Using Ai To Elevate People, Products And Decisioning

Consider this:

• 71% of non-federal acute care hospitals use predictive artificial intelligence integrated directly into their electronic health records.
• 76% of SaaS companies use AI in their products.
• 80% of marketers leverage AI for content creation.

Meanwhile, only 34% of insurance companies have fully adopted AI into their value chain. Although that’s up from 8% the previous year, it still lags far behind other industries. Given insurance’s reliance on complex data and risk assessment, adopting agentic AI should be a top priority for any industry player serious about long-term success. There are three key areas where it can elevate organizations across the insurance ecosystem:

• Scaling talent performance
• Innovating key products
• Transforming workflows and decisioning

Amplifying your top talent

In every industry, we lament that we can’t clone our best talent, but that constraint is starting to change. Advances in AI are making it possible to scale the impact of high-performing individuals, reducing the limits of human endurance, multitasking and even language barriers. At the center of this shift is the rise of digital twins.

Traditionally used to simulate physical systems such as jet engines or race cars, a new class —professional digital twins — focuses on cognitive behavior. These models replicate how top performers think, make decisions and communicate, capturing their expertise at scale. Although the implications are real, this shift doesn’t have to replace people; it can elevate both employee performance and customer experience. Let’s explore what that looks like in practice.

• Real-time augmentation of live work: A risk adjuster can instantly tap the top AI-trained analysts to get guidance, identify gaps, and receive a qualified go/no-go recommendation.
• Scenario-based sales training: Sales teams can engage with “super seller” models to handle objections, refine messaging, and even be pitched to understand high-impact narrative structures.
• Self-service tools and knowledge bases: AI-powered tools enable scalable self-service for customers, employees and partners, creating a “freemium” model in which foundational knowledge is broadly accessible and further enhanced in higher-value interactions.

Evolving your product capabilities

The most powerful aspect of AI is its ability to process complex scenarios across broad data sets quickly and effectively. Imagine creating standardized insurance solutions and real-time markets for highly bespoke risks. How do you insure a college basketball player’s future earnings if they return to school for another year? What about assessing the risk of non-petroleum cargo entering the Gulf of Oman? Or underwriting the thousands of satellites planned by Starlink and Amazon?

These are not edge cases; they reflect a broader shift. Risk is becoming more dynamic, interconnected, and difficult to model using traditional approaches. Static underwriting and historical data alone are no longer sufficient to keep pace with the speed and complexity of modern markets.

The possibilities are expanding as scenarios grow more complex. New markets are emerging —from drone-powered aerial delivery to autonomous fleets from Waymo, Uber and Zoox, to micro-nuclear facilities built by private organizations. If AI is not at the core of your product strategy, competing at scale will not be viable. You will need to either build these capabilities or access them through partners.

Systematizing knowledge, workflows and decisioning

As AI reshapes product capabilities, it is transforming how knowledge is captured, workflows are executed, and decisions are made. In the era of AI, organizations can automate manual tasks, enable self-service through digitized knowledge, and accelerate transactions across operations. AI not only improves efficiency but also enables more consistent, scalable execution across core workflows. From this, three key takeaways emerge:

• Knowledge digitization is foundational. Beyond digital twins, “digital memory” captures institutional knowledge and applies it in real time. Imagine a claims adjuster assessing a vehicle with instant access to similar cases and recent repair costs—driving faster, more consistent decisions using capabilities that already exist today.
• Automated workflows offer the most immediate return on investment. Repetitive tasks with standardized processes and approvals are prime for automation, improving efficiency and enabling organizations to redeploy talent, invest in growth or enhance margins.
• Autonomous decisioning is where the industry can leap ahead. Gartner estimates that 15% of decisions will be made autonomously by 2028. Given its reliance on benchmarks and repeatable scenarios, insurance is primed to expand the use of autonomous decisioning across straight-through processing, damage estimation, and scalable underwriting.

An inflection point

The stakes are high, and the economic benefits are significant. McKinsey estimates that generative AI could unlock $50 to $70 billion in annual revenue for the insurance industry. At the same time, AI is freeing underwriters and claims adjusters from tasks that once consumed up to 40% of their time. As McKinsey states, the impact is clear: U.S. managing general agencies have doubled premiums from $47 billion to $97 billion by using AI to cut quoting times, lower acquisition costs and improve margins.

So why does AI fatigue already seem to be taking hold? Because ambition has outpaced the underlying data and knowledge infrastructure required to execute. Moving from prototype to production requires building real systems that can capture and deploy institutional knowledge at scale, enforce governance and compliance amid uncertainty, and adapt as interfaces and user behaviors continue to evolve. Without that foundation, most efforts stall before they deliver meaningful impact.

Pick your phrase — we’ve reached a tipping point, critical mass, a point of no return. This moment demands real work and investment, and the cost of inaction will be far greater for those left behind.

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