Success with artificial intelligence (AI) requires more than technology. Insurers should address seven critical workforce, culture, and process factors to avoid costly missteps and unlock AI’s full potential.
A newly built home can be a beautiful creation, full of the owner’s long-held dreams – a custom kitchen, an in-home theater, an expansive finished-basement playroom for the kids.
Yet it is the far less eye-catching preparation work that allows the home to exist – the precise land grading, the carefully constructed structural drawings, the properly designed ventilation system.
For businesses, artificial intelligence (AI) can be the equivalent of that beautiful new home. But if executives focus on fancy features without first doing the foundational work, such metaphorical homes are more likely to crumble.
And crumble they do. A scrapheap of failed AI initiatives is starting to grow, much like the pile of dot-com disasters from late last century.
A recent MIT study found that 95% of GenAI pilot projects are failing.1 Many organizations are finding it challenging to harness the power of AI. Worse, they are failing to find much, if any, business value in their AI expenditures.
Insurance executives can navigate these pitfalls. By addressing seven workforce, culture, and process needs now, the likelihood of success can grow exponentially.
Being excited, rather than scared, about what AI can do is essential, and that mindset starts at the top. It is an insurance executive’s role to generate excitement about AI’s potential.
Employees should feel comfortable exploring what AI can and cannot do. For those working in companies where leadership rarely discusses the topic or, worse, where it is talked about as the ominous storm on the horizon, take the opportunity to evangelize and educate your leadership about AI.
Think of AI like a diamond. The most important process in diamond formation is pressure. Apply too little pressure and a diamond does not form; apply too much and the diamond breaks.
A similar concept applies to AI. If AI capabilities are placed too far from the business, they are more likely to struggle and generate little value. If they are put too close to the business, they are more likely to wilt under the weight of unrealistic expectations.
Insurance executives who find the sweet spot for AI capability – where it is close enough to, and aligned with, both business and technology – will be more likely to lead AI initiatives that thrive.
It is easy for insurance leaders to see how AI can be used in any process, but this is a trap. Those who fall into this trap tend to create many proof-of-concept projects that deliver little value. Further, these projects divert energy and resources from key initiatives that could grow their organizations.
Those initiatives are the big rocks. To find AI success, insurance executives must identify these select big rocks along the value chain and use AI where appropriate to help push them up the hill.
For example, RGA is an industry leader in biometric risk expertise. AI has been used for decades to hone that expertise. Perhaps unsurprisingly, several of RGA’s AI initiatives directly target this core strength.
In Europe, the Middle East, and Africa (EMEA), RGA has worked with insurers and bancassurers to combine their data with its biometric expertise to expand the availability of financial protection. Some examples include:
Wherever an insurer’s big rocks might lead, the biggest piece of its AI investment should follow.
Succeeding with AI requires continuously educating employees on AI essentials:
But AI education goes even further. It also helps break down some of the biggest barriers to adopting and succeeding with AI, chief among them:
Insurers can start with the basics. The key to getting the most out of GenAI today is to craft effective prompts. Not surprisingly, the three most popular courses in RGA’s Data Skills Academy are how to use GenAI tools, how to write effective prompts, and how to apply advanced prompt engineering techniques.
People in AI and technology speak a variety of languages – Python, R, and SQL, for example. People in business speak a different language, often full of accounting terms and business drivers.
To succeed with AI, at least one person from each side needs to speak the same language. AI leaders need to translate their knowledge into clear, non-technical terms common to the business and its leadership. AI cannot exist in a silo as a foreign entity with a language unto itself. It must meld into the wider business culture.
A building cannot stand on a weak foundation for long. AI has four foundational blocks for creating a strong, secure future.
There is nothing more demoralizing and detrimental to AI success than not knowing how it performs against quantifiable metrics or how to measure return on investment. Clear objectives that measure value are critical.
It can be difficult to estimate an AI initiative’s impact on the top or bottom line, but unclear results and ambiguous ROI will make it nearly impossible to gain the trust and funding necessary to continue AI’s transformative journey.
For example, in Europe, the Middle East, and Africa, RGA uses its value framework to prioritize and fund AI initiatives, monitor progress, and measure value to RGA and its clients. This is replicable in other regions. That ability to measure value, together with the focus on the big rocks, makes it possible to clearly state results and keep all stakeholders excited about what further AI applications could mean for the company.
AI will continue to transform our world – and the insurance industry. Succeeding with AI goes beyond data, algorithms, and technology. It starts with the realization that the most important driver is us – the human beings who can and should build the proper foundation to enable AI to live up to its life- and industry-changing potential.
Contact us today to explore how an AI-driven partnership with RGA could drive business results at your company.
