Two ideas simultaneously exist in strong opposition to one another like a messed-up Schrodinger’s cat: AI projects both fail and hold tremendous potential value.
In its “Rewired and running ahead” article, McKinsey shares that insurers considered AI leaders outpace AI laggards by six times total shareholder return. Ironically, Frank Landymore wrote in Futurism, McKinsey is also “terrified” that AI can do its job perfectly.
Conversely, MIT reports 95% of generative AI pilots are failing. That accounts for millions (if not billions) of wasted dollars.
An IDC report, commissioned by SAS, points to obstacles like fragmented data and data governance as reasons why even traditional AI projects never make it off the ground (Source: IDC eBook, sponsored by SAS, Data and AI Impact Report, EUR153787025, October 2025).
This tension between success and failure is cause for concern. The good news? You’re not alone.
Whether you’re a leader or an individual contributor at any size insurance company, feeling unprepared and overwhelmed is the status quo. AI capabilities are advancing rapidly, and it can be easy to feel like you’re being left behind.
Unsure where to start your AI transformation?
The path to successful AI starts with your approach to data and analytics. It can help to start small, building on existing strengths so you can experiment with AI tools, exercising existing muscles and developing new ones along the way.
Another strategy is to have trusted partners on board for your AI journey. They should understand your business, your strengths, and your level of maturity with data and analytics.
The right partner can accelerate your AI journey by finding practical ways to connect your organization’s capabilities and your individual goals to new and emerging technologies. A good partner can also help refine new or existing algorithms and processes, positioning you and your team to take advantage of the wave of AI capabilities on the way.
SAS draws on deep, hands-on experience working with insurers for more than 50 years to help you prepare for what’s next. To illuminate a path forward to an AI future, we’ve crafted the AI blueprint. Check it out to discover how the blueprint aligns vision with strategy and guides you in implementing AI initiatives that are both scalable and trusted.
The AI blueprint: A leader's guide for organizational trust and ROI during rapid change
You too can AI
Insurers are not just thinking about AI, they’re also budgeting for AI projects and planning for AI use cases – even if it’s unclear how they will successfully implement such projects. Consider the example of generative AI.
Despite a high percentage of GenAI pilot failures, 92% of respondents to a global GenAI in insurance survey shared that they were budgeting for GenAI projects. Their investments aimed to:
- Improve customer satisfaction and retention (81%).
- Reduce operational costs and increase time savings (76%).
- Enhance risk and compliance measures (72%).
At the same time, many respondents said they lacked sufficient budgeting for GenAI governance – and most governance frameworks were still in development.
Going all in with GenAI is no surprise, especially when we consider the results of the new SAS report. This new global research reveals that survey respondents scoring low on trustworthiness trust GenAI 200% (3x) more than traditional machine learning. That’s true even though machine learning is mathematically explainable and far more transparent.
The GenAI paradox reveals a humanlike bias: People tend to trust AI that feels intuitive and conversational, even when they know it may not always be accurate.
Business value of AI for insurance
Using AI in insurance can help you reduce manual tasks, analyze large data sets to detect patterns for better decision-making and enhance user experience. From a business value lens: Our new global research shows that nearly two-thirds of insurers place process efficiency and effectiveness as the highest AI value.
In this new research, 33% of insurers said creating an AI strategy road map was one of their execution priorities. But these same insurance organizations reported modest overall AI maturity. Only 7% described themselves as “transformative” (the lowest of all industries surveyed). And 14% remain siloed in their data infrastructure.
AI will not only yield better outcomes for your business – it will also make individuals more valuable in their careers. As Jensen Huang, Nvidia’s CEO, shared at a commencement speech at National Taiwan University, AI won’t steal jobs, but “someone who’s an expert with AI will.”
AI governance is essential for success with AI. Learn about a proven AI governance maturity model and how to move ahead.
Take the AI governance assessment and get customized recommendations
Make sure you’re ready for AI
Evaluating your existing data and analytics technology is a great way to assess your readiness for AI. Where do you already have strengths that might bolster your AI strategy?
Consider that most of the work required to enable AI will focus on data. That entails gathering it, prepping it, analyzing it, creating visualizations, and understanding relationships, trends and outliers in the data. These are all areas where you can begin assessing your organization’s AI readiness today.
Once your assessment is complete, you can start testing the waters. Remember, you don’t have to push the accelerator to the floor. With machine learning, computer vision, natural language processing, or other forms of AI, you can shorten the new policy acquisition process, settle claims or even fight fraud.
Prepare for the promised land
Every successful journey starts with good preparation, and AI adoption is no exception – similar to cloud computing and the emergence of the internet itself.
The World Economic Forum issued a statement two years ago that 25% of all jobs would change in the next five years (due to AI). McKinsey and Goldman Sachs corroborate this estimate with similar figures, and a recent Forbes article further extrapolates the impact to be as high as 60% of the global workforce by 2050.
It’s safe to assume that by 2040, every insurer will be an AI company. By then, insurance firms will rely on collaborative AI teams working alongside humans to achieve their organization’s goals. Prepare for the journey on your own terms, which ultimately reduces the risk of missteps along the way.
Jensen Huang’s quote merits revision: “Companies using AI will thrive, while others will fail.”
Which path will you choose?
