Fix the Foundation Before You Fund the Growth: Understanding Process Debt

 

Author: Lisa Soss, Founder, Soss Strategy Group

There is a moment that most COOs and operations leaders in insurance recognize, even if they don’t always name it.

Growth is up. Premium volume is increasing. The team is working hard. But something is getting harder. Decisions that used to be straightforward now require more coordination. New initiatives take longer to stand up than they should. Technology investments aren’t delivering the efficiency gains the business expected. And somewhere underneath all of it, the operations team is managing a quiet backlog of workarounds that nobody has time to address.

That moment is not a growth problem. It is a foundation problem. And whether you sit inside a carrier, an IMO, or an MGA, it has a specific name: process debt.

The Gap Between Ambition and Readiness

The industry’s appetite for innovation is real. According to recent research, 82% of insurers believe AI will define the industry’s near future — but only 14% have fully integrated it into their operations. That is not a technology problem. That is a readiness problem.

The barriers are consistent across organizations: 42% of firms cite legacy system integration as a primary challenge, 39% point to fragmented data environments, and 40% lack the in-house expertise to move meaningful initiatives forward. Beneath all three sits the same root cause — operational foundations that were not built to support what the business is now trying to do.

The organizations that close this gap are not the ones investing most aggressively in new technology. They are the ones that have done the harder, less visible work of cleaning up what is underneath.

What Process Debt Is — and How It Accumulates

Process debt is the operational equivalent of financial debt or tech debt, for my fellow techies out there. It accrues when organizations make short-term decisions about workflows, systems, and processes without accounting for the long-term cost of those decisions.

It rarely looks like negligence. It looks like pragmatism. A workaround gets added to manage an exception. A new step gets inserted into a workflow to prevent a handoff from breaking. A team builds tribal knowledge around a system limitation because fixing the system is not on the roadmap right now. Each decision, in isolation, is reasonable. Collectively, they compound.

At scale, the compounding becomes consequential.

How It Shows Up

The texture of process debt varies by organization type, but the patterns are recognizable.

At carriers, process debt often surfaces in data fragmentation. Research indicates the average insurer manages 17 separate data sources feeding premium processes alone. When that many systems hold different versions of the same customer or policy information, the gap between them is filled by human effort. Agents or operation teams call in and get different answers depending on who they reach. Websites and portals reflect data that does not match what is in the core system. Reporting takes longer than it should because someone has to reconcile records manually before numbers can be trusted.

This is rarely just a technology problem. More often, it is a foundation problem that technology has made visible. Each system was added to solve a specific problem. None of them were designed to work together in a unified way. And the people working between them are managing the inconsistency with effort that was never accounted for in the operating model. The downstream cost is measurable: nearly half of insurers now face settlement cycles exceeding 60 days, a direct consequence of operational drag that compounds across fragmented systems.

At IMOs and MGAs, process debt often accumulates through a different mechanism: compensating steps. A handoff in the contracting or onboarding process breaks down. An agent has a poor experience. The operational response is to add a step, such as a review, a check, or an additional approval, to prevent that failure from happening again. But the new step creates its own friction. Another step is added to manage that friction. Over time, a workflow that was once two steps becomes seven, each one added to compensate for the last.

Nobody designed the seven-step process. It evolved, one reasonable decision at a time. A breakdown occurred, so a review was added. A carrier requirement changed, so another checkpoint was introduced. A technology limitation surfaced, so a manual workaround filled the gap.

Over time, individual pieces of the workflow were improved, but the workflow itself was never redesigned. The result is a patchwork process that resembles Frankenstein’s monster: stitched together from years of fixes, exceptions, and compensating controls. Every individual piece has a reason for existing, but few people can explain how the entire process is supposed to work from end to end.

The Automation Trap

I’ve seen this pattern repeatedly across carriers and distribution organizations. By the time leadership recognizes the friction, the instinct is often to automate it.

The logic is understandable. If the process is slow and manual, automate it. If the team is overwhelmed, give them technology that reduces the manual burden. It seems like the obvious path to scale.

But if the underlying process is fragmented, situational, and undocumented, and it runs on tribal knowledge and exception-handling that no one has written down, automation does not eliminate the complexity. It amplifies it. You end up with a faster version of the broken process, one that is significantly harder to unwind because it is now embedded in the system.

As one recent industry analysis put it, AI does not just underperform on bad data — it amplifies the dysfunction already present, but at scale. The 17 data sources the average insurer manages do not become more coherent when automation is layered on top of them. They become faster at producing inconsistencies.

The organizations that benefit most from automation are the ones that have done the foundational work first. They understand their workflows well enough to define them clearly. They have identified where the exceptions live and made deliberate decisions about how to handle them. They have simplified before they have scaled.

Fix the foundation, then fund the automation. In that order.

It’s Not If. It’s When.

The operational debt that accumulates at the $500M stage looks manageable at $1B. At $2B, it begins to slow things down in ways the leadership team can feel. At $3B and beyond, it becomes the constraint on growth itself.

Technology is not going to slow down. The pressure to automate, modernize, and adopt AI-driven capabilities is only increasing. And for organizations carrying significant process debt, every new technology layer adds weight to a foundation that was not designed to hold it.

Process debt does not disappear on its own. It compounds. The question is not whether it will eventually constrain growth, but when. Organizations either address it deliberately before it becomes a problem, or they address it later when it has already become one.

The organizations that navigate this well treat operational debt the same way they treat financial debt: as something that accrues interest, requires active management, and becomes significantly more expensive the longer it goes unaddressed.

Where to Start

The first step is not a technology decision. It is a diagnostic one.

Before investing in automation, new platforms, or workflow tools, operations leaders should be able to answer four questions:

What are our core workflows, and are they documented? Not the official process. The actual process. The one that includes the workarounds, exceptions, manual checks, and compensating steps that have accumulated over time. To uncover it, talk directly to the people doing the work. The reality of a process rarely lives entirely in documentation, and the nuances that create operational friction often only surface through the individuals executing it every day.

Where does human effort compensate for system limitations? The places where a person is doing something that should be systematic are exactly where process debt is most likely hiding.

What does our team work around instead of through? The systems and processes that people have learned to avoid, route around, or manage manually are a direct indicator of where the foundation needs attention.

What would break if volume doubled tomorrow? The honest answer to this question is usually the most useful starting point for a prioritized remediation plan.

None of these questions require a technology investment to answer. They require time, intellectual honesty, and a willingness to surface what the team already knows but rarely says out loud.

The Competitive Advantage of a Clean Foundation

Organizations that address process debt before it compounds have a structural advantage over those that do not.

They can adopt new technology with less disruption, train new team members more effectively, and absorb growth without creating unnecessary complexity. Because they understand their operational foundation, they are making deliberate decisions about how the business scales instead of discovering its limitations in real time.

The goal is not operational perfection. The goal is operational clarity: knowing where the debt lives, understanding what it costs, and making deliberate decisions about what to fix, what to automate, and what to stop doing entirely.

Growth exposes operational debt. The organizations that address it intentionally are the ones that scale without being constrained by the very processes that got them there.