Study finds $18 trillion in enterprise value at risk as AI readiness challenges mount: Genpact Research
A new study by Genpact and HFS Research estimates that Global 2000 companies could unlock nearly $18 trillion in value by addressing longstanding operational challenges that continue to limit the effectiveness of artificial intelligence initiatives.
The research, based on a survey of more than 2,000 enterprise leaders across 16 industries and 14 business functions, argues that many organisations are focusing on AI deployment while overlooking structural issues that affect how work is performed, data is managed, technology is maintained and talent is developed.
The report refers to these challenges collectively as “enterprise debt” and identifies four categories that organisations must address to improve business performance and maximize returns from AI investments: data debt, process debt, technology debt and talent debt.
According to the findings, enterprises that actively tackle these issues could achieve up to 8% faster annual revenue growth and 16% annual cost savings. However, 85% of executives surveyed said enterprise debt is already constraining their ability to generate value from AI initiatives.
The findings come at a time when AI spending continues to rise. The report notes that organizations are now allocating nearly 13% of average functional spending toward AI, increasing the importance of having the right operational foundations in place.
Among the four categories identified, data debt emerged as a major concern. Researchers found that only 33% of enterprise data is currently suitable for AI applications, while data quality issues are affecting 42% of AI and analytics initiatives.
Process debt was also highlighted as a significant barrier. The study estimates that employees spend approximately 40% of their working time dealing with inefficient or manual processes. In such environments, AI may accelerate activities but does not necessarily improve business outcomes if workflows remain fragmented or poorly governed.
Technology debt continues to affect enterprise transformation efforts as well. Core systems across organizations are, on average, about 10 years old, while developers spend roughly 42% of their time maintaining existing infrastructure instead of creating new capabilities.
The report also points to talent debt as an emerging challenge. Only 32% of employees are considered AI-ready today, creating a gap between enterprise ambitions and workforce capabilities.
Balkrishan “BK” Kalra, President and CEO of Genpact, said organizations seeking to scale AI successfully must first understand and improve the processes that underpin business operations. He noted that process intelligence is becoming increasingly important as enterprises look to generate measurable outcomes from AI investments.
The report estimates that process debt and data debt each account for nearly $7.7 trillion of the total value opportunity identified. Manufacturing and healthcare were found to represent the largest combined opportunity, while financial services showed the highest concentration of data-related challenges.
Phil Fersht, Founder and CEO of HFS Research, said AI is forcing organisations to confront weaknesses that have accumulated over time. According to him, issues such as fragmented data, inefficient processes, legacy technology and workforce readiness gaps have become increasingly important determinants of competitiveness and growth.
Despite broad recognition of these challenges, the study found that only 6% of enterprises have successfully established and measured debt-resolution programmes at scale. More than half of surveyed organizations have yet to commit dedicated funding toward addressing the problem.