{"id":648242,"date":"2026-10-08T12:15:08","date_gmt":"2026-10-08T19:15:08","guid":{"rendered":"https:\/\/www.genesys.com\/blog\/post\/ai-is-changing-the-cx-operating-model-is-your-workforce-ready"},"modified":"2026-10-13T02:53:34","modified_gmt":"2026-10-13T09:53:34","slug":"ai-is-changing-the-cx-operating-model-is-your-workforce-ready","status":"publish","type":"blog","link":"https:\/\/www.genesys.com\/en-gb\/blog\/post\/ai-is-changing-the-cx-operating-model-is-your-workforce-ready","title":{"rendered":"AI is changing the CX operating model. Is your workforce ready?"},"content":{"rendered":"

[vc_section full_width=”stretch_row”][vc_row][vc_column][vc_column_text css=””]AI is transforming customer experience. That much is clear.<\/span>\u00a0<\/span><\/p>\n

Organisations are using AI to automate routine interactions, help employees work more efficiently and deliver faster, more personalised experiences. And as AI agents become increasingly capable of taking action on behalf of customers and employees, the possibilities continue to expand.<\/span>\u00a0<\/span><\/p>\n

But there\u2019s another transformation happening behind the scenes.<\/span>\u00a0<\/span><\/p>\n

As AI becomes more capable, it changes the organisation. Responsibilities that once belonged entirely to people begin to shift. Existing roles evolve. New ones emerge. And operating models designed around an entirely human workforce start to come under pressure.<\/span>\u00a0<\/span><\/p>\n

In other words, AI doesn\u2019t just change customer experience. It changes the work behind customer experience and, eventually, how the business operates.<\/span><\/p>\n

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AI is changing more than customer experience<\/span><\/span>\u00a0<\/span><\/span><\/h3>\n

Much of the conversation around AI in CX has understandably focused on what the technology can do. Can it automate an interaction? Help an employee find an answer? Summarise a conversation? Predict what a customer needs next?<\/span>\u00a0<\/span><\/p>\n

But as AI becomes more deeply embedded in CX operations, a different set of questions starts to emerge.<\/span>\u00a0<\/span><\/p>\n

If an AI agent handles an interaction, who determines what it can and can\u2019t do? How do you evaluate whether it performed well? How do you identify when an interaction was technically correct but still created a poor customer experience? Who decides when a human should step in?<\/span>\u00a0<\/span><\/p>\n

And who continually improves the system as customer expectations, business priorities and AI capabilities evolve? <\/span>\u00a0<\/span><\/p>\n

These aren\u2019t simply technology questions. They\u2019re questions about work, ownership, decision-making and accountability.<\/span>\u00a0<\/span><\/p>\n

Organisations have spent years optimising customer journeys. Now, many will need to think just as carefully about redesigning the organisations behind them.<\/span><\/p>\n

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When the operating model starts to strain<\/span><\/span>\u00a0<\/span><\/span><\/h3>\n

At first, organisations can often absorb AI into their existing structures.<\/span>\u00a0<\/span><\/p>\n

An operations team takes responsibility for a new AI use case. Quality teams begin reviewing AI-generated interactions alongside human ones. Managers add monitoring and improving AI performance to a growing list of responsibilities. Governance is handled by a cross-functional group alongside existing work.<\/span>\u00a0<\/span><\/p>\n

That can work\u00a0\u2013 until the scale and complexity increase.<\/span>\u00a0<\/span><\/p>\n

As AI becomes involved in more customer journeys, organisations face more decisions about performance, escalation, quality, governance, training and optimisation. And the traditional boundaries between teams can become increasingly difficult to maintain.<\/span>\u00a0<\/span><\/p>\n

Is AI performance an operations responsibility or a technology responsibility? Should quality teams evaluate only human employees or AI agents as well? Who designs a conversation when the customer might interact with a virtual agent, a human employee or both during the same journey?<\/span>\u00a0<\/span><\/p>\n

There\u2019s no single operating model that answers those questions for every organisation. But the underlying challenge is increasingly common: As businesses grow, complexity can outpace the operating model built to support them. AI can help organisations manage that complexity, but it also changes how work gets done, introducing<\/span> new responsibilities, <\/span>decisions<\/span> and ways of working<\/span>. You can\u2019t build an AI-powered future indefinitely on an operating model designed for a pre-AI world.<\/span><\/p>\n

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New responsibilities create new roles<\/span><\/span>\u00a0<\/span><\/span><\/h3>\n

At <\/span>Xperience 2026<\/span><\/a> in Las Vegas, Genesys Product Marketing Manager Yulia Kaurova led a breakout session exploring how AI is reshaping the CX workforce, including the new responsibilities and roles that could emerge as organisations adopt AI at scale. <\/span><\/p>\n

That\u2019s where the conversation about emerging CX roles becomes particularly interesting. Roles such as AI performance manager, conversation designer, prompt engineer and human-in-the-loop orchestrator offer examples of how the work could evolve. The titles themselves may change\u00a0\u2013 or never exist in some organisations. What matters is what sits beneath them: responsibilities that organisations increasingly need someone to own.<\/span>\u00a0<\/span><\/p>\n

An <\/span>AI performance manager<\/span><\/b>, for example, might monitor how AI performs against customer, operational and business outcomes, identifying patterns and opportunities for improvement.<\/span><\/p>\n

A <\/span>conversation designer<\/span><\/b> might shape interactions across human and AI touchpoints so that experiences remain coherent, useful and aligned with the organisation\u2019s brand.<\/span><\/p>\n

Prompt engineers or AI experience specialists<\/span><\/b> can help translate business objectives, policies and knowledge into the instructions and structures AI needs to perform effectively.<\/span><\/p>\n

And <\/span>human-in-the-loop orchestrators<\/span><\/b> can help determine when AI should act independently, when human judgement is needed and how work should move between the two.<\/span><\/p>\n

Some of these responsibilities will become part of existing jobs. Others might be distributed across several teams or develop into entirely new roles. Starting with responsibilities rather than titles also helps organisations avoid designing tomorrow\u2019s workforce around today\u2019s assumptions about AI.<\/span>\u00a0<\/span><\/p>\n

The more important shift is in the work itself.<\/span><\/p>\n

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The human premium changes, too<\/span><\/span>\u00a0<\/span><\/span><\/h3>\n

That shift also creates an opportunity to rethink how existing CX roles create value.<\/span>\u00a0<\/span><\/p>\n

Consider supervisors and quality teams. In a traditional operating model, a significant amount of their time can be spent manually reviewing interactions, checking compliance or identifying relatively routine performance issues.<\/span>\u00a0<\/span><\/p>\n

AI can take on more of that analysis at scale. But that doesn\u2019t make human expertise less valuable. It can make the uniquely human parts of the role more important.<\/span>\u00a0<\/span><\/p>\n

Instead of spending as much time finding the signal, people can spend more time deciding what the signal means and what to do about it. Their work can shift towards calibration, coaching, exception handling, judgement and process improvement.<\/span>\u00a0<\/span><\/p>\n

The same pattern can appear elsewhere in the organisation. As AI takes on more repeatable tasks, people can focus more of their attention on ambiguity, empathy, creativity and accountability.<\/span>\u00a0<\/span><\/p>\n

That\u2019s why the future of the CX workforce isn\u2019t simply a story about replacing human work with AI. It\u2019s about redistributing work according to what humans and AI each do well.<\/span><\/p>\n

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Redesign responsibilities before roles<\/span><\/span>\u00a0<\/span><\/span><\/h3>\n

So where should CX leaders begin? Not necessarily with a new org chart.<\/span>\u00a0<\/span><\/p>\n

Before creating a collection of AI-themed job titles, start by understanding how responsibilities are changing.<\/span>\u00a0<\/span><\/p>\n

Here are some questions to consider:<\/p>\n

One company, a much bigger pattern<\/span><\/span>\u00a0<\/span><\/span><\/h3>\n

That evolution is already beginning to appear in real-world CX organisations.<\/span>\u00a0<\/span><\/p>\n

One example explored during the Xperience session was Brazil\u2019s <\/span>DM<\/span><\/a>. Rapid growth through acquisitions saw the business double in size twice in two years, creating new layers of operational complexity. That created a need to think beyond technology alone and reconsider how work was structured, managed and improved.<\/span><\/p>\n

The specifics of DM\u2019s approach aren\u2019t a blueprint for every organisation. Nor should they be. Every company has a different mix of customers, channels, technology, regulatory requirements and organisational structures.<\/span>\u00a0<\/span><\/p>\n

What matters is the broader pattern.<\/span>\u00a0<\/span><\/p>\n

As AI changes the work, organisations eventually have to reconsider how that work is managed. And the earlier they recognise that shift, the more deliberately they can respond to it.<\/span>\u00a0<\/span><\/p>\n

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