If a job that took eight hours now takes one, most managers will call that a productivity gain.
They are right up to a point.
Economists usually measure labour productivity as output per hour worked. If we produce the same output in one hour instead of eight, productivity has risen whether we use the remaining seven hours to win another customer, dissect the accounts, or head for the beach.
The beach does not cancel the efficiency gain, it simply exposes the limits of the measure.
The conventional measures tells us how efficiently we produced what it can already see. It tells us very little about what happened to the released capacity, whether the output improved, or whether technology enabled valuable work that nobody previously attempted.
It is a stopwatch with a blindfold.
This matters as businesses adopt AI. Most discussion starts with replacement: write the report faster, answer the email sooner, produce the first draft without paying someone to stare at a blank screen.
Useful, certainly but not transformative.
If AI cuts eight hours of work to one and the business fills the other seven with more of the same work, the gain will eventually appear in output, margin or capacity. If the business simply adds seven hours of meetings, the technology has delivered potential while management has squandered it.
That is not a measurement failure alone. It is a management failure with excuses.
The larger opportunity comes when AI extends what a business can do.
Most SMEs sit on piles of data they barely use. The accounting system contains patterns in customer profitability, purchasing, margin leakage and payment behaviour. The CRM contains clues about lead quality, objections, lost sales, response times and repeat purchases.
Managers have always suspected gold lies in those records, but lacked the time, skills or patience to dig it out.
AI changes the economics of the digging.
A business can now examine why apparently similar customers generate wildly different margins. It can compare the language used by prospects who bought with the language used by those who disappeared. It can identify which products create revenue but consume so much handling, discounting and after-sales attention that they destroy profit.
None of this necessarily replaces an existing task. Often, nobody did the task in the first place.
That makes “hours saved” a miserably small way to assess the value.
The better questions concern decisions improved, waste prevented, risks spotted and identified opportunities leveraged.
My commercial history includes a significant time driving sales and profitability of dairy products.
Take yoghurt for example. The supermarkets report a yoghurt market. That broad category helps retailers size the category shelf space, then allocate space to product groups, brands and individual products. It creates a ‘category map’ products and preparation of management reports. It conceals much of the behaviour that drives the sale to the time poor, budget limited, harassed by kids shopping consumer.
Shoppers choose between fruited and plain, low-fat and full-fat, pot-set and stirred, indulgence and health, adult and children’s formats, single serves and family tubs. Then they behave inconsistently, driven by the behaviours at home that result in consumption. Shoppers have never read the segmentation presentation, and do not care about it at all. The same persons trolley can contain Greek yoghurt for breakfast, a sweet pouch for a child, a set in pot natural yogurt for a fussy daughter, and a premium dessert for Saturday night, and be different on every visit to the supermarket.
Traditional analysis could reveal these patterns, given enough clean data, statistical skill and time. AI does not invent the possibility. It lowers the cost and effort required to pursue it, then allows a manager to question the data repeatedly rather than wait three weeks for another report.
They can uncover submarkets hidden inside the convenient fiction called “the market”. It can improve assortment, pricing, promotion and product development. The value comes not from producing the old market report faster, but from seeing choices the old report blurred out.
Then comes the hardest category: genuine innovation. Not the pack design change, or the ‘new improved’ standard product, genuine, category generating new products.
Every important general-purpose technology eventually enables products, services and business models that people could not sensibly describe at the beginning. We struggle to measure these gains because measurement follows observable activity. It cannot count a commercial possibility before somebody discovers it.
Nor do conventional accounts handle quality and unpriced value in anything but hypothetical terms. A better diagnosis, a more useful forecast, improved process, or a tailored customer answer may create considerable value without creating a measurable additional unit of output. We do not need to throw out existing productivity measures. They answer an important question and allow comparisons over time.
Businesses need a second dashboard.
Alongside output per hour, managers should track the use of released capacity, the valuable work newly performed, improvements in product quality and management choices, improved cycle and takt times, and new revenue or margin that technology made possible.
That is the productivity challenge in businesses.
AI will save time. This is an opportunity also available to competitors and especially new market entrants, unencumbered by past choices and technology. The sustainable advantage will go to the businesses that know what to do with the no added cost capacity that better use of time can deliver.
As an interim GM 20 years ago I was contracted to a manufacturing business that while marginally profitable, was struggling with capacity. Analysis revealed what everyone knew at some level, that a key step in the manufacturing process was acting as a bottleneck, strangling capacity. A number of relatively simple process changes that required no capital and no personnel changes enabled the key step to operate an extra 6 hours a day, effectively nearly doubling capacity. That resulted in shorter customer lead times, dramatic increases in DIFOT, abolition of outside warehouse costs, and very happy customers increasing orders at the expense of competitors. The changes took a couple of months to work their way through the financial system, but when it did, the impact on profitability was substantial. However, it was only with hindsight and deep digging into the numbers, that the changes in productivity numbers could be tracked.
The foregoing examines the productivity challenge of an individual business.
In the recent budget, and following discussions, the Treasurer has loudly and consistently pointed to the ‘productivity package’ contained in the budget papers.
The challenge in delivering on the promise, and having the electorate understand the complexity of the productivity challenge faced by the Australian economy is immense. It is easy to pick at the edges and observe ‘nothing to see here’ because the policy documents are shallow, predictably lacking in any real and measurable project timelines and checkpoints, and the press releases are as useless as the claims of a carnival hawker. It is however a start.
