If you run a business turning over somewhere between R15 and R50 million, you have almost certainly had this experience. Your feed is full of AI. A competitor drops a hint about "our AI-enabled workflow". A supplier mentions their new "AI capability". The phrase AI enablement gets used in rooms you are in, by people who say it as though everyone present agrees on what it means. You nod along. And privately, you are not entirely sure what it would actually look like inside your business — a business that knows its trade cold, and does not have a data scientist, an innovation budget, or a spare afternoon.
That uncertainty is not a gap in your knowledge. It is a gap in the term. "AI enablement" has been stretched to mean everything and therefore nothing — from a chatbot on a website to a wholesale reinvention of a company. This post is an attempt to put a plain, usable meaning back into it, for a business like yours, with the hype and the jargon left at the door.
It is not the thing you are picturing
Start by clearing away what it is not. AI enablement is not a science-fiction robot. It is not, necessarily, a chatbot bolted onto your website. It is not a plan to replace your people. And it is emphatically not a moonshot that requires a research lab and an eight-figure budget — that version exists, but it was written about a company that looks nothing like yours.
It is also not new or exotic any more. Stanford University's Human-Centered AI institute, which publishes the most respected annual audit of the field, found that 78% of organisations reported using AI in 2024, up from 55% the year before. Whatever this is, most businesses are already doing some of it. It has quietly become ordinary.
Stripped of the mystique, AI enablement means something fairly mundane: using AI to take specific, well-defined, information-heavy tasks off your people — the drafting, the summarising, the sorting, the looking-things-up — so that their time goes to the parts of the work that genuinely need a human. That is the whole idea. Everything else is detail.
A mental model that survives contact with reality
The most useful way to think about the tools underneath all this is not as an oracle or an autopilot, but as an assistant: fast, tireless, and very good at a specific range of jobs. It produces a strong first draft in seconds. It reads a long document and pulls out what matters. It spots patterns across a lot of text. It never gets bored of repetitive work.
It is also, occasionally, confidently wrong, and it has no real judgement of its own. Which is why the sensible arrangement is always the same: the assistant does the drafting and the spadework, and a person checks it and makes the call. The value is not in handing over the decision. It is in the leverage on everything that leads up to the decision.
There is good evidence this leverage is real, and — more importantly for you — that it lands where a smaller business needs it most. In one of the largest workplace studies to date, economists Erik Brynjolfsson, Danielle Li and Lindsey Raymond tracked more than 5,000 customer-support staff given access to an AI assistant. Published in the Quarterly Journal of Economics in 2025, the study found productivity rose about 15% on average — but the gains were concentrated among the newer and less-experienced workers, while the most experienced staff barely changed. The tool worked like a leveller, spreading the instincts of the best people across everyone else. Customers were measurably happier, and staff were less likely to leave. For a business that cannot always afford to hire the most experienced person in the room, that is the headline: the technology is best at lifting ordinary work to a higher standard.
What it looks like on a normal Tuesday
None of this is abstract once you make it concrete. In a normal business, AI enablement is:
- Turning a rough scope into a first-draft quote or proposal in minutes, for you to check and finish.
- Reading a long supplier contract or technical spec and pulling out the handful of clauses that actually matter.
- Drafting replies to routine customer emails in your tone of voice, for a person to approve and send.
- Pulling a straight answer out of your own records — your invoices, orders and spreadsheets — instead of hunting for it across three systems.
- Turning a month of scattered customer feedback into the three themes worth acting on.
Notice what these have in common. None of them is glamorous. None of them would make a good LinkedIn video. All of them are hours your people currently spend on work that does not actually need them — and every one of those hours is money.
Why it is suddenly worth your attention
The reason this is a conversation for a R20-million business now, and not just for a bank, comes down to price and access. The same Stanford audit found that the cost of running a capable model collapsed — from roughly $20 to about $0.07 to process a million words-worth of text, in the space of about eighteen months. The same capability, for a fraction of a cent. And you no longer need to build or own anything to use it: you rent it over the internet, by the sip, and pay only for what you use.
That combination — cheap and rentable — is what moved AI from a capital project to something you can simply try. The barrier to a first step is no longer money or infrastructure. It is knowing where to point it.
The part nobody sells you
Here is the quiet catch. The phrase "AI enablement" points at the technology, and the technology is now the easy, cheap part. The hard part — the actual work — is figuring out which of your tasks it genuinely fits, and being honest about which it does not.
That is not a technical question. It is a question about your business, and you are better placed to answer it than any vendor with a demo. The businesses that get real value out of AI are not the ones who "adopted AI" as a project. They are the ones who looked at their week, found one task that was tedious, repetitive and made of words, tried the tools on it, and kept what worked.
So if you have been waiting to understand AI before you start, you have it slightly backwards. You do not need to understand the technology first. You need to look at how your people actually spend their days, find the task that fits the description above, and begin there. Understanding follows doing, faster than you would expect. And if that is the conversation you have been circling — which task, and is it even worth it — it is a short and unusually clarifying one to have with someone who does this for a living.
References
- Stanford University Human-Centered AI (HAI) — 2025 AI Index Report (organisational AI adoption rising to 78% in 2024; the fall in inference cost from roughly $20 to $0.07 per million tokens). https://hai.stanford.edu/news/ai-index-2025-state-of-ai-in-10-charts
- Erik Brynjolfsson, Danielle Li & Lindsey Raymond — Generative AI at Work, Quarterly Journal of Economics, Vol. 140, Issue 2 (2025); working paper via the National Bureau of Economic Research. https://www.nber.org/papers/w31161
Written by JP Dippenaar, Sixees Labs. Last reviewed July 2026.