By Jonathan Rusby, Director of P&C, INSTANDA
At Insurtech Insights Europe 2026, the most interesting shift wasn’t a new AI demo or a bold prediction about autonomy. It was something quieter, and fundamentally more important.
The industry has stopped talking about whether AI works and started talking seriously about how it scales and adds real value.
Across panels, workshops and side conversations, the message was consistent: pilots are no longer the problem. Execution is, and for large insurers in particular, scaling intelligence is less about AI’s capability and more about the operating model that sits underneath them.
In other words, becoming “AI-ready” isn’t an AI initiative at all. It’s a question of whether an insurer has built a change engine capable of moving quickly, safely and repeatedly across products, portfolios and partners.
From Pilots to Performance: What’s Changing
For the past few years, AI has dominated conference agendas. This year felt different.
The framing moved decisively from experimentation to outcomes; from “what’s possible” to “what actually works at scale”. Sessions focused on value realisation, governance, interoperability and trust. Even discussions around agentic AI were grounded in practical realities: redesigning workflows, rethinking data flows, and maintaining human judgement in decision‑making.
That shift was illustrated particularly well during Tim Hardcastle, Cofounder and CEO of INSTANDA,’s fireside chat with Jason Howes, Chief Transformation Officer at Allianz UK. The conversation was notably optimistic about AI’s role in the future of insurance but grounded in the realities of what it takes to make that promise deliver at scale. Rather than treating AI as a standalone breakthrough, they spoke about the importance of creating the right conditions for it to succeed: modern, flexible architecture; clear use cases tied to real business outcomes; and operating models designed to absorb continuous change.
Why Enterprise Insurers Feels the Pressure First
The industry constraint is no longer innovation itself. Insurers are not lacking in ideas and technology options; what they continue to wrestle with is industrialising change: converting ambition into action without adding fragility, risk or complexity. For many insurers, this challenge is amplified by the inherent complexity of their operations, including:
- Portfolios made up of thousands — sometimes millions — of individual assets
- Constantly shifting risk signals and external data sources
- Multiple distribution models running in parallel
- Delegated authority structures that demand consistency and local nuance
In that environment, speed is essential, but as INSTANDA’s Tim Hardcastle shared on stage with Allianz UK’s Jason Howes, “Uncontrolled speed is risky.”
Overlaying AI or automation onto brittle foundations amplifies architectural weaknesses, and that’s why so many transformation efforts stall between promising pilots and meaningful, enterprise‑wide impact.
The Real Requirement: an AI‑ready Change Engine
What emerged clearly at Insurtech Insights is that the winners will be the insurers that can change continuously, with confidence.
That requires a change engine built on three foundations.
1. Change must be scalable
As I discussed in a recent INSTANDA article on the importance of scalability, AI thrives in environments where products, rules and workflows can be adjusted frequently: not once a year behind a wall of approvals and code freezes.
If change still feels risky, slow or disruptive, intelligence will always be constrained by the system it sits within. The ability to adapt underwriting logic, pricing approaches or portfolio rules quickly, and safely, is a prerequisite for scaling anything more advanced.
2. Interoperability is no longer an IT concern, it’s commercial
One of the most telling signals this year was how often data processes and interoperability were discussed in explicitly commercial terms.
For insurers, value increasingly sits at the intersection: where internal data meets third‑party insight; where underwriting connects seamlessly with claims; where partners can plug in without friction. Interoperability isn’t about elegance: it’s about reducing drag, accelerating response and enabling ecosystems to function at speed.
3. Human judgement must be designed in
There was broad agreement that “human‑in‑the‑loop” must be a fundamental architectural decision. That means designing workflows where the technology is designed to enable humans intervene at the right moments, with the right context, and where accountability is clear.
Let’s consider underwriting as an example. Fully autonomous underwriting is neither appropriate nor regulatory compliant in many contexts; the real opportunity lies in deploying AI to accelerate analysis, surface insight and provide better context at the point of decision. When accountability, transparency and traceability are built into workflows by design, AI becomes a trusted decision partner — one that enhances consistency and confidence while leaving judgement firmly in human hands.
This is especially critical as regulators, partners and boards ask sharper questions about how decisions are made, not just what the outcomes are.
What “Good” Looks Like in Practice
When the right foundations are in place, the impact is tangible.
- Change stops being episodic and becomes continuous
- Underwriters and product teams spend more time applying expertise, and less time wrestling with systems
- New products and adjustments reach the market faster, without increasing risk
- Best-of-breed ecosystem partners integrate more easily
Most importantly, intelligence, whether predictive, generative or agentic, can be introduced where it genuinely adds value, rather than where the platform happens to allow it.
The Competitive Advantage is Compounding Change
The most important takeaway from Insurtech Insights Europe 2026 wasn’t about a specific technology trend. It was about direction.
Insurers that treat AI as a standalone project will keep running pilots.
Those that invest in a robust, governed change engine will compound advantage as every improvement makes the next one easier.
As markets soften, the ability to adapt, repeatedly, safely, and at speed, may be the most valuable capability of all.
Where INSTANDA Fits
This is exactly the challenge INSTANDA was built to address.
Becoming AI‑ready is ultimately about having the confidence to change, frequently, safely and at scale, across the full policy lifecycle. That requires more than point solutions or isolated innovation initiatives. It requires a platform designed to absorb continuous change: one that allows insurers to configure products, rules and workflows without friction; integrate seamlessly with data, analytics and ecosystem partners; and embed governance, traceability and human oversight directly into day‑to‑day operations.
If you’re thinking about what it really means to become AI‑ready in P&C, and how to build a change engine that can scale with confidence, I’d welcome the conversation.
