AI-Enabled Transformation: Game-changer or Hype

AI has become one of the most transformative technologies of the 21st century. Its ability to significantly improve efficiency, enhance decision-making and boost customer experience is shifting the way we manage transformation. But as its application expands, so too does the debate that surrounds its true potential.

Insurers are quickly getting to grips with the transformative power of AI to enhance customer experiences, deliver new insights and streamlined operations. Nearly all are in the process, or about to launch, the next wave of digital transformation initiatives to unlock new capabilities across their businesses. Though the track record of successful transformation is bleak - with estimates that between 70-90% of business transformations do not achieve their desired outcomes - a lot has been written about the pitfalls to avoid. Without trivialising their value, in my experience, these ideas are largely common sense but difficult to execute in practice given the multi-disciplinary teams and competing resource pressures that underpin many insurer’s efforts.

So how can AI have an impact? 

1. Technology conversion 

The most significant impact of AI to date has been through enhancing the speed and quality of transformation in legacy technology conversions, several off-the-shelf solutions already exist. The capabilities on offer can accelerate development cycles in migrating old to new code, enhance test procedures, and streamline the production of supporting documentation. Insurer’s massive data migration efforts can also be streamlined, as AI provides intelligent data quality analysis and automated mapping, transforming a painstaking process into a seamless transition. The results? A significant potential reduction in workload on some of the projects I have worked on of 80% or more.  

2. Change management and continuous improvement 

Change management and continuous improvement are areas ripe for AI's contribution, yet its potential remains largely untapped. AI can forecast training needs, gauge stakeholder engagement, and generate communications. A range of AI change management tools are emerging that incorporate solutions to improve the transparency of change including, chatbot services on change programs, knowledge base management and integration with popular collaboration tools. This will free up change managers to focus on the ‘human’ aspect of change and be better equipped to address these needs, all of which are critical for a seamless transition to the new ways of working.  

3. Process Design  

The insurance sector has yet to fully grasp how AI can improve solution designs and optimise process flows. Indeed, this is a developing field, and while AI can conduct workflow analyses to spot inefficiencies and simulate improvements, ensuring that the proposed changes translate to tangible operational performance gains is still elusive. In our view, this is primed for a human-in-the-loop approach. Having humans collaborate directly in the calibration approach will lead to the best outcomes in terms of accuracy, reliability, and flexibility, then relying solely on an AI approach. Additionally, it is important to carefully weigh up the costs and benefits of implementing these AI-driven changes. Despite these reservations, there are impressive examples of how AI can be used effectively, especially in setting up robust processes for monitoring and optimising the management of IT resources. 

4. Project Management  

AI-enabled project management tools are already a reality, using predictive analytics to help keep projects on course. These tools can dynamically adjust schedules, allocate resources, and flag risks, with potential solutions, before they crystalise into issues. While not an autopilot for effective project management, they serve as invaluable aides that provide insights to, and challenge, project teams to think differently and improve on existing project management tools and approaches.  
 

The Strategic Integration of AI 

For insurers to fully harness the potential of AI to support transformation, it needs to be strategically integrated from the start. This requires a paradigm shift: moving away from traditional methodologies and towards a mindset that embraces AI-enabled transformation. It's about knowing when AI adds value and when it could introduce unnecessary complexity. 

In my view, as we look ahead, there's a glimmer of hope that by leveraging AI responsibly, alongside a considered approach to addressing common transformation pitfalls, we will eventually see a reversal of the current bleak statistics — where, in the new world, most transformations will succeed and deliver value to insurers and their customers alike.