Genpact flags trust gap in agentic AI business transformation

Agentic AI business transformation is clearly on the radar for enterprises, but the latest Genpact HFS Agentic AI report shows that ambition is running ahead of reality. While most organisations believe AI agents will reshape how work gets done, they are still not ready to let these systems operate independently at scale.

The study, based on inputs from 545 senior executives across industries, reveals a strong belief in the future of autonomous AI systems. Yet, nearly 80 percent of enterprises continue to keep these systems under human supervision, signalling a clear gap between expectation and execution.

Trust and AI agent accountability framework remain weak

At the centre of this slowdown is trust, or the lack of it. Only a small share of organisations are comfortable giving AI agents full autonomy, and the concerns are not purely technical. Issues such as regulatory exposure, reputational risk, and lack of explainability continue to hold back decision-making.

This is where the need for a strong AI agent accountability framework becomes critical. Enterprises are realising that without clearly defined ownership of AI decisions and outcomes, scaling autonomy becomes risky. The challenge is not building smarter systems, but creating structures where accountability is clear and enforceable.

Services-as-Software (SaS) HFS model needs process rethink

The report also connects this shift to the broader idea of Services-as-Software (SaS) HFS, where AI agents take on execution roles rather than just supporting tasks. However, most organisations are still operating with legacy processes that are not designed for autonomous decision-making.

A significant number of respondents point to process readiness as the biggest barrier, highlighting that workflows still depend heavily on manual approvals and sequential steps. Without redesigning these processes, even the most advanced AI systems cannot deliver true autonomy.

Investment is rising but outcomes remain unclear

Interestingly, investment in agentic AI continues to grow, with enterprises planning to scale within the next 17 months. Spending is expected to increase sharply, but measurement frameworks have not evolved at the same pace.

Many organisations still rely on traditional productivity metrics, which fail to capture the value of autonomous systems. This creates a mismatch where companies invest in AI but struggle to quantify its real impact, slowing down broader adoption.

Skills and structure are shifting faster than expected

Another key insight is the changing nature of work itself. As AI agents take over coordination tasks, organisations expect flatter structures with fewer management layers. At the same time, the skills in demand are shifting towards managing and working alongside AI rather than building it.

This creates a new kind of gap, where structural changes are happening faster than workforce readiness. Enterprises need to rethink roles, responsibilities, and training to keep pace with this transition.

Autonomy needs more than ambition

The path to agentic AI business transformation is not blocked by technology but by readiness across trust, process, and people. Enterprises may be eager to move towards autonomous execution, but without strong accountability, updated metrics, and redesigned workflows, progress will remain slow.

The report makes one thing clear: the winners in this space will not be those who adopt AI the fastest, but those who build the right foundations to let it operate with confidence and control.

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