How to successfully integrate AI into your business

Introduction: the first step’s often the hardest

Whether it’s forking out for expensive software and not deploying it effectively, failing to define ROI or not engaging their people, organisations experience all sorts of challenges on their digital or AI journey. We’ve helped insurers of all sizes overcome these challenges to successfully integrate AI into their business. Here are our tips for a successful implementation, along with examples from our client base.

1. Define the business case

It’s the first and most obvious question: what are you trying to achieve? It’s no use spending big money on an AI platform without understanding the specific business challenges that you need to address. Instead of aiming for a vague improvement in efficiency, for example, set yourself a specific target of speeding up business analysis work by 30%.

Your purpose for bringing in AI has to align with your overall business objectives, whether it’s improving customer experience or cutting operational costs. You’ll also need executive buy-in. The board have to understand why you’re doing it and be aligned on their vision. If you can’t agree at leadership level, you have little chance of deciding on a strategic direction or getting your people onboard.

A digital-first global insurer and reinsurer has successfully implemented AI in its change function to improve internal speed. By building out storyboards for its business analyst user stories, it’s speeding up every part of the BA process and journey. This is all about optimisation and cutting costs within the change and transformation function. With AI agents deployed across half the organisation, they’ve now moved on to deploying agent bots outside the customer journey.

2. Adopt an integrated approach

Too many business functions undertake AI implementation projects in isolation, without a clear end goal or a plan to involve the rest of the business. We’ve seen clients buy software off the shelf, work with third parties, and start integrating and tooling without even knowing if the technology’s a good fit. This often results in low user adoption and understanding of the objective.

In one extreme example, a listed insurer spent over £100m on AI related initiatives without any idea of the ROI.  This is not fixing a particular problem – it’s investing in a platform and then trying to figure out what to use it for. What’s needed is a joined up approach connecting business units and people, with a clearly defined and well communicated objective.

3. Get the data right

You need comprehensive, consistent and quality data before you can implement an accurate and reliable AI system. Our clients have experienced challenges when their data lakes have not been formed in a way conducive to scaling AI across the organisation – particularly in the case of those which are part of a group. In theory, one centralised business technology hub means one source of data.

A global leader in insurance broking and risk management carried out a significant rationalisation of their existing estate as part of a five year strategy, to get as close as possible to that one true source of data across the organisation. Once you’ve developed a true tech and data hub that gives you the foundations for successful AI implementation, it’s then about where you position that hub – whether it’s with your technology team, innovation team or VC hubs.

4. Put your people first

There’s nothing more daunting for your workforce than having a significant piece of technology thrust upon them and being expected to adapt, without any explanation of the purpose or even how it will affect their roles. Adopting a people-first approach has been hugely effective for clients who have successfully delivered a complex tech transformation project.

This means understanding why you’re delivering this project and what it means for you and your people – and you can only achieve this by talking to them. Employee surveys will help you understand how your people are feeling about the implementation and fine-tune the platform to better suit their needs. Focus groups are an effective way of ironing out their concerns.

When undertaking a major implementation, a leading global insurance broker invested in a comprehensive training programme to bring its people on the journey. The focus was on demystifying AI, clearly articulating what it means for employees, and outlining every individual’s role in the transformation. Crucially, the programme emphasised the positive impact on day-to-day work and long-term career development, reinforcing that this is everyone’s project - not a top-down initiative imposed by leadership. By doing so, the firm created genuine engagement, ownership, and cultural alignment across the business.

Conclusion

When it comes to implementing AI, knowing where to start your journey is often the toughest step of all. But by identifying your goals, having a joined up approach, getting the data right and involving your people, you can successfully implement the right platforms to automate your processes, make better decisions and free up your people to focus on non-repetitive tasks.

As we strive to increase our understanding of the impact of AI in insurance, we’re conducting a survey of insurance leaders to understand how they’re utilising AI and how it’s affecting their organisation. The results will form the basis of a detailed report which we’ll be releasing later in the year. It would be great to have your input so if you have a few minutes to spare, please complete our survey here.

If you’d like more advice on successfully integrating AI into your business or you want to talk about finding the leadership talent you need to help deliver a tech transformation project, please get in touch with us for an informal conversation.