AI sprawl: Why your productivity trap is about to get expensive

I have seen this movie before.

A decade ago, at Tesla, our Finance team faced a data crisis. We had information scattered across accounting, supply chain and delivery systems, all disconnected, all using different structures. The engineering team was rightfully focused on Full Self-Driving (FSD) and manufacturing. So, we did what productivity-hungry teams always do: We built our own solution. We taught ourselves Structured Query Language (SQL), normalized the data with creative IF-THEN logic and created our own reporting database.

It worked beautifully. Until it became a governance nightmare. The VP of Engineering hated our siloed system with embedded business logic. We eventually handed it over to IT, but not before our workaround forced the company to finally resource a proper data team.

The pattern is always the same: Productivity-hungry teams build workarounds faster than the organization can govern them, and by the time leadership notices, the workarounds have become the infrastructure.

That was more than a decade ago. The pattern took years to unfold.

Today, I am watching the exact same dynamic play out in insurance and industries across the board, but compressed into months, not years. AI adoption is sprawling across organizations, led by the same productivity-hungry individuals, but without central platforms or governance. Leadership has not created space for safe experimentation, so adoption spreads like a city without a highway system. The difference? Back then, we were building SQL databases. In 2026, we are building AI agents. And the cost of fragmentation is exponentially higher.

What is AI sprawl?

AI Sprawl is what happens when the cost of building AI drops faster than an organization can govern it. Teams spin up models, agents and automations independently. Each one works in isolation. None of them connect. The result is fragmented data, drifting decisions and intelligent systems that quietly get abandoned.

It happens because execution has become cheap. Large Language Model (LLM) APIs, no-code tools and cloud infrastructure have made spinning up AI trivially easy. A claims team builds an automation to speed adjudication. Underwriting builds a model to assess risk. Customer service deploys a chatbot. Each initiative delivers local value. No single project looks like a problem.

But collectively, they create an ungovernable landscape.

Over the past 18 months, the GenAI acceleration intensified what IDC calls the GenAI scramble: scattered, fragmented and sometimes redundant applications launched by business-led initiatives without central oversight. Many organizations have fallen into what researchers describe as a productivity trap: Focusing on short-sighted value generation instead of scalability, which limits their ability to create reusable capabilities across departments.

AI sprawl is everywhere

A major property and casualty carrier recently invited us to speak with their innovation leadership about implementing process automation. We spoke with more than 10 key stakeholders across multiple lines of business and found more than a dozen different POCs and local solutions across claims intake, underwriting and fraud detection.

Six of them were solving overlapping problems. None shared data infrastructure. Two had been abandoned months earlier but were still running and still being billed.

This is not an outlier. It is the norm.

AI Sprawl persists because it is insidious, hiding in plain sight unless you look for it. Business units move fast, build independently and solve immediate problems. IT discovers shadow AI only when something breaks, when an audit is triggered or when a vendor renewal surfaces a tool, nobody knew existed. And this symptom multiplies as more innovative teams exist within the organization.

The 4 hidden costs of sprawl

AI Sprawl creates costs that compound over time, many of which are not visible in any single budget line. It results in a dangerous cascade of failures:

1. Governance becomes impossible. Companies cannot govern what they cannot see. When AI systems scatter across departments, audit trails fragment. Bias monitoring becomes inconsistent. Explainability standards vary by team.
2. Scaling stalls. Disconnected systems cannot integrate. Every new initiative starts from scratch instead of building on shared infrastructure.
3. Maintenance and redundant spending multiply. Teams that built AI to accelerate their work end up spending most of their time maintaining it. One carrier reported that 60% of their AI engineering capacity was devoted to maintaining existing tools rather than building new capabilities. Meanwhile, teams unknowingly pay for overlapping capabilities because nobody has a complete view of AI spending.
4. Talent drains away. The best AI engineers want to solve hard problems. When they are cornered into spending their time maintaining fragmented infrastructure, they walk out the door.

Why traditional governance fails

Seventy percent of large insurers are investing in AI governance frameworks. Yet only 5% have mature frameworks in place. This gap is not about commitment or resources. It is about a category mistake.

For the last two decades, enterprise software governance worked because the software itself worked a certain way. Systems were point solutions. A claims platform did claims. A policy admin system did policy admin. Each tool had a clear owner, a defined scope and a predictable boundary. Governance could wrap around the edges, through access controls, audit logs, change management, vendor reviews, because the edges were visible. We governed the perimeter because the perimeter was the product.

AI is not a point solution. It is foundational technology, closer to electricity or a database than to a piece of software. It does not sit inside a defined boundary; it flows across every process, every decision and every department that touches data. And because it flows, it cannot be governed at the perimeter.

This is why carriers applying the old playbook keep running in place. Policy documents, oversight committees and compliance checklists were designed to govern systems that stood still. AI does not stand still. It is built, modified, retrained and extended by the same teams it is meant to serve, often in the same week. By the time a governance committee reviews it, three more versions exist somewhere else in the organization.

The failure is not that carriers are governing AI badly. It is that they are governing it as if it were software, when it’s actually infrastructure. Infrastructure requires a different discipline: Shared foundations, common standards and the assumption that everyone will build on top of it. You do not govern electricity by reviewing each appliance. You govern it by standardizing the grid.

Until carriers make that shift, their frameworks will keep maturing on paper while sprawl compounds underneath.

3 questions every insurance CIO should be able to answer

If the failure of traditional governance is a category mistake, the first job of leadership is to check which category they are actually operating in. These three questions are not meant to produce tidy answers. They are meant to reveal whether you are still governing AI as software when you should be governing it as infrastructure.

1. Are you governing AI at the perimeter, or at the foundation?

Look at your current AI governance artifacts, such as the policies, the committees, the review processes. Are they designed to wrap around individual tools after they are built, or to set shared standards that every tool must be built on top of? Perimeter governance asks, “is this specific model compliant?” Foundational governance asks, “does every model in this organization inherit the same definitions, the same lineage and the same guardrails by default?” If your governance only kicks in at review time, you’re still treating AI like software. You’re already behind.

2. If you standardized one thing across your entire organization tomorrow, what would create the most leverage and why haven’t you?

Every carrier has a list of things they know should be standardized but have not been. Shared definitions for core entities. Common ways of handling unstructured inputs. A single source of truth for how decisions get logged. The question is not which item belongs at the top of the list; most CIOs already know. The question is what has been blocking the standardization: Is it political, budgetary, or organizational? Because that blocker, whatever it is, is also what is letting sprawl compound. Governance frameworks cannot fix what foundational decisions have been deferred.

3. When a new AI initiative launches next quarter, what will it automatically inherit from what already exists?

This is the real test. In a point-solution world, every new system is built fresh and governance is applied afterward. In a foundational world, every new system inherits shared standards, shared definitions, shared oversight before a single line of code is written. If the honest answer is “it will inherit nothing, and we will govern it after the fact,” then you do not have an AI governance problem. You have an AI foundation problem, and no amount of policy will close the gap.

The uncomfortable truth is that most carriers will answer these questions honestly and discover they are still operating from the old playbook. It is a signal that the work to be done is not more governance, but different governance, the kind that assumes AI is the ground floor, not the top floor.

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