How to improve operational efficiency with AI call summaries

Despite typically spending two to five minutes—often more—summarising calls manually, the results are often inconsistent, incomplete, or hard to use. The problems arise due to subjective opinion, memory recall and both writing skill and style. The consequence? Key information gets lost, and customers may need to repeat themselves. 

According to a 2024 PwC study, 56% of CEOs reported improved employee efficiency thanks to generative AI. Automating call summaries is one such opportunity—simple to implement, rich in benefits. 

AI can take over this task, ensuring every summary is structured, consistent, and immediately accessible. But how does it actually work? What are the real benefits? And what best practices can help you get it right? Let’s explore. 

The rise of automated call summaries

Operational efficiency is key to smoother interaction flow and better service delivery. Since wrap-up happens after every call, it’s no surprise that AI-generated call summaries have gained traction as a way to reduce agent workload and improve efficiency—but summarisation can come with challenges. 

A recent example is Apple’s decision to suspend its news notification summary service after inaccuracies were found in the generated content. However, this was due to the unique limitations of summarising short notifications without sufficient context, combined with on-device AI processing limitations. It highlights the importance of choosing the right technology—and using it in the right context.  

Reinvesting agent time

While spending a few minutes on a task might not seem like much, it adds up fast. Worst case: your agent spends 5 minutes writing up each call, with a target of 50 calls a day—that’s 250 minutes, or just over 4 hours. That doesn’t leave much actual talk time. No wonder it impacts productivity. 

By automating this repetitive task, agents can redirect their focus towards more strategic, high-value conversations. It’s not just a time-saver—it’s an efficiency gain that benefits the whole team. 

The increasing need for data structure and quality

Efficiency, AI quality monitoring, and enterprise-level supervision all depend on clean, usable information. Manual summaries are often inconsistent—each agent brings their own style, level of detail, and tone. The result is poor-quality, unstructured data that’s difficult to use. 

This creates two major issues: 

• Businesses lose out on valuable customer insights 

• Customers may have to repeat information due to incomplete or inaccessible histories 

AI-driven summarisation helps standardise the format and content of call wrap-ups, making data easier to use across teams and systems. 

AI summaries are quick and consistent

With generative AI, agent-customer conversations can be summarised instantly in a clear, structured, and usable format. But, not all summaries are created equal. If an AI solution isn’t properly configured, the result can be just as flawed—missing key points or lacking context, only at a higher cost. 

So how do you ensure the output is meaningful and actionable? 

Best practices for high-quality summaries

To get the most from AI-powered summaries, they must be tailored to your business needs. Here are three key principles: 

1. Define the right customer insights

Summaries should reflect the essential information your business needs. Decide upfront what should be captured, such as: 

• Customer satisfaction: What level of satisfaction was expressed? 

• Reason for the call: What issue or query was raised? 

• Agent’s response: What solution or advice was given? 

• Agent behaviour: What soft skills or communication style was demonstrated? 

By defining these elements, you ensure summaries serve both operational and strategic goals. 

2. Set up, test, and refine

Implementing automated summaries requires collaboration across business and IT/AI teams. It’s crucial to: 

• Choose the right tech stack: The solution must support transcription, speech recognition, summarisation, and seamless integration. Transcription quality is often overlooked but critical. 

• Test in real-world conditions: Check how the system handles different types of calls. Evaluate not just accuracy but also formatting elements like tone and length. 

• Train your teams: Help agents understand how the tool works and gather their feedback to improve adoption and outcomes. 

An iterative process will help ensure summaries are accurate, relevant, and fit for purpose. 

3. Monitor ROI

AI summarisation delivers value on multiple fronts: 

• Time savings: Less manual admin means more time for high-impact customer interactions. 

• Better customer insight: More complete and standardised records enable deeper personalisation. 

• Stronger loyalty: A better understanding of customer needs results in higher satisfaction and retention. 

• Happier agents: With tedious tasks handled by AI, agents can focus on meaningful work and enjoy a better day-to-day experience. 

Some benefits—like time savings—are immediate, while others—like improved customer insight—deliver long-term value. 

Odigo: AI tailored for professional-grade call summaries

While collaboration across teams is key, AI can be your strategic partner in turning conversations into actionable insight. 

With AI Orchestrator, Odigo offers a solution that fits your business context and uses leading language models to optimise customer service. 

Our approach is personal and adaptable. With 20+ years of AI experience, we work with you to define your use cases (goals, scope, formats, etc.) and configure the system using your industry-specific language for maximum relevance and clarity. 

After setup, we run tests and iterations to fine-tune output quality. And post-deployment? We stay involved, tracking performance and making sure your summaries deliver. 

Odigo offers a unified, high-performance platform that turns every conversation into a smarter customer experience. Ready to modernise your CX and get ahead of tomorrow’s challenges? 

Get in touch with our team