If AI Starts Making Decisions for Your Business, Who Explains Them? India’s Agentic AI Moment Just Got Real

Not long ago, artificial intelligence inside companies mostly meant experiments. Chatbots handled customer queries, analytics tools suggested actions, and humans still made the final calls. Now that comfort zone is ending. Across India, enterprises are beginning to deploy AI systems that do not just assist humans but increasingly act on their own.

That shift was sharply highlighted this week by Arundhati Bhattacharya, who now leads South Asia operations at Salesforce. Her message was simple but pointed: AI systems can no longer operate as mysterious black boxes. If machines are making decisions that affect customers, finances, or compliance, businesses must be able to explain how those decisions were made.

It is not just a technical upgrade. It is a trust upgrade.

India Is Moving from AI Experiments to AI Employees

Over the last two years, many Indian companies experimented with AI cautiously. Small pilot programs tested tools in customer support, data processing, or internal workflows. Most of these projects remained isolated, kept safely away from critical operations.

Now things are changing quickly. Businesses are deploying multiple AI agents that coordinate across departments. Instead of one system answering questions, companies are building digital teams where different agents handle billing, logistics, compliance, customer requests, and inventory decisions in real time.

Recent industry data shows nearly half of Indian organizations now run several generative AI use cases in live production environments, not just testing phases. In simple terms, AI has moved from demo mode to office floor mode.

And once AI starts acting instead of suggesting, accountability suddenly matters.

So, What Exactly Is Agentic AI?

The term sounds complicated, but the idea is simple. Agentic AI refers to systems that do more than respond. They plan, decide, and act.

Instead of just answering questions, these agents can:

• Analyze situations and decide next steps
• Execute tasks without waiting for human approval
• Adjust actions when conditions change
• Coordinate with other agents across systems

Imagine a customer requesting a refund. Instead of forwarding the issue to five departments, an AI agent checks policy eligibility, processes payments, updates records, and sends confirmation automatically. The customer gets resolution, not just instructions.

The system does not just respond. It solves.

That efficiency is attractive, but it also introduces a new question: how do you trust decisions you cannot explain?

Why Explainability Suddenly Became a Business Requirement

When AI only made recommendations, humans still held responsibility. But when AI begins approving transactions, routing complaints, flagging fraud, or managing supply chains, decisions must be auditable.

Executives and regulators now ask straightforward questions:

·       Why was this loan rejected?

·       Why did the system escalate this complaint?

·       Why did pricing change for this customer?

If the answer is simply “the model decided,” that is not good enough.

Explainable systems allow companies to trace decisions back to specific factors, thresholds, or rules. That transparency protects companies legally, builds customer confidence, and prevents hidden bias.

In other words, AI decisions must stand the same scrutiny as human ones.

Multi-Agent Systems Bring New Power and New Complexity

Bhattacharya emphasized another shift happening inside enterprises. Companies are moving from single AI tools to orchestrated systems where multiple agents collaborate.

Think of it as digital teamwork:

·       A primary agent interacts with customers.

·       Specialist agents handle payments, inventory, policies, or compliance.

·       An orchestration engine coordinates everything behind the scenes.

Airlines, retailers, and service providers are already deploying such systems. One airline using these AI workflows reportedly processes millions of refund queries faster while still escalating complex cases to human staff when needed.

Speed improves, but oversight becomes critical. When many agents act together, mistakes also scale quickly unless monitored carefully.

Why India May Lead This Transition

Interestingly, India’s complexity may actually be an advantage. Businesses here operate across multiple languages, regions, digital maturity levels, and regulatory environments. Systems built to function in this diversity often become globally adaptable.

Business leaders in India are also known for rapid adoption once value is proven. Instead of waiting years for perfection, companies test, learn, and scale quickly. Combined with national investments in AI infrastructure and training initiatives, India may shape how responsible AI deployment looks worldwide.

But leadership comes with responsibility. Scaling AI without governance could damage trust faster than it creates efficiency.

The Three Priorities Business Leaders Now Face

According to Bhattacharya’s perspective, companies entering this new phase must focus on three areas:

• Building AI literacy so employees understand what AI can and cannot do
• Creating governance frameworks that set clear limits and accountability
• Encouraging continuous learning rather than one-time deployment

Put simply, companies must train people, guide machines, and keep humans involved when stakes are high.

AI should enhance judgment, not replace it.

Trust Is Becoming the Real Currency

The conversation around AI used to focus on capability. What can AI do? Now the focus is shifting toward responsibility. Can AI decisions be trusted, reviewed, and corrected?

Organizations deploying AI agents increasingly realize success depends on three foundations:

·       Strong and reliable data

·       Clear policies governing AI behavior

·       Human oversight that can intervene when necessary

Without these, AI systems may perform brilliantly but still fail under scrutiny.

The Moment India Is Entering Now

India’s enterprises are entering what could be called the execution phase of AI. Systems are no longer side experiments. They are becoming part of everyday business operations.

That makes explainability no longer optional. If AI systems handle customer money, personal data, or regulatory processes, companies must show how decisions happen.

And this is where Bhattacharya’s warning lands. AI cannot remain a mysterious engine producing outcomes nobody fully understands.

Conclusion

AI in India is growing up. What began as experimentation is becoming infrastructure. Agentic systems promise speed, efficiency, and scale, but also demand transparency and accountability.

The winning organizations will not just deploy smarter systems. They will deploy systems people trust.

Because when machines begin making decisions, someone still has to answer the question: why?

And in business, “because the AI said so” will never be enough.