UOB polled business owners across the region and found something most vendors will not put on a slide: 65% of businesses have adopted AI in some form, but only 15% have reached what the study calls advanced capability.
Read that again. Four out of five companies using AI are stuck somewhere between "we tried it" and "it changed how we work."
The barriers the respondents named are where it gets interesting. Data and system readiness: 47%. Funding: 47%. Talent: 39%.
Funding and talent are the answers people give. Data readiness is the answer that is true.
The pilot always works. That is the problem.
Here is how it goes in real companies. Someone uploads three months of sales data to a chatbot and asks it to find the slow-moving stock. It comes back with a clean answer in eleven seconds. Everyone in the room is impressed. The pilot is a success.
Then you try to run it every week, across every product line, and it falls apart. Not because the AI got worse. Because the three months of data in the pilot were cleaned by hand by the one person who understood the mess. Nobody has time to do that every week for the whole catalogue.
The pilot worked on a sample. Production needs a system.
This is why the same study shows a 28-point gap in digitalisation success rates between large and small enterprises, and why only 69% of small enterprises here are digitalised against 93% of large ones. It is not that big companies have better AI. They have somewhere for the data to live.
What "data readiness" actually means
Strip out the consultant language and it means four unglamorous things.
One record per thing. One customer, one code. Not "ABC Trading", "ABC Trading Pte Ltd" and "ABC" as three separate lines that no tool can add together.
Numbers that update themselves. If your stock figure is a spreadsheet someone types on Fridays, every answer built on it is Friday’s answer.
History that survives people leaving. If the reason a customer gets 12% is in someone’s head, no model will ever find it.
A way to get the data out. If your system cannot export or connect, you will be pasting into a chat window forever — which is a demo, not a process.
None of that is AI. All of it has to exist before AI is worth paying for.
Our take: the order matters more than the tool
Business owners are being sold the roof before the foundation.
The sequence that works is boring and it is always the same. Get your master data clean — customers, products, suppliers, prices. Get your transactions into one system, so a sale, a stock movement and an invoice are the same event rather than three people typing. Then, and only then, put AI on top of it, where it has something real to read.
An ERP is not the exciting part of this. It is the part that makes the exciting part possible. Its actual job is to be the single place where things are true. Once that exists, AI stops being a demo and starts being a function: forecasting from real sales history, flagging margin drift as it happens, drafting the purchase order instead of the person who has done it 4,000 times.
Do it the other way around and you get exactly what the survey found. A pilot that impressed everyone, and no change to the P&L.
There is a second thing hiding in that data. 47% of businesses reported cost reductions from AI and 46% reported productivity gains. Those are real results, and they came from the companies that had somewhere to put it. The tool was available to everyone. The foundation was not.
What to do this quarter
Do not start with AI. Start with one question: if I asked for last month’s gross margin by product line right now, how long would it take and how much would I trust it?
If the answer is "a day, and not much" — that is your project. That is the whole project. Fix that, and AI becomes a small step afterwards rather than a leap you keep failing to make.
If the answer is "ten minutes, and completely" — you are in the 15%, and you should be moving faster than you are.
Rising costs were named the top challenge by one in three businesses in the same study, and improving profitability edged out growing sales as the top priority. That tells you what people actually need this year. Not a clever pilot. A business where the numbers are right and the manual work is smaller.
Read the original coverage of the UOB study.
Check your own readiness first
The ERP Readiness Scorecard asks eight questions on data, process, ownership and time — the things that actually decide whether a system project works. It will happily tell you "not yet", which is the most valuable answer it can give.
Originally published at The Gantry on 27 August 2026.
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