The 4 Types of Enterprise Debt

Last updated July 16, 2026

The conversation around AI transformation is prevalent, yet a recent Genpact study highlights a significant issue many organizations face: Enterprise debt. According to the study, 85% of companies recognize that Enterprise debt is hindering their ability to leverage AI effectively.

Here are the four main types of Enterprise Debt to consider:

• Technology debt (legacy systems): A well-known and unwanted friend for a number of enterprises, Genpact said development teams spend more than 40% of their time tending to existing technology debt, taking away time from building out new capabilities and catering to transformation initiatives. Technical debt remains a crushing problem for organizations because companies won’t shell out funds to upkeep technology until it becomes a business risk.

• Process debt (inefficient workflows): Costs derived from inefficient or manual processes. According to the report, employees spend around 40% of an average week addressing outdated processes.

• Data debt (poor data quality): This is the misalignment between the quality of an organization’s data and what they need for AI. Fragmented and poor quality data can become a bottleneck for AI projects.

• Talent debt (AI skills gaps): This includes the skills gap between the current workforce and what is required by human-agent operating model AI tools. Genpact estimates that only around one-third of the workforce is “AI-ready.”

The key takeaway? Many organizations are attempting to build AI capabilities on foundations that are not adequately prepared. Instead of immediately asking, "How can AI improve our business?", leaders should first consider, "What obstacles are preventing our organization from realizing the benefits of AI?"

Ultimately, the companies that will succeed with AI are not necessarily those that adopt the most tools, but rather those that work to eliminate the most friction.

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