Yes.
The mistake is believing that the business case for AI is simply replacing people.
If a bank employs 100 people costing £10 million and replaces them with AI costing £12 million, then viewed through the traditional cost-reduction spreadsheet AI has failed. Yet that calculation ignores what happens if those AI systems perform ten times as much work, operate twenty-four hours a day, respond instantly to customers, analyse every transaction simultaneously, detect fraud continuously, write software faster, personalise millions of interactions and allow the remaining humans to concentrate on decisions where human judgement matters.
The denominator has changed.
We should not measure the economics of AI simply as AI cost versus people cost. We need to measure AI input versus economic output.
This is where many organisations are getting the argument wrong because they are adding AI to existing organisations rather than redesigning organisations around AI. Giving every employee an expensive frontier model and encouraging them to use as many tokens as possible is not transformation. It is adding another technology expense to the P&L.
The answer is architecture.
Companies will route simple work to small, cheap models, complex reasoning to powerful models, deterministic processes to conventional software and human judgement to humans.
There is no point in using Einstein to summarise an email.
AI economics will increasingly depend upon selecting the cheapest intelligence capable of completing each task rather than throwing the world’s most powerful model at everything.
This is the same journey computing has taken repeatedly.
We did not abandon cloud computing because early cloud bills were unpredictable. We created FinOps, monitoring, workload optimisation and sophisticated infrastructure management. AI will go through the same process, except this time we will develop something closer to Intelligence Economics, where companies measure the cost of machine intelligence against the economic value generated by that intelligence.
This is where the CFO is focused most.
A CFO wants a chief of staff who prepares board materials, reads documents, summarises meetings, remembers actions, prepares daily briefings and keeps track of decisions. Hiring another senior employee creates salary, benefits, recruitment and management costs, whereas an AI system connected to the relevant corporate information can perform large parts of that function continuously.
Multiply that concept across an organisation and the economics become far more interesting.
The future bank will have AI credit analysts, AI compliance assistants, AI fraud investigators, AI software engineers, AI customer-service agents, AI treasury assistants and AI relationship-management copilots working alongside humans. Eventually those agents will begin communicating and transacting with other agents, creating an organisation whose productive capacity is no longer constrained by its human headcount.
That is where the productivity revolution lies.
The problem today is that many companies are measuring AI using twentieth-century management accounting.
They are asking how many jobs AI eliminates when they should be asking how much additional economic activity, revenue and results it creates.
There is also an important historical parallel.
The internet produced one of the greatest investment bubbles in history because companies correctly understood that the internet would transform the world but wildly misunderstood how quickly the economics would work. The dot-com crash did not prove the internet was wrong. It proved that price, timing and business models mattered.
AI is heading towards the same reckoning.
There will be wasted billions, abandoned projects, spectacular corporate mistakes and companies discovering that their shiny AI strategy consists of an enormous monthly token bill and some slightly better PowerPoint presentations. There will also be companies that redesign their operations around machine intelligence and achieve productivity levels their competitors cannot match.
That creates the dividing line.
The AI winners will not be the companies that spend the most on AI. They will be the companies that extract the greatest economic value from every unit of intelligence they buy.
For banks, this is particularly important because AI will not simply reduce the cost of banking. It will change the productive capacity of the bank itself.
A bank employing 100,000 people today will not need 100,000 people to produce tomorrow’s level of economic output, but that does not mean the objective is to replace 50,000 employees with 50,000 digital workers. The objective is to create an institution capable of doing several times more with a radically different combination of human and machine intelligence.
That is why the CFO is right to worry about the AI bill and the CEO is right to keep investing.
AI 1.0 was about adoption. AI 2.0 is about economics.
The winners will discover that the most important question was never about how much does AI cost, but how much is intelligence worth?
Some useful links:
https://www.accountantcheltenham.me.uk/news/ai-pricing-leaves-businesses-struggling-to-predict-costs
https://www.forbes.com/sites/jemmagreen/2026/07/02/ai-costs-more-than-the-people-it-replaced/
https://riseuplabs.com/cost-of-implementing-ai-in-business/
https://www.bbc.co.uk/news/articles/c872r52x7jgo
https://cfooffice.io/p/how-to-build-a-chief-of-staff-with
Chris M Skinner
Chris Skinner is best known as an independent commentator on the financial markets through his blog, TheFinanser.com, as author of the bestselling book Digital Bank, and Chair of the European networking forum the Financial Services Club. He has been voted one of the most influential people in banking by The Financial Brand (as well as one of the best blogs), a FinTech Titan (Next Bank), one of the Fintech Leaders you need to follow (City AM, Deluxe and Jax Finance), as well as one of the Top 40 most influential people in financial technology by the Wall Street Journal's Financial News. To learn more click here...