Adopting AI in customer experience requires more than technology — it demands a reimagining of the entire operating model. Organisations are now recognising the need to restructure teams, recruit new people, re-train existing employees, and pivot from a traditional contact centre approach to an experience economy model that creates emotional and measurable value.

For years, the traditional contact centre model has been designed for cost-efficiency. Customers call, agents respond, leaders track metrics like average handle time, and success is measured by how quickly and cheaply issues are resolved.

But today’s customers are different. They expect personalised, proactive, and frictionless experiences. They don’t want to wait in queues or repeat themselves across channels. They want organisations to know them and anticipate their needs. That shift is why AI isn’t just an add-on technology — it’s a catalyst for transforming the way businesses operate.

To thrive, organisations must pivot their operating model to embrace the experience economy. This means:

  1. Restructuring teams: Traditional tiered agent models are giving way to hybrid teams where human empathy meets AI precision. That may mean embedding data scientists, conversational AI specialists, and experience designers alongside frontline staff. It also means breaking down silos so marketing, service, and operations collaborate around a shared view of the customer.
  2. Upskilling employees: AI is not necessarily a threat to human roles — but it certainly is a multiplier of human potential. Agents need to be trained to use AI-generated insights, freeing them to focus on empathy, complex problem-solving, and brand building. The workforce of the future won’t just handle cases — it will curate experiences. In addition, operational teams behind the scenes need to adjust and will leverage AI to drive additional value and efficiencies.
  3. Redefining roles: AI handles repetitive tasks and surfaces insights in real time, while people bring emotional intelligence and creativity. Organisations must rethink job descriptions and career pathways to reflect this new partnership.

The operating model must also shift from being transactional to transformational. That means viewing each customer interaction not as a cost to minimise, but as an opportunity to deepen loyalty and increase lifetime value.

Benefits of adopting change:

  • More personalised and proactive experiences that build stronger emotional connections.
  • Increased employee engagement, as staff are freed from repetitive work and able to focus on meaningful interactions.
  • Organisational agility, with teams structured to adapt quickly to new tools and customer expectations.

Lessons & Common Patterns (from real Genesys customers)

When organisations adopt AI in CX and adjust their operating model, you’ll often see:

  1. From silos → journey-centric alignment AI works best when data, decisions, and processes aren’t locked in silos. Many companies reorganise around end-to-end journeys (e.g. “onboarding,” “service resolution,” “retention”) rather than departmental silos. McKinsey emphasises that redesigning the CX organisation & operating model is a key enabler of CX transformations.
  2. Unified tooling & data infrastructure AI and agent assist are only as powerful as the underlying data and integration. Companies often move from fragmented systems to common platforms or “digital cores.” Also, governance, data pipelines, and latency considerations become first-class in the operating model.
  3. Human + AI collaboration, not replacement Many models shift agents’ roles: from doing mechanical tasks to managing exceptions, coaching, reviewing AI suggestions, and handling more complex cases. The operating model must allocate that decision boundary clearly, and shift resource allocations accordingly.
  4. Governance, trust & feedback loops Introducing AI means you need governance (who owns the model, who monitors drift, who audits decisions), feedback loops (agents should flag bad AI suggestions), and transparency (so agents and customers trust AI outputs).
  5. Agile, iterative deployment & scaling Most successful deployments don’t go “big bang.” They pilot, iterate, scale. KPMG’s UK CX report recommends starting small with manageable use cases, then scaling once trust is built. The operating model must support experimentation, rollback, continuous improvement.
  6. Skills & resourcing change New roles emerge: AI/ML engineers, data scientists embedded in CX teams, AI product managers, “explainability / ethics” roles. The traditional org chart shifts.
  7. Performance metrics evolve Traditional metrics (calls handled, cost per contact) coexist with new ones: AI accuracy, human override rate, model drift, customer sentiment change, AI ROI per use case.

Consequences of standing still: The risks of inaction are stark. Organisations that cling to outdated contact centre models will face escalating costs, disengaged employees, and higher turnover. More critically, customers will leave for competitors that offer AI-powered, effortless experiences. Over time, the gap between leaders and laggards will only widen — and catching up will become nearly impossible.

The question is no longer if organisations should evolve their operating model, but how quickly. Those who move now will shape the future of customer experience. Those who don’t will be shaped by it.

Finding out more about AI That Scales: Strategy, Ethics and Innovation

In this new webinar series, Beyond the Buzz: Real AI for Real CX, Genesys experts will cut through the hype to show how AI can deliver measurable business impact today while preparing your organisation for tomorrow. From building a sustainable AI operating model to understanding the Genesys AI roadmap and ethics principles, you’ll gain the knowledge to adopt AI responsibly, scale effectively, and unlock true customer experience innovation.

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