India Inc seeks legal safeguards as agentic AI liability risks mount

Conventional wisdom suggests that proactive legal work protects companies from future liabilities, especially with emerging technologies like agentic AI. However, India Inc.'s current scramble to redraft contracts and stipulate AI responsibility may offer a false sense of security. This early push for safeguards likely misprices operational risk, assuming regulatory certainty will arrive before these systems become ubiquitous.

A lawyer-led risk preflight meets boardroom budgets

Lawyers and compliance teams are increasingly treated as a first line of defense in AI programs, not merely as a post-mortem check. The ET piece situates this as a shift from technical feasibility to contractual governance: as AI systems move toward independent decision-making, firms fear unknowable liabilities that could arise from autonomous actions.

The practical implication, executives tell the authors, is a reallocation of resources toward drafting and negotiating liability clauses that specify who bears costs for AI-induced mistakes and how disputes will be resolved. In financial services, manufacturing, and services alike, this lawyer-led preflight is entering RFPs and vendor negotiations as a visible cost category.

The article stresses the need for governance around “agentic” behavior, not just capabilities, and notes that such governance may become a prerequisite for pilots and scale. This framing suggests a new OPEX line item for risk consultation, contract redlines, and audit-ready liability schemas.

This same dynamic is being observed in boardroom conversations where risk officers push back on aggressive AI adoption timelines until liability maps are in place. The ET report frames the discussion as a preemptive move to build resilience into procurement and partner contracts, attempting to turn a potential liability spike into a defined contractual outcome.

While the piece does not quantify these costs, executives quoted or implied in the article describe a shift from mid-cycle governance to front-end risk budgeting. The practical upshot is a tighter linkage between AI design choices and legal exposure, with procurement teams now negotiating who owns data rights, decision-path traces, and accountability in edge cases.

What the article omits: slow, fragmented enforcement The article foregrounds proactive contract work but provides little about how regulators will texture liability in practice. The regulatory tempo in India, as described, is slow and uneven, with enforcement and precedent likely to lag the pace of AI deployment. This regulatory lag can create a window where contracts attempt to predefine responsibility, yet courts and regulators fail to offer predictable enforcement. For executives, that means a mispricing of risk: a cost of drafting expansive liability clauses today may not translate into reliable protection tomorrow if the legal framework remains unsettled. The ET piece hints at this but does not provide a roadmap for how to calibrate exposure to the real tempo of jurisprudence.

Moreover, the article does not map how different industries might experience divergent outcomes. In financial services, for example, the push toward autonomous decision systems intersects with consumer-protection regimes and banking liability norms; in manufacturing, product liability and safety compliance could interact with AI-driven process control in complex, context-specific ways.

Without granular guidance, the headline claim—contractual clarity—may become a general expectation that fails in practice when disputes arise near the edge of novelty. The absence of enforcement timelines and sector-specific guidance is, in itself, a risk signal.

From contracts to claims: mispricing risk under regulatory ambiguity If India’s domestic discussions translate into formal guidance, the mispricing thesis could tighten. The ET report points to a proactive stance that may shrink ambiguity in the near term, but ambiguity is not only legal; it is also operational. Contracts may specify who bears liability for AI mistakes, but they cannot anticipate every operational edge case or interpret evolving autonomy thresholds. This creates a bifurcation: organizations may believe they have carved out responsibility assignments only to discover that courts or regulators interpret autonomous actions differently, resulting in dispute frictions and unexpected costs. The absence of a clear, unified standard means that liability could shift across parties as cases unfold, underscoring the risk that today’s contracts become tomorrow’s litigation leverage.

As the article notes, the immediate move is contractual hygiene rather than a binding, nationwide framework. That hygiene reduces some risk but does not eliminate it; it instead redistributes it toward legal fees, insurance negotiations, and cross-border data and IP disputes.

In the long run, the value of the contract becomes a moving target as the definition of agentic AI, the threshold for liability, and the scope of responsibility evolve. For executives, the takeaway is not to fear governance but to recognize that governance is a living tool that must be continuously realigned with regulatory flux and courtroom interpretation.

Watchlist: signals that could widen the gap Executives should monitor a handful of signals over the next six to twelve months to test whether the mispricing thesis holds. First, any formal regulatory guidance in India that clarifies agentic AI liability definitions and enforcement would begin to compress the risk space and align contract terms with expected precedents. Second, the emergence of standardized liability insurance products for agentic AI would provide actuarial data to calibrate risk pricing and define coverage gaps. Third, court decisions on early autonomy-related disputes would establish precedents affecting how liability allocations are interpreted in practical disputes and could force rapid re-pricing of risk. If any of these occur, the premise that risk is mispriced based on anticipated clarity would weaken.

The article does not provide a compensation strategy for when those signals fail to materialize, leaving corporate risk offices with a dilemma: delay AI pilots to wait for clarity, or deploy with contractual guardrails that might prove insufficient in future enforcement contexts. Boards will need to consider multiple scenarios, including what happens if liability allocations are unsettled or if insurers reduce coverage for agentic AI risks.

The ET report thus presents a precautionary tale: the push for legal safeguards is prudent, but it may overestimate how quickly regulatory certainty will translate into practical risk reductions.