Editorial
Research Report
Mind The Agentic Action Gap: Stop Losing Money With AI Agents
AI agents are quickly becoming a core part of customer experience, handling support, onboarding and service delivery at scale. The promise is clear: faster responses, lower costs and less reliance on human teams.
It's a real opportunity, and the shift is already underway. But this framing also deserves a sanity check. Because while AI agents may behave like a workforce from a systems perspective, users don’t necessarily experience them that way. They experience them as part of a product. And in that context, the question is not only how many tasks an AI can complete, but how the experience holds up in practice.
Table of Contents
- What the Numbers Actually Say About AI in CX
- Customers Abandon AI When It Almost Works
- The Shift: Products Used With and By AI Agents
- Trust Becomes the Defining Factor
What the Numbers Actually Say About AI in CX
Let’s look at some numbers. According to Ada’s Agentic CX in 2026 report:
- 92% of enterprises plan to increase investment in AI for customer experience
- At the same time, only 24% of issues are fully resolved by AI without human involvement
- And just 32% of consumers rate their most recent AI interaction highly
Taken together, these figures suggest that while adoption is accelerating, outcomes are not yet keeping pace. There is a gap between what companies are investing in and what users are actually experiencing. And that gap shows up clearly in the experience itself.
AI in CX is technically impressive, but often experientially underwhelming. Responses can be correct, but unclear. Flows can be efficient, but confusing. Systems can scale, but still fail to guide users in moments of uncertainty.
Think of a common support interaction. You ask an AI chatbot about a billing issue. It gives you a technically correct answer, links to a help article and suggests a next step. But it is not clear whether your specific issue is resolved, what will happen next or whether you need to take further action. You are left piecing it together yourself. And in customer experience, that matters more than raw capability.
Related Article: The CX Cost of AI-Washing: Lost Trust, Bad Data, Real Risks
Customers Abandon AI When It Almost Works
The issue is not demand. Consumers are open to AI in customer experience. In fact, 59% say they prefer always-on AI over waiting for a human, but only when it actually resolves their issue. And that is the catch.
Interactions often get most of the way there, but break at the final step. As highlighted in the same report, many users experience AI that gets them 70% to 80% of the way, then fails.
And the result is not neutral. In fact, it can create a worse experience than if a human had handled it from the start. Customers do not abandon AI because it exists. They abandon it because it almost works. And this reveals a deeper mismatch in how AI is being framed and built.
Treating AI as a workforce leads to an operational mindset. Assign tasks, optimize outputs, measure throughput. But customer experience is not just a production line. It is shaped by context, trust, tone and clarity. What users need is not just answers, but guidance. Systems that help them understand what is happening, what to do next and whether they can rely on the outcome.
This gap is also reflected in how companies measure success. Most optimize for speed, cost reduction and deflection. How many interactions AI can handle without human involvement. But customers do not measure interactions. They measure outcomes. Whether their problem was solved.
The Shift: Products Used With and By AI Agents
At the same time, another fundamental shift is underway, because products are no longer only used by humans. Increasingly, they are used with AI agents, or by agents acting on behalf of users. A customer may collaborate with an AI while writing or troubleshooting. In other cases, they may delegate tasks entirely, asking an agent to find a product within a budget, compare options and complete the purchase on their behalf.
In practice, the agent does not just return options. It navigates the experience itself. It browses products, filters based on constraints, compares specifications and moves through checkout flows, interacting with the same interfaces a human would, even though those interfaces were never designed for them. It may select a product, but fail to interpret delivery constraints. It may complete a flow, but miss a critical edge case.
This changes the role of customer experience entirely. It is no longer just about designing interfaces for people, but about creating systems that work across multiple modes of interaction. When a user acts directly. When they collaborate with AI. And when an AI acts for them. In each case, the experience must remain understandable, navigable and trustworthy.
Today, most CX systems are not built for this. They are designed for direct human interaction, with structured flows and interfaces built for manual input. Agent-driven interactions are more dynamic. They require products to be legible not only to humans, but also to machines. They demand clearer structures, more explicit logic and more consistent behavior. In many ways, this becomes a question of accessibility. Not just for people, but for agents navigating the product on behalf of people.
As AI capabilities become more widely available, this challenge only becomes more important. The technical layer is quickly commoditizing. Many companies will have access to similar models, tools and agent capabilities. What will differentiate them is not the intelligence itself, but how that intelligence is experienced.
Related Article: Where AI Wins — And Where It Still Falls Apart in Customer Service
Trust Becomes the Defining Factor
Trust is built when systems communicate clearly, behave predictably and handle uncertainty in ways that feel transparent and controlled. It is reinforced when users feel guided rather than overwhelmed, and when outcomes align with expectations, whether they are interacting directly or through an agent.
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Sanity checking the agent workforce narrative means recognizing this shift. AI in customer experience is not simply an automation problem. It is an experience problem, one that becomes more complex as agents enter the interface layer.
The companies that succeed will not be those that deploy the most agents or automate the most tasks. They will be the ones that design systems where intelligence works seamlessly across human and agent interactions, and where the experience remains clear, reliable and trustworthy at every step. Because in the end, users do not engage with a workforce. They experience a product.
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