There are myriad examples of how AI is said to be transforming contact centre operations. But while there’s plenty of hype around systems and tools such as agentic or voice bots, these still appear some way off being realised for the majority of UK contact centres, or could be considered fanciful visions of the future, compared with other approaches that are already in far greater operational use.

Based on discussions at recent CCMA events, roundtables and circles, here are nine of the most commonly referenced AI use cases that have entered into operations today – and what they each entail.

Call Transcription

Converting voice interactions into text, in real time or shortly after a call ends. This creates searchable records for quality assurance, compliance and analysis, and forms the input layer for several of the other use cases on this list, including summarisation and sentiment analysis.

Call Summarisation

AI-generated summaries of a call or interaction, produced automatically at the end of the contact. This reduces the time frontline colleagues spend on after-call work, and gives the next person handling that customer’s query – be it another member of the frontline, a supervisor, or a different channel – a written record of what was discussed.

Agent Assist

Real-time prompts and information surfaced to a frontline colleague during a live call or chat, based on what the customer is saying. This can include suggested responses, relevant policy or procedure information, or next-best-action guidance, retrieved automatically rather than searched for manually.

Knowledge Management

The use of AI to organise, maintain and surface information used by the frontline and customers. This includes keeping knowledge bases up to date, identifying outdated or conflicting content, and improving how relevant information is retrieved during a query.

Sentiment Analysis

Automated assessment of customer (and in some cases the frontline colleague interacting with them) tone and emotion during an interaction, based on language, pace or vocal characteristics. This is used for quality monitoring, escalation flagging, and identifying trends across large volumes of interactions that would be impractical to review manually. AI’s use in analysing emotion is coming under increased scrutiny, as the EU’s AI Act outlines.

Email Drafting and Response Support

AI-generated draft responses to written customer queries, which a member of the frontline team then reviews, edits and sends. This is typically applied to higher-volume or more routine email and webform queries.

Chatbots

Automated, conversational interfaces that handle customer queries via text or voice without advisor involvement, for at least part of the interaction. Chatbots vary significantly in sophistication, from rules-based decision trees to AI-driven systems capable of managing more complexity, and typically include an escalation path to a human.

Translation Services

AI-powered translation of customer interactions, either in real time during a live conversation or applied to written communication. This allows organisations to support customers in a wider range of languages without requiring frontline colleagues that are fluent in each one.

Workflow Automation

The use of AI to trigger follow-up actions required after a query is resolved, such as updating customer records, initiating a refund process, or routing a task to a different team, without manual intervention from a frontline colleague.

What’s Next?

Where else is AI having an impact on your contact centre? These nine use cases reflect where we’ve seen the bulk of operational AI investment applied, rather than an exhaustive list. As governance frameworks, cost models and organisational readiness continues to develop, examples will only expand and vary further, and move from pilot to mainstream.