in this issue
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Explore how CX leaders can define what AI should own, where humans remain essential, and why trust and governance are critical to AI success. |
What Responsibilities Should AI Own?
by Execs In The Know
The use of artificial intelligence (AI) to support the customer and employee experience has accelerated from inquiry and investigation to an operational imperative. The economics, expectations, and unprecedented advancement have catapulted AI from an optional and limited innovation experiment to an absolute requirement that shapes trust, operational efficiency, and competitive advantage.
Worldwide spending on AI is forecast to total $2.59 trillion by the end of 2026, a 47% increase year-over-year.1 The rapidly expanding scope of this transformative technology and its impact on customer-facing organizations reveals the critical need to define ownership.
Organizations that are not focused on answering this question increasingly struggle to meet service expectations and control costs; however, those tackling it are strengthening the most practical way to deliver the speed, personalization, scale, and efficiency that customers expect. As with any operational requirement, CX leaders have an incredible opportunity to set the strategy and chart the course to define AI ownership.
The New AI Leadership Challenge
A critical part of embracing this new AI leadership challenge is recognizing that the technology is advancing faster than organizational decision-making. Unlike other technology implementations, the perpetual and dynamic acceleration of AI makes defining ownership, accountability, and governance somewhat fluid yet essential to success. Leaders need to set clear boundaries between AI and humans, specifically in defining new operating models and employee skills.
Instead of the traditional approach that includes standardizing or digitizing an existing process and follows predefined steps on a specific and predictable schedule, AI implementation is more dynamic. An AI operating model can interpret language, identify intent, generate recommendations, execute tasks, and improve over time. The model must be adapted to include stronger governance, human oversight, continuous testing, model monitoring, feedback loops, and clear boundaries.
Similarly, employees need different skills. Instead of standard training to learn a new interface in a traditional deployment, AI requires employees to learn how to work with an intelligent system that continually improves. Employees must have an enhanced skill set that includes AI literacy, critical thinking, and judgment. Prompt development, data interpretation, exception handling, and the ability to validate outputs are also critical skills for employees using modern AI tools. Leaders must take a cross-functional approach to ensure training, monitoring, and coaching are aligned around the critical new skills employees need to succeed in an AI-enabled operating model.
Where Should We Draw the Line?
Organizations must intentionally define which interactions are best handled by AI, which require human judgment and empathy, and how the two should work together to deliver the best customer outcome. As CX leaders work to strike this delicate balance, an excellent starting point is to understand what tasks customers are comfortable allowing AI to handle.
The voice of the customer will help reveal what customers value, where the frustration points are, and which interactions require empathy, judgment, and trust-building from their perspective. Industry leaders must use these signals to represent the customer and better understand their journey to and through the service experience. That data is critical input to operational planning as it defines the interactions that customers support AI resolution as well as those that need a human agent to resolve.
As development and deployment of AI assistants rapidly move forward, routine inquiries, summarization and insight generation, and transactional support have become table stakes. The best use cases are high-volume, repeatable work, including account updates, password resets, and order status checks. More advanced uses, such as proactive service interventions, automated quality auditing, and knowledge management that updates in near real time based on live interactions, are also sound choices for AI assists to handle.2
These types of interactions are more transactional and occur when a customer needs to solve an issue quickly, making them comfortable dealing with machine support. More complex use cases that further increase efficiency and deliver proactive service are being tested and deployed, but leaders recognize that relationship-sensitive interactions should remain in the human agent queue. Human involvement is critical in empathy-driven interactions, complex problem resolution, high-risk contacts, escalations and exceptions, and moments where trust must be built or restored.
Many organizations ask whether AI can perform a task. The better question is whether customers want AI to own that moment. Seventy-one percent of consumers feel neutral or better about AI-powered agents serving as the first point of contact during a customer care interaction.3
AI ownership isn’t about assigning work to machines. It’s about defining accountability.
Trust: The New CX Battleground
In an AI-enabled environment, trust is both an equalizer and a competitive advantage. Customers must believe AI is being used to support their needs in transparent and responsible ways to improve their experiences. Customers are not just evaluating outcomes; they are examining how those outcomes are being achieved.
Employees also need to understand this to build their confidence in the technology and processes so they can represent the brand promise during human-assisted interactions. For both groups, building trust increases the likelihood of engagement, self-service, and acceptance of AI-driven support.
The relationship between AI adoption and brand trust requires an intentional and well-planned operational shift. Organizations must now treat AI trust as a foundational business requirement rather than a mere compliance issue; without this shift, the full value of AI cannot be realized.
Trust underpins two critical outcomes:
| 1. | It enables organizations to realize value from AI investments by supporting sustained adoption and integration into core workflows. |
| 2. | It is essential if organizations are to successfully manage an expanding and evolving risk landscape.4 |
This trust-forward approach empowers organizations as it becomes a competitive differentiator, allowing brands to improve results with promotion and use of responsible AI. When organizations lead with trust, the brand promise comes to life, increasing consumer confidence, building deeper relationships, improving loyalty and spend, preventing legal liabilities, and driving additional innovation. A first step to achieving these benefits is building a strong governance practice.
Governance, Accountability, and Risk
CX leaders understand the importance of governance to create clear accountability and mitigate risk. A structured governing framework identifies operating rules, practices, and processes to control the organization and AI-powered initiatives. In the current AI era, the importance of governance has become even more important to the operational plan.
AI governance identifies the standards and guardrails that need to be in place and ensures that systems are safe and ethical. An effective governance approach and plan should balance efficiency gains with CX improvements and business risk. Some of the core elements of an AI governance plan include:
| Plan Element | Description |
| Purpose and prioritization | Aligns the business around why AI is being used, the problems it will solve, the criteria for prioritization, and the measures of success (e.g., improved self-service, reduced handle time, fewer repeat contacts). |
| Human boundaries | Defines the protocols for human vs. machine, escalation paths, approval thresholds, and moments of truth that require human intervention. |
| Risk and compliance controls | Identifies hot spots with privacy, security, bias, and regulations as well as potential AI weaknesses, such as inaccuracies, hallucinations, and drift. |
| Data governance | Pinpoints internal and external data that AI can access, ways to protect that data, and data quality measures. |
| Roles and rights | Names the AI owners across the organization, including sponsors, approvers, monitors, creators, and decision makers. |
| Transparency and trust | Establishes communication protocols with customers on the use of AI and how to get to a human. |
Importantly, AI governance is not just about ensuring compliance; it is also about sustaining ethical standards as AI is used. Over time, AI models can drift and lead to lower quality, reliability, and experiences for customers and employees.5 Governance plans must include triggers to ensure that when things go wrong, humans are notified and have the authority and skill to resolve issues.
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Redefining the Human Role As AI transforms the technology stack and operational model, leaders have an opportunity and a responsibility to define how it reshapes CX teams, roles, workflows, and the skills employees need to succeed. This work starts with a simple question: what work should remain human? CX leaders must now decide where AI should lead, where humans should lead, and how the two work together. Defining these boundaries is a strategic and governance imperative and will differ from organization to organization. However, core elements of the work to redefine the human role remain the same: 1.Not every interaction should be automated
2.Customer trust must be a top priority
3.AI requires oversight
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As work to redefine the teams, workflows, and skills advances, a key principle must guide this effort: human access and availability is an imperative. Organizations must design and offer ways for customers to reach a human agent if they feel the need, particularly in high-stakes interactions. The best customer service operations combine capable self-service with transparent access to human agents. When customers have access to human support but never need to use it, their trust in the brand grows.6
What CX Leaders Are Discussing Right Now
The topic of AI ownership, governance, and trust is on the minds of CX leaders around the globe. As AI reshapes our industry, the real advantage comes from learning alongside a diverse peer community. Execs In The Know, and particularly our KIA community, gives leaders a place to deepen their knowledge, challenge assumptions, and sharpen ideas together. AI is evolving too quickly for any one brand to discover, test, learn, and deploy in isolation. Leaders need to compare strategies, pressure-test ideas, share lessons learned, and understand what is actually working when it comes to AI ownership (and all else AI!).
Some key insights and questions are emerging across the CX community:
Being part of the conversation in CX leader communities helps organizations accelerate AI adoption. The dialogue can turn isolated exploration and experimentation into shared learning, practical governance, stronger workforce readiness, and more confident decision-making, particularly when it comes to defining roles and responsibilities for ownership.
Ownership Will Define the Future
As AI becomes more deeply embedded in CX, trust cannot be treated as a byproduct of good service; it must be designed, governed, and measured as a core feature of experiences. Customers still look for consistency, honesty, and predictability across every interaction, but AI introduces a new trust equation: organizations must be clear about when AI is being used, how it supports the experience, and where humans remain accountable. This is particularly important in sensitive or high-stakes interactions, where transparency can determine whether AI helps or hurts.
If the relationship is the product, then trust is not optional; instead, it is the foundation that makes AI-enabled customer and employee experiences credible. This is a leadership issue, not a technology conversation. Leaders already teach human agents how to build trust, and they must now apply the same discipline to AI agents through governance, quality, communication, and culture. In this era of CX, culture is driving trust, quality is revealing it, and trust is the equalizer and a competitive advantage.
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