Genpact research finds $18 trillion AI opportunity trapped behind enterprise debt

You can't build your AI future on broken foundations

Every major enterprise is running an AI program. Almost none of them are ready for it. Genpact, in partnership with HFS Research, surveyed more than 2,000 enterprise executives across industries and functions as part of our study on how four enterprise debts will make or break your AI future. The findings are confirming and clarifying: Ambition is high, readiness is low, and the gap between the two is compounding. The data is unambiguous: 85% of leaders say their underlying foundations, fragmented data, ungoverned processes, aging systems, and undertrained talent are actively working against their AI investments. Enterprises are directing 13% of average function spend toward AI. The foundations on which spending depends are not ready for it. Layering AI on top of processes that were never designed for it does not unlock value. It locks in the cost of the status quo at machine speed. 1 STRUCTURAL FAILURE, 4 COMPOUNDING DEBTS Enterprise debt does not appear on a balance sheet. It accumulates quietly, in systems held together by tribal knowledge, in data no one fully trusts. Often, processes are so layered with workarounds that they have become the workflow. And with a workforce so used to the dysfunction, they no longer notice it.

Genpact Research Finds $18 Trillion AI Opportunity Hidden Behind Enterprise Debt

The research defines enterprise debt as the accumulated drag on a business from outdated technology, poor data quality, inefficient processes, and underprepared talent. Left unresolved together, the four debts compound -- each one deepening the others -- into a structural ceiling on performance. In the AI era, that ceiling becomes a hard constraint. Data debt is the gap between the data enterprises have and the data AI needs. Only 33% of enterprise data is AI-ready today, and 42% of AI and analytics initiatives are already failing because of data quality issues. Process debt is the cost of how work actually flows -- manual, ungoverned, and hard to change. Around 40% of employee time each week is lost to inefficient or manual processes. AI deployed into ungoverned workflows does not fail visibly; it executes the wrong steps faster. Technology debt is the legacy infrastructure tax every modern initiative pays before it starts. Core enterprise systems are, on average, 10 years old, and approximately 42% of developer time goes to servicing existing debt rather than building new capabilities. Talent debt is the readiness gap between the workforce enterprises have and the human-agent operating model AI requires. Only 32% of the workforce is AI-ready -- and talent debt amplifies every other form of debt, silently slowing every resolution effort.

AI Unpreparedness Leaves $18 Trillion on the Table

Four enterprise debts are quietly working against every dollar organizations are investing in AI Global enterprises could unlock nearly $18 trillion in value by addressing longstanding operational challenges that continue to hinder AI adoption and business performance, according to a new study released by Genpact and HFS Research. The report, based on responses from more than 2,000 enterprise executives across 16 industries and 14 business functions, identifies four interconnected forms of "enterprise debt" that are preventing organizations from maximizing returns on their AI investments. Researchers found that companies addressing these challenges could achieve approximately 8% faster annual revenue growth and reduce costs by as much as 16% annually. However, 85% of surveyed leaders said these issues are already limiting AI-driven outcomes, while more than half acknowledged they have no funded plans to address them. With organizations now directing nearly 13% of average functional spending toward AI initiatives, the study suggests that foundational weaknesses are becoming increasingly costly. The report categorizes enterprise debt into four areas: data, process, technology and talent. Data debt remains a significant concern, with only 33% of enterprise data currently considered AI-ready. The study notes that 42% of AI and analytics initiatives are already being impacted by data quality issues. Process debt, meanwhile, stems from inefficient and heavily manual workflows. Researchers estimate that employees lose around 40% of their working time each week to such inefficiencies, limiting productivity and reducing the effectiveness of AI deployments. Technology debt reflects the burden of aging infrastructure. According to the report, core enterprise systems are, on average, 10 years old, while developers spend roughly 42% of their time maintaining existing systems rather than building new capabilities. Talent debt, which refers to workforce readiness for AI-enabled operating models, remains another critical challenge. The study found that only 32% of employees are currently equipped with the skills needed to operate effectively in AI-driven environments. Balkrishan "BK" Kalra, President and CEO of Genpact, said organizations must address foundational business challenges before expecting meaningful returns from AI investments. He emphasized the importance of understanding how workflows across an enterprise and using process intelligence to guide transformation efforts. The research estimates that process debt and data debt each represent nearly $7.7 trillion of the total value opportunity. Manufacturing and healthcare emerged as the industries with the largest combined potential gains, while financial services showed the highest concentration of data-related challenges. Phil Fersht, Founder and CEO of HFS Research, said AI is exposing weaknesses that organizations have often learned to work around over time. According to him, fragmented data, inefficient processes, legacy technology and workforce capability gaps are increasingly becoming barriers to growth and competitiveness. Despite widespread recognition of the issue, the report found that only 6% of enterprises have successfully implemented and measured debt-resolution programs at scale. More than half have yet to allocate dedicated funding to address these challenges, highlighting a significant gap between awareness and action.