What makes an AI model fit for purpose in contact centres.

03 November 2025 | eBook

What is an AI model?

I often refer to the term ‘AI model’ as a suitcase word – a term coined by Marvin Minsky, one of the pioneers of AI, to describe a word or phrase that packs multiple meanings into a convenient single term. When people talk about “AI models” in contact centres, they’re often unknowingly referring to an entire ecosystem of different technologies working together.

The problem arises when these components are treated as interchangeable. A language model cannot fix poor speech recognition, and a text-to-speech engine can’t compensate for a model that misunderstands customer intent. When organisations blur these distinctions, they risk poor design decisions, misaligned expectations, and disappointing outcomes.

Consider a voice-based AI assistant for example, a diverse ecosystem of AI models needs to be leveraged so that operators can automate inbound traffic and customer conversations. Each model has distinct strengths and limitations and requires different approaches and methodologies to make it ‘fit for purpose‘. A model is only fit for purpose when it can mirror the linguistic, behavioural, and intent-based nuances of real customer interactions. Understanding these approaches is crucial for organisations seeking to implement effective AI-driven CX solutions and advance their AI strategy.

The four-stage AI model framework.

When trying to categorise this AI model ecosystem, I often draw comparison to how a human agent would engage with a customer, separating it into four critical stages: Listen, Understand, Process and Respond.

Each of these stages requires different types of AI models working in collaboration to deliver seamless interactions and exceptional CX.

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