I was in deep thought reflecting on the recent AI update while slogging through a long list of AI use cases. The list included a quite a few transformative ideas but also included a lot of practical, focused, yet narrowly defined, use cases: “Use AI to summarize claims notes.” “Use generative AI to draft customer emails.” Efficient? Absolutely. Valuable? Probably. But a question surfaced—and stuck.
Are we building toward real transformation, or just checking off AI to-dos?
That sparked something. Maybe we’ve unintentionally narrowed our lens. Maybe we’ve started asking, “Where can we use AI?” when we should be asking, “What kind of business, process or experience are we trying to build—and how can AI, automation, and data help us get there?”
That’s not semantics. It’s strategy.
As a result, I switched gears from reviewing the list of use cases to putting one of the narrowly defined, yet valuable, use cases into action—using AI to help me transform my thoughts into a well-crafted message. There’s more “Sean” in here—minus the typos and sentence fragments—but the structure and formatting is all ChatGPT. Hopefully that blend works.
We Can’t Forget About Today But We Need to Stop Optimizing the Old Map
It’s tempting to treat AI like the strategy itself. But it’s not. AI is a power tool, not the house plan. When we anchor transformation around a list of AI features, we risk installing smart appliances into crumbling frameworks. The result? Faster output, but not a better business.
Instead of asking, “How do we use AI?”—what if we asked, “If we were building this company, capability, or process from scratch, with today’s technologies, what would we do differently?”
That’s a reframing that unlocks real transformation. It pushes us to rethink—not just automate—how we underwrite, serve, and engage.
Transformation = Vision × Orchestration
Let’s make this tangible. Real transformation in a life insurance business might include:
- Process Reinvention: Moving from siloed handoffs to AI-enabled, event-driven workflows—where automation, human judgment, and compliance logic operate in sync.
- Product Evolution: Offering dynamic policies tailored to real-time behavioral and wellness data, rather than static actuarial models.
- Customer Experience Reimagination: Designing empathetic, multi-channel journeys that respond fluidly to life changes—not just life events.
- Data & Platform Strategy: Leveraging APIs, orchestration layers, and personalization engines to turn insights into actions, not just dashboards.
AI is a catalyst in each of these—but it’s the orchestration that makes them sustainable and scalable.
Don’t Just Use AI—Build Around It
Let’s widen the lens. If we want to create something fundamentally better, we need to think in systems, not features. AI doesn’t operate in a vacuum—it thrives when connected to the digital plumbing of the business.
Here are a few examples of other digital building blocks that bring AI-powered transformation to life:
- Digital Experience Platforms (DXPs): These create seamless, consistent experiences across mobile apps, websites, and human interactions. Without a DXP, all the AI insights in the world won’t translate into action where it counts.
- Wearables (IoT): Smartwatches and fitness trackers are becoming pivotal in life insurance. They offer real-time health data that enables personalized risk models and dynamic engagement—key to product innovation and proactive service.
- CRM and Personalization Engines: You can’t personalize what you can’t operationalize. AI might flag a customer as likely to lapse, but unless your CRM can act on that signal in real time, it’s just another missed opportunity.
When we use these tools together, we are not just doing things more efficiently—we are doing fundamentally different things. That’s the shift from optimization to reinvention.
Risks Scale with Capability
We can’t talk about transformation without talking about risk.
- Cyber threats are escalating—with AI being used by attackers as much as defenders.
- Fraud patterns are mutating. Deepfakes and synthetic identities aren’t fringe—they’re here.
- Reputational risk is exponential. One misjudged automation, one model bias, one tone-deaf chatbot—and trust can evaporate.
- Regulatory uncertainty is increasing. Compliance can’t lag behind innovation; it must evolve alongside it.
In other words: the more we transform, the more deliberately we must design for safety, equity, and resilience.
Checklist or Compass?
Here’s a simple lens I keep coming back to:
- A checklist says: “We’ve deployed 15 AI use cases.”
- A compass says: “We’re building a business that’s faster, smarter, and more humane—enabled by AI, automation, and data.”
This is our moment to choose. Not just what tools we use—but how we use them, and to what end.
Leadership Means Asking the Bigger Questions
You don’t need to be an AI engineer to lead this work. But you do need to ask the right questions:
- Are we trying to optimize what we already do—or rethink what we could do?
- What value are we unlocking—for customers, for employees, for the business?
- Are we embedding accountability, ethics, and security by design?
- Are we building a resilient system—or just stitching together shiny features?
These questions don’t slow transformation. They focus it.
Final Thought: Let’s Not Just Use AI. Let’s Rethink the Business.
AI is one of the most powerful tools we’ve ever had. But it’s still just a tool. The opportunity isn’t in the tech—it’s in the house we now have the chance to build with it.