Key Takeaways

  • According to a report jointly released by tech company Genpact and global research and advisory firm HFS Research, the world's 2000 largest listed companies collectively holdtrillions of dollars in unrealized AI value, hindered by internal operational shortcomings such as poor data quality and inefficient processes.
  • The study, released on Monday, found that many enterprises have yet to address the fundamental issues limiting returns on AI investment.
  • "AI is exposing every weakness that companies have taken for granted for decades," said Phil Fersht, founder and CEO of HFS Research, in a statement. "Lack of process discipline, fragmented data, legacy technology, and talent gaps are no longer minor operational annoyances but direct barriers to growth, productivity, and competitiveness."

Deep Insights

The study estimates that the world's top 2000 listed companies have nearly $18 trillion in potential value locked away due to unresolved "enterprise debt"—a combination of outdated technology, poor-quality data, inefficient processes, and insufficient employee readiness.

HFS and Genpact said they calculated this total value by applying respondents' reported revenue improvements and cost reductions to the combined revenue base of the Global 2000 companies.

The report argues that technology debt, data debt, process debt, and talent debt are interconnected and often reinforce one another. Poor data quality hampers process improvement, while outdated technology makes effective deployment of AI tools more difficult. Skill gaps among employees further exacerbate the difficulty of addressing other challenges.

"These intertwined enterprise debts do not appear on financial statements, yet they quietly trap agentic AI in 'pilot purgatory,'" the report states.

The research shows that organizations that successfully address these issues can expect annual revenue growth to improve by approximately 8% and annual costs to decrease by 16%.

The study found that mature organizations do not address enterprise debt in a linear sequence. Instead, they operate in what the report calls a "two-speed" model: fixing foundational weaknesses while simultaneously advancing higher-impact transformation initiatives.

The report notes that although these investments rarely yield immediate quarterly results, they build organizational capabilities that allow other initiatives to scale and compound over time—thereby balancing the CFO's focus on short-term performance with the CEO's mandate for long-term transformation.

Among leaders surveyed by HFS and Genpact, 85% said enterprise debt is actually constraining the value created by AI initiatives, while more than half said their organizations lack a funded plan to address it.

Only 6% of respondents were classified as "mature debt resolvers"—organizations that have established, implemented, and scaled programs to measure and reduce enterprise debt.

"Mature resolvers treat debt resolution and agentic transformation as a single program, led from the top, funded as a portfolio, and advanced in order of capability building rather than merely patching visible pain points," the report concludes.