Today, artificial intelligence is reshaping insurance. Claims are being triaged faster, documents processed more efficiently, and customer enquiries handled with greater speed. On the surface, progress looks undeniable.
Yet beneath this momentum sits an uncomfortable truth: many insurers are not using AI to effectively transform how their businesses operate or create lasting competitive advantage. Instead, they are using it to simply do the same things they have always done, just a little bit faster.
With ever-higher policyholder expectations for fast, efficient service and personalized products, as well as increasing competition, the real risk for many insurers is not that their AI initiatives fail. Instead, it is that they succeed in ways that entrench the status quo, reinforcing operating models that were never designed for intelligence, adaptability, or continuous change. In doing so, insurers risk locking themselves into short-term, often illusory, gains at the expense of sustainable, long-term competitive advantage and meaningful digital transformation.
The efficiency trap
For understandable reasons, today's AI deployments are overwhelmingly focused on optimization. Automating manual steps. Accelerating existing workflows. Reducing operational friction at the edges of the organization. These gains are real but they are also fragile.
When AI is simply layered on top of policy-centric platforms, batch-based processes, and siloed data models, it inherits their limitations. The result is not a smarter or more sustainable business, but a faster version of the same one. Claims are settled more quickly, but still follow rigid paths, embedding inefficiencies and inaccuracies that fundamentally limit the upside. Underwriting decisions are automated, but remain constrained by static product logic. Customer interactions feel smoother, yet remain fragmented across the lifecycle, falling short of the seamless, omnichannel experience customers increasingly expect.
In this scenario, AI becomes an efficiency lever rather than a transformation engine, delivering gains that remain capped and inevitably decay without structural change.
The AI realism gap
Most insurers understand conceptually what AI can do for their business. The focus is often on continuous underwriting, hyper-personalization, proactive risk assessment, improved customer engagement, and products that adapt in real time to customer behavior and life events. These ambitions are not unrealistic. Critically, they are already table stakes in other industries.
However, insurers often discover a significant gap when their ambitions collide with organizational reality. Legacy core platforms were designed to warehouse data for stability, not adaptability. They prioritize control over change, products over people, and processes over decisions. As a result, customer data is often fragmented and incomplete, decision logic is buried deep in code, and even small changes require lengthy and expensive IT intervention.
The result — when well-intended, but fundamentally task-driven AI solutions are introduced into this environment, it is AI that is forced to operate at the margins. Intelligence is bolted-on rather than embedded, automation is localized rather than systemic, and innovation becomes constrained by the very foundations meant to support it.
This is not a failure of AI. It is a limitation of legacy systems.
Why this is now a CEO issue
Throughout its evolution, AI has been treated as a technology-driven initiative: something to be explored, experimented with, and implemented through point solutions and functional pilots. That framing is no longer sufficient for insurers to achieve their goals.
To address market challenges and realize the promise of AI, insurers must treat AI as a core business capability, driven strategically from the C-suite, because the implications are structural:
- Growth: Without the ability to use data at the core to adapt products, pricing, and engagement models, AI cannot generate meaningful, sustainable new value.
- Cost: Initial efficiency gains cap out quickly when AI remains at the periphery, ensuring change remains slow, complex, and resource-intensive
- Risk and governance: Ad-hoc tasks driven by AI that sits outside the core limits transparency and accountability, weakens auditability, and elevates regulatory risk.
- Talent: Knowledge workers increasingly expect direct access to intelligent systems and timely insights. Only AI embedded at the core can deliver this without forcing them to wait on prolonged, IT-led change cycles.
In this sense, AI is not just a capability, it is a stress test. It exposes whether an organization is built for adaptability, agility and long-term success, or merely geared to task-oriented optimization.
The three shifts required to unlock AI's real value
To move beyond incremental gains, insurers must address three foundational shifts.
1. From policy-centric to customer-centric design
Highly functional AI derives its power from data-driven context. That context does not live neatly inside individual policies or siloed systems. It exists across relationships, behaviors, interactions, and time. Platforms built around products will always be unable to support intelligence built around people.
2. From process automation to decision orchestration
The real value of AI isn't simply in executing tasks faster, but in shaping and sequencing decisions. This requires decision logic that is explicit, governable, and adaptable, not hard-coded into monolithic workflows.
3. From IT-controlled change to governed business control
Speed without control creates intolerable risk. Control without speed results in stagnation. The future demands both: business users empowered to direct systems in natural language, underpinned by embedded rules, domain knowledge, and compliance guardrails. These shifts are organizational and cultural as much as technical. But they have profound architectural implications.
The architectural reality
As AI matures, the limitations of bolt-on approaches are becoming clearer. Intelligence that sits outside the core struggles to scale, remains disconnected from critical processes, and increases complexity rather than reducing it.
The next phase of insurance will require more than core systems with AI features. Insurers will need an AI platform for insurance that can safely apply intelligence inside the systems that run the business.
That means a platform where AI is:
- Enabled — models and copilots can be applied across operations
- Grounded — in insurance-native knowledge and real operational data
- Governed — with security, controls, auditability, and explainability
- Actionable — able to execute outcomes through core transactions and workflows
In practice, this means taking AI out of the black box, grounding it in insurance knowledge, governing it with controls, and making every AI-assisted decision explainable, traceable, and executable inside core operations.
In this context, a platform is AI not because it includes AI features, but because it can reason and execute safely within real insurance constraints. Intelligence must operate inside the systems that govern policies, billing, claims, and customer outcomes. This principle underpins the EIS platform, which is designed to connect knowledge, reasoning, and execution across the core of the business.
This is because, without execution, AI remains a conversation. With execution, it becomes a powerful operational lever to delight customers, empower employees, optimize processes and create sustainable business advantage.
What AI platform really means in insurance
An AI platform is not defined by how many AI features it embeds. Rather, it is defined by whether intelligence can be applied consistently across the entire insurance and customer lifecycle with precision, control and traceability.
Achieving this requires a very different foundation: a cloud-native, API-first, event-driven architecture where intent can be expressed, decisions can be governed, and outcomes can be executed deterministically. Just as importantly, it requires credibility guardrails.
This is not about fully autonomous agents replacing insurer decision-making. It is not about accepting AI as "black-box" automation running regulated processes without visibility. Instead, in insurance, the only viable path to achieve business-driving results and sustainable scale is governed AI execution with human oversight and insurer-defined constraints.
AI will reveal what your organization is really built for
AI does not create transformation on its own. It amplifies whatever structure already exists. In organizations designed for adaptability, AI compounds advantage, accelerating learning, enabling new value creation, and supporting continuous reinvention. In organizations constrained by legacy thinking, AI accelerates complexity, reinforces rigidity, and delivers diminishing returns.
The question for insurance leaders is no longer whether to adopt AI. That decision has already been made. The real question is whether their organization is built to absorb it or whether AI will simply make existing limitations visible, faster than ever before.
In the AI age, transformation is not something you install. It is something your foundations either enable or prevent.
