Enterprises are writing off AI proofs of concept. Not so fast, says Axis Bank
Many big companies have started to skip AI proofs of concept and jump straight to scaling. Axis Bank thinks that is a mistake. The bank says AI proofs of concept, or POCs, still matter a lot, as long as each one has a clear path to going live. In short, do not throw out the test stage just because it feels slow.
This view comes from a bank that has already put real AI tools into daily use. So the argument is not theory. It is built on what worked and what did not. Below we explain what a POC is, what Axis Bank actually said, and why this debate matters for founders and Indian firms.
What is an AI proof of concept?
A proof of concept is a small test. A team builds a tiny version of an idea to see if it works before spending big money. With AI, a POC might be a chatbot tried out on one team, or a model tested on a small set of data.
The point is to learn fast and cheap. If the test works, you scale it up. If it fails, you stop early and save money. Lately, some leaders have grown tired of POCs that never become real products. They call this “POC purgatory.” So they want to skip the test and go straight to a full rollout.
What Axis Bank actually said
Prasad Lad heads the Business Intelligence Unit at Axis Bank. He agrees that POCs can drag on too long. But he warns against dropping them. His core point is blunt and clear.
“But there is no path to enterprise scaling till you have done the POC right,” Lad said.
In plain words: you cannot grow an AI tool across a whole company unless you first prove it works in a small test. The fix is not to skip the POC. The fix is to tie each POC to a real plan for going live. No clear path, no POC.
Proof from real AI at Axis Bank
Axis Bank backs this view with tools that already run inside the bank. The clearest example is a platform called ADI.
ADI is an internal knowledge tool. It works like a smart search engine for staff. Employees use it to find facts on products, policies, and procedures. Around 40,000 staff use it regularly, which is close to 40% of the bank’s roughly 100,000 employees. The bank has also built AI training that adjusts lessons to each worker’s role.
To support all this, Axis Bank uses a layered data setup. One layer controls how data enters the bank and watches for errors and “data drift,” which is when data slowly changes and breaks a model. Another layer organizes that data into clean, consistent tables. Strong data plumbing is what lets a POC grow into a live product.
Key facts
| Detail | What we know |
|---|---|
| Who is speaking | Prasad Lad, head of Business Intelligence Unit, Axis Bank |
| Core argument | Do not skip AI POCs; tie each to a clear path to production |
| Flagship tool | ADI, an internal AI knowledge search engine |
| ADI users | Around 40,000 staff (about 40% of ~100,000 employees) |
| Data setup | Layered data architecture with drift checks and curated tables |
FAQ
Why are some firms dropping AI POCs?
They feel POCs take too long and rarely turn into real products. So they want to skip the test and scale fast. Axis Bank says this can backfire.
What is “data drift”?
It is when the data feeding a model slowly changes over time. This can quietly break the model’s results. Good systems watch for it and raise an alert.
What is ADI?
ADI is Axis Bank’s internal AI tool. It helps staff search for company information quickly, like a private search engine for the bank.
Why it matters (especially for India and founders)
Indian firms are spending heavily on AI right now. The big risk is wasting that money on tools that never go live. Axis Bank’s advice is a useful guardrail. Test small, but always with a plan to grow. That keeps spending honest.
For founders selling AI to large companies, the lesson is sharp. Do not pitch a flashy demo with no roadmap. Show the buyer how the POC becomes a live, scaled product. The same grounded, useful approach drives India’s wider AI push, including local efforts like BharatGEN’s work on sovereign AI. It also shapes how states compete to host real AI and deep-tech work, a fight playing out in India’s space-tech race.
The deeper point is about discipline. AI hype fades fast. What lasts is a clear line from test to production. Banks deal in trust and risk, so they cannot afford sloppy rollouts. That makes their playbook worth copying.
The takeaway
Skipping AI POCs may feel fast, but it can lead to costly failures. Axis Bank’s message is simple. Keep the test stage, but make every test earn its place with a clear path to going live. Done right, a POC is not a waste of time. It is the bridge between a good idea and a working product.
