Contact Centre
Call Summarisation – the ultimate GenAI use case in contact centres

Ever since the launch of Chat GPT, businesses have been exploring use cases for generative AI technology in the contact centre. In its 2025 Service Centre Trends Report, PwC identifies 8 key use cases:
– Virtual channel assistant
– Content generation
– Virtual agent assistant
– Virtual customer assistant
– Text analytics
– Real-time speech and text translation
– Content utility functions, and
– Call summarisation
While virtual assistants (chatbots) and content generation tools grab the headlines, call summarisation is emerging as one of the most impactful, quick win uses of genAI in customer service operations.
What is Call Summarisation?
Call Summarisation refers to the automatic write-up and summary of a call between a customer and a call centre agent. Whereas it takes time and effort for an agent to type up notes, relying on full memory of the conversation, GenAI software can instantly produce a summary including agreed action items within seconds.
Productivity Benefits
The impact on agent productivity alone makes a compelling case for adoption of Call Summarisation. Contact centres implementing this technology report a 40% reduction in agent time that was previously spent on note-taking and summary writing.
The technology also frees up agents to focus on what they do best, which is solving customer problems. With agents liberated from the burden of manually documenting conversations, they can speak to more customers resulting in faster call answering and resolution times.
The improvements in note-taking accuracy are equally impressive. Manual summarisation can suffer from inconsistency and subjective interpretation. GenAI-powered summaries eliminate these human variables and ensure that every interaction is documented with the same level of detail and objectivity. This helps enhance the customer journey by eliminating the need for customers to repeat information when transferred between departments.
Finally, an often overlooked benefit of having accurate call summaries is increasing sales conversions. GenAI can analyse interactions to understand what factors, tactics, or messages actually drive conversions and what resonates with customers.
Real-world Impact
Let’s look at the savings that call summarisation could bring to a busy contact centre.
Imagine a 100-FTE centre where agents spend an average of 1 minute per call on After Call Work (“ACW”). With agents handling, say, 10 calls an hour over an 8 hour shift the savings look like this:
- 10 calls per agent per hour x 1 minute ACW per call x 8 hours = 80 minutes ACW per agent per day
- With 100 FTE, this equals 8,000 minutes, or 133 hours, spent on ACW daily.
- With an average cost of $50 (including wages and on-costs), ACW costs approximately $6,650 per day or $2.43 million annually.
Even conservatively, if GenAI call summarisation reduces ACW by 50%, that’s $1.2 million in annual savings. Boom!
The impact is even more dramatic in sectors with more complex documentation requirements, such as:
- Premium credit card concierge services, where agents can spend 10 minutes post-call documenting detailed customer requests and actions taken.
- Healthcare contact centres, where accurate documentation of patient enquiries and symptom reporting is crucial.
- Financial advisory services, where detailed notes about investment discussions and recommendations are required for compliance.
- Insurance claims centres, where agents must document extensive claim details and customer circumstances.
- Legal service hotlines, where precise documentation of legal enquiries and advice is essential.
These high-ACW environments stand to gain the most from GenAI call summarisation, not just in time savings but in improved documentation quality and compliance.
The Power of AI-driven Summarisation
Most contact centres find it challenging to extract insights from the thousands or tens of thousands of customer interactions they manage daily. But they are missing a goldmine for QA and continuous improvement.
It turns out that LLMs (large language models) are remarkably good at processing complex, non-linear conversations with precision. They can determine not just the topic and intent of speakers, but also capture the next steps and outcomes of a conversation with a consistency that human agents struggle to match.
This isn’t just about creating notes – it’s about transforming raw conversations into actionable intelligence.
Just as many businesses are now using AI to take minutes and note actions in their Zoom or Teams meetings, genAI can produce comprehensive summaries of phone calls and live chats, capturing everything from core issues to resolution steps while detecting customer sentiment and flagging critical action items.
Most systems can present the information they uncover in multiple ways, whether you need detailed paragraphs, concise bullet points, or structured data tables.
Making it work
While the potential benefits are clear, successful implementation of call summarisation requires careful planning, with data privacy and security among the most important considerations. Robust encryption and access controls are a must while ensuring compliance with data protection regulations. This ensures that sensitive information is protected and customer trust is maintained.
Integration of the technology with existing systems can present another critical challenge. Generative AI solutions should work seamlessly with existing contact centre infrastructure, particularly Customer Relationship Management (CRM) systems for maximum efficiency. The goal is to create a unified workflow where AI-generated summaries automatically update customer records, trigger necessary follow-up actions, and pop on-screen to guide advisors.
Technical compatibility is just the first hurdle. Successful implementation requires the genAI model to be configured for specific use cases and departments. Consider how interaction summaries will feed into the broader customer service ecosystem, from quality monitoring to agent training programmes. AI-generated insights should be available to all teams in the contact centre responsible for driving continuous improvement – from QA staff to team leaders and training managers.
The Future of Call Summarisation
As generative AI technology evolves, increasingly sophisticated capabilities will emerge. For example, many models now support multiple languages, making the technology viable for global operations. For organisations serving diverse customer bases internationally, the ability to apply learnings from one language or market to others can be a competitive advantage.
Beyond the basic summarisation of conversations that it does so well, genAI is becoming much more sophisticated in providing deeper insights into customer interactions. It can analyse sentiment, identify trending issues, and even suggest proactive measures to improve customer service. The evolution of these models from simple documentation tools to strategic insight engines is going to be a major driver in improving efficiency and quality in customer service.
The bottom line
In the rush to implement genAI, it’s all too easy to overlook some of the technology’s more practical applications in favour of flashier solutions. Call summarisation may not make headlines like chatbots, but its ability to improve operational efficiency, customer experience, and business intelligence makes it a standout use case in contact centres.
Interested in Call Summarisation for your business? Contact us!
Last updated on: February 2, 2025