AI Moves from Theory to Practice in Contact Centres

The AI Landscape in Contact Centres

For the last three years, contact centre suppliers have been announcing Artificial Intelligence (AI) products.  Some announcements have been genuine and new, others have just been a rebranding of existing products.  Either way, the talk has all been about AI. 

For the first time, 2025 has seen a real change, with organisations using AI products in the contact centre for real operational benefit, not just to test the new technology.  AI adoption in the contact centre is now increasing at speed.  It turns out that the contact centre can really benefit from AI and help to deliver a great citizen experience in an efficient way. 

How Can AI Empower Citizen Experience?

In the public sector, citizen experience is about supporting a wide range of people, many of which are likely to be vulnerable.  In this context, AI is not about pushing people to some poorly designed robotic chat service – but about providing real world value. 

AI Use Cases in Citizen Experience

Some of the top uses of AI in the contact centre include: 

1. Taking Notes – Contact centre agents have never been great at taking notes.  AI does this quickly, efficiently and accurately.  The result is reducing call length, reduced wrap-up time and increased accuracy of notes – driving long term citizen service. 
2. Outcome Codes – Many contact centres use call outcome codes, sometimes called wrap-up codes.  Typically, these are the reason for the interaction (call, email or chat session).  But agents often pick the wrong one and many only select the option at the top of the list.  AI can do this quickly and accurately.  
3. Quality Assurance – Many contact centres have one or more QA people.  These typically spot check and score interactions.  This can then lead to training for individual agents.  But spot checking is slow, expensive and often miss the most important cases.  AI can effectively score 100% of interactions – something that would be impossible for a human QA team.  The result is a significant increase in efficiency, more targeted additional support and training and an overall improved citizen service. 
4. Agent Assist – Many public sector contact centres cover a very broad range of complex issues and requirements – often a much more diverse range of issues than in the private sector.  Even the best trained agent can struggle to keep up with this much information, especially when it is changing.  AI Agent Assist tools listen in to the interaction and provide real time information for the agent, e.g. the correct answer, links to other resources, advice on next steps, etc.  The result is reduced interaction lengths, more accurate information for the citizen and improved satisfaction levels. 
5. Next Steps – Many contact centre interactions need some follow-up tasks which are often completed by different teams.  Efficiently identifying actions and correctly passing them on is a challenge.  This is a key area of customer dissatisfaction, especially if they have to make contact again and explain the issue for a second time.  AI Next Steps can suggest actions to the agent, as a result of listening into the interaction.  If accepted by the agent, these can be efficiently transferred to other teams.  In addition, automated processes, such as sending a text message or email, can be triggered. 

Conclusion

These AI tools are relatively simple to implement but have a big impact on both the citizen experience and the efficiency of the operation.  The ability to improve service and reduce cost doesn’t come along very often!