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

AI Ambition Meets Operational Reality

Global enterprises are racing to deploy AI, but Genpact research conducted with HFS Research reveals a stark disconnect between ambition and readiness. Surveying more than 2,000 enterprise executives across 16 industries and 14 business functions, the study uncovers that organizations could unlock nearly $18 trillion in value by addressing what researchers call enterprise debt—the accumulated drag from outdated technology, poor data quality, inefficient processes, and underprepared talent 1

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. Companies are now directing 13% of average functional spending toward AI initiatives, yet the foundations supporting these investments remain fundamentally unprepared 3

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The data reveals a troubling gap: 85% of leaders acknowledge that fragmented data, ungoverned processes, aging systems, and undertrained talent are actively working against their AI investments 1

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. This AI unpreparedness represents more than a temporary setback—it creates a structural ceiling on performance that becomes a hard constraint in the AI era. Organizations addressing these foundational flaws in AI investments could achieve approximately 8% faster annual revenue growth and reduce costs by as much as 16% annually 3

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Four Debts Compounding Into Structural Failures

The Genpact research identifies four interconnected forms of enterprise debt that prevent organizations from capturing AI-driven economic value. Data debt represents the gap between the data enterprises possess and the AI-ready data that algorithms require. Only 33% of enterprise data meets AI readiness standards today, and 42% of AI and analytics initiatives are already failing because of data quality issues 2

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. Process debt stems from how work actually flows through organizations—manual processes consume around 40% of employee time each week, and AI deployed into ungoverned workflows executes the wrong steps faster rather than fixing underlying inefficiencies 2

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Technology debt reflects the legacy infrastructure tax that every modern initiative must pay before it starts. Core systems average 10 years old, and approximately 42% of developer time goes to servicing existing debt rather than building new capabilities 2

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. Talent debt captures the workforce readiness gap—only 32% of employees currently possess the skills needed to operate effectively in AI-driven environments, and this deficit amplifies every other form of debt 2

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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 3

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The Action Gap Widening

Despite widespread recognition of these challenges, the study exposes a significant gap between awareness and action. More than half of surveyed leaders acknowledged they have no funded plans to address enterprise debt, and only 6% of enterprises have successfully implemented and measured debt-resolution programs at scale 3

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. Balkrishan "BK" Kalra, President and CEO of Genpact, emphasized that organizations must address foundational business challenges before expecting meaningful returns from AI investments, highlighting the importance of understanding how workflows operate across an enterprise 3

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Phil Fersht, Founder and CEO of HFS Research, noted that AI is exposing weaknesses organizations have learned to work around over time, with fragmented data, inefficient processes, legacy technology, and workforce capability gaps increasingly becoming barriers to growth and competitiveness 3

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. The research suggests that layering AI on top of processes never designed for it does not unlock value—it locks in the cost of the status quo at machine speed . For enterprises watching AI competitors pull ahead, the message is clear: the race to AI adoption will be won or lost on the strength of operational foundations built today.