Tech debt is a leadership problem, and the real question is whether leadership is willing to confront it with the same rigor applied to growth bets. For many organizations, debt accumulates through a series of rational, well-intended trade-offs, where very real short-term delivery pressures, from funding cycles to risk tolerance, outweigh investments in long-term resilience. Over time, those deferrals harden into constraints that shape what a company can realistically build, ship, and support.
“Tech debt is really just deferred decisions. If leaders don’t explicitly choose what to do or what to defer, the organization defers it accidentally,” says Keith Eich, Vice President of Product and Technology at Harbor Compliance. After more than two decades leading product and technology teams across media, legal services, technology, healthcare, and now compliance, Eich has come to view technical debt as a proxy for leadership decisions. The cost of that deferral rarely appears immediately, but it does compound through outages, slower delivery, and even burnout.
Tech Debt Is a Leadership Problem
Short-term wins can be tempting, particularly in competitive tech environments, but when delivery speed is favored over sustainability, debt accumulates and introduces structural fragility that limits future options. That fragility emerges from a web of strategic choices, incentives, and trade-offs that shape how teams are asked to deliver and what they’re rewarded for prioritizing. For example, engineering teams may be measured on feature throughput or deadline adherence, while resilience, maintainability, and risk reduction remain implicit expectations rather than explicit goals. Teams work within those parameters, where prioritization, incentives, funding models, and risk tolerance are defined at the leadership level.
Eich likens unmanaged debt to an interest-bearing obligation that leadership never formally approved. “Teams weren’t failing because they couldn’t build,” he says. “They were failing because they were shipping today and ignoring the compounding cost of tomorrow.” Eich argues that this can result in “innovation theatre” where an organization appears productive on paper while absorbing hidden costs behind the scenes.
The Myths Distorting Executive Decisions
One reason technical debt persists is that it’s often misunderstood at the executive level. Eich points to several myths that consistently skew planning conversations. The first is the belief that technical debt is simply refactoring, a preference rather than a business concern. “The reality is that it’s a business tax,” he says. It shows up as higher incident rates, customer-impacting bugs, and teams spending more effort to deliver the same outcomes.
Another misconception is that debt can be paid down later. In practice, later rarely arrives. Backlogs grow, complexity compounds, and organizations drift into what Eich calls innovation theater. Commitments are made, deadlines slip, and eventually, a reliability or security event forces an emergency rewrite under pressure. Perhaps the most damaging myth is the false choice between innovation and debt reduction. Unmanaged debt eventually kills innovation. “The real question leaders should ask is what level of debt keeps us fast and safe,” he says. Treating the issue as a binary turns planning into a political tug-of-war rather than a strategic exercise.
Making the Invisible Visible
Eich’s first move for any CTO/CIO entering a planning cycle is to name and measure debt. Unknown risks are dangerous. “If it’s not on a list somewhere with an owner and some level of risk, you’re at risk,” he says. A technical debt register doesn’t require executives to dive into code. It does require a prioritized view of ownership, business impact, warning signals, and due dates. Visibility creates accountability and replaces vague unease with concrete trade-offs.
The second move is to shift the conversation from individual projects to portfolio allocation. Rather than debating initiatives one by one, leadership should agree on percentage-based allocations across innovation, modernization, and debt reduction. Those ratios may vary, but the discipline matters. “Agree to it and then deliver on that,” Eich says. The goal is to prevent the same trade-offs from being relitigated every quarter.
Not all technical debt is created equal, and a one-size-fits-all response often creates more risk than it removes. Treatment plans must be grounded in a business context. Some issues warrant targeted refactoring. Others are better addressed through replacement, version upgrades, or intentionally bundling remediation into larger initiatives. “What matters is that every debt item has an owner, an interest rate, and a due date,” he says.
Returning to First Principles
Looking ahead, Eich expects artificial intelligence and low-code tools to both reduce and increase technical debt. Automation can eliminate toil and surface hidden issues earlier. It can also create duplicate logic, unmanaged workflows, and shadow systems when governance lags adoption. “The biggest risk isn’t bad AI,” he says. “It’s ownership.” When critical processes are built without lifecycle management, versioning, or accountability, organizations inherit fragile systems with no clear stewards.
Eich’s prescription is to treat automations like production software, applying basic discipline around testing environments, monitoring, documentation, and maintaining registries for AI and automation just as rigorously as for technical debt. In the end, Eich returns to first principles. The question of technical debt versus innovation is one of capital allocation and risk management. When debt is invisible, optimism dominates planning and reality arrives as a surprise. Naming it allows leaders to regain control of the trade-offs that shape long-term performance.
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