Insurance's value is a myriad of things. Insurers' problems are, too.
We can't move without insurance, yet we don't trust it and often don't value it, either. It's a cost, a necessary evil, essentially a direct debit on the balance sheet of our lives and businesses we would rather not have.
Here we are at the tipping point where math and neurons can think for us, and at levels of "intelligence" we are often told we can't even comprehend. Despite this, most of what we are artificially trying to make more intelligent is simply what we do today. And to many of us, this doesn't seem right at all.
The issue for strategic thinkers remains "value chain" thinking, where we focus on minimizing costs and maximizing distribution (channels, coverage, capacity). This puts us at a permanent disadvantage, where new value, through new working models in new technology, is pushed aside for cost savings and efficiency. Worse, when we try to do this with prediction token engines, we are constantly backpedaling because we live in an industry that needs us to be highly deterministic. This is one of the key reasons we remain in pilot purgatory with AI far too often.
We need to solve the meaningful problems we face and start to evolve our business and technology architectures into ecosystems capable of maximizing the knowledge of a customer (and their risks) and acting on this as near to real time as needed.
To do this, we have to address major issues or misperceptions:
- Many insurers are building houses on sand by layering AI over a "messy middle" of fragmented data and customer-blind legacy processes. AI isn't a repair kit for insurers' broken business models.
- If we apply AI to a fractured, policy-centric design, we just get fractured, policy-centric mistakes - at scale and at speed. We are simply automating the friction, industrializing the silos, and alienating the customer faster than ever before.
- The insurance industry is obsessed with plugging in AI, but it's still in pilot purgatory. And that's because layering GenAI over outdated data structures and silos means we aren't innovating; we're building a house on quicksand.
Framing the answer to this paradoxical state
This is, therefore, an enterprise design problem, where policy-centric architectures have to give way to customer-centric enterprises.
Building AI into this new model is vital, but so is building in risk, regulations, compliance, auditing and legal. If things move in real time and intelligently, so will all these things as well.
We need to move from a "data & AI" strategic frame where these things become almost self-serving toward an "intelligent" business model, where data is seen as a perishable asset, constantly mined for insight and acted on as close to real time as is needed, but in a controlled, deterministic and responsible way.
To make this possible, we need to deal with the messy middle. That's because operations in insurance are the big unlock - where the magic (or the misery) happens. If the middle is a black box of manual hand-offs and disconnected spreadsheets, AI will choke on it anyway.
Insurance is a process-heavy industry, one where simply making a claim also means the insurer understands the wider context we are in, that it will focus communications on the best resolution path, that other communications or needs are sympathetically managed in this context, like a repairer, and so on. It's multi-faceted, and the operations, customer experience, and data that weave it together need to be symbiotic. We are at the point now where operational efficiencies and better customer experiences are mutually beneficial, and not the opposing forces they are all too often seen as.
To get to the end state where AI actually works and starts to create new value, we need an evolutionary model to aim for. And we need to clean up this messy middle and orchestrate the flow of outcomes more intelligently - I tend to call this intelligent orchestration. Systems of intelligence are hyped and relevant, but systems of outcome are needed to make them count.
In conclusion
Foundationally, we need a robust data orchestration layer (not more data storage), but insurers need a unified data model, built around the customer. Data should be fluid, so events are available and usable when they need to be.
Insurers need to be able to interoperate agents, with telemetry across their estates, all the way into employee and customer use. And they need a deterministic framework that harnesses agentic solutions and ensures human intervention. But it also needs to be deliberately designed to maximize human interaction when it's needed.
AI is an outcome, not the goal, and once insurers solve the enterprise design problem and move from policy-centric to customer-centric via intelligent orchestration, AI likely becomes the hero. A hero they can control, manage the risk of, and interoperate and adapt at will.
