The grassroots approach to AI adoption has probably gone as far as it’s going to go.
Point solutions are impressive—but rarely scale. In-house pilots generate strong early results, only for performance to deteriorate for lack of consistent data.
As for copilots, people generally appreciate tools that draft emails, summarize documents, or eliminate busywork. But enthusiasm tends to wane when the tool stops assisting and starts deciding. Handing approval authority to a model can feel indistinguishable from handing over your badge and desk.
That's not irrational. Nobody organizes a grassroots campaign to accelerate their own obsolescence.
The next big push in enterprise AI, when it comes—and I suspect it will come sooner rather than later—will come from above: top down, through the capital structure.
Trillions of dollars are being invested in the infrastructure required to power the AI economy. The institutions providing that capital aren't investing on faith. They have underwritten a thesis: that vast investments in computing, data centers, power generation, networking and models will produce vast economic returns.
For that thesis to work, somebody has to create the demand.
Consider who is financing the AI buildout. BlackRock, Blackstone, Apollo, KKR, Goldman Sachs and other institutions that manage or deploy trillions of dollars are helping finance hundreds of billions of dollars in AI infrastructure. Many of those same institutions are among the largest shareholders of America's public companies, including insurers.
At Travelers, for example, Vanguard, BlackRock, State Street and Fidelity together account for roughly one-third of the company's shares. The financial system is simultaneously financing the supply of AI infrastructure and owning the enterprises whose adoption of that infrastructure will be necessary to justify the investment.
There's an almost poetic financial loop here worth noting: Blackstone's AI infrastructure investments are partly financed with insurance capital. Blackstone now manages $290 billion in insurance capital, while its infrastructure and data-center strategy explicitly connects digital infrastructure with its insurance-capital platform.
All of this means the capital funding AI infrastructure and the capital owning the companies expected to use it increasingly overlap.
Put more plainly: the same capital on your shareholder register may also be financing the infrastructure that needs you to become an AI customer.
The pressure will run downhill. Capital markets will lean on boards. Boards will lean on CEOs. CEOs will hang targets on business units. And eventually the questions will get very specific: What percentage of your workforce is AI-enabled? What's the productivity delta? Where's the ROI? What are your competitors doing with AI that you aren't? What is stopping you from moving faster?
Delivering solid business results is, today, enough. In the near future, how those results are delivered—through what combination of human effort, automation, and AI—will increasingly factor into executive evaluation.
Insurance leaders can prepare now. That does not mean recklessly deploying AI everywhere. It means creating the conditions under which AI can produce durable, defensible business results.
Governance is part of that infrastructure. Done badly, governance becomes an institutional “no.” Done well, it becomes a mechanism for saying “maybe” or “yes,” depending on the use case, the risk, and the evidence.
There is also substantial work between an AI pilot and a legitimate business result. Enterprise information must become usable by machines that can reason and act on it. That means building ontologies, pipelines, object models, knowledge graphs and relational data stores; connecting systems never designed to work together; and establishing the controls that make machine decisions auditable.
None of that is especially glamorous. All of it takes time.
Which is why waiting for questions from investors, analysts, and boards may be the wrong strategy. By the time those questions arrive, you do not want to be explaining why you haven't started. You want to be showing what you're building, what it does, and what it's worth.
The AI infrastructure industry needs demand. Build your own AI infrastructure to create demand for theirs. Build before they ask.
