Dive Brief:
- The rapid rise of artificial intelligence is deepening the complexity of information technology landscapes, setting up many companies for a technical debt “tsunami” in 2025 and beyond, according to research firm Forrester.
- More than 50% of technology decision-makers will see their technical debt rise to a “moderate or high level of severity” in 2025, with that number projected to reach 75% by 2026, Forrester predicted in a recent report. Technical debt refers to costs incurred from putting off technology upgrades or modernization.
- “There’s a massive amount of technical debt in IT infrastructures,” Forrester Principal Analyst Carlos Casanova said in an interview. “It’s really this perfect storm of technology growing, companies being far more distributed and AI coming into the equation, which will make the problem exponentially worse.”
Dive Insight:
The research highlights the growing challenges that businesses must navigate as they rush to adopt AI and avoid falling behind competitors.
“CFOs should lead the charge in addressing the enterprise’s accumulated technical debt,” consulting firm Protiviti said in a 2023 report, which noted that organizations spend an average of 30% of their IT budgets and invest a fifth of their IT human resources on technical debt management.
AI tools, including the generative variety, are now the highest contributors to tech debt along with enterprise applications, according to a report published last month by Accenture. In the U.S. alone, tech debt costs $2.41 trillion a year, the report said, citing 2022 figures from the Consortium for Information and Software Quality.
The trend will likely accelerate as 52% of organizations plan to allocate more funds toward generative AI heading into 2025, Accenture said.
“Generative AI is leading to a classic catch-22,” according to the consulting firm. “On the one hand, it is creating new technical debt. On the other hand, when used appropriately, generative AI can help manage tech debt remediation as well as minimize tech debt creation.”
Technical debt is the result of a range of practices, including making temporary fixes that inevitably become permanent, not updating solutions that become outdated, favoring fast technology delivery over long-term benefits, or implementing one-off solutions to meet business priorities, McKinsey analysts said in an article last year.
“Many of these decisions make sense at the time and are necessary,” they wrote. “But complexity builds, and future projects become more difficult. This vicious downward cycle translates into an enormous cost for the business in the form of lost opportunities and wasted resources.”
AI is only compounding the problem, according to Accenture’s report. Among other hurdles, some companies have platforms that were built with human interactions in mind and aren’t ideal today for many generative AI implementations, it said.
