Beyond the pilot: Is AI changing how India Inc works?
Artificial intelligence has been a recurrent theme in Indian boardrooms over the past year, with questions about where to begin, how fast to move, and what competitors are doing. Now comes the harder question: is AI improving business performance?
Most companies still rely on measures inherited from traditional software, including tracking pilots, licences, active users, or prompts. These numbers show activity, but not whether customers are better served, employees are more productive, or important work gets done faster. The true value of AI lies in how it impacts the business process through the work completed, the full cost of producing a successful outcome, and the confidence people have in the result.
With AI now applicable across so many parts of the business, a simple scorecard can help enterprises judge whether it is delivering value.
Measure the work, not the activity. The strongest deployments begin with a clear definition of useful work. For a customer service team, that could be an issue resolved. For an engineering team, it could be a code change that passes its tests. The outcome will vary by business, but it should be visible in the system where the work is done.
In India, many enterprises operate across large customer bases, high transaction volumes, multiple languages, and complex handoff chains. An impressive demonstration is a good starting point, but value appears when AI improves the speed or quality of everyday work.
Cost per outcome changes the equation. Cost per token is useful, but too narrow for a business decision. The full cost of an AI task includes employee time, retries, review, correction and delay. A less expensive model can end up costing more in practice if people have to make several attempts or spend too much time checking its results. A more capable model may cost more per request and still deliver better economics by producing a usable result faster.
For many customers, therefore, the true result of leveraging generative AI lies in discovering the value of deploying AI—rather than simply in the number of prompts generated or tools deployed. Companies need to know what it costs to complete a task to the required quality, versus just the cost of an AI model they deploy.
Additionally, trust has economic value. As AI moves into important workflows, dependability becomes part of the return. Accurate and consistent outputs reduce review and rework. They also give teams the confidence to use AI more widely. This is why some of the more instructive deployments are taking place inside the enterprise.
This is why some of the more consequential deployments are happening in environments where quality, governance and human judgment already matter, such as in pharmaceuticals. In fields like these, the real question is not whether AI can generate an output—but whether people can rely on it enough to incorporate it into everyday decisions and operations with the right safeguards and oversight.
That question is particularly relevant in India, where many enterprises are already used to managing complexity with discipline. For modern AI, the cost sensitivity is high, human review often remains an important layer, and workflows can be fragmented across teams and systems. In such settings, trust is what determines whether AI remains a side experiment—or becomes part of running a business.
The final defining point to note for utilizing modern AI is scale, which completely changes the economics of AI. Indian enterprises are accustomed to managing complexity and building with efficiency in mind. When an AI-enabled workflow performs well, even a modest gain can spread across thousands of employees or millions of customer interactions. At that scale, the meaningful test is whether useful work grows faster than the total cost of producing it, while quality holds or improves.
The next phase of enterprise AI will be about execution. It will mean backing the use cases that work, stopping the ones that do not, and continuing to keep humans in the loop where judgment matters most. The winners will not be the companies that ran the most pilots—they will be the ones that can clearly and repeatedly show that AI is completing useful work within real business processes, at a lower cost per successful outcome, and with enough trust to scale.
Nitin Bawankule is head, enterprise sales, India, OpenAI.
