Where to start with AI in a South Asian enterprise

Start with a decision, not a technology

Many AI initiatives begin with a technology. A board member has seen a demonstration, or a competitor has announced an assistant, and the brief becomes "we need a chatbot" or "we need to use AI." That is a solution looking for a problem, and it tends to produce a pilot that impresses in a meeting and then quietly stops being used.

A better starting point is a decision or a task. Look for something that happens repeatedly, costs money when it goes wrong, and has a clear owner. How much raw material to buy next month. Which deliveries go on which truck. Whether an invoice matches its purchase order. Who answers the same customer question for the hundredth time this week.

Three questions that rank the options

Most operations will produce a long list. Three questions sort it quickly.

How often does it happen? A decision made every day or every week gives a system thousands of chances to add value. One made once a year rarely justifies the effort.

What does a wrong answer cost? Be concrete. Excess stock written off, a stockout that lost a key account, overtime spent on manual reconciliation. If nobody can put a rough figure on it, it is probably not the place to start.

Is the data already being captured? The best first projects run on data the business already records, even if it is spread across systems. A project that first requires a year of new data collection is a second project, not a first one.

The opportunity that scores well on all three is usually not the most exciting idea on the list. It is the one most likely to pay back.

Check the data before the vendors

In most organisations the data needed for a first project exists, but it lives in an ERP, a set of spreadsheets, an email inbox and a WhatsApp group. Before comparing vendors or tools, spend two or three weeks finding out what is actually there, how far back it goes and how much it can be trusted.

This step is unglamorous and it decides most outcomes. It also produces something valuable whatever happens next: a clear picture of what the business knows about itself.

Keep the first project small enough to finish

A first project should reach production in a matter of weeks, not quarters. One product line, one site, one region. Define in advance what success looks like and how it will be measured, so the result is a fact rather than an opinion.

Small is not the same as trivial. A forecast for the twenty products that drive most of the volume, running reliably every month, is worth more than an ambitious platform that never quite launches.

Plan for the people, not only the model

The most common reason a working system fails is that the people meant to use it do not. Decide early who will use it every day, where they already work and what language they think in. A forecast delivered inside the spreadsheet a planner already opens will be used. The same forecast in a new dashboard behind a new login often will not.

What a good first year looks like

One system live and measured against an agreed target. A second opportunity, chosen from the same ranked list, under way. And a data foundation that is cleaner than it was, which makes every project after the first faster and cheaper than the one before it.