Robert Clark is the founder and CEO of Cloverleaf Analytics, a leading provider of insurance decision intelligence solutions.
Insurers are not short on technology questions right now.
One of the biggest is whether they should build AI and analytics capabilities internally or buy them from a partner. It is a fair question. It is also incomplete.
The better question is whether those investments are improving the decisions that drive the business. Are they improving underwriting, claims, risk selection, retention, pricing discipline or operational performance? If the answer is no, then the company may not be choosing the wrong technology. It may be solving the wrong outcome.
AI And Analytics Are Inputs
AI models, predictive analytics, dashboards and automation tools can all be valuable. But they are inputs. They are not the end result. A model that identifies a profitable segment but never changes underwriting appetite has limited value. A dashboard that shows claim deterioration without triggering action is insufficient. The goal is not more data. It is not more analytics. It is not even more AI. The goal is to make better decisions faster and with greater consistency.
That sounds obvious, but many technology programs are still measured by deployment or activity. Those indicators matter, but they are different from business impact.
PwC’s 2026 Digital Trends in Operations survey found that 60% of organizations said poor data quality has affected their ability to achieve value from digital initiatives. The same research found that 89% of respondents gave at least one reason their technology investments had not fully delivered expected results, with integration complexity topping the list. That is the issue for insurers. AI can surface signals, but poor data quality and disconnected workflows can keep those signals from driving business change.
This Is Where Decision Intelligence Matters
Decision intelligence changes the conversation because it moves the focus from technology ownership to decision ownership.
Data can show what happened. Analytics can explain patterns. AI can surface signals and suggest actions. Decision intelligence connects those capabilities to the business choices that matter.
For an insurer, that may mean deciding whether a claim should be escalated, whether a risk should be priced differently, whether a customer segment is becoming less profitable or whether a portfolio is moving outside the company’s appetite.
The point is not to remove human judgment. In insurance, human judgment remains essential. The point is to give that judgment better information, better timing and structure.
Build Where You Differentiate
Insurers should own the decisions that define who they are. That includes underwriting appetite, claims philosophy, pricing strategy, risk tolerance, customer experience priorities and institutional knowledge.
Those are not generic capabilities. A company should not outsource the logic that defines how it evaluates risk, treats policyholders or balances growth and profitability. This is where some build arguments are valid. If a capability reflects proprietary knowledge or a decision process that truly separates one carrier from another, internal ownership can be the right path.
But owning the decision does not always mean owning every piece of technology underneath it.
Partner Where Speed And Scale Matter
Some capabilities are essential, but they are not always where an insurer creates unique market differentiation.
Data ingestion, integration, reporting, alerts and workflow connectivity all matter. Without them, AI has little chance of producing consistent value. But rebuilding every layer internally can become expensive and slow.
PwC also found that only 24% of executives ranked reducing enterprise complexity as a top factor in build versus buy decisions. That is a warning sign. If complexity is not part of the decision, the organization may buy or build tools that make the operating model harder, not better.
A platform decision should not be about pride in ownership. It should be about decision performance.
The Build Momentum Is Real
AI is also changing the economics of building.
Retool’s 2026 Build Vs. Buy Report found that 35% of teams had already replaced at least one SaaS tool with a custom build, while 78% expected to build more custom internal tools in 2026.
That trend should get the attention of insurance leaders. Companies now have more power to create tools that fit specific workflows. That can be useful in insurance, where generic software often misses nuance.
On the other hand, building more tools does not automatically create better decisions. It can also create more fragmentation, more governance risk and more systems that IT and business leaders eventually must reconcile.
AI can make building easier. It does not automatically make building smarter.
The Case For Blend
The strongest model for insurers is not build or buy. It is blend. Insurers should build and own the decision logic that makes them different. They should partner for the platform capabilities that help those decisions move through the business faster, with more consistency and with better governance.
A carrier can own its underwriting appetite without building every data pipeline. It can own its claims philosophy without creating every workflow tool from scratch. It can own its pricing strategy while still using outside technology to connect data, monitor performance and surface insights.
The point is control where control matters.
Decision intelligence provides that framework. It helps leaders separate what they need to own from what they need to accelerate.
Decision Velocity Will Separate Winners
The next competitive advantage in insurance is decision velocity.
The carriers pulling ahead are the ones with clarity around which decisions actually drive outcomes, which capabilities are core to the business and where external partners can accelerate execution without adding complexity.
That’s the real build-versus-buy conversation. It has less to do with owning software and more to do with owning the decision.
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