Gartner predicts that more than 40% of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. For companies investing in AI, that raises a critical question: Who is responsible for making sure it delivers results after launch?

For CX leaders, that question has immediate operational consequences. When AI gives customers outdated information, repeatedly mishandles exceptions, or produces inconsistent answers, someone needs to identify the problem, determine its cause, and have the authority to act. Without clear ownership, those issues can persist across hundreds or thousands of customer interactions.

We’ve Been Asking the Wrong Question

Companies have focused on what AI can do, how quickly they can deploy it, and how much it can save. But what happens after AI starts making decisions that affect customers?

An AI system may handle thousands of customer interactions a day, applying policies, answering questions, and determining when to escalate. Some problems surface immediately. Others emerge gradually as information changes, exceptions accumulate, or customers encounter the same issue repeatedly.

Responsibility often crosses organizational boundaries. IT manages the technology while CX owns the customer relationship. Operations may recognize recurring problems without having the authority to change how AI behaves. Leadership expects results, but the people closest to the customer lack a clear path to getting problems resolved.

When AI Problems Become Management Problems

A customer reports an incorrect AI-generated answer. An agent notices the same exception appearing repeatedly. Customer satisfaction begins to decline. Each issue gets addressed individually, but nobody connects the dots to recognize a larger problem with how the AI operates.

That disconnect creates a management challenge. CX leaders need visibility into recurring issues, the ability to identify whether they stem from outdated knowledge, system behavior, or an underlying workflow, and the authority to make changes. Without that coordination, individual fixes may resolve immediate customer complaints while the underlying problems continue affecting other customers.

What Ownership Should Actually Look Like

AI ownership rarely sits with one person or department. Keeping AI performing requires clear accountability across five areas:

• Knowledge Operations — Keep the information AI relies on accurate and current as policies, products, and customer needs change
• Quality Operations — Monitor AI performance against defined standards, identify recurring problems, and determine where improvements are needed
• Exception Operations — Manage situations that require human judgment, preserve context, and identify patterns that reveal broader system weaknesses
• Governance Operations — Establish clear decision-making authority, operating standards, and accountability for what AI is permitted to do
• Tuning Operations — Use operational feedback to improve AI behavior through changes to prompts, rules, knowledge, and workflows

Each of these areas requires dedicated attention and coordination so that problems identified through customer interactions translate into decisions and improvements across the AI operation.

Accountability Determines What Happens After Launch

AI continues answering questions, processing requests, and interacting with customers long after the people responsible for deploying it have moved on to other priorities. But as the business evolves, the decisions AI makes must evolve with it.

Clear ownership starts with knowing who monitors AI performance, who has the authority to make changes, and how problems identified through customer interactions drive improvements. As AI takes on more work and becomes more deeply embedded in the customer experience, these responsibilities become even more critical.

The real test of AI ownership comes when something needs to change. Once a customer-facing problem is identified, can the people responsible determine its cause and take action? That ability to turn operational insights into meaningful improvements is what keeps AI performing as customer needs and business conditions evolve.

Learn more about SupportNinja’s Human-in-the-Loop (HITL) AI Operations.