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Bridging the gap between AI hype and contact centre reality

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Minimising AI risk- a step-by-step guide blog

We have all heard the promises. The enterprise technology sector is flooded with claims that you can automate the vast majority of calls overnight. But for most customer experience leaders sitting in a legacy contact centre, the reality looks very different.

In a recent keynote at the Future CX Summit, Dr Mike Banbrook addressed this exact disconnect. He pointed out that while the transformative power of AI is real, organisations fail when they attempt to make a single massive jump to full automation. Bridging the gap requires understanding the technology, avoiding common deployment traps and following a structured roadmap.

The four layers of AI in CX

The first step to escaping the hype is understanding that AI in the contact centre operates across four distinct layers:

  • Deterministic AI
    This is highly controlled and prevents hallucinations. It runs behind the scenes to make operations accurate and efficient.
  • Generative AI
    This layer adds conversational naturalness and handles complex follow-up questions.
  • Governed AI
    Crucial for connecting to backend systems, this layer acts as an auditor to block unauthorised tasks before they occur.
  • Agentic AI
    This grants full autonomy to resolve tasks. Starting your AI journey here is an incredibly dangerous place without the preceding layers established.

Why popular AI strategies fail

Many organisations try to test the waters with an isolated proof of concept. They place a shiny new AI bot behind an old "press one, press two" menu. These legacy routers often have a 20 to 30% misroute rate. Your new voice bot is suddenly fighting against a broken front door, leading to a terrible user experience and preventing human agents from building the muscle memory needed for proper hand-offs.

Alternatively, companies attempt the "Big Bang" approach. They plan massive overhauls that require mapping every customer journey, integrating countless APIs and navigating endless security boards. By the time this project is halfway complete, the foundational AI model has already evolved multiple generations. The technology becomes obsolete before it even launches.

The self-funding three-phase roadmap

To eliminate implementation lag and establish a proactive ecosystem, Banbrook recommends a phased approach where early successes fund subsequent development.

Phase 1 (Intent and Deflection): start at the customer front door. Replace your legacy menus with a simple deterministic AI asking "How can I help you?" alongside a few follow-up questions. This takes about two weeks and yields roughly 20% of the total potential benefit.

Phase 2 (Identification): layer in deterministic identification steps like voice biometrics. This builds on your new front door and drives further efficiencies.

Phase 3 (Self-service and actions): with intent and identification firmly established, you can safely introduce generative, governed or agentic AI to handle backend actions.

By starting small and fixing the entry point first, you can deliver the reality of AI without the risks of the hype.

Contact our team at Convai to learn how we can enable rapid digital transformation for your contact centre.

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