Refactoring isn’t just technical—it’s an economic hedge

Refactoring is not a technical clean-up activity. The choice to refactor is an economic decision about how you allocate human effort. Refactoring in this sense goes beyond cleaning up code—it includes improvements to architecture, operations, and decision structures, because the real cost of legacy systems rarely lives in the code alone. 

Refactoring is necessary to maintain system stability. But in an environment where your competitors are accelerating with AI, even when systems appear healthy, achieving stability through manual intervention is increasingly structurally expensive. The question is no longer whether systems still work, but whether organizations can afford the way they are keeping those systems working.

The price of stability

“Service-level agreement (SLA) is being met.”

This sentence appears in countless operational reports, and when it does, it describes a situation that’s essentially stable. Dashboards are green. Availability targets are being satisfied. From the outside, the system looks healthy.

What these reports rarely show is the human cost behind that stability: how often IT staff had to manually intervene, how often experienced engineers were pulled into firefighting, and how much tacit knowledge was consumed to prevent small issues from becoming visible failures.

That was, historically, how organizations absorbed architectural strain: through people rather than structure. Complex systems stayed afloat thanks to the constant work of experienced operators. Ambiguous designs were compensated for through manual procedures, escalation paths, and institutional knowledge. As long as services stayed up, these efforts remained largely invisible.

This was not a failure of engineering discipline; it was a rational economic choice. When compute resources were expensive and automation progressed slowly, relying on human effort to bridge structural gaps often made sense. Costs accumulated gradually, and organizations had time to adapt, so refactoring could be postponed without immediately threatening the business.

What has changed since then is not only technology, but the relative price of computation itself. As compute costs collapse and automation accelerates, decisions built on those earlier assumptions quietly remain in place—not as deliberate strategies, but as hidden economic hedges organizations continue to rely on without fully realizing it.

Why incremental fixes are no longer enough

This doesn’t mean that organizations stayed static. When pressure increased, they naturally sought local optimizations. They introduced new tools, they added automation around existing processes, and they modernized platforms.

However, they did all that without revisiting underlying organizational boundaries. That reduced pain temporarily, but without structural change it also institutionalized the very cost patterns it aimed to relieve. Automating around structural debt does not remove that debt; it stabilizes it.

In structurally sound environments, routine operations are automated, predictable, and inexpensive. In structurally constrained ones, the same outcomes depend on continuous human intervention. The warning signs are familiar to anyone close to operations:

• SLAs are met, but only through constant manual coordination 
• Incident response depends on a shrinking group of experienced individuals 
• Operational procedures grow more elaborate and exception-driven over time 
• Improvements focus on coping mechanisms rather than eliminating root causes  

What makes this dangerous is not failure, but normal operation. Costs rise quietly, hidden behind the appearance of stability.

Where human effort is being squandered

In every organization, human effort is finite. The question is not whether people are working hard. The question is what their effort is being used for.

In many environments, skilled engineers are not spending their time learning, experimenting, or improving how systems work. Instead, they are spending it compensating for structural limitations—applying patches, managing exceptions, and coordinating manual workarounds just to keep systems stable.

This distinction matters. Human effort invested in learning and iteration increases future capability. Human effort invested in patching structural gaps only preserves the past.

For a long time, this trade-off was acceptable, and refactoring was framed as a technical concern—important, but optional. As long as systems appeared stable and service levels were met, choosing to do nothing felt like prudent risk management. Using people to absorb complexity made sense when systems changed slowly and alternatives were limited. 

Today, that same pattern has become a hidden cost driver. Every hour your team spends maintaining fragile behavior is an hour not spent reducing that fragility. Every workaround delays the next structural improvement. Over time, organizations find themselves allocating more and more of their high-skill workers to preservation rather than progress.

Refactoring changes this allocation. By moving recurring effort out of manual processes and into structure, organizations free people to focus on learning, experimentation, and adaptation. The goal is not to eliminate human involvement, but to ensure that human effort is spent creating future capability rather than endlessly maintaining past decisions.

Designing systems that do not blame people

The goal of sustainable refactoring is not to blame developers for complex code, operators for manual procedures, or teams for firefighting. The real objective is to design systems that do not require such heroics to function. Systems that rely on heroics also rely on silence: knowledge concentrates, learning slows, and responsibility becomes implicit rather than shared.

Refactoring makes that implicit knowledge explicit—turning personal survival skills into shared, repeatable standards. Refactoring, done well, shifts reliability away from individuals and back into the organization itself. That means making deliberate decisions about:

• Which problems must be eliminated structurally rather than managed operationally 
• Where manual intervention is no longer an acceptable long-term strategy 
• Which failure patterns the organization will not allow to repeat  

When these decisions are explicit, refactoring stops being episodic and starts becoming preventative.

Generative AI exposes the real cost of structure

Generative AI has made these issues all the more acute. AI doesn’t reduce complexity by itself. But in organizations with clear boundaries, explicit responsibilities, and consistent structure, automation can be accelerated and operational costs will drop rapidly.

Organizations without these advantages face a stark reality. They are paying humans to compensate for structural defects. Using generative AI and automation tools to scale their business won’t fix their chaotic architecture; it will simply scale up the chaos. In this sense, AI acts as a structural filter—creating advantages for clean systems, and amplifying cost where hidden debt remains.

Used well, automation platforms and generative AI reduce marginal cost dramatically; used prematurely, they expose structural weaknesses and increase human burden. When it comes to rolling out these tools, the sequence of steps matters:

• First, correct structural distortions through refactoring 
• Next, establish decision mechanisms that prevent recurring failure  
• Finally, apply acceleration technologies in a deliberate fashion 

Reversing this order leads to predictable—and expensive—outcomes.

When cost rises faster than visibility

Automation, cloud infrastructure, and generative AI are rapidly lowering the baseline cost of running well-structured systems. As a result, even when an organization that relies on manual interventions appears stable, its costs are rising relative to more forward-looking competitors that are structurally absorbing cost through automation and simplification.

In an AI-accelerated environment, doing nothing is no longer a neutral choice. It is an active decision to keep paying the price of the gap between what structure and automation could absorb and what human operations are still compensating for. It means silently accepting a worsening cost position, even when nothing is visibly “broken.” Inaction becomes a relative disadvantage that compounds over time.

Stability achieved through exhaustion is not stability

Choosing not to refactor is not a passive decision. It is a commitment to continue spending human energy and organizational resilience to compensate for structural shortcomings.

In slower eras, this trade-off was survivable. At this turning point, as AI and automation are becoming more and more widespread, it isn’t survivable anymore.

If there is one message worth sharing with executive leadership, it’s this: Refactoring is not technical debt management. It’s economic debt repayment—and it’s a decision about how much strain you’re willing to place on your people.