If your GenAI programme is a chat tab people copy and paste into, you haven’t changed the work. You’ve added a step. That step is the tax: switching screens, rewriting, checking, and then typing it all back into the system that actually runs the job.
That "extra step" was cool at first, now its everywhere and annoying. In insurance, it becomes a problem fast. You’re making regulated decisions from messy evidence, on top of old cores, with customers who escalate the second something feels unfair. A chat helper on the side doesn’t hold up for long.
So 2026 is when the pilot era ends. Not because the tech turns into magic, but because budgets, risk teams, and ops stop paying for toys.
Here are my 10 calls for 2026 and what they mean for insurers (retail follows the same logic, but insurance will feel it first).
Copilots help people write and summarise. Fine. But they don’t move a claim or a quote.
The pattern that wins is an agent that owns a narrow workflow end-to-end.
In 2026, teams stop paying for “assistants” and pay for workflows with controls.
The model isn’t the blocker. Integration and control are.
Most failures are dull: wrong permissions, weak tools, brittle exception handling, no audit trail.
If an agent touches claims, payments, declines, fraud flags, or pricing, you need clear answers:
In 2026, “agentic” stops meaning “cool demo” and starts meaning “production service”.
GenAI spend is messy: tokens, retrieval, vector infra, monitoring, humans in the loop. If you can’t tag cost to a workflow, every budget review turns into a fight.
Insurers move to hard unit metrics:
In 2026, programmes that can’t show unit economics get cut. Programmes that can, get rolled out.
You can’t scale what you can’t see. Most failures aren’t one big model mistake. It’s the slow bleed: latency, rework, hidden safety issues, low adoption.
A control tower becomes mandatory because it’s how you defend decisions to risk, audit, and regulators.
Minimum view:
In 2026, “we shipped something” becomes “we run it like ops”.
Model capability is converging. The gap is whether your system can pull the right evidence, with the right permissions, and show it cleanly.
Insurance data is fragmented and versioned: policy wordings, endorsements, claim notes, bordereaux, third-party reports, emails.
If your context layer is weak, your agent will:
In 2026, the boring plumbing becomes the main work: governed data products, semantics, permissioning, retrieval quality, and eval sets built from real claim chaos.
Text-only GenAI hits a wall in insurance because the expensive work is visual and document-heavy.
Multimodal is where you get the big wins:
In 2026, insurers that stay text-only will look busy while competitors remove layers of manual handling.
Most governance today is paperwork. It slows delivery and still doesn’t prevent incidents.
In regulated workflows, governance has to be built in:
In 2026, the fastest teams are the ones with repeatable governance, not bespoke politics.
Arguing about hallucinations is like arguing whether software has bugs. Yes. The point is how you design around it.
Mature patterns look like:
In 2026, anything that can’t justify outputs won’t be allowed near customer-facing or decisioning workflows.
Claims ops and underwriting support are drowning in admin. They will build helpers. Blocking them just pushes it into shadow AI.
Without guardrails, you get:
In 2026, winning insurers give people a safe runway:
Cost-out gets you in the room. It also caps fast. The lasting budget shows up when GenAI drives commercial outcomes.
The growth plays that stick:
In 2026, insurers that connect GenAI to growth keep momentum. Everyone else becomes a perpetual pilot factory.