Last summer, Qover’s CEO explored how the insurance sector could harness AI to maximise customer outcomes and efficiency. He highlighted that the natural entry point for AI adoption in insurance – like in most industries – would be in frontline customer interactions.

Fast forward several months later, and we now have hands-on experience engaging with AI tools and related automation. That’s why we’re starting a new AI blog series designed to share practical insights into how companies like Qover can effectively integrate AI into their operations.

We don’t have all the answers – far from it. But we do want to contribute to the ongoing conversation about the real-world utility of AI and encourage others to do the same.

This first post in our three-part series will focus on the lessons we’ve learned about the business impact of AI – written by me, Chief Customer Officer at Qover (Ed Ackerman, reporting for duty).

In the next instalments, we’ll explore the change management implications of AI adoption and conclude by examining the next frontiers for AI beyond customer-facing capabilities.

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Navigating the AI hype cycle

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Despite ChatGPT-4 being widely released only two years ago, it feels as though we’ve already reached the trough of disillusionment. The initial excitement around generative AI tools has settled, and businesses are now grappling with the real challenge: turning AI potential into meaningful outcomes.

Big Tech and startup investors have poured billions into AI, but whether these investments will drive the mass adoption to fulfil these big bets remains an open question. Some analysts predict 2025 will be a make-or-break year for AI – will we look back on it as the year AI revolutionised industries at scale? Or will we see a burst of the AI bubble, followed by a slower, more measured adoption curve?

At Qover, we see AI’s potential and are investing heavily in its capabilities. However, I also acknowledge the reality: as pioneers in our sector, we will experience both the advantages (first-mover benefits) and the challenges (navigating the unknown and course-correcting when necessary). We’re committed to being at the forefront of AI adoption in insurance, but we want to do so responsibly, ensuring the needs of our partners and their customers remain the priority.

Qover’s customer sentiment analysis showed us that customers value speed almost above all else – speed of accessing answers to queries and speed of claim settlement, as Accenture has also found.

This insight led us to prioritise AI claims processing applications that would significantly improve speed to outcome and resolution efficiency. However, we recognised that achieving these gains couldn’t come at the expense of customer satisfaction or quality of service. Our goal was to enhance both speed and accuracy simultaneously.

To do this, we focused on two key AI applications:

1. Supporting written communication with customers: using generative AI to refine, speed up and enhance responses.
2. Enhancing claim evidence capture: automatically extracting and analysing documentary evidence to expedite claim decisions.

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Watch the webinar: benefits & risk of AI in claims →

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The mantra: hurry up and slow down

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In our experience, hurry up and slow down is the mantra. AI is undeniably transformative. However, expecting AI alone to generate substantial returns overnight without broader operational changes is perhaps naive.

‍AI-driven improvements require investment in multiple areas: for example, better documentation to feed AI models, enhanced process flows to manage human-in-the-loop exceptions and revised quality assurance mechanisms to measure the effectiveness of AI-human collaboration.

At Qover, implementing AI in customer service and claims processing was never going to be dependency-free. Instead, it demanded significant improvements to our supporting capabilities. Here’s what we’ve learned along the way.

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Lesson 1: AI’s power lies in high-quality inputs

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A key finding in our AI journey is that people are, in general, quite happy to interact with AI. The AI Act required that we clearly indicate when AI is being used and provide an alternative, non-AI option for those who prefer it.

In our claims project, we found that less than 1% of claimants opted for the non-AI version. This suggests that what truly matters is not whether the agent is human or AI, but its competence. A poorly trained human agent can be a major turn-off, whereas a well-trained AI agent that delivers fast and accurate responses is well accepted.

Over time, as AI systems become even more capable, I believe users will care less about who or what is on the other end, as long as the service is competent and efficient.

AI-powered agents are impressively articulate, but their accuracy is only as good as their training data. Generative AI tools, like ChatGPT and Gemini, are excellent at producing professional, well-structured responses. However, they need well-organised, up-to-date and highly detailed information to deliver truly useful answers.

In the insurance sector, policy documents are critical, but they don’t always cover practical, real-world applications. Human agents instinctively know the nuances of FAQs and edge cases – AI, on the other hand, needs those insights documented explicitly.

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Our approach at Qover:

• When deploying an AI agent for a new product, we spend the first few weeks reviewing every response manually. Our human agents provide feedback, refining AI outputs until it's as good as human output. That way, our AI agent achieves customer satisfaction scores comparable to human agents.
• Post-launch, we analyse queries that AI couldn’t answer and expand its knowledge base accordingly. This leads to a 50% increase in the number of queries the AI can resolve over time.
• We also invest in the AI literacy of our staff to foster awareness and proper oversight.

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Bottom line: Our experience shows human expertise must be systematically documented to unlock AI’s full potential – which takes significant time and effort.

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Lesson 2: AI’s success depends on robust upstream and downstream processes

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When returning from an operation to repair a tendon, we need time to rebuild the supporting muscle through physiotherapy. Your achilles (heel) doesn't work in isolation. 

Much the same, you can't simply insert AI into a process without levelling-up the business muscle around it; everything will fall over somewhere else in the chain.

For AI claims processing, for instance, success depends on:

1. High-quality document capture: ensuring that claim documents are clear, well-structured and digitised correctly.
2. User-friendly experiences: making the claims submission process intuitive and ensuring customers provide accurate inputs.
3. Sensible prompt engineering: refining AI queries to extract meaningful and relevant data.
4. Seamless AI-human integration: transforming AI-generated insights into actionable claims decisions.

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What we’ve learned at Qover:

• Well-structured claim forms can enable automation of up to 50% of claims without AI.
• Around two-thirds of our automation success comes from rule-based verifications. We achieve this by improving the questions in our claims forms and converting key policy conditions into system-run checks.
• To tackle the remaining one-third of claims, AI is essential. But for AI to work effectively, prior investments in data accuracy, flexible validation frameworks and seamless user interfaces are crucial.
• Using AI for document verification, we’ve been able to automate up to 25% of complex claims, significantly reducing manual workload and improving efficiency.

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Bottom line: It’s important that AI claims processing adoption be accompanied by robust process and user experience improvements across the board.

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Lesson 3: Optimisation is an ongoing process

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As with people, AI needs time and continuous learning to achieve mastery. Organisations expecting overnight success will be disappointed. Getting to launch is step one; realising the full benefits of AI requires patience, experimentation and determination. 

Before launching AI initiatives, it’s essential to clearly define:

• The core objectives the AI solution is meant to achieve.
• Metrics to measure success before and after implementation.
• A structured optimisation plan post-launch to ensure continuous improvement.

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Our AI optimisation approach at Qover:

• Every AI automation project enters a hypercare phase post-launch, where project teams dedicate over half their time to monitoring performance, gathering frontline feedback and fine-tuning the AI.
• This allows us to swiftly address false positives, enhance AI understanding and improve its integration into workflows.

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Bottom line: Companies should plan for a period of AI fine-tuning to maximise effectiveness and long-term gains.

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Final thoughts

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AI has already demonstrated its potential in improving customer experience and operational efficiency. However, unlocking its full power requires careful planning, investment in the team’s AI literacy, strengthening supporting processes and a commitment to continuous improvement.

For insurance companies and other industries navigating AI adoption, our advice is simple:

• Be bold in experimenting with AI, but realistic about its limitations.
• Ensure high-quality inputs underpin AI initiatives.
• Think holistically about how AI integrates into broader business workflows.
• Plan for ongoing optimisation rather than a one-and-done implementation.

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This is just the beginning of our AI journey. In part two of this series, we’ll explore the change management implications of AI adoption and how businesses can effectively integrate AI into their operations without disrupting their teams and processes. Stay tuned.