As the industry leaves the summer months, companies are now showcasing why their solutions and approaches are fit for the coming year.
These include the growing expectations for responsible AI adoption as concerns over its capabilities outside of human oversight grow.
As organizations prepare for 2027, leaders will be required to monitor how agents perform in real customer journeys, identify unexpected behaviors, and understand interactions happening beyond their own channels.
Vangie Cleversey, Chief Customer Officer at Acquia, explained to CX Today why organizations must keep observing and learning from AI agents after they have been deployed.
“The companies I worry about are the companies that agents run free,” she said.
“They’re doing great things, they’re providing efficiency, but they’re not observing, watching, and learning from the patterns of those actions.”
Why You Shouldn’t Leave Your AI Alone After Launch
Organizations must now recognize that deploying an AI agent should be treated as the beginning of an intelligence cycle rather than the final stage of an AI project.
Once an agent is operating in live customer journeys, there must be clear oversight into whether it is achieving the outcomes it was designed to deliver.
This means deciding your AI goals in advance: “What are the outcomes that you’re trying to achieve through our platform?” Cleversey asked leaders.
“And then where do agents step in to help you with those outcomes?”
This includes defining objectives, permissions, data boundaries and behavioral guardrails while continuing to assess those controls against real-world interactions.
Ensuring continuous observation also enables businesses to uncover behaviors that weren’t visible during testing.
“We’ve seen customer paths for certain issues that we never thought would happen, but they do,” she said.
These unexpected journeys can reveal service, product, and data gaps, meaning organizations without a mechanism for capturing and analyzing those patterns may see efficiency gains while missing cx deterioration evidence.
She emphasized:
“You’ve got to build that loop.”
Introducing a loop connects deployment with observation, analysis, and ongoing improvement, allowing customer interactions to inform how the agent operates and where human intervention is required.
The need for this discipline is becoming more pressing as organizations move agents into more important roles, as McKinsey’s 2026 AI Trust Maturity Survey found that only around 30% of organizations had reached mature levels of strategy, governance, and controls for agentic AI.
Furthermore, Gartner predicted that more than 40% of agentic AI projects could be cancelled by the end of 2027.
For CX leaders, the ever-growing standards as we enter the next quarter means greater emphasis on proving that agents can perform reliably after launch.
As a result, continuous monitoring is becoming a standard part of the business case for agentic AI, revealing the globally acknowledged danger of ‘drop-off’ deployment.
How To Build a More Responsible CX Before 2027
As AI grows more capable across their systems, CX teams need to understand what happens beyond their own websites and support channels, as traditional measures such as containment rates and average handling time do not reveal whether customers are achieving better outcomes.
“It is much more than what they’re doing in your own products, in your own site,” Clevereley noted.
“It’s about what they’re doing in the ecosystem that then impacts how you support customers, how you evolve your products to support them.”
This expectation now requires organizations to actually identify how AI systems influence their decisions and where journeys break down.
The trust-by-design approach means governance needs to be built into the CX journey from the beginning, so customers understand when they are interacting with AI, what the system is authorized to do, and how they can reach a human when needed.
“Customers want to do things themselves as far as they can go,” she emphasized.
“And when that moment comes where they’re like, I’m done, I need help, I need a human, we have to allow that to continue to happen.”
Human escalation needs to be designed as a core part of the automated journey, giving both agents and employees access to customer history, transaction details, and interaction context so that customers do not have to repeat their problem.
Furthermore, CX teams should examine what happens after an escalation through complaint volumes, resolution quality, and successful escalations to reveal whether an agent is completing a task while creating friction elsewhere.
She concluded:
“Now you can see it through agentic transcripts.”
By analyzing both traditional and customer-centric pattens across interactions and customer segments, organizations can use this to improve agent behavior, employee training and escalation processes.
For CX leaders, preparing for 2027 means connecting governance, customer intelligence, and human support across the wider customer ecosystem.
You can check out the full interview above with Vangie Cleverley for additional insights on how leaders can ensure a competitive advantage before the new year.
