In 1987, economist Robert Solow observed: “You can see the computer age everywhere but in the productivity statistics.” This became known as the Solow Productivity Paradox.

Despite huge investment in IT during the 1970s and 80s, productivity growth remained weak. The technology largely did what it promised – payroll, inventory, accounting, spreadsheets – but the gains didn’t show up clearly in the data. Economists debated why: some pointed to measurement problems, others to time lags, or the need for organisations to adapt their processes. It wasn’t until the 1990s that productivity growth really picked up, which economists put down to wider IT diffusion combined with organisational change.

We’re seeing something very similar with GenAI today:

• Massive investment
• As yet, very little evidence of any meaningful productivity impact
• Organisations trying to bolt GenAI onto existing processes rather than rethinking them

But there’s also an additional wrinkle this time – a capability misalignment gap.

In the 70s–80s, IT, whilst crude compared to what we have today, was generally capable for the jobs it was applied to. The “paradox” came from underutilisation and lack of organisational change – not because the tech itself failed at its core promises.

With GenAI, the technology itself isn’t yet reliable enough for many of the tasks people want from it – especially those needing precision, accuracy or low fault tolerance. For business-critical processes, it simply isn’t ready for prime time.

That means two things:

1. If it follows the same path as earlier IT, we could be a decade or more away from seeing any meaningful productivity impact.
2. More importantly, technology alone rarely moves the productivity needle. Impact comes when organisations adapt their processes and apply technology where it is genuinely fit-for-purpose.