CCW Digital Magazine: Agentic + Generative AI

AGENTIC AND GENERATIVE AI THE NEW FRONTIER OF CX MAGA ZINE

LEADING STRATEGIES TO ELEVATE EXPERIENCES FROM FAST FOOD TO FINTECH

NAVIGATING UNKNOWNS ENSURING LONG TERM SUCCESS IN THE ADOPTION OF EMERGENT AI

A Smarter Future, Grounded in Humanity

Technology may power tomorrow, but principled intention and people will shape it.

When the Internet became accessible to the public in 1991, two schools of thought dominated: this will make things easier, faster, better, and more connected, or this will uproot and dismantle our ways of living and working. Thirty years later, we see that both sentiments were validated: technology is only as beneficial as the intentions of its operators are principled. As AI has transitioned from a topic of science fiction to a mainstay of our daily conversations, similar dialectics have arisen.

Our goal with this inaugural issue of the CCW Digital Magazine is not to repeat the same AI cliches we’ve been inundated with in recent years but rather shed light on exactly how AI fits into the modern business machine.

There is an abundance of content freely available showcasing AI’s ability to increase productivity, reduce costs, improve customer outcomes, and ultimately transform business. Our goal with this inaugural issue of the CCW Digital Magazine is not to repeat the same AI cliches we’ve been inundated with in recent years but rather shed light on exactly how AI fits into the modern business machine. Through interviews with CX practitioners and industry visionaries, we have compiled diverse perspectives on the challenges and implications of integrating AI tools into human-led processes.

Recognizing that there are real-world priorities and concerns with nascent AI that go beyond business strategy, we hope this magazine empowers decision makers to become educated on what AI really does and how it can improve the way we interact. For customers and employees (or in other words, people), decisions about AI have the potential to profoundly change experiences as we know them. By grounding AI success parameters in the tangible, positive experiences of all people, we do our part in creating a world that is powered by technology without becoming beholden to it.

AUDREY STEEVES audrey.steeves@cmpteam.com

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3 ANALYST ROUNDTABLE

CCW

6 OWN YOUR FUTURE: NAVIGATING AN AI-POWERED WORLD

Authors Antara Dutta and Winnie Kroculick share a framework for embracing AI with confidence.

9 DESIGNING PROACTIVE EXPERIENCES

Strategically using AI is the difference between data overload and competitive advantage.

13 AI AND THE FUTURE OF CONTACT CENTERS

The next generation of Amazon Connect is a game changer for contact centers.

16 THE CUSTOMER-CENTRIC REALITY CHECK

Jan Young shares how prioritizing company-wide collaboration is essential for fostering customer centricity.

19 COMPLEXITY BURNOUT: THE OVERLOOKED REALITY OF THE AI REVOLUTION

What does “AI for simple issues, humans for complex ones” really mean?

23 BALANCING PERSONALIZATION AND PRIVACY

Navigating heightened customer expectations and growing security concerns.

24 FROM IVR TO AGENTIC AI: THE CONVERSATION REVOLUTION YOUR CUSTOMERS ACTUALLY WANT

Agentic AI brings a welcome alternative to outdated IVR systems.

25 REFRAMING THE CONVERSATION: ADDRESSING 8 UNAVOIDABLE MYTHS

AI is transforming customer experience— but not in the ways we may expect.

27 YOUR CUSTOMERS’ TRUST IS AT STAKE—AND AI WILL MAKE OR BREAK IT

Anita Toth shares how deep research, emotional insight, and smart AI use can turn customer doubt into long-term trust.

30 TRANSFORMING CX: HOW AI IS REDEFINING THE CUSTOMER JOURNEY

UJET’s Chief Business Officer shares challenges facing today’s contact centers.

31 DECODING ENTERPRISE AI

It’s a question of when—not if—AI will lead to organizational restructuring.

33 VOICE AI: THE NEW FRONTIER OF CUSTOMER EXPERIENCE

New Voice AI is redefining what it means to be “human-like.”

35 4 DEI COMPANY CULTURE MUST-HAVES

A commitment to DEI is critical for fostering a people-centered workplace.

37 LEVERAGING AI FOR A MODERN RENTAL EXPERIENCE

RentSpree is simplifying the rental process—and AI is the star player.

AI HYPE MEETS REALITY:

Redefining the Contact Center Experience

As generative and agentic AI spark excitement—and anxiety—leaders grapple with balancing human touch, evolving expectations, and the future of customer service.

She leads CCW Digital article development and contributes to the analyst team by supporting online events. Her current work focuses on the intersection of customer centricity and employee experience. ANALYST

BRIAN CANTOR

Brian Cantor is the Managing Director of Customer Management Practice’s Digital division. Driven by a passion for helping brands better empower their employees and more meaningfully connect with customers, Brian oversees research, product development, editorial vision, and commercial strategy for properties like CCW Digital. Reaching a community of almost 200,000, these digital properties offer industry-leading commentary, research reports, and virtual event sessions.

Far from a “boardroom manager,” Brian routinely speaks at leading customer contact events and directly engages with global enterprises and innovative start-ups via training and advisory services sessions.

BROOKE LYNCH

Brooke Lynch is the Divisional Director of Digital. With a background in television news and production, she’s worked across industries covering B2B marketing, procurement and finance events.

Her current work highlights challenges and opportunities for customer experience and contact center leaders, with a recent focus on e-commerce, retail, and technology.

AUDREY STEEVES

Audrey Steeves is a content analyst for CCW Digital. She is an experienced content writer with a background in healthcare and technology across consumer and B2B audiences.

We’ve had a lot of widely touted trends in our space, but none have completely consumed dialogue the way agentic and generative AI have. What is driving this unprecedented hype?

BROOKE: For contact center leaders, it is a mix of excitement and fear of the unknown. Today’s customers expect a lot; they want seamless and intuitive support but they also want access to a human. Navigating expectations for cost cutting and innovating while also maintaining a human touch is not easy. Change is hard, contact centers are evolving rapidly and AI is expediting this process. At the end of the day, the hype is real. Generative and agentic AI do have the power to change things, which I think is ultimately causing this stir.

AUDREY: I think the ambiguity of the language contributes to the hype and mystique of generative and agentic AI. Applied AI has been a part of our technology landscape for years, flying mostly under the radar in terms of discourse because it can be explained in straightforward terms that make sense to even those who are unfamiliar with AI. Part of what makes generative and especially agentic AI feel so new and different is that so few people have experienced them first-hand, and an even smaller portion of those people can speak to exactly how they work and the types of change they can effect.

BRIAN: Excellent point. We often think of uncertainty as a negative quality, but in this case, it represents a breath of fresh air. Because what we absolutely are certain about is the fact that customer and employee experiences are falling short. We know customers choose between accepting impersonal, unintuitive “self-service” or jumping through hoops, answering repetitive questions, and enduring long wait times – only to speak to someone who is so bogged down by inefficient systems that they can barely deliver anything more “human” than a dated FAQ page. By representing something different – even if there is tremendous ambiguity about what that difference is – AI could finally be the answer to delivering the experiences customers demand and deserve.

Are technology providers missing the mark when it comes to AI use cases? Are there better examples to highlight?

AUDREY: The biggest issue is that use cases mostly seek to demonstrate how every feature in an AI product can be used in a cookie cutter situation, or how a full-stack platform can transform contact centers at a macro level. These examples don’t resonate with customers at all; they want the micro, they just want to hear how the technology will make their experiences easier/better/faster. We need to move away from thinking that customers will be impressed by hightech solutions simply because they’re high-tech–those days are over. Customer confidence still needs to be earned, and you can’t do that by showcasing products as these all-powerful, all-knowing Swiss Army knives of customer experience.

BRIAN: When it comes to consumerfacing automation, there have been two major challenges. First, these selfservice platforms lacked meaningful conversation capabilities. Second, they were predicated on providing formulaic responses to straightforward inquiries. If a customer was inclined to contest the standard policy or even just follow a slightly different method of pursuing a resolution, self-service was not for them. By demonstrating natural language understanding, AI demos are beginning to address the former challenge. Unfortunately, few are conquering the second challenge. They operate in this fantasy world where customers not only behave predictably but always accept the simplest answer to their problems. If you want to prove AI will be transformative, show me an “AI agent” that can appease a customer who wants a refund for a used product outside the return window. Show me an “AI agent” that can appease a customer who wants significant compensation for a food delivery that was 25 minutes late.

Consumers fear that AI will mean the loss of the human touch. Brands say no way. Who’s right?

BROOKE: If I’m being realistic, I would say that there will be a bit of a loss when it comes to the human touch. However, if done correctly, it won’t be as bad as customers might fear. If brands can deliver self-service experiences that feel seamless and easy, then there will be far less backlash. If brands make it extremely difficult to reach a person and self-service experiences fail, customers will fight for this human touch even more.

AUDREY: I would add that as long as customers value human-centric customer service, it will never go away. We’re already seeing major backlash to companies that try to move too quickly into service that is devoid of human touch for the sake of efficiency and cost savings, and I think we can expect that trend to continue.

What about the fear of job loss? For much of the past decade, there was widespread confidence that AI would not replace agents but make them even more important. Recently, however, we’re seeing more and more people discuss the potential negative impact on headcount. Where do you stand?

AUDREY: This is going to come down to the ethos of individual business leaders and how much they actually value keeping humans on their payroll. Both things can be true–an increase in the accessibility of automating technology reduces the market value of rote customer service task completion. But agents with a deep understanding of their customers and processes will be critical in training, monitoring, and guiding AI agents, at least for the next few years. So I think we’ll see a reduction in headcount for frontline roles, but that doesn’t mean all of those folks will be out of a job. Similar to what we’ve seen with outsourced manufacturing in the past, companies that fail to invest and appropriately value human labor often come up short in quality, consistency, and of course in issues of ethics.

BRIAN: To mitigate the fear of job loss, three things need to happen. For starters, leaders have to actually empower agents to tackle “complex work,” through both training and giving them the freedom to ditch the script, make autonomous decisions, and go “above and beyond” for customers. But insofar as there will ultimately be fewer “complex customer service issues” than simple ones, we also need to precisely define the other tasks “next-generation” agents will handle. Simply saying “meaningful work” is insufficient. Finally, leaders have to secure the budget needed to provide agents with compensation befitting that work. Without providing better incentives and career paths, contact centers stand no chance of attracting or retaining consultative talent.

Only 17% of consumers trust chatbots; how can we elevate AI-powered selfservice and overcome this stigma?

BROOKE: Based on past experiences, customers have a poor perception of chatbots. This sentiment won’t change overnight, but the more resolutions and positive interactions customers see, the more likely they will be to change their minds. To elevate the view of AI-powered self-service, organizations must empower the channel to resolve customer concerns. To enhance adoption more quickly, they should give customers the option to escalate, showing them that they will not be trapped in an endless loop of inefficient support. From there, customers can form new opinions on self-service as they engage with more effective tech.

BRIAN: We all love the claim that customers “just want their problem solved.” There is undeniable truth to that. What is very debatable, however, is what getting their problem solved actually entails. Digitally savvy customers already have plenty of options for learning the standard, policy-based answer to their question. If they engage, whether with a chatbot or agent, it is because they do not accept or understand that “standard” resolution. This means that solving their problem involves either going off-book to provide a more customized outcome or at least going off-script to explain the situation in a more personalized way. If a bot or AI agent can truly do that, all indications are that customers will embrace it. This doesn’t require throwing all caution to the wind, but it does mean giving bots some degree of freedom to deliver real resolutions.

Not simply about self-service, AI is often positioned as an employee experience play. How can AI help elevate the workforce?

BROOKE: AI has so much potential when it comes to the employee workflow. Generative AI is an incredible tool for summarizing customer feedback, recognizing trends, capturing customer sentiment, and minimizing post call work. All these features enable employees to focus more on the customer during key moments, elevating the coveted human touch. Beyond this, AI can also empower managers to better understand agent performance and pinpoint skills of high performers to continually train and

coach employees effectively. By gaining a better line of sight into customer interactions, organizations can gain deep knowledge of the most urgent challenges and foster a culture of improvement.

Is the “AI for simple issues, agents for complex ones” dynamic really that appealing to employees?

AUDREY: Whether or not AI will lead to the elimination of roles is top of mind for agents. Agents are rightfully skeptical when we talk about this dynamic because they know that what drives implementation decisions is the bottom line, and how agents feel about that is a moot point. That being said, most agents are drawn to CS because they enjoy human connection. After all, agents are often roles with low barriers to entry and there are plenty of other disciplines that don’t require an interest or aptitude in interpersonal customer communication. So assuming that most agents derive value from meaningful customer interactions, technology that clears the queue of short, repetitive conversations and allows them to dive into those will likely be genuinely exciting and motivating.

BRIAN: Audrey is absolutely right that the typical, human-centric agent will relish the idea of replacing robotic “password reset” inquiries with actual conversations. But we can’t forget that conversations are not all harmonious – many will involve argumentative customers, who are dealing with highly sensitive problems for which there is no obvious permissible solution. This will not only require them to be “on” at all times but also subject agents to an intense emotional burden. Ensuring agents that they will be supported with helpful tools and wellness efforts will be essential.

What are some of the more traditional risks we need to consider with AI, and how should we be addressing them?

AUDREY: One of the most notable differences between LLMs and other computing algorithms is that when we code something wrong, it generally just won’t work. Broken code doesn’t hallucinate, maybe it gives an incorrect output or no output at all, but when these issues reach the customer it is usually starkly clear something went wrong. When AI hallucinates, the customer may never know that something went

wrong. When LLMs are trained or white labeled without airtight parameters for compliance, regulation, and privacy, they risk hallucinating or breaching data, which have myriad negative effects for the customer that far outweigh the inconvenience of a digital touchpoint not functioning as it should. So in considering the benefits of how AI can improve processes and touchpoints, they shouldn’t solely be measured against the performance of current systems.

The assessment should also recognize the downsides are much, much more severe.

What are some of the unexpected risks?

BROOKE: One of the unexpected risks is a higher standard of support for agents. While we’ve certainly talked about the fact that they will be taking on more complex work, we don’t necessarily discuss the fact that they may be the only human left throughout the customer journey. As we are able to automate more of the journey, customers will have less human-led touchpoints. That will put a lot more pressure on agents to be the expert, there to solve any and every problem. It will also require greater patience, empathy and confidence. Customers will put a lot more weight into agent-led interactions once they can self-serve effectively.

BRIAN: I concur with Brooke’s take on the elevated agent standard. I do, however, want to introduce another point of consideration that many are overlooking: the value in having agents handle “simple issues.” AI may be as good as, if not better than, human agents at processing basic transactions or answering straightforward questions. What it is not necessarily as good at, however, is maximizing these “moments of truth.” When a human agent is at the helm, what starts as a simple “Internet outage” call can turn into an empathetic conversation that not only strengthens the relationship but opens the customer to potential upsell efforts. If AI does its job and meaningfully reduces the inquiries that reach live agents, will customer relationship building become the collateral damage?

OWN YOUR FUTURE:

Navigating An AI-Powered World

BY BROOKE LYNCH

is everywhere. It has become our search engine, it curates our entertainment, powers our smart home devices and acts as our personal voice assistant. It is now synonymous with innovation and growth, and it has put brands on the map in 2025.

It has also sparked fears of job loss, a decline in creativity and a crutch for critical thinking. It is being increasingly referred to as a human replacement in many industries.

It is these two conflicting thoughts that drive a polarizing reaction to the technology. But, regardless of where you align on the AI argument, it is here to stay. Learning to adapt, understand, and leverage the tool will be a differentiator as we move forward. It is no longer an if, but a definite part of our future.

Antara Dutta and Winnie Kroculick, Authors of Own Your Future: AI for All, offer insight on how to unlock your potential with the transformative power of AI. Dutta, a luminary figure in the financial services space and Kroculick, who has led digital transformation for Fortune 100 financial services companies, share their unique vision for the future of AI.

Their framework offers a deeper look at how every individual engages with the technology. Categorizing people as learners, leaders and changemakers, the pair shares guidance on how to navigate AI with these innate characteristics in mind.

Learners are looking to gain tools to master AI with simplicity, make confident decisions and drive personal growth. Leaders are working to harness AI and create a lasting impact within their organizations. Lastly, changemakers drive disruption and amplify impact within their industry and across their networks.

Pinpointing who you are within these profiles is critical to moving forward with AI. At a time when innovation continues to push forward, it is essential to understand how to navigate new technology with confidence.

In a recent interview with Dutta and Kroculick, CCW Digital discussed the impact of AI on customer service, the future of the employee experience, and the role of their framework in moving forward with AI. Here are a few key themes that emerged during our conversation:

The Future of Work/Life With AI

In AI for All, Dutta and Kroculick emphasize the idea that AI really is everywhere. It is the mindset that AI will become integrated in all areas of society, business and technology. It goes beyond the contact center, and even the workplace; it is being used as a tool in classrooms, retail spaces, and entertainment.

Adapting to AI and leveraging it intentionally, then, is critical to navigating the modern world. And in many ways, we can all benefit from this AI-powered environment. While there are certainly fears about it diminishing our critical thinking skills, Kroculick believes that AI will make us smarter.

She shares, “You are going to scale human intelligence with artificial intelligence… As you leverage artificial intelligence, and you do something that you maybe would not have done in the past, you are going to get a little bit smarter.”

Take the learning, fail fast and then iterate again… Don’t be afraid of uncertainty. Take it for granted. There’s going to be failure, and you’re going to learn from it.”

When used effectively, AI can enhance our skills and prompt more creativity. It has the power to remove redundant and unproductive work, giving humans the time and space to do more. With the right mindset and skills, everyone can leverage AI intelligently.

So, What’s Next?

With new skills and an eagerness to start on their AI journey, how should individuals in the customer service space begin?

The pair suggests taking the leap, there is no time better than the present to begin innovating. But keep in mind, it is largely a learning experience and may require a few tries before you figure out how to proceed.

“Take the learning, fail fast and then iterate again… Don’t be afraid of uncertainty. Take it for granted. There’s going to be failure, and you’re going to learn from it,” Dutta states.

It is this dedication to trying, iterating and improving that will set individuals apart on their AI journey. By prioritizing curiosity, understanding the benefits and working to improve, you really cannot go wrong.

As AI appears in more and more aspects of our lives, it is critical to not just learn to tolerate, but embrace the tool. In the future, AI for all will only be applicable for those who take the leap and leverage the tool with confidence.

Antara Dutta emerges as a luminary figure in the realm of financial services, distinguished by her astute leadership and transformative vision. With a distinguished track record, Antara has orchestrated strategic investments to yield remarkable returns. As a leader at esteemed institutions like PayPal, Barclays, JP Morgan Chase Antara has led groundbreaking initiatives aimed at revolutionizing core banking platforms, refining client journeys, and redefining digital marketing paradigms. Her strategic acumen has manifested in tangible enhancements to revenue streams and profitability across diverse business verticals.

WINNIE KROCULICK, MBA, PMP

Winnie Kroculick epitomizes the archetype of a versatile transformation program and product leader, boasting over two decades of profound experience in steering outcomes within digital marketing, payments, and servicing domains for Fortune 100 Financial Services Companies. Her narrative is one characterized by a steadfast focus on strategic planning, seamless stakeholder communication, and the art of crafting compelling business cases, all of which have left an indelible mark on the digital trajectories of major financial institutions.

ANTARA DUTTA

DESIGNING PROACTIVE EXPERIENCES

The thirst for intelligent decision-making has come with an unintended consequence: data overload.

BY AUDREY STEEVES

Customer feedback occupies a paradoxical space in the world of business, wherein the infrastructure to collect it has been in use far longer than today’s best practices of how to most effectively utilize it. Even in the days of pen-and-paper forms, the dominant customer feedback collection strategy was to collect now and analyze later, leading to enormous quantities of responses that have only grown in the decades since. This strategy has been amplified exponentially by our modern customer journeys’ abundance of touchpoints, resulting in a new challenge for leadership: how to examine this mountain of data.

Not long ago, business leaders racked their brains to try to figure out how customers think and feel, confident that they would be able to tailor their products and services to customers’ needs if only they had access to their thoughts. Now, for better or for worse, it’s not hyperbole to claim many organizations do have direct access: data points across myriad experiences supported by detailed demographic,

psychographic, and behavioral information. As technology enables accessible widespread data collection, market competition increasingly takes place along the lines of how this data is used to drive experiences.

Where AI Comes In

AI represents a path forward through the abundance of data. With the right approach, AI can both aid in illuminating the stories data can tell and the way these insights are applied to customer experiences. By intelligently digesting customer data, you will unlock the ability to deeply understand customer needs and proactively design experiences for them.

Like any other tool, AI is only as valuable as its outcomes. What halts progress for many leaders is the notion that use of AI–due to its amorphous intelligence–is sufficient for it to provide value. Widespread use of AI for CX currently takes the form of self-service. Self-service, primarily in the form of chatbots and voice AI,

add value by operating within existing principles: automation and expedition optimize processes by reducing the resources needed without diminishing experiences. Rather than focusing our efforts on never-ending optimization, the opportunistic value of AI in CX will be in the new experiences we create that anticipate customer needs.

Data is, by its very nature, historic. The best example is like driving a car looking through the rearview mirror. Our guests absolutely want speed, but what they really want is to understand what’s happening before it’s even happened.”

Swain, COO

Rob

of KFC in his Qualtrics X4 Keynote

Understanding Proactive CX

Proactive CX is the strategic usage of data to innovate experiences that anticipate customer needs and desires. This can look like outbound experiences that leverage personal and aggregate data to offer unexpected value. It may also take the shape of predictive experiences that prompt next steps in the journey using the data available.

Outbound CX has been gaining traction and popularity as a way for brands to capitalize on the short distance between digital upselling and digital CX. Outbound CX isn’t inherently proactive–outbound experiences that don’t leverage specific and relevant data will ultimately result in more of the same results you’re currently seeing. And while there is merit in pursuing sales wrapped up in outbound customer experiences, a direct selling experience rarely offers the level of customer centricity that customers appreciate and seek out.

brands. Most prudently, anticipatory experiences must be implemented with tact and sensitivity, otherwise there is risk that the personal nature can create a negative experience.

“The ‘blue dot consumer’ is the world. They stand perfectly still in the center of the frame at all times. They take a step left, they expect your brand to take a step with them. This changed everything,” Ken Hughes said in his Qualtrics X4 keynote. “It’s personal, you don’t go on Google Maps and follow someone else’s blue dot around. You’d end up at their destination, not yours. It’s all about personalization. It’s your journey.”

In healthcare, a predictive experience may look like a doctor’s office reaching out by sending an email to set up an annual appointment; it may add a personal flair to the experience by suggesting appointment times based on the patient’s past bookings. This

The ‘blue dot consumer’ is the world. They stand perfectly still in the center of the frame at all times. They take a step left, they expect your brand to take a step with them. This changed everything.”

Proactive CX has a role in every industry, although it may look different according to the level of familiarity customers have come to expect from

level of personalization is appreciated: a reduction in patient effort is often met with gratitude. On the other hand, if the email also includes how long it will take the patient to drive from their

home address to the doctor’s office, some patients may be put off by this added level of personalization. The value added by personalization cannot be outweighed by the potential negative effects of customer concern that their data is unsafe or being used in nefarious, or even just superfluous, manners.

Integrating Data At Every Level Of Decision Making

When it comes to the amount of customer data that never makes its way through the organization to product, marketing, and customer success teams, many leaders may as well be sitting on a gold mine. Data siloing, outdated systems, and bureaucracy prevent organizations from truly being guided by the enormous volume of data they are collecting.

Promoting the value and utility of aggregate customer data is a cause that will likely need to be championed by CX leaders, but will require multidisciplinary coordination and strategy. The “siloing problem” goes beyond data, and CX leaders often find themselves frustrated at not being a part of high-level strategy conversations. Contact center leaders are invaluable because their analysis can and should be used to identify targets for improvement across all areas the entire customer journey. As customers’ expectations heighten to match the capabilities they know are available, brands that previously held a competitive advantage in their product quality and simple, convenient delivery will be undercut by a new competitive edge: highly personalized, proactive CX.

Ken Hughes in his Qualtrics X4 Keynote

Amazon recently announced the next generation of Amazon Connect, easily enabling AI and generative AI across all customer touchpoints. How is it so easy?

It’s native AI built directly into Amazon Connect, so it’s easy to set up, will continually improve with updates, and requires no integrations. With Amazon Connect it’s just a matter of turning it on. If you want to use content summarization, if you want to use contact categorization, if you want to use generative AI-powered chatbots, they’re all built in. Pre-set integration means you get to move at the speed of business, instead of having to worry about what models to use or complex prompt engineering. This was one of the biggest requests we heard from our customers - make it easier to use AI in my contact center.

How do you think that will benefit the contact center operations?

When generative AI became a priority for companies, there were a lot of questions around where to start. Ensuring that the AI is trained properly, is set up with the appropriate parameters, and is not going to lead to hallucination, are all critical considerations every time a new integration is implemented. With native AI built directly into Amazon Connect, we’re able to rapidly innovate and deliver new capabilities that remove the guesswork for our customers. This allows businesses to quickly implement and benefit from advanced AI features without worrying about complex integrations or keeping up with the latest AI developments. Amazon Connect is all about making it easy and costeffective to leverage AI and generative AI across all customer touchpoints. You don’t have to go procure some analytics service. It’s already part of Amazon Connect, and it’s all part of the pricing model. So, you don’t even have to think about pricing differences as you go, it’s just there, ready for you to consume whenever you’re ready.

Thinking about pricing, we found that 71% of contact center leaders cite the high and unpredictable cost of AI as a hindrance to adoption. How does the Amazon pricing model address some of those concerns?

Amazon Connect has a usage-based pricing model. You start by turning on the features you want to use for each channel, whether it’s voice, digital, or outbound. This is differentiated in the industry, because instead of paying for user licenses or tokens or some variation therein, we charge a “pay-as-you-go” model. You only pay for the resources you use. And now that includes all the AI that we have available in Amazon Connect under one price. So, you no longer have to make big upfront decisions around your AI strategy, you can just turn it on and try it and you’ll only pay for what you use. It’s all there. It’s ready to go and makes it very easy to consume advanced AI tools.

When we think about the challenges that small and medium businesses face when adopting AI and how that compares to much larger businesses adopting enterprise-level AI, do you have any insights into how these challenges are different and how Amazon can suit both of their needs?

I think that the challenges SMBs face are not entirely different from large enterprises. Everybody’s a little bit scared that strategic decisions being made today will have negative consequences down the line. “Are we making the investment in the right areas?” and “Are we building things we should be building?” are the kinds of questions we hear all the time as companies struggle to keep up with the rapidly evolving AI landscape. Even large enterprises aren’t going to want to build something that doesn’t deliver any kind of differentiated value. This is especially true if you’re resource constrained, like a smaller company might be. So, these challenges exist across the board. With Amazon Connect having that AI built into the service, it allows them to take advantage of the AI tools where it matters most, where it’s going to provide the most business productivity. Whether it’s a front-end virtual agent with an agentic AI experience or it’s using generative AI for context summarization and advanced analytics on contacts, the tools are readily available when you need them. Whether you’re five agents or fifty thousand agents, it doesn’t matter, it’s all part of that same model.

What do you see as some compelling use cases for agentic AI that you think will be transformative?

There’s a lot of variability in the definition of agentic AI. As we think about agentic in customer experience, the big question is, “Can agentic AI do things we used to have to rely on a human agent for?” For example, is an IVR that we ask for an account balance at a bank an agentic experience? If you broaden the definition out far enough, the answer is yes because it did something an agent could do. Is it really complex? No, not at all. IVR experiences can be “agentic” but as someone who’s been in the industry 30 years, I can tell you that’s not new science, and we’ve been able to do that for a very long time. Making this example a little more complex: let’s say I am talking to my virtual agent, and say, “I would like my account balance, and if my account balance is more than $50,000 I want you to take 10% of that money and transfer it into my Roth IRA.” Typically, if you did that to even a relatively modern AI-enabled IVR, it’s just not going to be able to process that. Agentic AI is an implementation of a wide variety of tools, and generative AI is a piece of it. That’s particularly exciting in the customer experience space, because it helps us keep those human agents available for increasingly complex conversations. As we increase that ability of agentic AI to handle complex questions, the other neat thing about agentic AI is it’s able to learn from itself and get increasingly competent at handling those questions faster and more accurately. We can just keep turning up that volume a little bit on how much we can ask agentic AI to do for us.

CX REFRAMED

THE CUSTOMER-CENTRIC

Reality Check

Jan Young unpacks what it really takes to embed customer centricity across teams, and how AI can only be an asset if it’s implemented with clear intentionality.

Jan Young in conversation with CCW Digital

Q:In your own words, could you share a little bit about your background and your current role and what excites you about it?

A:My name is Jan Young, and I’ve been in tech for over 20 years. I try not to acknowledge just how long it’s been, but I really love the innovation in tech, and I’ve probably innovated my career, frankly, several times over within tech. I’ve done project, product, and account management, sales, marketing, professional services, customer success. I tried a little bit of coding, but it didn’t really stick. Luckily, there’s so many no code options now, so I know enough to be dangerous. And it’s really because I love understanding the whole workflow. While I’ve really focused my attention on customer success, the reason why I have CX after my name and CX as part of all the communities that I’ve built, including StepUpXchange, is because what I appreciate about CX is looking at the whole journey, from a prospect to when they turn into a customer. And that is critical, that whole go-to-market alignment and understanding from that customer perspective. That’s what

appeals to me, and that’s why I really appreciate the discipline. But I think that we’re evolving. And I think that CX and CS and tech and everything that’s happening with AI and the economics of it all is changing so much that, whatever we call ourselves, we just call ourselves innovators, and that’ll work.

Q:I wanted to talk about customer centricity today. Customer centricity plays a big role in all of those disciplines, though some CX leaders don’t always see it that way. In your experience, what does customer centricity mean? And how should business leaders be evaluating it?

A:It’s interesting, because so many companies will say that they’re customer centric, and then they’re really just all about themselves. We’re self-involved human beings and it’s hard to think outside our own experiences. In terms of how to transform our companies to being customer centric, part of it is leading with trends and the data, and bringing that to the company, in order to understand how it relates to each discipline within the

company. That way they can understand their relationship to the customer and how they’re contributing, so that they can be more customer centric.

At least for customer success leaders, too often there’s a lack of data and a lot of “I feel” statements with ARR (annual recurring revenue) attached to it. That doesn’t get anybody’s attention because it’s an “I feel” statement. It doesn’t really tell you anything, right? But when you have the data to back up what has happened and hasn’t happened, and what your next steps ought to be, there actually is data that allows us to do more things with it. And so it’s by putting these matters in terms of data that we can then bring to our colleagues, and help them understand that this is something worthy of their attention. And it will likely require them to change their behavior when you show them the customer economics, as well as connecting that to what things have or haven’t transpired in that customer journey, and what kinds of things you need to actually take action on. That is how you backup what product changes need to happen.

And sometimes it’s about changing your processes or changing your ICP, or focusing on portions of your ICP. It’s when you connect the customer economics to your margins and how much it costs to deliver something. So often we’re human gap fillers, and we’re filling in for a product that isn’t fully built… and that is the most expensive way to deliver it. So you show them the cost of what it takes for a human to deliver these features of the product that we didn’t finish building, that our customers absolutely need. And usually we have some customers who’ve churned because we didn’t deliver it in a profitable and efficient way. As in, this is how many customers we currently are delivering in this very costly way. And then here’s the cost of if we actually built it, and then we’d be done… and then all of a sudden you can get the attention.

It really does take everybody. You can’t do it alone if you’re not working with all the other groups, so that they’re finishing building the product, so that they’re delivering in a more efficient way, so that they have a more refined ICP, so that they’re selling efficiently, so that you can reduce your cat cost. If you don’t think about how you’re working with everyone, so that they can all be more customer centric, then you’re not going to be a profitable company. So this idea of being a martyr and just like, “I’m going to make us customer centric,” doesn’t make you customer centric. You’re literally failing at your job if you’re trying to just do it yourself.

Q:Very well put. Going back to what you said about the economic argument for customer centricity, are there any frameworks or high level principles that you think are absolutely necessary to get started on building out that argument?

A:It comes back to the basics, which is: understand your revenue, your costs, and your margins. If you understand that on a per account basis, then you know which of your customers are your most profitable and your least profitable, and you can take a look at your customers by segments and tiers. I like to differentiate between segments and tiers, if segments are the customer behavior, that customer perspective, then the tiers are the company perspective, or what I can afford to deliver for people. Investors are only going to care about your CAC to LTV, which is your customer acquisition costs to lifetime value, but the way that they are calculating those things are very specific to investors. And that’s not necessarily what you need to know. Talking to your CFO is also really important, because then you understand their perspective and what’s important to them.

If you’re thinking of a framework, I guess what I would say is more like what steps you’re going to take, and the first step is having your revenue, your cost, and your margins on a per account basis, and you can slice and dice from there. And that’s how you start to understand, your break even points and ARR. There might be some segments and tiers that you’re never going to break even on. And so if that’s the case, then you either need to take a look at, can we deliver it in a way that’s more economic, or should they even be our ICP? Even if your business is overall profitable, there might be a group of customers that are actually a drag and preventing you from the type of hockey stick growth that your investors are looking for. So by analyzing it in these ways, you start to understand what kinds of actions you can actually take.

Q:It sounds like one of the key barriers here is that we are collecting so much data, but without the kind of interdisciplinary approach that you’ve been talking about, it is really hard to tell the whole story because people are so siloed in their individual departments.

A:We definitely can’t afford to be siloed anymore, that’s absolutely certain. So whatever you do to work so hard to understand your customer, you have got to do that with your colleagues. That’s what you think of as first-team thinking, when you start to think about what you’re all trying to accomplish together, as opposed to just thinking of your own individual disciplines. And that’s really the only way you can step up to executive leadership, because without that first-team thinking you’re not really contributing to the business.

Something I’ve been thinking about a lot lately is when you have a customer journey, and there’s so many different versions of it and there’s the key data for each stage. When you think about it from a project manager perspective, like “what is your critical path in this journey? What do the customers need to do by when?” There are milestone events that are key activities that need to happen in that stage, some sort of transformation or outcome that the customer is looking for, and there are exit criteria. Before getting to onboarding or implementation, the exit criteria might be scheduling the kickoff meeting, right? Think about those different elements that make up your critical path or your success path. Then think about the fact that, if you look back on some of your customers, if you weren’t intentionally trying to get them to go through all of those things and make certain they achieve them. Invariably,

there will be some customers somewhere that never signed off on onboarding, and because of that, they never really felt like things were finished. And they keep kind of going back to that, and it keeps creating all these problems.

If you spend any time looking at what marketing and sales does they’re looking at their conversion rates for just a single economic event: the revenue event, is that initial sale. I’ve worked with companies that had seven stages to the sales process. It doesn’t matter how many stages– they know what they’re doing in each stage and what the next step is, and they’re measuring. They’re trying to see how long people stay in it and how they can be more efficient with it. But we don’t do that once they become customers. But we should, because when we can measure it, then we can anticipate and project where they are on track to have earned the right to do the renewal sale and the expansion sale.

If you define these things, and you can see how long it took them to do it, then you can measure these things, and you can start to do it in more automated ways and build reports off of it. You can add triggers off of it, you can prioritize activities off of it, all of the things that then AI can allow us to do more efficiently and effectively.

Q:I think that really speaks to how opportunistic and possible this is for so many different companies. It sounds like it is a labor of putting the story together and

And if you don’t have data, use AI to help you point out what’s the most critical data that you’re missing, so you can work on that. You can just start with something small, if you need to, because once you start, you can build from that. But it’s important not to stop. Don’t just generate more unstructured data. AI can give you all kinds of information if you use it.

JAN YOUNG, FOUNDER, JANYOUNGCX CONSULTANCY

pitching it back to get their buy-in. But it does sound very possible, almost like it’s right under our noses, because it’s just a matter of measuring, and not a matter of generating anything new. Do you have any examples of some of those places AI fits into this process at the organization level?

A:There are a few things. First of all, we need to just start using it in ways that are a bit more transformative. I take my AI notetaker with me everywhere and it’s really helpful. But what’s more helpful is if you can pull data from it. If you don’t know what your success path is, and you don’t know what’s prioritized and what activities you’re really trying to ensure that the customer experiences and achieves, then you don’t know what data you’re trying to pull together. So you do have to do some of the basics first, and then you can use AI to help you understand some of those things. So if you look at the trends across whatever data you do have, as imperfect as it might be, then you can at least start to learn where you have data gaps and where you do have data trends.

Now that agentic AI is coming about, it’s really worth your time to start to understand how agentic AI works and how to use multiple agents in coordination with each other to do some work that can identify trends, then ask the next question, then do the next step. We have so much data, too much to be able to use, unless you’re using AI. So first you want to understand what you’re trying to drive towards.

Then you need to understand what is the quality of your data. You can then start working in parallel to fixing some of the data, and use AI to help point out which areas of data you need to fix.

But then keep going, keep trying to learn what trends you can take from it, and start to use AI in the ways that are coming up, so that you can get in on the ground floor and build it and incorporate it into your processes. If you’ve got data, you’ve got a way to use AI. And if you don’t have data, use AI to help you point out what’s the most critical data that you’re missing, so you can work on that. You can just start with something small, if you need to, because once you start, you can build from that. But it’s important not to stop. Don’t just generate more unstructured data. AI can give you all kinds of information if you use it.

Jan Young is the Founder of JanYoungCX consultancy, StepUpXchange (a projectbased learning community), and CxXchange (a free peer-led CS community). An award-winning leader, she’s served as VP Client Services for two exited startups and has advised founders since 2016. Her mission: transform CS Leaders into Business Leaders. With expertise across GTM, she brings a cross-disciplinary approach to drive Customer-Led Growth.

creatively, or meaningfully engage with customers, the most charismatic and emotionally intelligent candidates have no reason to view the contact center as an attractive, long-term home.

By spurring a pivot to critical thinking, creative problem-solving, and meaningful customer conversation, the AI transformation stands to make the prospect of being a contact center agent more compelling. It keeps existing agents more engaged, while making the role more suitable for those who value dayto-day variety and human connections.

Most contact center leaders are confident this pivot will resonate with employees; nearly 90% say their existing agents are willing to take on more complex interactions, and that says nothing of the new, highcaliber talent who will finally see contact center tasks as worthwhile.

Granted, it is important to approach this transformation realistically. Agents are rational human beings, and so while they may crave higher-value work, they are not necessarily begging to work harder for the same money. As they create more value for the business, they will expect to be more valued

by the business. Businesses, indeed, will have to improve compensation and career pathing to more suitably accommodate the next-generation agent.

Complexity Burnout: The Downside Of The AI Revolution?

When it comes to the AI revolution, the cloud of idealism has not simply been obscuring the compensation question. It has also been masking the potential downside of human interaction.

Although customer conversations can be joyous, they can also be emotionally draining. As they pivot away from simple, transactional matters and start diving into nuanced, challenging, high-stakes problems, agents will encounter a wide palette of emotions. This palette will include rude customers who feel their ongoing issue is the end of the world. It will include aggressively demanding customers who feel their status as a buyer warrants them to unrealistic compensation. It will include frightened customers who are going through devastating financial or medical situations.

Notably, it will also – and perhaps often – involve saying no to customers in these emotionally heated, high-stakes situations. Agents will have to reject refund requests and then deal with a barrage of obscenities and threats as the customers refuse to accept the outcome. They will have to break bad financial news and deny medical claims knowing that the lives of callers will be catastrophically changed.

Collectively, these interactions can take a major toll on agents. Left unchecked, this emotional burden can result in disengagement, disillusionment, and disloyalty, affecting performance in the short-term and retention in the long-term. Indeed, the pivot that is supposed to increase employee satisfaction and longevity could end up having the reverse effect.

To minimize this adverse effect, it is imperative to mitigate the risk of emotional drain. This, first and foremost, requires an elevated commitment to the idea of employee empowerment.

The customer experience is completely and directly related to the employee experience. The employee experience is the customer experience. The employee is our first customer.

– DANNY MEYER, FOUNDER UNION SQUARE HOSPITALITY GROUP in his Qualtrics X4 Keynote

By optimizing training to not only arm agents with more nuanced product knowledge but stronger de-escalation skills, contact centers will prevent conversations from going off the rails. Agents will be more capable of providing quick help and more agile and proactive in addressing souring sentiment. The resulting conversations will be easier, less hostile, and less draining.

Investment into modern, AIpowered workforce and data solutions will amplify this empowerment effort. With easier access to customer data, sentiment insights, product knowledge, and recommended solutions, agents will have what they need to deliver more productive and personalized care. This capability, too, will keep interactions on track – and in an emotionally harmonious space.

Agent-centric contact center leaders will additionally commit to agent autonomy. Enabling agents to make independent decisions about “off script” resolutions will not only lead to more fruitful customer support but demonstrate a crucial sense of trust in employees. That belief will simultaneously make employees more confident when navigating complex issues and more willing to “defend” their brand in a heated conversation.

Employee Wellness: A Pivotal Contact Center Priority

Committing to agent empowerment will reduce complexity burnout, but it will not fully eliminate it. Employees will still be navigating through a constant sea of emotionally and intellectually challenging issues, and they will still “feel” the impact of this work.

A robust employee wellness program, therefore, represents a nonnegotiable tenet of the AI-powered

contact center. Through incentives, flexible work opportunities, investment into workplace culture, mental health initiatives, and peer support, successful contact centers will do everything in their power to support their employees. They will demonstrate the same empathy for their workers that they expect those agents to demonstrate when supporting customers.

Although the nature of contact center wellness programs will differ based on specific company, industry, and employee needs, one universal tenet will be an emphasis on open-door communication. If they are to feel thoroughly valued by their business, employees cannot sense the slightest bit of doubt that leadership cares about their feedback. They require unequivocal comfort to not only express how they feel but also make recommendations that could lead to more effective customer service – and less frustration during interactions.

To bring employee wellness to fruition, leading brands treat it as an actual function of their operation. Raising Cane’s, for example, leverages a “Cane’s Love” department to optimize its employee experience.

“Non-negotiable is making sure the crew is always treated right and appreciated,” declared founder and CEO Todd Graves. “We have a department called Cane’s Love, it’s a whole department, all working on respect, recognition, and rewards. We’ve had that since our tenth location.”

Unprecedented Stakes Of “Happy Agents = Happy Customers”

On the surface, the call to invest in employee empowerment and wellbeing may not seem like groundbreaking thought leadership. After all, the

claim that “happy agents equal happy customers” has been a staple of contact center discourse for decades. And at customer service events like Customer Contact Week, employee experiencecentric sessions, discussions, and exhibitors are always incredibly prevalent.

The challenge has been a lack of stakes. Although contact center leaders would obviously prefer to have happy agents answering their calls, an agent does not necessarily have to be over-themoon to answer a basic question about a lost username or delayed shipment. Unenthusiastic agents are ultimately still capable of handling simple tasks.

The rise of AI alters this landscape. With bots and AI agents increasingly handling straightforward issues, agents will be engaging in more meaningful, emotionally charged conversations. Since agent demeanor will play a direct role in these experiences, cultivating employee happiness becomes far more important.

The ongoing AI transformation will thus create a more visible, more impactful separation between those who “talk the talk” and those who “walk the walk.” Brands that see employee happiness not as a poster in their office but as the fabric of the operating strategy are the ones that will empower agents to thrive in the new, AI-driven normal.

Committed to creating a “good time” for their employees, companies like the aforementioned Raising Cane’s are built for a world in which the power of human connection has never been greater.

“How do [we] make the crew members’ journey with us to where they can grow, and it’s going to be fun and rewarding,” explained Graves regarding the brand’s employee experience philosophy. “When I started the first Raising Cane’s, it was different from what I went through when I worked in restaurants, which was ‘do this, do that.’ There was no music played, I was like ‘man, I don’t want that. I want to have a good time … we’re going to have fun, we’re going to play music, we’re going to wear casual uniforms, we’re going to have a good time.’”

BALANCING PERSONALIZATION AND PRIVACY

Personalization will be the way brands make experiences stand out in our busy world.

BY AUDREY STEEVES

Customers are more anxious than ever. Keeping personal information secure is a growing source of concern amidst constant news of data breaches, AI mishandling of sensitive information, and the relentless digital scams that affront us. For years privacy concerns were the responsibility of the customer to manage, but as every organization expands their reach into more dynamic digital marketing and CX, customers can’t be expected to shoulder it all. Failing to give digital security the appropriate resources and consideration is the fastest way to lose customer trust, and when customer trust is lost to security failures, it’s almost impossible to win back.

Each digital touchpoint has vulnerabilities, and the susceptibility to compromise is significantly greater when organizations have disjointed processes without comprehensive oversight. Unfortunately, many mature businesses have seen their departments grow independently of each other, leading to a fractured organizational structure whose fissures are felt in subpar customer journeys.

At the same time, AI has enabled limitless opportunities to personalize experiences. Where applied AI transformed customer management by powering the analysis behind CRMs, agentic AI is taking CX into a new dimension by removing the bottleneck of manpower that has prevented businesses from providing customers with individual, personalized attention.

Setting The Stage For Personalization

The abundance of digital channels make brands feel omnipresent in our lives, with 59% of leaders saying the rise of digital engagement has led to greater skepticism around customer data and privacy. Thus, customers need to see how their information improves the experiences they have with brands,

otherwise they are deterred from sharing. Customers are inundated with touchpoints that ask them for their information, leading to both fatigue and skepticism. Companies that have not yet streamlined their collection touchpoints across the organization would be better off focusing efforts on this project before pursuing more personalized CX.

CX teams are positioned to take on a much more central role in the organization, and they must be staffed by individuals with both strong data analysis capabilities and a deep understanding of customer journeys. Breaking down false walls to make teams more collaborative and data less siloed may be a major upheaval, but conveying the urgency and importance of remaining ahead of the CX curve is a vital responsibility of contact center leaders.

Weighing Risks And Priorities

Personalized customer experiences overtly reveal that your brand is collecting and using customer information, and must be orchestrated with extreme tact. Failing to review such initiatives for appropriateness can be disastrous for a brand. These risks must always be balanced with clear and concrete value in the form of proactive experiences that reduce their effort, offer delight, and/or save time.

In recent years, retail brands have generated buzz by offering an opt-out for certain holiday promotions, like Mother’s Day and Father’s Day, that may evoke feelings of grief and sadness in those who have experienced loss. While the intent of the opt-out is personal and empathetic, the opt-out prompt itself functions the same as a holiday promotional email in bringing customer awareness to their loss, and does not offer a heightened level of sensitivity. As generative AI is folded into these processes, the stakes get even higher. Ideally, AI should assist in identifying

customers’ preferences and configure their communications accordingly, avoiding situations like the redundant opt-out. In the same vein, a mainstream AI tool could identify a shopping trend and prompt a customer the following, “We’ve noticed you haven’t bought any Women’s Retail products in the past year. Click here to stop receiving Women’s Retail advertisements.” This is personalization in that it uses real customer data, but its effect, when sent to someone who has gone through a divorce or lost their wife, may trigger a deeply negative experience. Even if a miniscule fraction of customers have this kind of reaction, all it takes is one customer sharing their insensitive experience online to seriously damage a brand’s image.

In the pursuit of more personalized experiences, there are other ways generative AI can wreak havoc when left unchecked. We have all seen examples of egregious AI hallucination online: obvious gibberish or chatbots spewing nonsense that are easy to write off as nothing more than bad, but harmless, customer service. As generative AI improves and companies provide better guidance to their LLMs, these hallucinations will become less obvious and more dangerous.

With the average customer having little understanding of the mechanics and laws that govern digital data collection, seeing an increase in touchpoints, channels, and automation may stoke apprehension. The crucial guidance here is to consider the enormous volume of digital interactions and communications customers are faced with every day from all of the businesses they patronize. Ensuring marketing, customer success, and support are streamlining their collection touchpoints and communications has two-fold benefits: enhanced internal coordination and more intentional customer exchanges.

The Conversation Revolution Your Customers Actually Want

JAIN, CEO, OBSERVE.AI

AIagents are transforming contact centers, dramatically outperforming traditional IVR systems in cost savings, speed, and customer experience. We spoke with Observe.AI CEO Swapnil Jain to explore this technology’s capabilities to help leaders understand its immediate opportunities and long-term implications for modern customer service operations.

Why Is Agentic AI A Game-Changer?

AI agent technology is far superior to previous generations of conversational AI. What makes this technology transformative is the fundamental shift in how we use these systems to build AI agents. You don’t have to be prescriptive to the nth degree anymore and meticulously describe every single step of a process, focusing on “how” something should be done. Now, you simply instruct an AI agent on “what” needs to happen, and it figures out the entire process on its own. These systems work based on outcome-driven models, use thinking ability to execute multi-step workflows autonomously, and completely eliminate the headache of programming complex IVRs.

Also, your customers don’t want to wait on hold to talk to a human. They want instant resolution. These newage voice AI agents are conversational, intuitive, and perfect for handling those 30% of calls where people prefer not to speak with a human agent. This is the efficiency breakthrough contact centers have been waiting for.

Will AI Replace Human Agents?

The future isn’t about replacement; it’s about powerful collaboration. AI will tackle all routine, deterministic calls that your human agents never enjoyed anyway, freeing them to handle the complex, high-value interactions where human empathy and expertise truly shine.

AI can also handle the upfront information gathering, like collecting phone numbers, email addresses, and basic details, so when your human agents join the conversation, they’re fully prepared with all relevant information and can focus on delivering exceptional service.

What Call Types Suit Agentic AI Best?

Agentic AI excels in three key areas. First are routine calls with straightforward responses and actions, which make up around 60% of your daily calls. AI agents can quickly and easily perform tasks for customers, like booking appointments, checking a claim status, or explaining credit card transactions.

Second is information gathering to determine intent before transferring complex cases to humans. This can range from identity verification and compliance questions to prequalifying sales opportunities.

Third is the proactive outreach, with AI contacting customers about appointment reminders, conducting surveys, or notifying them of qualifying offers.

What’s The Best Way To Get Started With Agentic AI?

Our customers usually start by replacing their current IVR systems. They spent a lot of time and effort automating use cases, so initially, they start slowly to safeguard the customer experience. But with every switch, they almost instantly see a positive impact with AI agents, so they are getting more confident and excited to transition faster. You can also use conversation intelligence to analyze your conversations for L1/L2/L3 call drivers to determine which use cases are easier to automate and which are not. This will help ensure successful automation at scale, optimal utilization of your human agents, and great experiences for your customers.

What Does The Future Of CX Look Like?

The future of customer service is omnipresent, with AI agents seamlessly built into your everyday devices. Imagine all your preferred brands integrated with voice assistants, allowing you to say something like ‘Hey Alexa, is my healthcare claim approved?’ and receive an instant response. Of course, human customer support will still be essential for complex issues requiring empathy, nuanced judgment, and creative problemsolving that AI cannot fully replicate. The ideal future combines AI efficiency with human expertise when it truly matters.

SWAPNIL

REFRAMING THE AI CONVERSATION:

Addressing 8 Unavoidable Myths

BY BRIAN CANTOR

Roughly 3-in-4 consumers feel their typical brand experiences are inconvenient. Nearly 85% feel they are impersonal. Unsurprisingly, 55% feel the state of customer service is moving in the wrong direction.

AI For Simple Issues, Agents For Complex Ones

AI Will Not Produce Job Loss

I see how valid these statistics are in my own life as a consumer. I, too, feel nothing but frustration when I encounter poor technology, convoluted journeys, and unsatisfactory resolutions during support interactions.

As a result, I am thrilled that the customer contact community is embracing AI as a way to revolutionize every facet of the customer journey. I want to access better self-service. I want brands to predict my needs. I want to talk to agents who are free to focus on the issue at hand.

But because we all want this, we ultimately contribute to a hype cycle that overstates what AI can do in some areas, while understanding its impact to affect others. On the end of the spectrum, this hype cycle causes us to overemphasize certain concerns – and downplay more realistic risks.

As we congregate at events like Customer Contact Week, let’s make it a point to address these missteps. Let’s make it a point to rethink some common AI action calls and deploy this technology in the most productive way possible.

Valid in theory, the statement may prove reductive in practice. Many thought leaders, for example, are neglecting that brands and customers may disagree on what constitutes a simple issue. As a result, brands may drive or even force customers to use AI-powered self-service in situations where they would really prefer a live agent.

It is also important to remember that “simple questions” may require more than “simple answers.” A customer complaining about a late delivery may not simply be looking for a bot to provide an updated ETA; they may want a human apology coupled with a reshipment or make-good.

The “AI for simple issues” cliché may, moreover, cause businesses to focus on the wrong metrics. They may celebrate gains in self-service containment or reductions in inbound contact volume, while neglecting unfavorable changes in customer effort score or satisfaction.

Finally, the emphasis on simple issues understates all the progress AI technology has made in recent years. The best AI selfservice and agent assistance platforms are not merely fancy FAQ pages; they are conversational engines that deliver real value.

There is absolutely a world in which contact centers could avoid job loss. There are numerous potential tasks for today’s frontline agents to handle, and that says nothing of the fact that using AI to scale customer engagement could fuel business growth and create an even greater need for personnel.

However, there is skepticism about whether contact center leaders are actually building this world. Despite the “value center” rhetoric, many are investing in AI with the goal of reducing costs. It is thus fair to question whether business stakeholders will willfully sign off on new AI technology and a plan to not only keep all employees but give them raises befitting higher-value work.

Further, the contact center community is still struggling to define the exact “complex work” agents will handle. There are inherently fewer challenging customer service issues than simple ones, which means there will not be enough customer service work to keep all of today’s agents on the front lines. Leaders will have to determine where these agents will go – if they cannot, they will never make the business case to keep them.

AI Will Elevate Self-Service

Capable of understanding natural language, personalizing communication, and taking real action, AI technology absolutely can transform self-service.

But the reality is that self-service woes have not merely been the consequence of poor technology; they have also been a product of restrictive strategy. Many companies do not want their self-service to be a haven for highly personalized, actionoriented engagement – and some fear they outright cannot allow that due to the regulatory landscape.Without overcoming this risk aversion, their self-service experience will be tantamount to putting lipstick on a pig.

AI Agents Must Mirror Human Behavior and Conversation

Of course AI developers are not wrong to incorporate human conversational elements in their technology. And of course technology that makes agents more capable of forming connections – such as real-time translation –is a step forward for the customer experience.

Many brands are, however, making two mistakes when it comes to pursuing “human” AI experiences. First, they are forgetting that the real reason customers seek human agents is because they trust those agents to hear them and respond accordingly. They are not necessarily after the jokes, small talk, or symbolic “smile.” As a result, a bot that mimics human conversation – but lacks the ability to deliver tailored resolutions – is not going to suffice. On the other hand, a bot that presents as robotic and menu-oriented but recognizes context and solves problems will always resonate.

AI Will Never Replace Humans – And Their Empathy

Whereas some brands are working to deploy human-like AI agents, others are rebelling against the trend. They view humans as fundamentally superior and actively trumpet access to live agents as part of their value proposition.

Although it is critical to provide customers with the choice of human support, especially with so many still unsure about the value of chatbots, it is misguided to position agent access as an irrefutable selling point.

The truth is that AI-powered selfservice empowers customers to engage asynchronously on their own terms, thus providing a clear convenience advantage.

And while today’s AI agents are not capable of rivaling humans when it comes to conveying emotional sympathy, there are situations in which they could be even more empathetic. Empathy is ultimately about understanding what matters to a customer at a given moment of truth, and if speedor consistency are top of mind, AI can actually represent the more human-centric option.

AI Augments Agent Performance, Making Them Even More Supportive

Yes,by eliminating tedious simple issues, AI will empower agents to devote more mental energy to high-value conversations. And by streamlining data and knowledge access, AI will allow agents to focus less on what to say and more on how to say it.

But there is a fine line between using AI copilot solutions to assist agents in making a connection with customers and using them to script conversations. If the agent is neither properly trained on the “why” behind key policies nor empowered to deviate from those prompts, they will be just as robotic as the chatbots they are meant to outperform.

In fact, an overreliance on agent assist could spur an underemphasis on meaningful training. And if agents do not have an opportunity to internalize the knowledge they are gathering, they will essentially become that cab driver who only knows how to navigate when the GPS is turned on and giving them very specific directions.

AI Is Only As Good As Its Training

If used as a call to rigorously select the right training model, evaluate AI performance, and scrutinize every database and knowledge entry, this statement is a valuable one.

Unfortunately, it is also stifling AI development. Knowing their frameworks are not unified, some businesses abstain from meaningful investment. While this may seem sensible from a risk management standpoint, it delays organizations’ ability to learn how AI tools will perform in a live setting.

It also downplays the critical role AI can play in creating the optimal framework. By elevating knowledge management and unifying actionable intelligence, AI can clean up a broken operation – and create a platform for better automating agent and customer experiences.

AI Is Simplifying Customers’ Lives

The best AI absolutely can enhance lives. It can make performing key tasks easier, while also generating an unprecedented return on effort.

It is important, however, to remember that the prospect of any extra effort could be a deterrent. Unwilling to see the forest for the trees, many would choose the higher floor of the known over the elevated ceiling of the unfamiliar.

This means that AI that requires a new action – prompting an intelligent search tool to put together a list of restaurants, telling an app to summarize an email thread, authenticating one’s account before talking to a chatbot – may struggle to catch on, even if it theoretically creates value over the status quo.

Mindful of this, customer-centric organizations will consider AI applications that can predict customer needs and proactively take actions. For example, instead of asking someone to log their gym visits or drinking habits to inform their fitness tracker, why not use a combination of GPS tracking (they were at a gym, they were at a bar) and historical patterns to predict what they were likely doing and how they should adapt?

Your Customers’ Trust is at Stake—And AI Will Make or Break It

Emotions drive customer decisions. CCW Digital and Anita

Toth discuss how orchestrating customer journeys to leverage both AI and human ingenuity will set your brand apart.

Q:

Could you start by sharing your background and an overview of your experience, and what makes you qualified to speak on the customer journey?

A: My name is Anita Toth, I am a hidden revenue hunter. My company is Hidden Revenue Hunters. For 20 plus years, I’ve worked in a university research institute with qualitative research methods, and for the last seven years now, have been working with SaaS companies. In particular I’ve been learning to capture the Voice of the Customer and use it to reduce churn and to increase customer lifetime value.

Q:

What are some of the things you’ve learned in the research world that you’ve brought over to your work in the CX space?

A: With respect to qualitative research, people think, customer interviews are just asking a bunch of questions, right? Well, it’s similar to how you can have a haircut at home or you can have a professional cut your hair. In both cases, you’re going to have your hair cut, but you will have different outcomes. And so it’s the same with this. There are specific ways to create questions, to help build trust, to ensure you’re validating the right information. So someone who’s a professional who does this for twenty years in a university research institute is going to have very different results from somebody who just thinks they can ask a bunch of questions and hopefully get the answers they’re looking for. So really, that’s the difference. It speaks a lot about how to do them properly and with

scientific rigor, and that’s what tends to be missed when people sort of run their own interviews and surveys without any knowledge. It’s like the home haircut.

Q: That’s a great example. When we talk about qualitative research in this context, we generally mean Voice of the Customer or Voice of the Employee programs. How can leaders apply these research concepts without the depth of experience that you have?

A:All of us start at imperfection, and then over time, improve. The biggest thing to realize is that there are different tools in your VoC or VoE toolbox. So call something like customer interviews, “the jackhammer” because it goes really deep, but it doesn’t go really broad. You’re getting a small group, like 30 customers, 30 employees, and you’re getting their opinion on usually two to three different topics. And you’re going deep. You want to find out what they’re thinking, what they’re feeling, what they like, what they don’t like. Surveys would be more like “the chisel,” which is very surface level. You can send surveys out to thousands of people, but you’re not going to get super deep answers. So you want to use the right tool for the data you’re looking to collect.

Surveys and interviews can work very nicely together because in interviews you’re going to find there are certain things that come up time and time again. So you want to use a survey at that point to see if that’s true across our larger customer base or employee base. It’s surface level, but it is either validating or refuting the information that you found in your customer interviews.

You don’t have to do them perfectly. If you can create an interview guide with questions that are stronger, you can get a really good idea of what’s going on with your customers and your employees in terms of how they’re feeling, what they’re thinking, what they like and what they don’t like, and maybe something new that you never learned before.

Q: Historically, we’ve relied on people using their intuition and their learned experiences, but now it’s more pattern recognition. I’m curious how you’ve seen this shift to pattern recognition over experiencebased understanding changes things?

A: I think the biggest change is now, as humans, we don’t have to do all of this laborious work that we used to. However, AI is not perfect, and you still have to validate the patterns it has recognized. So if you’re collecting what customers are doing, and you’re finding, that this is what we’re hearing from our customers, and AI hasn’t picked it up, this disconnect is where experience comes in. Human oversight is still critically important. So as advanced as AI is, it still makes a lot of mistakes. I see it kind of like a toddler. It is still learning and doing things quite imperfectly. So in some ways, it’s more important to use human experience to ensure that the patterns it’s recognizing are valid and true.

Q:

There are tons of mistakes you can make in adopting AI too quickly or in a disorganized way. Are there any particular missteps you think should be avoided?

A:

So there are two pieces of research that have recently come out. One is in the Journal of Hospitality Marketing & Management, and another is through KPMG, and they both say that there’s a bit of a disconnect in AI right now. Companies are really excited to be adopting AI, and yet the disconnect is customers are actually very risk averse to it. They don’t trust it. So both of them have done studies, and sales and purchases drop when customers are interacting with AI. Customers are not on the same excitement level that companies are. And so what it’s doing is it’s eroding trust… customers still want to know they’re talking to a human. They want to know that what they’re interacting with is a chatbot. It’s okay if it’s identified as a chat bot, but when it’s identified as a real human and it’s not and they can pick that up, then that greatly

CMP Research Prism

Technology Assessment Framework for Customer Contact & CX

The Prism assesses solution providers with insights and feedback from three perspectives:

analyst user marketplace

how each provider es and informs ent decisions.

Transforming CX: How AI Is Redefining the Customer Journey

CCW Digital spoke with UJET’s Chief Business Officer, Baker Johnson, about the challenges facing today’s contact center leaders and how AI can offer

Q:

What are the top drivers of transformation in CX today?

A: AI is clearly the catalyst breaking the status quo, forcing organizations to confront their legacy data, workflows, processes, and technology. But this isn’t just about adopting AI point solutions; it’s about recognizing that AI’s potential demands a fundamental rethink and a clean-sheet approach to how we operate and engage with customers. By forcing this critical self-assessment and modernization, AI is paving the way for genuine transformation, enabling us to orchestrate truly intelligent customer journeys.

Q: What do you see as the North Star of customer experiences as ways of working and measuring outcomes evolve?

A: The North Star isn’t elaborate interactions, but minimal ones. Consumers want the least engagement necessary to fulfill their needs. The ideal is for technology and even the experience to fade, leaving a direct brand-consumer relationship. Our evolution should focus on enabling this minimal, frictionless interaction through AI orchestration that anticipates and resolves needs proactively.

Q: How have customer expectations changed in recent years as customer contact efficiency and personalization has improved?

A: The improvement in basic efficiency and personalization is debatable. The real shift is towards near-zero effort. Customers expect brands to understand their needs implicitly. They demand contextually aware interactions that anticipate and resolve issues proactively, ideally without significant engagement. It’s time to let go of the illusion that siloed channel strategies can meet these expectations.

Q: What is the most critical area for contact center leaders to focus their AI investments?

A: The focus shouldn’t be on AI point solutions for basic automation, but on AI that orchestrates truly autonomous customer experiences. The opportunity lies in leveraging AI to proactively understand intent, resolve issues with minimal human intervention, and create frictionless intuitive interactions that drive value and revenue.

Q: What do you see as the greatest areas for opportunity in improving contact center efficiency?

A: Efficiency is a byproduct of minimizing customer effort. The greatest opportunity lies in anticipating needs and resolving issues autonomously - providing instant answers and resolutions with minimal interaction, reducing manual intervention and streamlining the experience. True efficiency transforms the contact center into a seamless part of the brand experience.

Q: At which points in the customer journey are customers most primed to react positively to customer-facing AI?

A: The concept of specific “primed” moments is outdated. Customers react positively to AI when it consistently minimizes their effort, expecting instant and convenient resolutions. AI that proactively anticipates needs, resolves issues quickly with minimal interaction, or provides personalized guidance without complexity will always be welcomed, delivering consistent value through effortless experiences.

Q: How does UJET technology make a difference in the contact center?

A: While our modern-device approach and AI orchestration are key differentiators, it’s essential to

understand that UJET offers a complete, end-to-end contact center platform. This includes a modern and intuitive agent desktop, a full suite of omnichannel capabilities encompassing inbound and outbound interactions across all channels, along with robust reporting and analytics. Our platform is designed to be the single pane of glass you need to begin your CX transformation today. Features like our intelligent routing, native mobile SDK, and integrated AI are all part of this unified offering. The key takeaway is that UJET provides everything an organization needs –from a powerful agent workspace to foundational omnichannel capabilities and cutting-edge AI – to modernize their contact center and start delivering exceptional, effortless experiences right now, ensuring they don’t get left behind in this rapidly evolving landscape.

BAKER JOHNSON, CBO, UJET Baker Johnson is focused on driving corporate growth by evangelizing how UJET’s ultra-modern, customerand-user-centric approach to CX is radically disrupting the Contact Center ecosystem. He brings more than 15 years of leadership experience to the role driving branding and data-driven strategy transformations to fuel SaaS growth.

DECODING ENTERPRISE AI

The rise of customer expectations and advancements in technologies are one and the same. As AI enables brands to innovate on the speed, quality, and efficiency of experiences, customers come to expect that level of service everywhere they go. This relationship between technology and customer demands has existed as long as the free market, but AI empowers an unprecedented acceleration that may render businesses and entire industries obsolete if they don’t keep up with the market.

Ten years ago enterprise-level AI was touted as a way to get ahead, differentiate, and trail blaze. Today, it’s a lot more like Excel. You may be able to get by without it, but at a certain point the inefficiencies of its absence will be too great for any amount of top talent or ingenious products to surmount. After all, these are big, abstract ideas that have tangible impacts on millions of working people. In a conversation with Peter Armaly, an executive and GTM thought leader with experience across sales, marketing, product, and customer success,

we connect some of these market-level transformations of business to the day-to-day experiences of those working to create better customer experiences.

As someone who has worked in both sales and on support teams to improve customer centricity, do you think it’s high time for organizations to restructure their teams?

PETER: I would have answered this question differently maybe five years ago before AI became more prominent, and I would have answered it by saying it would be hard to reorganize the way things work conventionally. So many of the successful companies that get that right depend on individuals who can influence the way those handoffs happen. Who has a better methodology for how information passes through organizations, but I think too much has historically depended on the heroics of people.

CCW DIGITAL IN CONVERSATION WITH PETER ARMALY

This is one reason why I’m quite excited for large enterprises–AI, if it’s done well, should act as a really smart governor of all those kinds of handoffs and all the integrated processes, because it’ll surface truths and gaps and weaknesses. AI will help them see where those weaknesses are, and then present the case for how things could be made better, and it’ll be harder for the silos to continue to exist as little fiefdoms, if presented with really detailed proof of why the processes need to be improved. Because at the end of the day, they’re not delivering what the company wants. And if a company’s not delivering, they’re not going to retain customers, let alone get customers. So in my opinion, I think AI will offer that opportunity to big reorganizations. I’ve been talking about customer centricity for 20 years. I felt oftentimes that I was talking kind of loftily over people’s heads, because it was pretty abstract. It was hard to nail down concretely at the task level and at the organizational level. But I feel now AI will be the right tool to really break down silos eventually.

Could you speak to the difference between using AI merely as a tool and AI as a partner?

PETER: The way I see it talked about quite a bit in conversations in communities I’m a part of and in things I read, is that there is too much emphasis placed on how individuals are using generative AI. It’s dazzled the world. There’s no doubt about it. I mean, I use it all the time, a lot of people use it all the time, and it can offer wonderful opportunities for improving what you do on a day-to-day basis, primarily when you’re a writer or creator, things have vastly improved in terms of speed and even expansion of ideas.. But I think that’s

all been a big distraction for companies. I think there’s too much leadership mind share devoted to,”I need to enable my team to use AI and give them licenses for OpenAI or whatever, and just let them run with it and hope that they’ll come back with great ideas and they’ll have this individual productivity.” I think that’s counterproductive. I actually think there should be more of a centralized oversight or governance of AI, and it should be seen more as a way to holistically attack the inefficiencies of this big organism we call a company. AI, as I said earlier, should be able to surface improvements and opportunities for accelerating the value chain that the customers will benefit from. And ultimately the company does as well. So in terms of partnership, companies should think of partnering with AI to do all kinds of low level analysis, collection of data and information, and then the consolidation of it to offer up ideas and insights and recommendations. That way, a centralized team, or maybe a number of centralized teams, can assess and decide how these are great ideas. And maybe we should be implementing these things over time, and really attack the future of business more from managing large projects that are AI-fueled. That’s how I see AI being a really vital partner for companies in the next five years.

Do you have any thoughts on how people can be educating each other and upskilling themselves to use AI?

PETER: When we’re talking about people working at companies, I think people just need to remind themselves they’re paid to do a job. Most people are not paid to experiment in the work they do, and so you need to be careful. You need to be careful how much time you spend getting all excited and trying to play with something which might

distract you from performing your duty. So I think people need to get educated on reorienting their mind around, “my value as an individual contributor should be complete understanding of the processes that I’m involved in. What role do I play in this value chain from start to finish?” Let’s just take a simple example. Products are created by a software company. They’re sold, they’re marketed. They’re sold, they’re implemented in the customer environment. Then they’re supported, and eventually they’re retired, or they’re renewed or refreshed in some manner. Where you are on that spectrum of work, it doesn’t really matter. You have to understand every, every facet of that role, and that’s where your value will come into play when AI is moving into your environment, because you want to be part of that conversation, of helping the company leverage AI to make things faster and more accurate and more consistent.

We should admit humans are not good at scale, at doing things faster, with consistency and with accuracy. We often make mistakes. We just don’t have the capability to do that. We obviously need technology to do that, and AI is differentiated from other technologies in its intelligence. And so I think that as an individual contributor, you should become expert at all the details of the work you do and the value that you’re adding along that value chain, and be able to kind of communicate that, articulate it, so that you get invited to the right conversations around AI.

It is going to replace people. I think we should just be truthful about that. I think there will be smaller organizations, and that’s where companies are hoping to derive greater margins and increased profit, but if you want to be part of that future, then you need to demonstrate you have that ability to rise above the rote tasks that you’re doing and and offer up ideas for how things could be improved.

PETER ARMALY

Peter Armaly is a senior-level executive with over 25 years of experience spanning sales, marketing, product, and customer success. He now focuses his energy in the post-sales world, especially within professional services, where he blends his array of experience into thought leadership that seeks to improve customer outcomes.

HAKOB ASTABATSYAN, CEO OF SYNTHFLOW AI

What’s exciting for any business is that AI operates 24/7 – it never gets tired or angry because of customer interactions. Those who invest are not only able to make significant cost savings but also increase the volume of interactions and protect their reputation in the long run.

Businesses are already benefiting significantly from voice AI agents, and there is still immense potential as new advancements continue to evolve and breakthrough. The concept of autonomous agents was once deemed science fiction, but they are becoming a reality.

Our customers are experiencing impressive results, such as 2.5x more qualified appointments, a 24% increase in answered calls, 12% more closed sales and a 25% increase in client satisfaction.”

voice interactions demands exceptional precision and scalability. Companies like Synthflow AI, which handles over three million calls daily, highlight the significant hurdles—and opportunities— in this space, notably addressing accuracy across multiple languages and maintaining low latency at scale.

Enterprise-grade voice AI providers such as Synthflow AI are accelerating the adoption of this powerful technology with compelling advantages. Synthflow AI offers rapid deployment tailored specifically to business use cases, launching fully operational phone call agents within just 1-2 months, significantly faster than competitors who typically take over 3-6 months. Cost efficiency is another advantage, as Synthflow provides human-like conversational experiences for as low as $0.08 per minute, making it the most economical solution available compared to industry benchmarks.

Additionally, Synthflow AI places high importance on data security and compliance, meeting stringent standards such as HIPAA, SOC 2, and GDPR, ensuring robust data protection for

sensitive information. The company’s commitment to exceptional customer support includes dedicated Solution Architects and Customer Success teams who ensure seamless integration and optimal performance of AI agents.

Looking ahead, the trajectory of agentic AI suggests rapid advancements. Experts predict AI will increasingly augment human capabilities rather than replace them, enhancing interactions to become more natural and effective. The industry anticipates a future where screenless interactions become prevalent, and AI-to-AI communications will automate complex business processes, opening entirely new markets and use cases.

For companies looking to adopt agentic AI, strategic decision-making is crucial. Astabatsyan emphasizes careful partner selection, recommending thorough technology assessments to ensure compliance, affordability, and advanced capabilities. Early and accurate implementation can significantly reduce deployment timelines and secure competitive advantages.

The unfolding AI revolution positions voice technology at the core of next-generation customer experience solutions. Agentic AI is no longer a futuristic vision—it’s a presentday competitive edge. Businesses that quickly embrace this technology stand to lead in customer satisfaction, operational efficiency, and innovative service delivery, reshaping customer experience for the foreseeable future.

Unlike text or image-based AI, voice data includes a rich tapestry of accents, emotions, and spontaneous speech nuances, requiring immense computational and algorithmic sophistication.

BY SHIWON OH

DEI Company Culture Must-Haves

IN A TIME OF UNCERTAINTY

It’s 2025. I wake in the morning, and my routine remains the same. After snoozing my alarm at least once or twice, I hurriedly wash up, change into my work-from-home clothes, and sit at my desk with a cup of tea. (Osulloc’s honey pear—my supply runs dangerously low.)

I breathe and, per my therapist’s instructions, notice how the act of inhaling and exhaling feels in my chest. Like a hot air balloon, I contract slowly. My ribcage graciously expands to make room for fresh air and grounds me in the present. I’m safe and warm. The sun paints the sky with compassionate shades of yellow and pink; a fresh new day awaits me.

After a beat of silence, I finally swipe open my phone. As is the norm for my new reality, a barrage of unwelcome news immediately threatens to rob me of my peace. PBS Shutters DEI Office. JP Morgan, Mastercard Face Key DEI Battles in 2025. Disney Scales Back Content Warnings on Classic Films Amid DEI Shift. My lungs squeeze, my stomach plummets, and I’m ready to expel every ounce of gentleness

that had embraced me just moments earlier. I take a slow sip from my mug.

Anti-DEI Rhetoric Harms Everyone, Not Just Underrepresented Groups.

Diversity, equity, inclusion, and accessibility (DEIA) ensures underrepresented individuals are visible. It has no other motive but to educate, equip, and empower employees who often feel unseen or unheard.

As an Asian woman, I’m hyperconscious of every room I walk into, knowing that everything that makes me me is deemed foreign: the color of my skin, the shape of my eyes, and my choppier accent that occasionally slips off my tongue.

The safeguards that aim to protect and spotlight my lived experiences reassure me that people like me bring value to the table because of our differences. Despite this truth, DEI has become a subject of political attack, and its removal in academic and professional spaces has already led to major ramifications.

Organizations are loudly distancing themselves from diverse hiring initiatives while academic groups reassess DEI-relevant grants. Amazon, Google, and other corporate giants continue to withdraw from key policies. Google recently removed Black History, Pride, Indigenous Peoples, and Hispanic Heritage Month, and key holidays like Holocaust Remembrance Day from its calendar product.

When people in power strategically erase history and close doors to equitable opportunity, they create the perfect breeding ground for injustice to fester. It can creep up on us in the form of microaggressions or come directly for our throats through outright discrimination. It doesn’t just harm those impacted—it forces everyone to take several steps back from meaningful progress, sowing discord and division at every level.

DEI Fuels A Stronger Work Culture.

Many want continuous improvement and innovation in the business world, and DEI provides the blueprint. One study from Harvard Business

HOW RENTSPREE IS

Leveraging AI for a Modern Rental Experience

BY BROOKE LYNCH

AIhas empowered brands to innovate and improve inefficient processes. It offers a pathway to a more streamlined customer experience; it can enhance personalization and communication.

With new technology, organizations can take a once commonplace, but often convoluted process, and simplify it for the modern customer. There is no better example than the rental process — a typically frustrating, lengthy, and high-stakes journey.

RentSpree, a top-rated, trusted tenant screening platform, has leaned into AI to simplify the traditional rental process. The company has a mission to revolutionize the residential rental application process and help tenants find a home faster.

Michael Storey, Chief Experience Officer at RentSpree, shared insights on how the organization is leveraging AI to better connect with customers through deeper personalization and intuitive communication.

Although AI has, at times, gotten a bad rap for creating more generic, de-humanized experiences, when used intentionally it can empower brands to build lasting connections. RentSpree’s vision expands beyond simplifying the rental process, it strives to create meaningful connections between renters, agents and landlords. By focusing on trust, efficiency and satisfaction, the brand is committed to elevating the residential rental experience.

Storey states, “In an era defined by rapid technological advancement, it’s crucial to remember that at the heart of every successful business lies human connection.”

In the rental industry, this human connection is more important than ever. With heightened mortgage rates and steadily increasing home prices, it is a daunting time for many Americans. By making the rental process more seamless, and human, customers gain a renewed sense of optimism.

“The rental process can be complex and inefficient, but at RentSpree, we’re harnessing the power of AI to simplify it and elevate the customer experience. By automating routine tasks and providing personalized insights, we’re freeing up time for our customers to focus on what truly matters. Ultimately, our goal is to create a seamless and enjoyable rental experience that fosters lasting relationships,” Storey shared.

We’re working on our product experience to make the rental process smarter and more seamless at RentSpree. By using multiple data sources—like property details, location, state and county compliance rules, typical customer preferences, and best practices—we’re working on building AI-driven experiences that minimize the information our users need to input. With AI we can train a model to build these data connections at a much deeper level of personalization than was previously possible.”

MICHAEL STOREY CHIEF EXPERIENCE OFFICER RENTSPREE

It is this level of seamlessness today’s customers are looking for. When organizations have the data to streamline operations and give customers a head start, it makes the process that much more intuitive. AI offers users a curated experience that feels tailored to their unique needs. At a time when as few as 16% of customers state that their typical customer service interaction feels personalized, it is clear that companies must focus on leveraging data to establish more informed experiences.

Finding a new home can be stressful enough for renters, making this process more seamless and prioritizing an intuitive journey is critical. Additionally, spending less time on the administrative, routine tasks gives customers the time to dedicate to the moving process.

When thinking about the future of AI, it is these examples that show us the human side of new technology. By automating key areas of the journey, organizations empower customers to spend more time on moments that actually matter.

RentSpree has also leveraged AI to power a chatbot and internally as a tool to generate call notes, summaries and reduce manual note-taking. Overall, this has empowered the team to assist customers more effectively while spending less time on unnecessary tasks. By removing some of these inefficiencies, employees can spend more time supporting the customer.

With more and more brands leveraging AI, it is exciting to see use cases like this. Leveraging the technology as an augmentation tool to streamline the journey and empower employees to better connect with customers is admirable.

In 2025, these are the experiences customers are looking for and brands that deliver will continue to thrive. By prioritizing both a streamlined journey and a humancentered approach, organizations can strike a unique balance that truly meets customer expectations.

DIGITAL MAGAZINE

ABOUT CCW DIGITAL

As the world’s largest customer service resource, CCW Digital provides 180,000+ members with tools and insights for optimizing their customer contact operations. Through research-driven market studies, virtual events, webinars, analyst reports, advisory services, and its quarterly magazine, CCW Digital drives critical conversations on customer experience design, employee engagement, brand reputation, business intelligence, and the growing impact of artificial intelligence.

CCW Digital is a part of Customer Management Practice

www.customercontactweekdigital.com

Art and Design: Synergy Design

Editor: Audrey Steeves

Marketer: Melinda Acuna

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