Whether it be document classifiers, chatbots, or risk models, most insurers have tested AI in some form. But few have seen those experiments translate into enterprise-wide impact.
Insurance leaders already understand that AI matters. The real question is how to scale it. Only 22% have AI in production, and fewer than 5% are realizing revenue from it. That disconnect signals a need for leadership to shift from exploration to scaling. Accelerators can make that shift real.
What’s holding back AI scaling
Scaling AI is less about building flashy models, and more about getting the foundations right. That includes:
- Reliable infrastructure to deploy and manage models across teams and use cases
- Clean, connected data that feeds those models in real-time
- Cross-functional delivery models that let teams test fast, track impact, and improve
Gartner calls this 'AI engineering,' the discipline of turning isolated models into repeatable, scalable systems. KPMG highlights the same bottleneck, that most insurers lack the operational agility and data readiness to get AI out of the lab and into daily workflows.
This is where most carriers stall, as AI requires coordinated execution across people, data, and platforms.
What scaling AI looks like in practice
A leading insurer needed to speed up claims resolution while maintaining accuracy and compliance. They had already introduced basic AI for fraud alerts and document tagging, but those efforts were isolated and did not deliver enterprise-level impact.
Partnering with Visionet, they deployed our Claims Assist Accelerator to address a high-friction area: triage and intake during First Notice of Loss (FNOL).
Here’s what made the difference:
- AI at the right moments: GenAI and machine learning were used to extract structured data from documents, emails, and PDFs, automating steps like claim validation, prioritization, and document classification.
- No system disruption: The solution integrated through APIs, working alongside existing claims systems. There was no need to replatform or overhaul legacy infrastructure.
- Fast, measurable impact: The insurer reduced cycle time, improved triage accuracy, and lowered manual workload—all within a matter of weeks.
Visionet’s Claims Assist approach proves that AI doesn’t need to start with a blank slate. It can deliver real, production-ready impact by enhancing what carriers already have.
Four moves that help CXOs scale AI with impact
The insurer followed a clear pattern that other carriers can adopt. These four moves consistently separate stalled pilots from scalable wins:
1.Focus where friction meets volume:
Start with use cases that are both painful and repeatable. Claims intake and triage, document processing, and quote generation are prime targets. They’re process-heavy, have clear ROI potential, and don’t require major system changes to improve.
2.Prioritize plug-and-play AI:
Scaling AI doesn’t require rebuilding infrastructure. What matters is whether solutions can work with your existing systems. Domain-trained accelerators speed up time –to- value by eliminating custom build cycles and minimizing integration effort.
3.Design for trust and compliance from day one:
Responsible AI isn’t optional. With regulations like the EU AI Act coming into effect, insurers must bake in model explainability, bias monitoring, and governance early. Visionet’s case shows how this can be delivered without adding overhead via built-in audit trails and drift monitoring.
4.Deliver quick wins to build momentum:
The path to scale starts small. A 10–12 week pilot that reduces triage time or automates intake builds internal buy-in and unlocks funding for broader rollout. This is where accelerators are essential, they de-risk the pilot phase and give teams proof that AI works in their environment.
Visionet’s approach to scale AI
AI at scale doesn’t start with a blank slate. It starts with what insurers already have and improves from there. That’s the lens Visionet brings to every engagement. Instead of lengthy R&D cycles or greenfield builds, Visionet provides ready-to-deploy AI modules tailored to insurance processes. These models are trained on real insurance data, built to integrate with common policy and claims systems, and designed to solve operational problems that actually exist.
This approach shortens delivery cycles from quarters to weeks. It’s not just about pre-built models, Visionet’s framework also includes:
- Data pipeline orchestration that brings structure to messy, siloed data
- Embedded AI governance with audit trails, monitoring, and model explainability
- With a modular design ,capabilities can be adopted incrementally, ensuring flexibility to adapt as business demands evolve
- Collaboration with in-house teams, not replacement, enabling insurers to scale internal skills and own their AI roadmap
What should be on every CXO’s AI agenda
Scaling AI isn’t just a tech decision, it’s a leadership one. It requires clarity on where to start, what to fix, and how to move. Here’s a simple framing:
- Where are your biggest operational bottlenecks?
- Is your data reliable and accessible enough to feed AI?
- Do your teams have the tools to experiment, measure, and adjust?
- Can you start small and scale smart without waiting on major system overhauls?
If not, it’s time to rethink your roadmap.
Let’s talk
Visionet helps insurance leaders move beyond pilots. Our accelerators, data stack, and industry expertise enable you to scale AI faster—delivering measurable results instead of endless cycles.
Reach out to explore a smarter AI agenda for your business.
