Talkdesk’s Kevin McNulty on why AI isn’t a magic fix for CX—and what real transformation will require in 2026 and beyond.
Artificial intelligence is everywhere in customer experience—or so the marketing claims. Yet beneath the buzzwords and glossy demos, many enterprises are discovering that “AI-powered transformation” often looks suspiciously like old workflows with new labels.
In this conversation, Kevin McNulty, Senior Director of Product Marketing at Talkdesk, cuts through the hype to examine what’s actually changing—and what isn’t. From the limits of single-agent AI to the overlooked importance of data architecture, McNulty offers a grounded view of how AI is reshaping customer service, marketing, and trust itself—and why the next phase of CX will be defined less by automation and more by orchestration.
Excerpts from the interview;
What’s the most overhyped belief about AI-powered customer experience that enterprises need to abandon?
The most overhyped belief is that a single AI bot can solve complex customer experience challenges simply by being added to an existing workflow. Many organizations assume that plugging in an AI model will somehow create transformational change. In reality, that approach delivers only marginal gains.
True transformation happens when companies move away from isolated AI models performing narrow tasks and toward multi-agent orchestration frameworks. In those environments, AI systems can reason together, coordinate actions, and execute end-to-end outcomes. If your operating model still depends on humans stitching together fragmented workflows, you’re not transforming customer experience—you’re merely augmenting it.
At Talkdesk, we think about AI not as a feature but as a workforce. And like any workforce, AI requires structure, orchestration, governance, and shared context. Once organizations start thinking this way, they also begin to recognize the need for quality management—setting standards, measuring performance, and ensuring accountability. That shift in mindset is where real transformation begins.
Many brands claim they’re moving CX from a cost center to a growth driver, yet their operating models still resemble 2015. What’s the structural barrier holding the industry back?
It comes down to data fragmentation.
Enterprises have invested billions in digital transformation, but customer data remains scattered across CRMs, ticketing platforms, policy systems, and even individual agent desktops. You simply cannot drive a growth-oriented customer experience if your AI lacks a unified, trustworthy understanding of the customer.
There’s also an uncomfortable truth the industry doesn’t like to acknowledge: AI cannot fix bad data. In fact, generative AI that retrieves the wrong information with great confidence is worse than no AI at all. The solution isn’t to keep layering intelligence on top of broken systems—it’s to fix the underlying information architecture first. Only then can organizations unlock meaningful value from AI.
As AI systems take on more of the customer journey, how should brands design experiences for a world where AI intermediaries increasingly act on behalf of customers?
The first requirement is a common language. If AI agents are going to interact with other AI agents, they need shared protocols and governed, traceable knowledge. We’re starting to see early standards—things like agent-to-agent frameworks—emerge, but we’re still at the beginning.
From a design standpoint, this requires a fundamental shift. When AI agents act on behalf of customers, you’re no longer designing linear journeys. Agents can move faster, multitask, and adapt dynamically, which means experience design becomes less about scripting flows and more about defining decision frameworks.
Humans follow scripts. AI agents follow policies, context, rules, and constraints. That’s what designers need to focus on building.
Another critical concept is outcome unification. Behind the scenes, AI-to-AI interactions will become incredibly complex, involving multiple systems and agents collaborating simultaneously. But from the customer’s perspective, the experience must feel seamless. Even if five agents contributed to an answer, the customer should receive a single resolution, a single narrative, and a coherent outcome—without contradiction.
You’ve argued that humans won’t be replaced anytime soon. Where should AI take the lead in customer service to force the industry to evolve?
Knowledge management is the most logical place for AI to lead.
In customer service, the biggest bottleneck is rarely agent capability or staffing levels. It’s that organizations don’t truly understand or manage what they already know. Knowledge is scattered across call transcripts, internal documents, policy files, and historical interactions—and it’s often outdated the moment it’s documented.
AI can transform knowledge into a living, governed system. It can continuously structure unstructured data, surface gaps, keep content fresh, and proactively suggest improvements.
Take the airline industry as an example. A rare policy question may already have been answered once—buried inside a 15-minute call transcript. AI can surface that answer, formalize it, and ensure the next customer receives an instant response instead of repeating a long, frustrating interaction. That’s where AI can dramatically improve both efficiency and experience.
Industries like insurance are rapidly deploying AI agents at scale. When does efficiency begin to erode trust—and how should companies manage that risk?
Trust erodes the moment AI becomes opaque or unaccountable.
There’s enormous pressure to automate, and that pressure can push organizations to deploy AI beyond what their data quality, governance, or policies can support. Our advice is straightforward: autonomy must match data quality. If your data is incomplete or unreliable, your AI should not be fully autonomous.
Explainability must also be non-negotiable, especially in regulated industries like healthcare and financial services. Customers need to understand when they’re interacting with AI and how decisions are being made. Transparency is essential to maintaining trust.
Human-in-the-loop systems are not a crutch—they’re a safety net. The best AI systems escalate intelligently, not because they’re confused, but because they recognize when confidence drops. Responsible automation has to be designed from the beginning. It cannot be retrofitted after something goes wrong.
What three shifts will define marketing and martech in 2026—and why should brands pay attention?
The first shift will be the rise of agentic website experiences, which will begin to replace traditional SEO as the primary discovery model. Websites will no longer rely on navigation or keyword-driven search as their main entry point. Instead, AI agents will guide visitors through conversations. That fundamentally changes digital marketing—fewer page views, more interactions, and a shift from optimizing for keywords to structuring knowledge for machines.
The second shift is the convergence of the marketing stack and the contact center stack. Historically, marketing has owned the top of the funnel while contact centers managed post-purchase interactions. AI collapses that divide. The same data, knowledge, and workflows can power both, enabling far more automation and continuity across the customer journey.
The third shift is outcome-based pricing. As AI adoption accelerates, organizations want clear proof of value. Pricing models will move away from seats and licenses toward outcomes delivered—tasks completed, issues resolved, value created. That will fundamentally change how ROI is defined and how technology investments are justified.
Every year brings a buzzword. What’s the most overhyped promise in marketing right now—and what’s the reality behind it?
The most overhyped promise is the idea of an all-in-one AI agent that can manage the entire customer journey with a single prompt.
The customer journey is inherently complex. It involves identity verification, intake, troubleshooting, compliance, updates, and personalization—often across multiple channels. A single AI agent cannot reason across all those domains with the level of accuracy and specialization customers expect. It will either oversimplify, hallucinate, or fail when it encounters nuance.
The reality is that effective customer experience requires a team of specialized AI agents, each focused on a specific capability, coordinated by an orchestrator that manages conflicts and unifies outcomes. One agent solving everything is a compelling story—but it’s not a realistic one.
For marketers entering an AI-driven era, what’s one piece of advice to stay relevant in 2026 and beyond?
Break the rules.
We’re at the very beginning of this AI era, and this is the moment to rethink everything. Don’t limit yourself to how marketing worked in the past. Younger marketers, in particular, aren’t burdened by legacy assumptions—and that’s a strength.
Imagine the experiences you’d want as a customer. We’ve all used tools like ChatGPT or Claude. Think about what feels intuitive, helpful, and human—and then think about how to build that. You no longer need deep coding expertise to experiment and create.
Marketing success will increasingly be measured by outcomes, not clicks or forms. Challenge assumptions. Break existing workflows. Invent new ones. This moment feels as transformative as the early days of the internet, and it’s moving just as fast.
There’s a natural instinct to fear AI, just as there was fear when the internet emerged. But history shows that these shifts don’t eliminate opportunity—they expand it. The marketers and brands who lean in early will help define what the future looks like.
