Even as AI ambitions in Indian organizations are gathering momentum, businesses are still struggling to build the governance and trust frameworks needed to scale the technology with confidence. According to ServiceNow’s Enterprise AI Maturity Index 2026, while 54% of Indian organisations are deploying AI agents, only 11% have moved to autonomous workflows.
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The gap points to a growing paradox wherein enterprises are increasingly willing to let AI participate in business operations, they are not yet equally prepared to manage the risks that come with it.
The concerns are understandable. As AI moves beyond generating insights and assisting employees to executing tasks, accessing enterprise systems and making decisions, the consequences of an error or misuse become significantly greater. Transparency and the potential for misinformation are concerns for 60% of Indian organisations surveyed by ServiceNow. Regulatory and compliance complexity worries 55%, while 50% cite data privacy and security.
The challenge, however, is not confined to India. Forrester’s global research highlights a similar disconnect between AI ambition and governance readiness. Its November 2026 report, AI Governance Has a Strategy Problem, finds that only 42% of AI decision-makers say governance strategy is included in their organisation’s official AI and agentic AI strategy.
Forrester points out that having AI policies is not the same as having a governance strategy. While policies may define acceptable usage and establish basic safeguards, organisations need a more comprehensive framework to determine which AI applications to deploy, what risks are acceptable, who is accountable for decisions and where human oversight is necessary.
This becomes particularly important with agentic AI. Unlike conventional AI tools that primarily generate content or recommendations, agents can access data, interact with applications and execute multi-step workflows. Enterprises must therefore govern not just what AI produces, but what it can access, what actions it can take and when human intervention is required.
For Indian enterprises, this governance deficit could become a significant brake on AI adoption. Without confidence in the reliability, security and accountability of AI systems, organisations are likely to restrict their autonomy, limiting the technology to relatively low-risk applications rather than deploying it across critical business processes.
Building trust, therefore, is the crucial bridge in accelerating AI adoption. It must be embedded throughout the AI lifecycle—from selecting and developing models to deployment and continuous monitoring. This requires clear accountability, strong data governance, regular testing and independent risk assessments, controlled access for AI agents, and human oversight for consequential decisions. Employees must also understand how to question AI outputs and recognise when human judgement is essential.
Ultimately, the next phase of enterprise AI adoption will not be determined simply by how many organisations deploy AI or how many agents they introduce. It will depend on how confidently they can trust these systems to operate securely, transparently and responsibly. Governance is what makes adoption at scale possible.
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