As AI becomes part of everyday business in India, the bigger opportunity may lie in helping companies connect data, context and human judgement to make better enterprise decisions at the pace of market volatility.
On a trading desk in Mumbai, the same signal can produce very different calls. One investor may hedge, another may hold and a third may add to a position. The difference is not the information in front of them, but how they read it against timing, exposure, risk and what they believe happens next.
Indian businesses are beginning to encounter something similar with AI. The technology has become remarkably good at finding the signal, detecting patterns, forecasting demand, flagging anomalies and processing information faster than a team could manually. Yet much of enterprise investment remains focused on making existing systems work faster, rather than improving the quality of the decisions those processes support.
A recent Gartner survey of finance leaders found that 45% of CFOs said their AI investments lean towards productivity, while only 20% of finance AI projects lean towards improving decision quality. This gap often widens when organisations fall into the 'data perfection trap' and suffer from decision paralysis while waiting years to cleanse internal data, rather than deploying an infrastructure that works with existing data to make immediate, agile choices.
Businesses have never lacked data. What they often lack is a shared view of context, because different teams look at the same business through entirely different lenses. Finance looks at budgets, margins and cash. Sales looks at customers and pipeline. Operations thinks about capacity and fulfilment. Supply chain teams focus on inventory, suppliers and logistics. All these perspectives matter. The challenge arises when a company must bring them together and decide what to do next.
{{/usCountry}}Businesses have never lacked data. What they often lack is a shared view of context, because different teams look at the same business through entirely different lenses. Finance looks at budgets, margins and cash. Sales looks at customers and pipeline. Operations thinks about capacity and fulfilment. Supply chain teams focus on inventory, suppliers and logistics. All these perspectives matter. The challenge arises when a company must bring them together and decide what to do next.
{{/usCountry}}This is where AI's role becomes more interesting. Instead of being seen only as a tool that produces information, it can increasingly form part of a wider decision-making environment, one that helps companies test different possibilities and understand the consequences of each choice.
Business planning has traditionally depended on forecasts around growth, costs, demand and investment. Business rarely moves in a straight line. What AI changes is the speed and depth with which businesses can test those assumptions. Scenario modelling that once required specialist teams and lengthy modelling cycles can increasingly be explored through more intuitive, natural-language interfaces and agentic capabilities.
What if festive-season demand grows 20% faster than planned? What if a new facility takes longer to become operational? What happens to profitability if costs move in a different direction? For Indian companies operating in a fast-moving economy, this ability to model alternatives before committing resources could be particularly useful. Yet, seeing the consequences of a decision is not the same as making it.
Growth may come at the cost of margins, resilience may cost more, a long-term market bet may depress returns today. Choosing between them remains a matter of judgement.
At an Indian enterprise, a growth decision may begin with sales but will also affect hiring, production, investment, cash flow and customer commitments. A procurement decision may influence margins and customer commitments. A pricing decision may change demand, inventory and profitability. The bigger the decision, less likely it is to belong to one department.
The next phase of enterprise AI could, therefore, become less about adding another tool to every function, and more about helping different parts of the organisation see how their decisions intersect. The opportunity is a connective layer that lets finance, operations and sales see the same regional variation at the same time, instead of discovering it in three different meetings a month apart.
India offers a compelling setting for this shift. Companies here often operate markets that differ significantly in consumer behaviour, price sensitivity, infrastructure, distribution and availability of talent. A strategy that works in one city or state may need to be adjusted considerably in another. At the same time, many Indian enterprises are becoming larger, more digital and more global.
Few other markets ask a single enterprise to reconcile this much internal variation into one number on one slide, which makes this less a footnote to India’s AI story and more the story itself.
That scale brings complexity, but it also creates an opportunity to rethink how decisions are made. An expansion plan for a consumer goods company, for instance, may look attractive at an all-India level but produce very different economics across cities once local demand, distribution costs, hiring and supply constraints are factored in. The ability to see those differences earlier can lead to better choices about where to invest, how quickly to expand and where to hold back.
That may be one of India’s more interesting AI opportunities, using technology not simply to manage scale, but to make better decisions within all the variation that comes with it. The businesses getting real value from AI right now are rarely the ones with the most sophisticated models. They are the ones using AI to surface where a national assumption breaks down, before that shows up as unsold stock in one state and stockouts in another.
There is also an important human dimension to this conversation. Businesses are not simply mathematical equations. They are shaped by people, relationships, ambitions and choices that cannot always be reduced to a set of numbers.
A business leader may decide to continue investing in a market because of its long-term potential, even when the immediate returns are still developing. A company may prioritise customer trust over a short-term financial gain. These are questions of strategy, risk and priorities, that ultimately require judgement.
AI’s role may not be to remove people from decision-making, but to give them a clearer view of the choices in front of them. If leaders can see relevant information, possible outcomes and competing trade-offs more clearly, they can spend less time assembling the picture and more time deciding what it means. That is a far more meaningful role for technology.
The first phase of enterprise AI was largely about capability, what can the technology generate, analyse or predict. The next phase may be about what businesses can do with that capability.
For Indian enterprises, the opportunity is not simply to add more intelligent tools. It is to use AI to understand choices, anticipate consequences and make better calls when the answer is not obvious. Because in business, as in markets, identifying the signal is only the beginning.
The harder part is knowing what to do next. Get that right, and India’s scale stops being a planning problem and becomes what it should have been all along, the advantage.
(The views expressed are personal)
This article is authored by Amit Bagga, managing director, APAC, Anaplan.