A Year Ago, I Was Looking Too Far Out
A year ago, I wrote about the future of life insurance as a 25-year horizon. I still believe the core point: the winners will be the organizations that modernize decisioning, customer experience, governance, and trust together.
What changed is the timeline.
The future I was describing no longer feels distant. It feels close enough to shape operating decisions now. Frontier models improved faster than most leaders expected. Reasoning is materially better. Agentic AI is no longer theoretical. AI-enabled coding and software delivery are mature enough to change how quickly teams can build, test, and adapt real business systems.
That does not mean every bold claim about AI will come true on schedule. It does mean the window between experimentation and operating-model change is compressing.
I do not think life insurance leaders can treat this as a long-range planning topic anymore.
What Feels Different Now
Last year, it was still reasonable to talk about AI as a capability to pilot around the edges. Underwriting assistance. Claims triage. Better servicing. Those still matter, but they no longer capture the full shift.
What feels different now is that the underlying tools are broad enough and usable enough to change system design, not just local workflow performance.
Reasoning models can hold more context, make better structured judgments, and support more complex task flows than they could a year ago. Agentic patterns are making it practical to coordinate work across multiple steps, systems, and roles. AI-assisted development is making it easier for teams to translate ideas into working software quickly enough that strategy and execution are starting to move at the same speed.
That combination matters more than any individual model benchmark.
The real shift is not that AI got smarter. It is that the cost and friction of redesigning work just dropped.
Why This Matters More in Life Insurance Than People Think
Life insurance is full of long-cycle decisions, fragmented processes, and trust-sensitive moments. That used to make change slower. In some ways, it still does.
But it also makes this industry unusually exposed to a capability jump.
When better reasoning can improve underwriting support, when agents can orchestrate servicing across systems, when software teams can rebuild internal tooling in weeks instead of quarters, the bottleneck moves. It is no longer primarily technical feasibility. It becomes leadership clarity, operating-model readiness, and data discipline.
That is why I think some incumbents are at risk of misreading the moment. They see the regulatory complexity and legacy architecture and assume the pace of change will stay slow.
I think the opposite is more likely. The institutions that figure out how to combine trust, data, and faster execution will widen the gap quickly.
My Current View of the Next 12 to 24 Months
I do not think the next year or two will be defined by one dramatic breakthrough. I think it will be defined by compounding operational advantages in a few specific areas.
- Underwriting and risk assessment will become more adaptive. Not because humans disappear, but because the decision support around them gets materially better. More context. Better summarization. Stronger pattern recognition. Faster iteration on rules, models, and evidence.
- Service operations will change shape. The combination of conversational AI, workflow orchestration, and better reasoning means policy servicing does not have to feel like a maze of handoffs and status checks. The winners will reduce friction without turning the experience into automation theater.
- Product and distribution teams will have more room to experiment. If internal software delivery keeps accelerating, carriers will be able to test new experiences, advisor tools, and servicing models faster than their old delivery cadence allowed.
- The bar for leadership decisions will rise. When teams can build faster, leaders cannot hide behind slow execution as an excuse for unclear thinking. Strategy gets exposed faster. So does organizational confusion.
My explicit stance is this: the next competitive advantage in life insurance will come less from having access to AI and more from being able to reorganize around what AI now makes possible.
What I Am Less Convinced About
I am less interested today in broad future-state speculation for its own sake. I care more about which assumptions are breaking right now.
For example, I am less convinced that long-horizon platform roadmaps are the right anchor for this moment. If AI-enabled development keeps improving, some of the build-versus-buy decisions organizations locked in a year ago may need to be revisited.
I am also less convinced that incremental pilots are enough. They still help teams learn. But if the tooling has reached the point where operating models can move, then endless piloting becomes a way to avoid redesign.
And I am less convinced that traditional role boundaries will hold. The teams that win here will blur old lines between product, engineering, operations, data, and risk. Not because governance matters less, but because coordination matters more.
What This Changes for Leaders Right Now
If I were advising a life insurance leadership team today, I would push on four questions.
The leadership challenge is shifting from predicting disruption to absorbing it.
- First, where has the timeline in your strategy quietly changed without the strategy changing with it?
- Second, which internal constraints are still real, and which ones are now habits protected by old delivery assumptions?
- Third, where can reasoning models, agents, and faster software delivery create a compounding advantage if you redesign the process instead of automating the current one?
- Fourth, does your leadership team actually have a shared view of what the next two years could look like, or are different functions still planning against different futures?
This is the part that matters most to me: the leadership challenge is shifting from predicting disruption to absorbing it. The companies that move well will not be the ones with the best slide deck about AI. They will be the ones that change decision rights, team structure, tooling, and measurement fast enough to match the new capability curve.
The Future Stopped Feeling Abstract
When I reread what I wrote a year ago, I do not think it was wrong. I think it was early.
The direction still holds. Life insurance is going to keep evolving around better data, better decisioning, better customer experience, and stronger trust. What changed is that more of that future is now within reach of ordinary execution, not just long-range planning.
That should change the tone of the conversation.
This is not just about preparing for the future anymore. It is about recognizing that parts of the future are already here, unevenly distributed across organizations, and moving faster than many leadership teams are prepared for.
Questions Worth Sitting With
Which part of your 3-year roadmap is already outdated because the capability curve moved faster than expected?
Where are you still planning for AI as a feature instead of planning for it as a redesign pressure on the business?
What would you rebuild differently today if you assumed reasoning, orchestration, and software delivery will all be materially better again a year from now?
And if that is true, what are you waiting to change now?