusive interview with Declan Ivory, VP, Customer Support at Fin, sharing his insights on the future of AI support, redefining what ‘complex’ work means and how AI can help manage well-defined work, leaving humans to focus on ambiguous, nuanced and sensitive interactions that require human judgement.
What do customers expect from modern experiences today? There is a marked shift happening in customer expectations, where for a long time ‘fast and friendly’ was good enough, to an environment now where customers expect immediate engagement as almost table stakes and the expectation is moving more to a seamless experience where ‘proactive, consistent, professional, and high quality’ support is the bar. This then leads to the following key trends:
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Customers expect support whenever and wherever they need it. This equates to immediate access to ‘support’ where customers don’t have to navigate your organisation’s structure to know where they need to seek support. It means 24X7 availability, in whichever channel makes sense for them, and ideally in their own language. They expect context to be maintained even if they switch channels or if multiple people need to be involved in resolving their issue. Not having to constantly repeat themselves is a given.
The common theme here is that customers value their time and are constantly looking for the effort and friction of the support experience to be reduced or eliminated. As part of this seamless and effortless experience it is expected that AI agents and humans function as one continuous, memory-aware system that doesn’t waste their time or make them repeat themselves, and they’ll punish (with churn) any brand where that system visibly breaks.
What does “AI resolves the most complex queries” mean for the future of support? It is well accepted that an AI agent can resolve informational queries with a very high degree of accuracy and deliver a really good customer customer experience, meeting many of the evolving customer expectations of the support experience e.g. 24X7 availability, immediate response, omnichannel and multi-lingual support.
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MARKET STUDY That has led to an assumption that AI will handle anything routine or simple, and that humans will handle the more complex and nuanced work. The reality is that AI capabilities are improving all the time and AI agents have moved well beyond the realm of just handling informational queries. Comprehensive AI Agents, like Fin, can now resolve really complex queries and tasks for customers. In many instances Fin is resolving greater than 80% of support interactions autonomously. It can also undertake different roles across the customer journey i.e. sales, success, and service. What does this mean for the future of support as we know and define it today? The scope of support as we think about it today is being redefined. A customer views every interaction they have with a business as some form of support. With AI Agents, like Fin, quickly becoming an intelligent front door for your business then a customer can get the majority of the support that they need via an AI Agent. Where does this leave the boundary between an AI Agent and human involvement? What this means in practical terms is that the dividing line stops being “simple vs. complex” and becomes “well-defined vs. ambiguous”, where human judgement and experience can deliver the most value for a customer. It also means that humans undertaking support activities are focused on two areas; • Resolving some complex, nuanced, and ambiguous issues that customers have. The definition of what constitutes the need for human involvement will change from one business to another, but irrespective of where the boundary is, the reality is that it will change as AI becomes even more capable and the expectation on the skills and expertise the humans bring to the experience will also be increasing all the time. • Humans have the best appreciation and understanding of the customer experience and therefore represent the best source of feedback and recommendations on how AI can be enhanced to improve the customer experience. The role that humans play in optimising the experience that an AI agent can deliver should now be underestimated.
It is critical that businesses invest in their AI agent but it’s just as critical that investment is made in humans to ensure that they are enabled to deliver the maximum value for customers and for the business in a world where an AI agent is managing the bulk of customer interactions independently.
How does this shift the role of the human? What can leaders expect from the modern hybrid/AI-powered workforce? There is a very clear shift in the role of the human in supporting customers. It’s not a case of humans doing less work; it’s actually a case of humans doing different, and more value adding, work. AI agents, like Fin, absorb the routine and predictable (event complex activities) customer interactions, leaving humans to focus on the more ambiguous, highly complex, high-value, or sensitive relationship interactions. The reality is that humans are freed up in a modern hybrid AI Agent/ Human workforce to spend more time with customers in a consultative capacity, thinking well beyond the problem presented by the customer and delivering real value as part of the interaction. For CX leaders the practical implications are: • Role redesign rather than headcount reduction. Using the bandwidth freed up by using an AI agent for the majority of customer interactions means you can think radically differently about the role of humans. They are handling the most ambiguous and nuanced interactions that AI cannot handle and are therefore valued for the deep subject matter expertise, judgement, and relationship skills that they bring to the table. Thinking of support as a value driver for the business rather than a cost centre, means that the capacity freed up by an AI agent can be reinvested in compounding the value for customers and for the business. This is real and necessary business transformation in a world that is being redefined by AI. • Increasingly humans will not just be involved with customers, but will also play a key role in optimizing how AI is performing for the business. In addition to the deep trouble shooting and relationship skills that humans are expected to have in this new world, new skills are required such as prompt engineering, knowledge creation and management, ability to review AI effectiveness, and the ability to drive continuous improvement of the AI agent experience.
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MARKET STUDY • Career paths will change for humans. The traditional entry level roles in support will disappear over time and the roles left will be more valuable long term career options for people where the skills they bring to the table are highly valued given the nature of the work that humans will undertake. This has implications for hiring, onboarding, learning and development, and job family design. The leadership role in this hybrid world fundamentally changes from being obsessed with queue management and human agent productivity to one where the focus is on customer experience and impact. This includes being sensitive to where and when humans need to be involved and moving beyond today’s model of an AI agent ‘escalating’ to a human, to a model where an AI agent and a human collaborate as necessary to drive the best outcome for a customer.
Is “good” the fastest experience, the most accurate response, or the friendliest conversation? What does a perfect customer experience look like in practice? Traditionally there has been inherent tension between ‘fast’, ‘accurate’, and ‘friendly’ with trade offs often necessary across these dimensions to try and optimise the customer experience, with organisations often picking the wrong trade-offs and sometimes providing a worse customer experience. The use of an AI agent opens up the possibility of delivering ‘perfect customer experience’ but it does require a continuous improvement mindset and the right investment across the AI agent and human support teams to deliver the maximum value for customers. Customers increasingly have an expectation of being able to engage immediately with support (whether that be an AI agent or a human) and having their issue resolved or task completed with the minimum amount of effort possible.
In practice ‘good’ or ‘perfect’ looks like: • Resolution, not just response. A fast reply that doesn’t solve the issue is worse than a slightly slower one that does. Resolution at the first point of contact matters more than speed alone. • Consistency across channels and hand-offs, so the customer never has to repeat themselves or reexplain context. • Appropriate tone, not maximum friendliness. Friendliness that doesn’t fix the issue reads as hollow; the real signal of care is reduced effort and better follow-through. The use of an AI Agent aligned with human support can transform the customer experience when deployed intentionally and with the correct level of operations rigor or operational excellence to drive continuous improvement where AI capabilities and customer expectations are fast evolving.
What should leaders do before committing to the AI journey? Too often businesses will deploy AI for customer interactions without a well thought through strategy. It’s never a case of AI for AI’s sake. This is an approach that will ultimately be doomed to failure. Here are some of the key things that a CX leader should consider:
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First and foremost, define the business problem you are trying to solve or the business outcome you are trying to drive. Without a clear vision or strategy around what you are trying to achieve then it’s challenging to know exactly where and how you need to focus on some of the other challenges such as data preparation.
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Having settled on the problem to be solved, then knowledge and content become critical infrastructure for the successful deployment of an AI agent. Audit and consolidate the relevant knowledge and content base for the problem you are solving for. The rate and quality of ‘resolution’ of an AI agent is directly related to the quality and completeness of the underlying knowledge that the AI agent has access to.
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Aside from the initial audit of content, it’s really important to have a really well defined and engrained process for constant monitoring and tuning of content to maintain and improve resolution rates. AI agent solutions, such as Fin, should offer capabilities to continuously assess the effectiveness of content and to suggest areas for improvement.
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Launching new products or making changes to existing products provides an opportunity for content to become out of sync with how the product actually works and can then impact the performance and experience of an AI agent. Ensuring that content development is a key part of any new production introduction (NPI) process is critical for success. This requires cross functional collaboration with engineering, product marketing, GTM enablement and support enablement. The investment here will pay huge dividends e.g. at Fin, our NPI process is deeply ingrained in how we operate and Fin, our AI agent, will typically resolve >70% of customer issues on the day of launch.
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Map the actual conversation taxonomy i.e. what percentage of volume is truly routine vs. genuinely complex, before setting resolution-rate targets, so you’re measuring against reality, not aspiration. For each category in the taxonomy it’s also critically important to understand what is limiting the ability to drive the highest resolution rate possible. In many cases the limiting factor will be access to underlying operational and business systems to enable deep personalisation and to enable the completion of tasks for customers. Setting up the appropriate APIs or MCP servers can be a huge unlock to improving the customer experience.
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Define what “resolved” means for the business before launch, since how you account for a resolution can vary e.g. what happens if a conversation is reopened?, what happens when you intentionally have a human in the loop? This can lead to a focus on outcomes rather than resolutions and this can lead to yet another uplift in customer experience when you focus on the overall outcome rather than did the AI agent resolve the issue autonomously.
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Pilot on a bounded segment first (one channel, one use case) rather than flipping it on everywhere at once; this is how you catch knowledge gaps before they become customer-facing.
Putting the right effort into data preparation will pay huge and compounding dividends in the customer experience and the business outcomes achieved.
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MARKET STUDY Predictions for the future of AI-driven support: where are we now vs. where are we going? Where we are now?: We have moved quickly to a situation where there is widespread recognition that an AI agent must be part of a modern support strategy and the majority of organizations have either experimented or are planning to experiment in 2026. There is also clear evidence of companies moving beyond the experimentation phase and using AI at scale i.e. AI resolving > 70% of support interactions, as a key component of their support experience for customers. AI has also moved beyond just answering informational queries and is competently handling complex multi-step procedures and processes that enable actions to be completed for customers. This is meeting a growing expectation from customers that businesses that they deal with will use AI to improve the customer experience. This expectation is built on the customer’s own personal experience of using AI tools. Where are we going? There are a few key shifts happening: • There is a move from just focusing on reosolution rate to look at automation rate which is more focused on outcomes and not just resolutions. It’s no longer just about ‘did AI answer this’ but ‘how much of the end to end work in achieving the required outcome did AI actually own’. This has major implications for ‘conversation design’ with an AI agent and humans collaborating where necessary and the idea of a one way handover from AI to human will no longer be a limiting factor i.e. AI and humans are involved where and when it makes most sense. • Agentic AI is blurring the lines between sales, success, and service, tearing down the long standing organisation siloes and divisions that have dictated how a customer deals with a business. An AI agent acting as an intelligent front door to a business unlocks a totally different way of thinking about how to handle the entire customer lifecycle or journey. New business models and organisation structures will emerge to optimise for the customer experience.
COMPANY
Vanta is one of the fastest-growing companies in security and compliance automation – a category where precision, trust, and speed matter equally. CHALLENGE
Vanta’s support metrics are publicly available on their website for everyone to see, and the sales team often references that page in conversations. So if anything changes there, it can affect customer confidence and have an impact beyond support. As customer volume grew, their previous fragmented setup, a helpdesk with an AI agent layered on top, was reaching its limits: their AI was deflecting around 45% of chat conversations, had stopped improving, had no coverage over email, and managing separate tools made it harder to deliver a consistent customer experience. "Each tool solved a part of the problem, but together they created complexity and made it hard to deliver the kind of seamless, omnichannel experience we wanted for our customers," says Margarita Wilshire, Director of Customer Support. "It felt like we had outgrown the setup, and it was only a matter of time before it stopped holding." RESULT
Vanta tested Fin, an AI Customer Agent against their existing AI on 400 real conversations. Fin was the clear winner, with a resolution rate of 73% versus just 49%, was better at handling nuance, and could extend to email, their highest-volume channel. They rolled out Fin on their existing helpdesk first, proved the impact, then migrated fully to Intercom Helpdesk. Today, Fin fully resolves 71% of the chat conversations it’s involved in, with ~97% CSAT, and they're expanding that same seamless experience across email and Slack, creating one front door for every customer question.
• Within a very short period, AI-powered support will cease to be a category in its own right and the focus will be on AI-powered customer experience, with it being a given that AI is managing the bulk to support activities, and eventually it will also be taken for granted that all customer interactions are managed by AI, with human collaboration as necessary. Modernizing Service Experiences with AI and Digital | www.customercontactweekdigital.com
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Anthropic is an AI research company that focuses on building safe, reliable artificial intelligence that’s aligned with human values. CHALLENGE
As a pioneer in the AI space, Anthropic's platform and products are constantly evolving, with new features and models being added all the time, bringing sharp spikes in support volume on top of fast-growing demand. Their support team started with just one person trying to keep up, serving users that range from free consumers to enterprise API customers, each with different needs, expectations, and levels of technical complexity. "I was just trying to survive. Volume was growing fast, and I was making constant trade-offs," recalls Isabel Larrow, Product Support Operations Lead. The team knew that providing highly tailored support at that scale would be extremely difficult without AI, which prompted their search for the right AI Agent. RESULT
In 2024, they selected Fin, an AI Customer Agent, after a focused evaluation – choosing to implement rather than build in-house. Fin was live in under a week, and within the first month it had already saved the team more than 1,700 hours. But the team knew Fin wouldn't succeed in a vacuum: it needed to be part of how they worked. So they treated it like a system to run, with weekly reviews of unanswered questions, quarterly content sprints, team-wide hackathons, and clear escalation rules so enterprise and edge cases go straight to the team. That investment kept compounding. Today, Fin is involved in 80% of all their incoming queries with a resolution rate of 79% and translates to more than ~560K resolutions every month. With that volume off their plate, the team now focuses where human judgement matters most: deep enterprise issues, compliance edge cases, and proactive outreach. And they're also exploring how Fin can support more of the user journey, extending its impact beyond support into areas like proactive help in onboarding, education, and community.
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Lightspeed is a global commerce platform serving around 145,000 retail and restaurant locations. Its software powers everything from point-of-sale and payments to ecommerce and inventory, which means support has to be fast, accurate, and always on. CHALLENGE
As Lightspeed grew, two big challenges loomed: volume and complexity. Tens of thousands of support requests were coming in each month, spanning multiple products, languages, and time zones. Meanwhile, years of mergers and acquisitions had left the company with a multitude of systems: numerous Salesforce instances, overlapping ERPs, and siloed data. At the same time, expectations kept rising across the board. Customers wanted instant answers, leaders wanted lower costs, and the team had to deliver both without tearing down and rebuilding the entire stack. RESULT
In early 2023, Lightspeed became one of the earliest adopters of Fin, an AI Customer Agent. Fin went live within weeks, without requiring Lightspeed to rip out or replace the systems already there. “Fin worked with our complexity, not in spite of it,” says Yamine Gluchow, VP of Information Systems. As the team invested in content, training, and adoption, performance kept improving – from around 20% resolution in the early days to over 70%. Today, Fin is involved in 88% of all support conversations, resolves 72% of them end to end, which adds up to more than 43,000 resolutions each month, in 12+ languages across more than 100 countries. CSAT has remained stable, and even improved in some markets, proving AI hasn't come at the expense of customer experience. And it's not just simple queries: Fin is increasingly resolving complex, multi-step, market-specific requests.
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RESULT
CHALLENGE
Within months, Fin was handling more than half of all conversations and resolving 68% of them. In areas like email-based billing management, Fin was exceeding human performance, consistently delivering 80%+ CSAT. Manual triage was greatly reduced, average handle times dropped, and the team unlocked nearly $1M in annual efficiency gains.
Rocket Money is a leading personal finance app that supports millions of users on their financial journeys – from managing subscriptions, to tracking spending, and reaching savings goals. Rocket Money's product touches sensitive parts of a user's financial life, so support has to be fast, accurate, and deeply reliable. As the customer base grew, so did the complexity of support. The team was handling over 60,000 conversations each month, relying heavily on button-based workflows to route requests. Agents were spending hours on manual triage, which got in the way of providing the best possible support for their customers, especially on urgent financial questions. "We wanted a system that could support customers reliably, at scale, and still feel personal because we recognize our customers' financial journeys are deeply personal," says Michelle McGowan, Director of Operations.
The team started small, introducing Fin, an AI Customer Agent, into just 10% of conversations, with every workflow tested closely and highrisk issues escalated immediately.
The result is a new operating model: customers get fast, accurate answers instantly, while the support team has shifted into AI-focused roles. Instead of spending hours manually rerouting conversations, they're now optimizing Fin, improving workflows, and focusing on the edge cases where human judgment makes the biggest difference.
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Beyond Containment: Designing Customer-centric Service in 2026 Introduction Across customer contact, leaders are finally moving away from broad strokes innovations that look good on paper but do little to address the issues customers have with today’s service experiences. In the absence of long-term results from AI applications, success has been challenging to measure and is often misidentified as more efficient experiences.
“The problem is that efficiency and customer experience are not necessarily the same thing,” says Chris Karp, Chief Growth Officer at Afiniti.
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MARKET STUDY Continuity and simplicity are non-negotiable “A shorter interaction is not inherently better if the customer’s issue remains unresolved, just as an AI interaction is not successful simply because it avoided reaching a human agent.”
Currently, 49% of customers do not see AI as actively improving customer experiences, and only 45% of customers believe experiences have improved at all in the past year. And yet organizations are investing millions into improving their experiences, predominantly using AI and automation to do so. This gap is striking, and in order to understand why customers and organizations are so misaligned, leaders must investigate more thoroughly what constitutes improved experiences in 2026. As customers exhibit less patience and tolerance for friction, brands have an increasingly narrow window of opportunity to get the interaction right. Customer scrutiny is especially high at the beginning of an interaction, when a confusing interface or slow load time could be enough for them to abandon it altogether. According to data collected in CCW Digital’s annual customer preference survey, only 17% of customers would consider abandoning a telephone interaction with a human agent, whereas 40% consider abandoning an experience with a self-service portal/app, and 38% consider abandoning an exchange with a chatbot. If heightened customer expectations for control over and freedom within journeys is a trend we can expect to continue, these abandonment rates are also likely to keep rising. Call latency, flows that require customers re-enter details across multiple steps, and difficult to find information all have definitive negative effects on the customer experience, but are too often rolled up into one “negative experience.” In order to understand what drives low CSAT and NPS, we have to address the challenges that have emerged in response to the most recent wave of AI innovation.
Many of today’s interactions begin in self-service, involve automation behind the scenes, and then move the call to a human agent. From the perspective of the customer, this is not far from the traditional process of interfacing with a Tier 1 agent to explain an issue before being escalated to a specialist. In both cases, the customer expects that their context will travel with them. 36% of customers express that they are frequently required to repeat information when interacting with businesses for customer service; the repetition of information across one or many channels is consistently a top pain point for customers. Pioneering brands have demonstrated an impressive ability to retain customer context across channels and touchpoints to create seamlessness no matter how much time passes between interactions. While experience design gets more complex behind the scenes, CX leaders must ensure the customers themselves do not have to understand or manage the complexity that powers these experiences.
“The best experiences will combine AI’s speed and scalability with human judgment and empathy, without making the customer navigate the boundary between them.” says Chris. “Ultimately, customers will care less about the sophistication of the technology and more about whether the experience felt easy, informed, and effective.”
Misguided conclusions drawn from outdated metrics Measurement stops being effective when the dimension being quantified begins to stray away from the value that we’re trying to assess. Deflection is the most prominent example of this: initially used as a barometer to assess the efficacy of self-service, deflection has now shown to have unintended, frustrating consequences for customers. Initiatives that shuffle customers from one channel to another on the basis of cost-per-contact may see short-term savings, but this will ultimately harm CX and erode customer success. None of the metrics organizations lean on to catch this, CSAT, NPS, even resolution rate, actually confirm whether an interaction protected or grew customer lifetime value, which is the one number that reflects whether a business is winning or losing the relationship over time.
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MARKET STUDY Generally, traditional contact center metrics are not going away, but their relative importance is changing. Even North Star metrics must be taken with a grain of salt in this new environment:
“CSAT captures how a customer feels about one experience, while NPS offers a broader view of the relationship,” says Chris. “Both remain valuable, but neither confirms that the right outcome was achieved.” Day-to-day KPIs offer a more accurate view into whether the outcomes we’re orchestrating are customer-centric or not while FCR offers one of the most straightforward barometers for how outcome-oriented a customer contact operation is. AHT, containment rate, and cost per contact remain useful measures of operational efficiency but mean little when taken alone. “A contact center can perform well against every operational target and still fail to produce the right outcome for the customer or the business,” notes Chris.
Solving for more than efficiency It is not enough to implement AI solutions that offer improved operational efficiency alone, but failing to capitalize on AI transformation puts an organization at risk of falling behind the ever-rising standards customers have for customer service in 2026. Afiniti’s approach is simple: connect the four layers of the contact center without requiring organizations to replace the technology they already have. Expecting enterprises to buy into a complete systems overhaul is unrealistic, and most will continue to operate across multiple AI models, CCaaS platforms, workforce technologies, and automation tools. This is the reality of contact centers in 2026, most are unique configurations of tools and technology that lose their value when they do not work together.
When taken with the limited bandwidth of CX leaders, streamlining orchestration across all dimensions of the technology stack has become critical. Instead of constantly sourcing and replacing tools that are incompatible or redundant, Afiniti allows CX leaders to keep their existing ecosystem, reduce or eliminate inefficiencies in the seams between them, and achieve better outcomes than they could with any all-in-one platform.
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Afiniti connects the customer to the right outcome, not just the next available agent. At the interaction layer, the difference between a great result and a missed one comes down to one decision: who connects with whom and when. Afiniti Pairing uses patented AI to match each customer with the available agent most likely to convert, retain, or resolve. It improves that decision behind the scenes, without requiring organizations to change their agents, contact center platform, or customer journey.
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Afiniti resolves interactions, not just deflects them. At the automation layer, success is too often defined by whether an interaction was contained or deflected. Afiniti Agents supports real-time, speech-to-speech interactions without transcription delays, helping organizations increase capacity, improve availability, and reduce cost to serve. It handles complex conversations, adapts as they unfold, and recognizes when a human is better placed to help. If escalated, the conversation context moves with the customer, so they do not have to start again. Containment is only useful as one metric to measure AI agent performance, but prioritizing it over resolution rates erodes the customer experience and impacts retention. Ultimately customers don’t want to be contained, they want to be serviced.
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Afiniti turns data into decisions, not reports. At the intelligence layer, leaders spend time chasing reports, reconciling numbers across systems and debating which dashboard to trust. Afiniti Intelligence unifies data from across the contact center, including CCaaS, CRM, WFM, IVR, and AI agents, into a single intelligence layer. It uses AI to surface issues, explain what changed and why, and recommend what action to follow. When the CFO asks what the AI investment delivered, the answer can show its effect on revenue, retention, and customer lifetime value.
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Afiniti operates above the stack, not inside it. At the operational layer, organizations often rely on internal teams or third parties to identify changing conditions and manually update routing, scheduling, and other workforce management rules. Afiniti Orchestrator continuously monitors demand, capacity, and business priorities, identifies when operating conditions shift, and adjusts decisions across the operation. By operating above the stack, Afiniti is able to connect data across systems and align their configurations to determine the optimal outcomes, and then take direction action to further those outcomes at the system level. This reduces manual rule management and helps teams respond before the opportunity to act has passed.
Most platforms still treat modernization as a technology replacement exercise. The contact center does not need to be dismantled to be transformed. The greater opportunity is to make the existing ecosystem more intelligent and orchestrate it around revenue, retention, and customer lifetime value.
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Where Trust Comes From: Designing Human Experiences in the Age of AI Introduction The tenets of a good customer experience have remained unchanged despite the channels and mediums it takes place in changing tremendously. Customers are seeking speed, effort, and accuracy in their interactions with brands, which are taking place across more touchpoints than ever. Over time, baselines have shifted: expectations are much higher today than they were even five years ago. The evolution of customer service has not been all progress, with impersonal service experiences, like robotic IVRs and useless
chatbots, introducing new derision from customers. But news of brands that successfully leap forward in the quality of their customer experiences proliferates and sets a new standard at the speed information is shared, and so complacency is not an option for today’s CX teams. When innovation is moving faster than results and outcomes, centering initiatives on time-honored customer preferences is the clearest path towards more customer-centric experiences. However, in the contact center, too many leaders organize their systems around the technology. This is not just meant metaphorically–as
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MARKET STUDY tools are layered onto the tech stack, ROI calculations and integration specifications displace the customer’s needs and wants at the root of change. Despite the promise and potential of self-service, 70% of customers still prefer service provided by a live human. With the amount of information available online, this persistence to speak with another human is not just a symptom of suboptimal self-service, rather, it is evidence of the human agent being a core component of what inspires the confidence and certainty that truly closes the loop of a service interaction. The communication and trust felt by the customer are not just features of the experience, they are the customer experience.
What is the first action you take to resolve a customer service issue? Speak with live representative over the telephone 42%
Exchange messages with a live representative 10%
Exchange text messages with a live representative using a mobile device 7%
Exchange emails with a live representative 6%
The right balance for CX in 2026 Contact center leaders are now tasked with bridging the gap between the volume of context and data that can be used to create better experiences, and supporting the agent to infuse the interactions with empathy and consideration. “Customers expect a modern experience to be fast, effortless, and relevant. They do not think in terms of channels, systems, or internal departments, says Harry Folloder, Chief Commercial Officer at Krisp. “They expect the company to understand their situation, retain the context of earlier interactions, and help them reach an outcome without unnecessary friction.” In balancing speed, effort, and accuracy, contact center leaders are right to focus on initiatives that make incremental improvements in each arena. Unfortunately they are frequently pursued in isolation: a chatbot that answers immediately but pulls the wrong information, a thoughtful and comprehensive email response that takes 5-7 business days to reach the customer.
Have a video call with a live representative 6%
Go to a self-service web portal/mobile app 5%
Speak with a human-like Al bot 4%
Exchange messages with a chatbot 3%
Call and used touch-tone keypad inputs 3%
Use video tutorials / online courses 3%
Exchange text messages with an SMS text bot 3%
“Speed is not enough. Customers need to understand and trust the interaction,” says Harry.
Call and used basic voice recognition 3%
Exchange email messages with an email bot
In measuring the attributes that contribute to an ideal service experience, speed ranked highest, but was closely followed by the preferred type of agent (preference for human over AI, or vice versa) followed by the level of empathy exhibited throughout the journey.
2%
Go to an online Knowledge Base or FAQ 2%
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MARKET STUDY Trust starts with communication Today’s technology is able to enhance conversations in countless ways. Customers and agents speaking languages and dialects at different levels of proficiency are able to clearly communicate with the help of platforms like Krisp’s, effectively eliminating barriers that used to be considered insurmountable. Still, even a tool with 100% translation accuracy is not able to guarantee a perfect transmission of information from one person to another without a shared contextual framework of expectations and intentions. In order to move towards more perfect understanding, we need to ensure every dimension of communication is factored in. Harry breaks down those key components:
Continuity: “Every handoff, repeated question, and lost piece of context tells the customer your operating model is organized around your systems instead of their problem” he says. This aptly frames continuity failure as an organizational choice, not a fluke or an agent shortcoming. This remains top of mind: 36% of customers say they are often prompted to repeat information within customer service interactions.
Context: With the volume of data available for organizations to operationalize, the application of this information is dependent on strategic hierarchies of importance. “The organization must explicitly preserve contextual data across all touchpoints, operating as a singular, deeply connected entity rather than a fragmented collection of siloed internal departments” says Harry, emphasizing the customer’s inability to distinguish different aspects of the experience from the brand’s overarching image and experience.
Comprehension: While many leaders have set their sights on implementing technology for next-generation innovations on the customer experience, the most overlooked aspects of the experience are often the most basic. “Translation quality is bounded by transcription quality,” explains Harry. “Transcription quality is bounded by signal quality. If the audio is degraded by noise, cross-talk, or accent friction, every downstream AI system inherits that degradation. Garbage in, garbage out applies to voice AI exactly the way it applied to twenty years of data warehousing.” This is how simple misunderstandings cascade into negative experiences.
A multi-faceted approach to improved conversations Too many contact center leaders relegate faltering communication to circumstantial issues. In actuality, communication can be optimized through a mix of shortand long-term initiatives. 1. R eview your channel mix When it comes to channel mix, CCW Digital has found the top driver for updating the mix is direct customer feedback. If that is truly the case, how can we reconcile the abundance of investment into self-service and autonomous technology, or even backend operational improvements and analytics, when the phone channel remains undersupported? If the vast majority of customers still prefer to speak to a human, decentering the phone channel is counterintuitive. “Digital channels work well when the customer’s intent is clear and the path is predictable, but they degrade when the issue has exceptions, missing context, or real commercial risk,” says Harry. “Voice lets the customer and the agent test understanding, correct assumptions, and negotiate the next step in real time, which reduces the number of turns required to reach a decision.”
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MARKET STUDY 2. A lign technology to business goals, not just CX outcomes Traditional CX metrics are mapped to traditional contact center operations, but the introduction of AI has upended a lot of these calculations. Previously reliable benchmarks for first contact resolution and handle time are rendered inert by the capabilities of agentic AI. The burden of the resolution quality sits more squarely on agents, who must now navigate complex systems and complex customer cases as automation takes on the basic tasks. “Automation will absorb more simple volume, so the interactions that reach a human will be harder, more emotional, and more commercially important,” says Harry. “That changes the baseline. A center can improve operationally while its average handle time and customer effort scores get worse, which is why leaders will need to redesign scorecards around resolution quality rather than raw speed.”
When customers and agents are able to do so in their natural language, without shouldering the burden of making sure they are being heard and understood, they are able to focus more on addressing the situation at hand. Translation or noise reduction alone are not able to offer an adequate level of reliability to delegate this consideration, but as a voice productivity platform, Krisp’s outputs adjust for the intricacies of dialect, proficiency, accent and more.
Conclusion Contact center leaders are inundated with the ways technology can improve their processes and systems. At a certain point, we are solving technology problems with more technology, and in many of these instances the value and costs calculations are opaque in the face of this technology market’s uncertainty. At the same time, the value and importance of human-tohuman communication in customer service remains an unshakeable pillar of CX. Communication is at the center of everything, yet, technology providers demand that we reorient around their solutions.
3. H onor the human experience for agents and customers Leaders incorrectly characterize changes to the agent role to merely reflect a reorganization of time and responsibilities, but it goes far deeper. The notion of “cognitive load,” now a ubiquitous buzzword in AI discourse, represents the finite capacity of agents to be “on” and ready to provide excellent service. When it comes to maximizing agents’ time and capabilities, tools that eliminate this cognitive friction have a multiplicative effect on existing agent strengths.
“One rule we hold to: technology should operate on the conversation, not replace the person having it,” says Harry. “Accent conversion smooths phonetic edges while preserving tone, emotion, and identity. Agents resist technology that changes who they are. They embrace technology that helps them do their job.”
“The metric most operators undercount is agent cognitive load,” says Harry. “When a conversation is easier to parse, agents stop working in second gear. That flows into resolution rates, CSAT, and attrition.”
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MARKET STUDY Success stories follow: COMPANY
Automated Health Systems cut handle time 31% and lifted every KPI with Krisp Voice Translation CHALLENGE
COMPANY
NTT Data delivers 95%+ CSAT for a global insurance carrier with Krisp Accent Conversion
CHALLENGE
Automated Health Systems supports callers across public health programs, many of whom do not speak English as a first language. Reaching them had meant pausing the conversation to bring in a third-party interpreter, which added time and friction to every non-English call.
When a global insurance carrier moved its IT service desk from an EMEA base to NTT Data’s centers in India, it refused to lower the bar. NTT Data had to meet the same stringent CSAT target its European operation carried: 94.5% on a penalty-driven SLA, one of the most demanding in its portfolio.
RESULT
The biggest risk in that move was voice and accent friction
AHS replaced that model with Krisp AI Voice Translation, moving multilingual support directly into the existing call flow. It did so during a period of rising demand: regulatory changes pushed volume up by roughly 15,000 calls per month, about 90,000 additional calls from February to August 2026. Every metric AHS tracks improved anyway. KEY OUTCOMES:
• 31% reduction in Average Handle Time • 32-second improvement in Average Speed of Answer, and abandoned calls down from 5% to 3.08% • CSAT up from 81% to 86% • 96% translation accuracy scored on 100% of calls • 9 in 10 multilingual calls completed in-flow, and a 500-agent rollout compressed from over a month to a single day Together, AHS and Krisp show that multilingual support can move from a slow dependency into an in-call capability.
RESULT
NTT Data removed it by deploying Krisp AI Accent Conversion from the first day of the engagement, and exceeded the target every month from the start. KEY OUTCOMES:
• 95.17% average CSAT against a 94.5% target: the toughest bar in the portfolio, beaten every month • FCR above target for both regions served: 73.7% for EMEA, 75.8% for NA • Zero voice- or accent-related DSATs across the period • Live from day one, with the license base since growing 3.6x NTT Data and Krisp prove that a stringent onshore experience standard can be held from day one on an offshore desk, with agents judged on the quality of their support, not the sound of their voice.
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COMPANY
Arrivia Boosts NPS by 99% with Krisp CHALLENGE
Arrivia’s international call centers are the backbone of its high-touch travel services, but accent clarity and background noise created friction in customer interactions. While agents were highly skilled, some customers struggled to understand them, impacting satisfaction and efficiency.
“Krisp creates wins for our customers, company, and team members. It enables higher-quality interactions, unlocks new opportunities for our agents, and drives real productivity gains. Our partnership with Krisp has truly elevated our ability to deliver exceptional value.” —T ravis Markel, Chief Operating Officer at arrivia
RESULT
To solve this, arrivia deployed Krisp’s AI Accent Conversion, enhancing speech clarity while preserving natural tone and flow. This followed the successful adoption of Krisp’s Noise Cancellation, eliminating background distractions and improving agent focus. The Results: • + 99% in NPS • + 26.1% in sales conversion • + 14.8% revenue per booking • +13.5% in agent performance With noise and accent clarity optimized, arrivia transformed its call experience— unlocking higher customer satisfaction, agent performance, and revenue growth.
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Becoming Resolution-First: Reorienting CX Around What Matters to Customers Introduction It has never been less clear what makes an experience “customer centric.” AI has disproportionately accelerated innovation in different parts of the organization, and the result has pitted key experience attributes against each other: speed versus accuracy, efficiency versus comprehensiveness, empathy versus clarity. What is clear is that technology can inadvertently create a wedge between brands and customers, and leaders are struggling to understand the right balance of AI and humans where they can respectively create the most value.
At its core, an excessive focus on technology capabilities in isolation has distracted leaders from centering their projects around customer outcomes. There is an abundance of anecdotal evidence that many of today’s AI-driven experiences demand unwarranted customer effort. This creates customer frustration, but it also sheds light on where journeys and interactions are unintuitive and could be improved. “Customers do not define a modern experience by how much technology is involved. They define it by how little effort the experience requires,” explains Jon Lunitz, Vice President of Datamark. “They expect organizations to recognize them, retain
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MARKET STUDY context across channels, resolve issues quickly and provide access to a knowledgeable person when technology reaches its limit.” It is this concept of retained context throughout the experience that differentiates a conventional approach to CX adorned with novel technology from a truly next-generation CX program.
Building next-generation experiences Traditional ways of measuring CX are insufficient in this new environment. According to leaders, the most critical service experience attributes are speed, agent optionality (human or AI), and empathy. These dimensions ranking above personalization and predictive/proactive engagement may mark a shift away from that which seems technologically advanced but fails to actually impress or retain customers. Instead of modernizing the way experiences are delivered, effective innovation focuses on the outcome or action being delivered. “The modern experience is not digitalfirst or human-first,” says Jon. “It is resolution-first: technology provides speed and continuity, while people bring judgment, empathy and accountability when the situation demands it.” So what does it mean to be resolution-first in practice? By examining the changes taking place across customer contact technology, consumer preferences, and the role of human agents, we can identify opportunities for improvement that reorient the customer contact operation around real outcomes instead of metrics.
Self-service has yet to hit its stride Currently, leaders report that 56% of contacts start in self-service. By the end of 2030, they expect this number to climb to 77%. At the same time, faith and confidence in self-service continues to waver. When we consider the importance of channel optionality to both customers and organizations, the pressure to use self-service is noteworthy: 89% of customers feel like companies have been more likely to require them to use a self-service tool before or instead of speaking to a live agent in the past year. Still, when we polled business leaders, only 48% claim to require customers to start in self-service. This gap emphasizes the critical and often overlooked component of perception and
how much of the customer experience is shaped by expectations. This freedom of choice in channels is not to be underestimated; the ability to quickly escalate to a live representative is the number one quality that makes customers more likely to use self-service at all. Further evidence that metrics tell an incomplete story can be found in the comparison of live versus self-service performance. CSAT is higher in live service for 60% of organizations, but is roughly the same as self-service for 36% of businesses. FCR is higher in live service for 43% of organizations, and roughly the same as for another 42%. The fact that outcomes are equitable for a significant portion of businesses just a few years into agentic AIenabled self-service casts a promising outlook on what it will soon accomplish. The real challenge for leaders will be managing perception and building or rebuilding confidence in these interactions.
Knowledge management directly shapes strategy Perhaps the most profound shift taking place in customer contact is happening within knowledge management. Historically, knowledge was written for agents and updated periodically. Today, the same knowledge must support agents, chatbots, AI assistants and customer-facing self-service—consistently and in real time. If that underlying content is incomplete, outdated, or contradictory, every channel simply delivers the wrong answer faster.
“Knowledge management is shifting from a static reference library into the intelligence layer that powers the entire customer experience,” says Jon. This change is structural at the most rudimentary level, requiring recalibration from leaders across different departments.
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MARKET STUDY Modern knowledge management must: • Capture new information directly from customer interactions and expert employees
Technology can still create an authentic experience when it: • Clearly identifies itself as automated
• Identify content gaps based on failed searches, repeat contacts and escalations
• Understands the customer’s context and intent
• Deliver answers based on the customer, issue and channel—not merely return documents
• Provides an immediate path to a human when confidence, sentiment or complexity changes
• Apply clear ownership, approvals and version control to AI-generated content Market-leading brands are redefining the role of knowledge from a resource available to a few leaders, whose judgement defines how it is actioned on throughout the organization, to an always-on layer that could soon be accessible to anyone with decisionmaking responsibilities.
T3 is the new T1 Better quality and access to knowledge is just one of the myriad factors making the agent role in 2026 a far more dynamic one. 76% of leaders report that their self-service offerings are successfully resolving Tier 2 interactions, such as account-specific or guided issues that follow established processes. Human agents are no longer needed for the bulk of these routine interactions, but that does not mean they’re expendable. As organizations employ fewer expert agents, they also risk losing the institutional knowledge those employees carry. The priority is not simply documenting what experts know, but creating a continuous system that captures, validates and distributes expertise. Organizations should intentionally determine which interactions are best handled by technology and which require human judgment. For example, some customers may prefer the privacy and neutrality of an automated collections experience. Conversely, a prior-authorization issue, sensitive conversation or complex escalation typically requires an empowered agent who can listen, interpret nuance and take ownership. The focus should remain on interactions that are authentic, not in how they’re delivered, but in the transparency of what the customer receives.
• Resolves the need without unnecessary friction
Reducing agent headcount may be effective for shortterm cost recuperation, but it is hard to put a price tag on the institutional knowledge lost when these decisions are made on cost basis alone. And as the need for agents in AI supervisory roles continues to grow, the significance of that institutional understanding will continue to be elevated.
Conclusion Most leaders are finding that rethinking their approach to CX technology is long overdue. Analyzing technology needs, weighing options, managing implementations and maintenance, and ongoing measurement are all new responsibilities that most often end up on the desk of CX leaders, stretching their talent and time thin. Rather than attempting to scratch the surface of each of these essential and growing facets of business, proactive leaders are turning to BPO partners like Datamark, whose business has evolved beyond the traditional outsourcing partner. “The BPO’s role is evolving from managing contacts to reducing avoidable demand, improving containment and eliminating the problems that created the interaction in the first place,” says Jon. This keeps the loop closed: the BPO analyzes where self-service fails, identifying the reasons customers still require assistance and continuously improving both automated and human journeys. Ultimately, automation is changing work, but it is not eliminating anything. As routine contacts are automated away, the remaining interactions become more complex and require stronger talent, better knowledge management, and more sophisticated operations. “The biggest misconception is that outsourcing is primarily a labor-arbitrage strategy,” says Jon. “Cost remains important, but modern outsourcing creates value far beyond a lower hourly rate.”
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MARKET STUDY The best BPOs do not simply operate a client’s existing process more cheaply; they help redesign it, combining people, technology and operational expertise to improve outcomes continuously. With their customizable approach, Datamark has a model for organizations at every stage of technology maturity: • Client-owned technology: Operate within the client’s stack while adding complementary capabilities such as accent neutralization, automated quality monitoring, agent assistance and interaction analytics. • BPO-owned technology: Provide and manage the full technology ecosystem, reducing the client’s implementation, integration and administrative burden. • Hybrid technology: Combine the strongest components from both environments without forcing unnecessary replacement of tools that already work. Success stories follow:
COMPANY
Shell Fuel Rewards
CHALLENGE
Agents navigated multiple systems while manually documenting customer interactions.
COMPANY
Enterprise Contact Center
CHALLENGE
Traditional training provided limited handson practice, delaying agent readiness and productivity. RESULT
Simulation-based training reduced training time by approximately 20%, AHT by 9%, and aftercall work by more than 7%.
COMPANY
Municipal 311 Contact Center
CHALLENGE
Manual reconciliation across payroll, CRM and WFM systems delayed invoicing and created limited billing visibility. RESULT
DATAMARK developed a real-time Power BI billing dashboard, reducing the billing cycle by more than 50% while improving accuracy, forecasting and transparency.
RESULT
DATAMARK deployed DataScribe for realtime transcription and automated summaries, reducing administrative work and allowing agents to focus on the customer.
COMPANY
Retail Contact Center
CHALLENGE
Agents spent too much time searching fragmented knowledge sources for accurate answers. RESULT
DATAMARK implemented DataSmart, an AIpowered knowledge solution that reduced AHT by 6% and improved quality by 2.1%.
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Appendix Insights iQ™: CX Analytics That Tells You Why, Not Just What | iQor AI in Customer Service: Why Automation Alone Fails | iQor
How AHS cut handle time 31% and lifted every KPI with Krisp Voice Translation (Automated Health Systems case study) Real-Time Voice AI for Contact Centers | Krisp
Fin Blueprint Hub Fin Demo
Explore Afiniti’s Outcome Orchestration Suite See why leading brands choose Afiniti
Case Study: Accelerating Time To Proficiency Signals And Sentiment: The Future CX Data Stack
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2026 Editorial Calendar Market Studies
Seminar Series OCTOBER
State of Enterprise AI
SEPTEMBER
Modernizing Service Experiences With AI & Digital DECEMBER
NOVEMBER
Redefining Customer Contact: Trends, Challenges, Opportunities
Redefining Customer Contact: Trends, Challenges & Opportunities
DECEMBER
Inside the Data: The Contact Center of 2026
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Meet the Team ANALYST:
SALES:
Brian Cantor Managing Director CCW Digital
Simon Copcutt Head of Strategic Accounts CCW Digital
EMAIL BRIAN
MARKETING:
EMAIL SIMON
ANALYST:
Andy Kuang Senior Marketing Manager CCW Digital
Brooke Lynch Divisional Director CCW Digital
EMAIL ANDY
MARKETING: Melinda Acuna Marketing Manager CCW Digital EMAIL MELINDA
ANALYST:
EMAIL BROOKE
SALES: Ben McClymontt Divisional Sales Director CCW Digital EMAIL BEN
CUSTOMER SUCCESS:
Audrey Steeves Senior Content Analyst CCW Digital EMAIL AUDREY
Emily Curran Customer Success Coordinator CCW Digital EMAIL EMILY
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