Research: Technical Debt and Process Shortcomings Trap Enterprise AI in a 'Pilot Predicament'
According to a joint report by Genpact and HFS Research, the world's top 2000 listed companies are leaving nearly $18 trillion in unrealized AI value on the table due to 'enterprise debt' (shortcomings in technology, data, processes, and talent). The study indicates that only 6% of companies can effectively address such debt, while those that succeed are expected to achieve annual revenue growth of 8% and cost reductions of 16%.
Key Findings
- The world's 2,000 largest listed companies are collectively sitting on trillions of dollars in unrealized AI investment value due to internal operational weaknesses such as poor data quality and inefficient processes, according to a report jointly released by technology company Genpact and global research and advisory firm HFS Research.
- The research, released Monday, found that many companies have yet to address the foundational issues limiting returns on AI investments.
- "AI is exposing every weakness that enterprises have taken for granted for decades," said Phil Fersht, founder and CEO of HFS Research, in a statement. "Missing process discipline, fragmented data, legacy technology, and talent gaps are no longer operational annoyances—they are direct barriers to growth, productivity, and competitiveness."
Deep Dive
The study estimates that the world's top 2,000 listed companies collectively hold nearly $18 trillion in unrealized value due to unresolved "enterprise debt"—a combination of outdated technology, poor-quality data, inefficient processes, and insufficient workforce readiness.
HFS and Genpact said they calculated this total value by applying respondents' reported revenue uplift and cost reduction estimates to the Global 2000's aggregate revenue base.
The report argues that technology, data, process, and talent debt are interconnected and often reinforce one another. Poor-quality data hampers process improvements, while outdated technology makes it difficult to deploy AI tools effectively. Workforce skill gaps further compound the difficulty of addressing other challenges.
"These interconnected enterprise debts do not appear on financial statements, but they are quietly keeping agentic AI trapped in pilot purgatory," the report said.
The research shows that organizations that successfully address these issues can boost annual revenue growth by approximately 8% and reduce annual costs by 16%.
The study found that top-performing organizations do not tackle enterprise debt in a linear sequence but instead operate in a "dual-speed" mode: fixing foundational weaknesses while advancing high-impact transformation initiatives.
The report noted that although these investments rarely deliver immediate quarterly results, they build organizational capabilities that enable other initiatives to scale and compound over time—balancing CFOs' focus on near-term performance with CEOs' mandate for long-term transformation.
Among leaders surveyed by HFS and Genpact, 85% said enterprise debt is actively constraining the value generated by AI projects, while more than half said their organizations lack a funded plan to address it.
Only 6% of respondents were classified as "mature debt solvers"—organizations that have established, implemented, and measured enterprise debt reduction programs.
"Mature solvers treat debt resolution and agentic transformation as one integrated program, owned at the top, managed as a portfolio, and sequenced by capability building—not just fixing visible pain points," the report said.