For most customer service leaders, the past two years have been dominated by one question: how do we do AI?

Pilots were launched, proofs of concept explored, and vendors brought in to demonstrate what might be possible. Yet for many organisations, very little has changed in practice. AI exists but only at the edges.

The reality is that GenAI only becomes transformative when it starts to influence how work actually gets done inside the contact centre. And that’s where progress often stalls.
 

When AI moves beyond experimentation, it stops being a side project and starts challenging long-established ways of working. Agent roles shift from handling transactions to managing exceptions. Supervisors move from monitoring queues to orchestrating how work flows between bots, humans, and systems. Decision-making, once instinctive and manual, becomes increasingly automated, visible and customisable. 
 

These changes are uncomfortable because they expose questions many organisations haven’t yet resolved. Who owns an interaction when it passes between automation and a human agent? How do you ensure consistency across voice, chat, and digital channels when AI is involved? And how do you maintain trust with customers and employees when decisions are no longer made in a single place?
 

This is where many GenAI initiatives lose momentum. Not because the models don’t work, but because the surrounding environment isn’t designed to support them. Workflows remain fragmented across tools. Customer context is lost between channels. Governance is applied after the fact, rather than embedded into how interactions are handled in real time.
 

The organisations beginning to see meaningful returns are approaching the problem differently. Instead of treating AI as a standalone capability, they’re focusing on how interactions are routed, escalated, and resolved end-to-end. They’re designing operating models where automation, agents, and supervisors work from the same context, within clearly defined guardrails.
 

Crucially, they’re recognising that scale doesn’t come from more intelligence alone it comes from orchestration. From ensuring that AI fits naturally into existing service environments, rather than forcing teams to work around it.
 

As customer expectations continue to rise, standing still isn’t a neutral choice. AI that’s poorly integrated doesn’t just fail to deliver value it creates friction, erodes trust, and adds complexity to already stretched operations.
 

The next phase of GenAI in customer service won’t be won by the smartest model. It will be won by organisations that rethink how conversations, decisions, and workflows are brought together and make AI work within the reality of the contact centre, not alongside it.