Four 'enterprise debts' blocking $18 trillion in AI value, research finds
A new study has found that the world's largest companies are collectively leaving nearly $18 trillion on the table from "enterprise debts" that hold back organisations from unlocking AI's full potential at work.
The findings come from a joint report by professional services firm Genpact and analyst group HFS Research, based on responses from more than 2,000 senior executives across 16 industries.
The report argues that four "enterprise debts" are forming a structural ceiling on AI performance across Global 2000 companies.
These debts refer to the accumulated drag on a business from outdated technology, poor data quality, inefficient processes, and underprepared talent.
Despite 85% of surveyed leaders acknowledging that these debts are hampering their AI returns, more than half reported having no funded programme in place to address them.
It comes as AI spending continues to climb, with nearly 13% of average function budgets now directed toward AI initiatives.
"Resolving these debts is the largest underutilised performance opportunity in business today," said Balkrishan "BK" Kalra, President and CEO, Genpact.
"You cannot out-innovate broken foundations. Understanding exactly where these debts live and how to resolve them requires context-rich process intelligence."
Where the debts sit
The first category, data debt, speaks to a quality problem rather than a volume one. It refers to the gap between the information enterprises currently hold and the quality AI systems require.
According to the research, only 33% of enterprise data is AI-ready, and 42% of AI and analytics initiatives are already failing because of data quality issues.
Process debt describes the drag created by manual, ungoverned workflows.
Around 40% of employee time each week is lost to inefficient or manual processes. Notably, the research warns that AI deployed into poorly governed workflows does not fail visibly, it simply executes the wrong steps faster.
Technology debt reflects the cost of maintaining legacy infrastructure before any new initiative can begin.
Genpact's research shows that core enterprise systems, on average, are already 10 years old, with approximately 42% of developer time going toward servicing existing debt rather than building new capabilities.
Talent debt captures the readiness gap between the current workforce and the demands of an AI-integrated operating model. Only 32% of the workforce is considered AI-ready. The research notes that talent debt compounds the other three, slowing every resolution effort.
"AI is exposing every weakness enterprises have spent decades learning to live with," said Phil Fersht, Founder and CEO of HFS Research.
"Poor process discipline, fragmented data, legacy technology and talent gaps are no longer operational nuisances. They are now direct barriers to growth, productivity, and competitiveness."
Addressing these debts
Addressing these debts, the study projects, could deliver around 8% faster annual revenue growth and 16% in annual cost reduction for organisations that act.
Despite these gains, the report found that just 6% of respondents have completed debt resolution programmes and measured the results.
The report calls these organisations "proven debt resolvers" and notes that while the gap in awareness between this group and the broader field is modest, the gap in execution is significant.
"The $18 trillion opportunity belongs to the organisations willing to confront these debts head on instead of masking them with more technology spend," Fersht said.
Kalra added that companies that commit to addressing these debts will not just gain a few points of advantage.
"They will gain market share by a factor," he added.
