Companies eager to deploy generative AI and autonomous AI agents must first address fundamental weaknesses in their technology infrastructure, data quality, business processes and workforce readiness, according to Livia Badea, Vice President and Global AI Innovation Center Lead at Genpact.
Speaking at the Innovation Forum 2026, organized by The Diplomat-Bucharest, Badea said a recent study conducted by Genpact in partnership with HFS identified four major categories of organizational debt that continue to hinder successful AI adoption.
“We call them the four debts of modern organizations: data debt, technology debt, process debt and people debt,” she said.
According to Badea, many organizations are rushing to embrace GenAI and agentic AI without first assessing whether their internal foundations can support these technologies.
“Before we start talking about GenAI, agentic AI and all these new technologies, we need to look inside our organizations and understand whether the foundations we already have can support the deployment of these systems,” she said.
She noted that many companies are enthusiastic about AI-powered transformation but lack the infrastructure required to make it successful.
“We see many clients excited about the idea of agentifying their operations, but they simply do not have the necessary technology infrastructure. They are running legacy systems that do not communicate with each other, and their data is scattered across multiple environments,” Badea said.
As a result, many AI transformation programs must begin with foundational investments in technology and data architecture.
“For these organizations, the first step is not AI. The first step is building technology foundations and data foundations, the backbone that will later support transformation,” she said.
Data quality remains another critical challenge. “If your data is incomplete or inaccurate, you cannot generate meaningful value from AI. You will create false positives and still need people performing extensive quality checks. At that point, you are paying both for technology and for manual intervention,” Badea explained.
She argued that process debt may be the most difficult issue for organizations to confront.
“The most painful area today is process debt. It is difficult for organizations to admit that their processes are inefficient or fragmented,” she said.
Drawing on her experience in business services transformation, Badea noted that many organizations continue to rely on outdated operating procedures while overlooking numerous undocumented exceptions.
“Companies often have standard operating procedures that have existed for years, but they also have hundreds of industry-specific or country-specific exceptions that remain undocumented and exist only as tribal knowledge,” she said.
Without addressing these underlying inefficiencies, organizations risk accelerating problems rather than solving them.
“If we do not examine and improve our processes first, we are simply automating chaos,” Badea warned.
The fourth pillar of successful AI transformation, she said, is people.
“We need to bring employees with us on this journey. We need to help them understand what AI can do, how they can use it and how it can become a colleague rather than something that threatens their jobs,” she said.
While some companies have reduced headcount as part of their AI strategies, Badea noted that Genpact has taken a different approach.
“Some companies have laid people off. We have hired thousands of technical professionals. Our goal is not to do the same work with fewer people—it is to create more value and do more,” she said.
According to Badea, the biggest mistakes occur when organizations focus on solving only one category of debt while ignoring the others.
“Most organizations are dealing with a combination of two, three or even all four debts. If you fix technology but not processes, you create chaos. If you improve processes without the technology backbone, you create chaos. If you ignore data, you create chaos. And if you ignore people, you end up with a beautiful tool that nobody knows how to use,” she said.
To address these challenges, Genpact has spent several years building a structured AI workforce development framework based on future skills.
“We defined our upskilling strategy around future AI roles,” Badea explained. She outlined three key categories within the framework.
“The first category is AI Fluent professionals—people who understand what each technology does, its limitations and the situations where it should or should not be used. The second category is AI Practitioners, who use these tools daily in their work. The third category consists of Builders, the professionals who develop and deploy AI products and solutions,” she said.
Badea concluded that successful AI transformation requires balanced investment across technology, data, processes and people.
“You cannot neglect any of these areas. Sustainable AI transformation happens only when all four foundations evolve together,” she said.