The hidden drag, quantified: Technical debt’s penalty on value and growth

A tale of two hypothetical enterprises

In the Deloitte Insights article, “Tech decisions can drive big earnings-per-share gains,” we found that companies that increase investment across IT, data, and AI can nearly double earnings per share (EPS), potentially unlocking up to $277 million in competitive advantage.² While these gains highlight the upside of bold technology strategies, leaders need to balance them against persistent pressures on operating costs, margins, and profitability.

As AI and automation ambitions accelerate, two questions arise regarding enterprise strategy: How can organizations pursue bold technology transformation without compounding future technical debt? And, how can leaders unlock latent value that already exists within their technology footprint? For example, tech leaders may have ideas for how to use existing tech more effectively but have limited influence or incentive to share them. Or, an organization may have processes that could be automated by AI capabilities already paid for but not currently used.

We compared two simulated S&P 500 companies to examine the potential impact of different strategic choices on technical debt and latent potential. In our analysis, both companies start with the same level of technical debt and latent potential, but the first treats its debt like many companies do in our experience: It allows debt to accumulate faster than it’s remediated. The second company takes action to improve infrastructure maturity or data maturity through actions like cloud migration or data cleansing, resulting in dramatically different outcomes.

The first simulated company is an S&P 500 organization with a median IT budget, moderate digital maturity, a fairly-strong (but not exceptional) ability to manage change, and limited AI activity beyond pilots. Its strategy is to keep infrastructure and AI and data capabilities broadly stable with the company’s current reality (reflected in the model by an average starting score for each attribute). Based on historical S&P 500 patterns, this company starts at an EPS of US$2 in year 1 and grows steadily to US$5.17 by year 5, reflecting typical market performance.

The second simulated company has a strategy related to infrastructure modernization or data value to help provide leaders with a clearer picture of how specific decisions related to bold technology strategies may affect tech debt and latent potential in the next two to five years.

Here’s what we learned.

How infrastructure interventions impact tech debt

Infrastructure modernization often leads to long-term reductions in technical debt. When we compare the two hypothetical enterprises, the second company that has a more robust infrastructure modernization strategy (leveraging solutions such as multicloud or microservices) reduces tech debt by 10% in the first year, reaching 18% over five years relative to company 1. The difference between the two examples is the leap in technical maturity. While the average company operates at about 65% of its potential (a 0.65 on our scale), the leading enterprise pushes its capabilities 23% higher than that baseline through aggressive modernization (figure 1).

How data transformation can reduce tech debt

When leadership makes deliberate investments in data modernization and simplification—rather than assuming that data-driven value creation will naturally overcome underlying technology constraints—organizations tend to be better positioned to reduce technical debt and unlock sustainable value. Data transformation is not simply about replacing legacy systems; it typically involves reconfiguring and optimizing existing assets to generate new business impact. That’s exactly what we found when comparing an organization that maintained average data maturity with one that strengthened its data capability.

Organizations can make deliberate investments in data modernization by cleaning up how data is managed and governed. This in turn improves business results across three core areas:

• Assets: The tools that drive an organization’s technological base
• Methods: The capability and process levers that determine how efficiently assets are converted into business value
• People: The human capacity to sustain transformation
  

During an 18-month gradual implementation period, increasing data capability by 35% in our model was associated with a progressively larger—but still incremental—reduction in technical debt relative to an average organization: By year 2, our ambitious company had approximately 1.3% less tech debt than the average company, 3.2% less by year 3, 5.3% less by year 4, and 7.1% less by year 5 (figure 2).

In one example, a global retailer migrated to centralized control over data assets (permissions, auditing, and lineage), striving to improve transparency and trust. By decommissioning legacy code and patterns, the retailer reduced technical debt and streamlined operations.⁵

How data transformation can unlock latent potential

Latent potential can be unlocked when organizations strengthen their data capabilities—even when they don’t tackle technical debt first. This might be achieved through better data governance, quality, access, and data reuse to improve decision velocity and execution effectiveness across the enterprise.

In the model, a 35% increase in data capability produced a material reduction in latent potential relative to an average organization after an 18-month gradual implementation period. In this case, less latent potential is a good thing, as it shows that less value is being “hidden” within the tech stack. While reductions in latent potential were modest early on—about 1% less than the average company by year 1—the effect then compounds as data-driven operating capacity scales: Company 2 saw approximately 8.5% less latent potential than the average company by year 2, 22.9% less by year 3, 38.6% less by year 4, and 52.5% less by year 5 (figure 3).

Optimizing the tech estate

Most organizations can’t “AI their way out” of technical debt. Infrastructure modernization is necessary as well as strong data strategies. C-suite leaders should consider how they make technology-strategy decisions—moving beyond isolated initiatives to understand their organizations as interconnected systems, where each decision shapes multiple downstream outcomes.

This analysis demonstrates that targeted, sustained interventions—particularly in infrastructure modernization and data capability—can materially reduce technical debt and unlock latent technology potential. While infrastructure investments play a critical role in stabilizing and simplifying the tech estate, strengthened data capability can drive compounding value over time, improving decision velocity, execution effectiveness, and ultimately financial performance.

Effectively managing technical debt is not simply a matter of cost control or operational hygiene. When approached deliberately, it can become a lever for new revenue creation, improved profitability, and increased earnings per share. For leaders navigating AI-driven transformation, the opportunity lies not only in deploying new technologies but in reshaping the system that determines how value is created—and sustained—over the next two to five years.