The word “productivity’ has a lot to answer for. By definition, productivity is a broad metaphor for efficiency. AI is supposed to benefit productivity.
Does it? Many people say it doesn’t.
The almost-new buzzword for AI problems is “rework”.
Rework is the additional work required to correct inadequate work. It’s an obvious grind. It’s a built-in form of inefficiency and the exact antithesis of productivity. AI generates a lot of rework, which is exactly the fundamental problem with AI that most experts predicted.
Nobody’s congratulating themselves for pointing out the obvious. Nor should they. The real issue isn’t even AI. The critical business issue is oversight. Management, even at the most senior levels, exists largely for the purpose of oversight.
The somewhat disingenuous theory that you can blame the AI for everything doesn’t work. Wherever you are in the pecking order, it’s your problem at some point. The inevitable messes and backlogs caused by so much extra rework can be quantified.
Harvard Business Review, that sultry siren gossip of American business, found that AI tools didn’t reduce work. They intensified it and increased workloads. They blurred roles. AI synergized a reworking of workplace practices, with added workloads and “diversifications” as the usual outcome. It’s a very interesting and surprisingly tactful read, so check out that link.
There’s a much less tactful way of describing the whole idea of AI issues in business, particularly at the employment and use of resources levels.
Consider:
What is any business task? It’s a role including related jobs using X amount of business resources at Y cost.
That role must be supervised and documented up and down the food chain, costing Z.
The value of X should therefore be X – Y+Z. The outcome should be sustainable, easily measurable, economic, and therefore productive and profitable.
Well, is it? Bad job design is famous for adding costs and inefficiencies that generate more costs. I worked in the employment sector for a decade or so, and really, the Middle Ages often looked downright glamorous by comparison. Job design is now a synonym for the Black Death.
Let’s kill a few myths. It’s not people causing the problems; it’s truly godawful costing. If you have fewer people, that doesn’t mean these costs miraculously disappear. They rewrite themselves into the script for business operations.
If you include AI, these pre-existing task inefficiencies (what a horrible expression) are already there and ready to roll to cost you more money, plus the upkeep of the AI systems, integration with your business systems, etc.
This elegant presentation of issues leads to a few questions:
Do you actually need AI right now, this minute? Probably not. If you have an established cost structure, you’re probably OK for now. You also need time to navigate the best deals, get the full suite of services required, and factor those costs into your business plan.
Do you need any added unquantifiable costs? No. Imagine someone selling you a service that you’ve somehow managed to survive without for decades. Your knowledge is more anecdotal than practical. The incentive is “savings” that you should be able to do in your head in a few seconds.
What about AI upkeep? The usual Handy Dandy form of AI is pretty two-dimensional. You get a chatbot, the ability to write reports which you then have to also check, and an integrated calculator function which can keep your accounts section merrily beavering away on jobs they didn’t need to do previously.
What about security? Unknown. Do you need more or less security issues? There’s not even a theoretical plateau for AI security yet.
What about next-generation AI? This is unanswerable and likely to be equally unquantifiable. AI is making itself obsolete on a more or less weekly basis. DeepSeek terrified the AI sector until somebody found out how to do the DeepSeek role for about a tenth of the cost, a few days later.
Does this state of techno-flux look productive to you? It can’t, and it won’t. Keep an eye on AI productivity for a regular search of the tech news and expect the obvious.
Business hardheads don’t believe anything until they see verifiable proof.
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Disclaimer
The opinions expressed in this Op-Ed are those of the author. They do not purport to reflect the opinions or views of the Digital Journal or its members.
