Build Once, Adapt Often: A Better Way to Scale AI
.png)
Insurers recognise the value AI can bring, from pricing and underwriting to distribution and operations. And adoption is growing.
A recent Deloitte survey found that 76 percent of insurers are exploring generative AI in at least one business function. But most of those initiatives are still stuck in the scoping or proof-of-concept stage.
AI enthusiasm is not the issue. The problem is how AI solutions are being designed or delivered. Off-the-shelf tools work in isolation but rarely stretch to other use cases. Progress slows when you have to go back to the drawing board for every new challenge.
What works is building from the ground up, using AI in ways that reflect how insurance actually runs. That means breaking capabilities into reusable parts, aligning with real workflows and designing for adaptability.
Success comes from creating systems that grow with the business and deliver value from one use case to the next.
AI only adds long-term value when it scales. We’re seeing insurers making early progress, but the structure underneath is not designed to grow. One-off builds get stuck in silos. The same tasks get solved more than once.
To move forward, AI needs to be built for reuse from the start. That means working with the data, systems and handoffs that actually exist, not an idealised version of the workflow.
At vector8, that starts with a clear blueprint:
● Agents that bring multiple skills together into coordinated, multi-step workflows
● Skills that define task-specific logic tailored to real business processes
● Accelerators that provide reusable components for common AI capabilities like extraction or classification
● A model gateway that connects everything to the right model, enabling smart routing, monitoring and control
This kind of setup unlocks new possibilities. A pricing validation accelerator used in underwriting could support the auditing team with minimal changes. A tool trained to triage claims could also streamline customer service. A recommendation engine built for product matching could flex to support lead scoring or retention.
As Giles Mazars, Group Chief AI Officer at vector8 explains, “You already see three levels — reusability of models, reusability of those accelerators to fulfil use cases, and reusability of individual skills. That’s exactly the architecture we promote.”
Scaling AI in insurance works best when there is a clear view of what’s in place today and what needs to happen next. Rather than aiming for full automation from the start, insurers benefit more by building steadily, with each step creating the conditions for the next.
To help frame that progression, Mazars draws a comparison with autonomous driving. He explains, “We chose to compare AI maturity to the levels of autonomy in driving — from level one to level five. It helps people understand where they are and what the next step looks like.”
At the early levels of maturity, AI supports a single task. In the driving analogy, that’s the equivalent of cruise control: one function, operating independently. In insurance, that might mean identifying errors in a submission or tagging and routing emails. At level two, functions begin to coordinate. AI might extract information, cross-check values and trigger a follow-up. Several tasks work together, but still within a defined scope.
As maturity increases, those components become more connected and more capable. Like moving from assisted driving to partial automation, AI in insurance starts handling more of the process from start to finish with less oversight. Eventually, that leads to systems that can orchestrate complex workflows, bringing together multiple accelerators and models to support dynamic, end-to-end outcomes.
Most organisations will never need to reach level five, and in many cases, they shouldn’t. Full autonomy isn’t always the goal. What matters more is matching the level of automation to the task. In some areas, like compliance or complex underwriting decisions, a human will always need to stay in the loop.
Scaling AI without the right foundation creates drag. Projects stall, duplication creeps in and outcomes stay limited to a single team or line of business. Even when the use case is strong, the benefit doesn’t spread.
A more flexible approach avoids that. Reusable components reduce time to deploy. Adaptable accelerators and a reactive model gateway keep infrastructures efficient. And solutions can move with the business, not hold it back.
The benefits stack up when AI is designed to be reused:
● Faster delivery: Teams don’t need to start from zero for every new use case. When the foundations are shared, each rollout gets easier.
● Lower cost to operate: You use the right model for the task. That keeps costs under control without compromising quality.
● Smarter use of talent: People spend less time fixing broken handoffs or repeating the same work in different systems.
● More consistent outcomes: Shared components create a baseline of quality that’s easier to maintain and improve over time.
This kind of impact isn’t theoretical. We're starting to see it in areas like submission turnaround times, renewal cycle speed, operational overhead, broker response rates and pricing accuracy.
And as Mazars points out, the value extends beyond the task itself. “Reusability isn’t just for us. It’s for the customer.” AI investment, made in the right way improves experience across the board.
Insurers need solutions that can move with the business, not sit beside it. That means treating AI as a system to design, not a tool to bolt on. A system built for change, where progress isn’t held back by past decisions.
When AI is designed this way, each project does more than solve the problem in front of it. It clears the path for what comes next.
We help insurers design AI systems that are practical, reusable and ready to grow. Whether you’re stuck in pilot mode or planning to step up your progress, we can help you unlock more value from what you already have.
Talk to our team to see what’s possible.
Too many solutions are built in isolation. Without flexibility and modularity, use cases stay siloed and the value stays local.
Anywhere work is repeated: submission handling, triage, risk scoring, pricing checks. Reuse helps teams avoid rebuilding the same logic across lines and systems.
Design for reuse from the start. Build around how the work really happens. Keep the system flexible so you can adapt without starting over.
Uncover fresh perspectives with our handpicked blog posts on AI advancements.
.png)
