Today, AI agents have become part of mainstream technology vocabulary, with enterprises and even individuals using them to execute specific, outcome-oriented tasks. ‘The ROI of Gen AI and Agents’, a report we brought out in collaboration with Omdia (Informa TechTarget), highlighted that 66% of organisations in India are already utilising or planning to embed agentic AI within the next year, versus 56% globally.

As AI models become more powerful and accessible, Indian enterprises have a clear opportunity to embed intelligence directly into everyday workflows. Given that enterprises now generate unprecedented volumes of operational data, AI agents can unlock new levels of productivity when used well. With these intelligent systems, organisations can finally move beyond analysis to coordinated action.

On the flip side, being slow on the uptake means that companies will remain constrained by manual processes, fragmented AI deployments, and slower decision-making. Not only does this limit productivity and ROI from AI investments, but it also makes it harder to scale innovation, respond to market changes, and stay competitive in an increasingly data-driven world.

When AI agents are equipped to determine when actions should occur, orchestrate workflows across enterprise systems, and execute them within governance and policy constraints, it forms the foundation of what we call the Agentic Enterprise.

The agentic enterprise

The agentic enterprise redefines how enterprises operate. It embeds intelligence directly into business processes to deliver outcomes at the speed of decision-making. Rather than merely responding to queries, intelligent agents identify the right actions to take and seamlessly orchestrate them across enterprise systems. AI evolves from a support tool into a core operational layer that actively coordinates and executes work across the organisation.

Realising this vision at scale depends on a unified foundation that can connect intelligence, data, and governance across the enterprise.

Why a control plane matters

AI assistants today can generate plausible responses, but there is still a gap between artificial intelligence’s promise and real business impact. For example, they often lack the business context, governed access, and deep integration with enterprise data that is needed for making sound decisions.

A unified ‘control plane’ can bridge this crucial gap, acting as the coordinating layer that aligns AI models, enterprise data, applications, and governance policies. This ensures that intelligence is not only generated, but translated into action in a consistent, secure, and governed manner.

Here is where a strong data foundation becomes non-negotiable. AI-driven decisions cannot be reliable unless they are based on solid enterprise data that is not only governed, and contextualised, but also easily accessible. When structured and unstructured data come together with operational context, and policy guardrails, every action taken by an AI agent is both relevant and trustworthy.

When this happens, business users can interact with AI in natural language and receive role-specific outcomes, without relying heavily on technical teams.

This is when the true power of the agentic enterprise emerges.

Key building blocks of the agentic enterprise

The biggest shift that an agentic enterprise brings is moving from generating insights to driving actions and outcomes. For instance, if a sales operations leader requests a weekly view of sales pipeline changes and risks, they don’t receive a pile of reports to consolidate manually. Instead, they receive a decision-ready brief with root-cause insights, prioritised risks, and suggested next steps.

To succeed in the agentic enterprise, however, a foundation of trust is essential. The foundation must anchor in the key tenets of governance, security, and accountability. Enterprises should invest in mature data governance capabilities to deploy AI responsibly while mitigating regulatory risks.

For example, finance teams can request a variance analysis against the forecast along with a summary for leadership. The system then generates a breakdown of key variance drivers, highlights anomalies, and produces a polished, executive-ready narrative grounded in governed KPIs.

Robust security frameworks are also essential not only to protect data at rest, but also models, training datasets, and inference pipelines from unauthorised access or adversarial threats.

Privacy, too, is non-negotiable. Enterprises must be able to harness AI even over sensitive data, while enforcing stringent privacy standards. In addition, interoperability becomes essential as AI assets move across environments. The AI assets require consistent policy enforcement, lineage tracking, and model governance.

Equally important is resilience to support business continuity and disaster recovery. And as AI systems grow in complexity, observability becomes a key enabler. It provides the visibility needed to understand, troubleshoot, and optimise AI-driven processes.

Finally, even in the agentic enterprise, humans remain a critical part of the loop. While AI agents can accelerate decision-making and execution, human oversight is irreplaceable to ensure accountability, ethical alignment, and contextual judgment, especially in high-stakes scenarios.

The path to the agentic enterprise is not simply about deploying more AI. It is about building the right foundation. With the right approach, enterprises can empower their teams to access and act on the most relevant data, knowing it is governed, secure, and aligned with business policies.

The future of enterprise AI will not be defined by how well systems can analyse data, but by how effectively they can act on it. And in that future, the agentic enterprise will set the standard for turning intelligence into impact. For enterprises, the time to start building that robust data foundation starts now.

(The author is managing director- India, Snowflake. Views are personal.)