How to balance governance and innovation in the agentic AI era

Unlike traditional AI assistants that primarily respond to user prompts, agentic AI systems can make decisions and execute actions autonomously across business processes. This creates opportunities to accelerate workflows and improve productivity but also introduces new challenges involving risk and organizational readiness. The shift is already well under way.

In a recent HCLTech survey, 87% of the participating enterprises reported using generative or agentic AI in IT operations, underscoring the growing importance of effective governance. These challenges were central to a recent HCLTech and Google Cloud roundtable on the business impact of agentic AI in which the participants explored what it takes to move from experimentation toward enterprise-wide adoption. Agentic AI is showing promise across a wide range of business functions, including IT operations, software engineering, customer experience, HR, marketing, finance, and sustainability.

Across these areas, intelligent agents can streamline complex workflows; automate routine tasks; and support faster, more informed decision-making. Realizing potential But realizing that potential requires more than deploying new technology. Successful implementations begin with identifying high-value use cases, establishing measurable key performance indicators (KPIs), and connecting AI initiatives to specific business outcomes.

Roundtable participants highlighted concerns about cost, ROI, and insufficient testing, reinforcing the importance of a measured approach to adoption. To manage these risks, organizations are typically starting with lower-risk business processes, demonstrating tangible value before expanding their use of autonomous agents as confidence and capabilities mature. Those concerns are reflected in HCLTech’s AI Impact Imperatives 2026 research, conducted by Omdia, which found that respondents expect approximately 43% of their major AI initiatives to fail.

Rethinking governance for autonomous systems As AI moves from assisting employees to acting on their behalf, traditional approaches to governance and compliance may prove inadequate. Autonomous agents introduce operational and reputational risks that require organizations to reconsider how decisions are monitored, audited, and controlled. Governance must be built into agentic AI initiatives from the outset rather than added after deployment.

That includes establishing clear boundaries for autonomous agents and determining where human intervention remains necessary. Human oversight is particularly important in high-stakes areas such as financial transactions, regulatory compliance, cybersecurity, and decisions affecting customers or employees. The appropriate level of oversight should reflect the potential business impact and risk associated with an agent’s actions.

Clear accountability, transparent decision-making, and escalation mechanisms can help organizations balance greater autonomy with continued human control over critical decisions. Preparing for new ways of working Technology alone won’t determine whether agentic AI succeeds at enterprise scale. Organizational readiness is another major hurdle, requiring businesses to align data, governance, people, and processes around common objectives.

Change management and AI literacy are particularly important. Employees across technical and nontechnical roles will need new skills to supervise agents, evaluate their decisions, and collaborate effectively with autonomous systems. As agents become increasingly integrated into business operations, organizations will also need to reconsider how their performance is managed and measured.

Over the next several years, enterprises may see the emergence of hybrid teams in which employees focus more heavily on strategic decisions and oversight while AI agents take on more operational responsibilities. Achieving this vision will require organizations to rethink operating models and invest in workforce readiness alongside technology. Ultimately, the autonomous enterprise will be defined by how effectively AI agents are integrated into business operations and deliver measurable results, rather than simply by how many agents an organization deploys.

Learn more about how HCLTech and Google Cloud are helping enterprises navigate the transition to agentic AI.