The Contact Centre in the Age of AI

The Contact Centre in the Age of AI

Capturing the full value of AI calls for a broad rethink of contact centre design: how work is divided between technology and people, how frontline expertise is deployed, and how customer insight informs business decisions. The opportunity lies in building a service function equipped to resolve customer issues effectively while extracting greater commercial value from the intelligence it generates – strengthening its position on the enterprise growth agenda.

n/aContents

Thank you for reading Unpacking The Customer Support Metamorphosis

Close Capability Gaps; Empower Frontline Talent

As AI begins to absorb more routine customer enquiries, the interactions left to frontline advisors are likely to involve greater complexity, higher stakes, and heightened sensitivity. This puts a greater premium on human expertise.

Yet only 16% of CX leaders cite elevating agent talent as a strategic priority between now and 2030. This raises a question about workforce readiness. Without investment in the skills agents need – their judgement, their decision-making, their emotional awareness – organisations risk optimising technology while underpreparing the human layer that customers depend on.

^{AI and technology advancement}

^{Data and insight capability}

^{Capacity scaling}

^{Innovation and new services}

^{Talent elevation}

Deep dive: The Contact Centre of 2030

Areas of focus for contact centre investment between now and 2030

Redesign Frontline Roles

Building an AI-enabled contact centre requires specialist roles that complement conventional agent teams and co-ordinate automated decision-making and human oversight. These include AI supervisors who monitor and optimise automated agents; journey designers who shape interactions across channels and systems; and workforce transformation leads who empower employees with the skills needed to work alongside AI.

Together, these roles provide the structures to institutionalise AI governance and orchestration; they facilitate AI adoption and ensure the technology delivers better customer outcomes.

^{AI supervisors}

^{Journey designers}

^{Workforce transformation leads}

^{AI performance analysts}

^{Knowledge engineers}

Deep dive: The Contact Centre of 2030

Emerging roles CX leaders are prioritising or planning to invest in by 2030

Make the Contact Centre an Intelligence Hub

The contact centre is one of the few parts of a business that hears directly from customers in real time, at scale, and often at moments that matter most. These interactions generate rich signals of customer behaviour, sentiment, and friction.

Right now, though, only 15% of organisations report that integrating their contact centre more fully into the enterprise is a primary focus. This points to significant untapped potential to apply customer insight to broader decisions across marketing campaigns, pricing strategies, product development, and lifecycle management.

Listening creates value only when the
business acts.

Where CX leaders see the greatest opportunities to increase contact centre value

^{Revenue enablement}

^{Trust and transparency}

37%

49%

Deep dive: The Contact Centre of 2030

35%

31%

15%

48%

^{Proactive service}

^{Brand differentiation}

^{Organisational learning}

^{Enterprise integration}

Put Culture and Autonomy First

Successful AI adoption depends on more than deploying new capabilities. Organisations need a culture that enables advisors to exercise judgement where human understanding adds the greatest value. Without this, rigid processes and narrow performance targets risk limiting the benefits AI can drive.

This is about redefining frontline decision rights: reviewing scripts, average handle time (AHT) targets, and approval processes to give advisors appropriate discretion when working with AI. CX leaders must empower their advisors to assess AI-generated recommendations, adapt to individual customer circumstances, and override automated outputs when human judgement is important.

“Build trust by empowering advisors to take ownership of customer responses and resolutions. They shouldn’t feel like robots delivering stock answers or be driven by six-minute ticket targets. Give them the autonomy to make informed decisions that resolve issues and leave customers satisfied.”

Jason Richardson, Head of Customer Experience, Abbott Lyon

Reconsider Operating Models Before Automation

A pervasive failure mode in AI transformation is layering automation on top of broken or obsolete operational workflows. Automating an inefficient process merely executes friction at machine speed.

To avoid this, organisations must audit existing ways of working and re-evaluate core customer journeys before selecting AI technologies or defining automation rules. The cost of skipping this step is rarely visible at launch. It surfaces months later in rising contact volumes, frustrated customers and a business case that never quite materialises. Redesign first, automate second: sequencing is what separates AI that scales from AI that simply amplifies.

“Teams must be dynamic and adaptive, able to distinguish signal from noise. Transformation can start today by upskilling technology teams in business and business teams in AI and technology. As these capabilities converge, the line between IT and business will become increasingly blurred.”

Dr. Hady Khalaf, Chief Capability Officer, Bel Group

What's Next?

There’s a difference between having an AI strategy and making it work in a live customer operation.

The most useful lessons often come from the people dealing with the trade-offs firsthand: what to prioritise, where implementation gets complicated, how teams respond, and which early assumptions stand up to reality.

Join us at the CCW Europe Summit to hear directly from customer management leaders sharing their experiences, lessons learned, and practical insights from the frontline of change.

explore the agenda