The true cost of AI (Part One)

There is a rather awkward conversation beginning in boardrooms about artificial intelligence. For the last few years, the question has been what AI can do, how quickly companies can deploy it and how many people it might replace, but the question now landing on the CFO’s desk is much simpler: how much is this stuff costing us?

The answer is becoming uncomfortable.

One of the more striking examples comes from McKinsey, a firm making substantial revenues advising corporations about AI transformation.

Releasing their latest survey about AI usage across industries they find quite a few anomalies:

• 20% of organisations say AI operating costs, including tokens, are already constraining AI usage.
• 28% are spending more than 10% of their entire technology budget on AI, yet 60% still expect AI investment to increase over the next year.
• Nearly nine in ten organisations use AI, and 44% are now scaling it across the enterprise, but only 37% report a positive EBIT contribution.
• Crucially, only 14% actually reduced their workforce because of AI last year, despite 32% having expected reductions.

It’s well worth a read.

What I found particularly interesting is that McKinsey’s very own CFO Eric Kutcher has warned that the rate at which the firm’s AI expenditure is increasing cannot continue, saying there is “no way” the company can afford another year of growth at the same rate.

That is McKinsey’s people’s usage of AI to assist in reporting to client.

McKinsey reportedly uses AI for as much as 30% of its tasks, which means the problem is not failed adoption but successful adoption.

The bottom-line is that the more people who use AI, the bigger the bill becomes.

That reveals one of the fundamental differences between AI and the previous generation of enterprise software.

Traditional software was largely predictable. Buy 10,000 Microsoft licences and the CFO knew roughly what the annual bill would be, whereas AI increasingly operates like electricity, cloud computing or mobile data because you pay according to consumption.

Every prompt consumes tokens, every answer consumes tokens and, critically, every AI agent working autonomously in the background can consume thousands or millions of tokens without the employee who started the process seeing what is happening.

Agentic AI makes the economics even harder because one instruction from a human can trigger dozens of machine-to-machine actions, queries and decisions.

Goldman Sachs forecasts cited in the reporting suggest token consumption could increase 24-fold between 2026 and 2030, meaning companies are heading towards an extraordinary expansion in AI consumption even as the price of individual tokens continues to decline.

This creates the great AI paradox: AI is becoming cheaper and AI is becoming more expensive at the same time.

The unit cost of intelligence is collapsing, but the quantity of intelligence companies consume is exploding.

The experience of Uber illustrates the problem.

Uber exhausted its entire 2026 AI coding budget in four months, despite achieving extraordinary adoption among its engineers. By March, 84% of engineers were using Claude Code and around 70% of committed code originated with AI, but Uber executives found that token consumption did not translate directly into useful features delivered to customers.

Microsoft has faced similar concerns over AI coding costs, while Nvidia’s vice-president of applied deep learning has said that his team’s compute costs now exceed the cost of the employees using that compute.

AI costs more than people!

That turns the original AI employment argument upside down.

There is also a deeper problem because the AI invoice is only one part of the bill. The true enterprise cost includes preparing and cleaning data, rebuilding data architectures, cloud infrastructure, GPUs, specialist engineers, security, compliance, model monitoring, integration with existing systems and continuous maintenance.

Riseup Labs estimates that data preparation alone often consumes 30% to 50% of an AI project’s budget, while ongoing maintenance can consume another 15% to 30% of the original implementation cost every year. Production infrastructure can easily reach tens of thousands of dollars per month before the organisation begins counting the people required to operate it.

Then there is integration.

AI sitting in a browser is cheap. AI embedded inside the operational nervous system of JPMorgan, HSBC and Deutsche Bank is not. It has to understand customers, products, permissions, regulation, workflows, data, legacy systems and decades of accumulated corporate complexity, which means that the expensive part of enterprise AI is often not artificial intelligence at all but making the enterprise intelligible to artificial intelligence.

Banks will experience this more intensely than almost any other industry because their technology estates contain decades of legacy infrastructure combined with enormous regulatory, security and data obligations. Putting a clever chatbot on top of that architecture is easy. Rebuilding the bank so that intelligent agents can safely operate inside it is a completely different proposition.

There is another uncomfortable factor hidden beneath today’s prices because the AI industry is still subsidising adoption.

The largest AI providers are spending extraordinary amounts on infrastructure while competing aggressively for customers, meaning today’s enterprise AI price does not necessarily represent the long-term economic cost of providing the service.

Forbes argues that normalisation of AI pricing could push enterprise bills substantially higher, particularly as providers move heavy users away from flat-rate subscriptions towards consumption-based charging.

In other words, corporations are worrying about the AI bill while AI remains comparatively subsidised.

That should concentrate the CFO’s mind.

But it leads to the more important question … is it worth it?

More on that tomorrow ...

 

Some useful links:

https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai#/

https://www.linkedin.com/posts/mckinseys-cfo-just-said-he-cant-afford-share-7499044034385207296-olXQ/

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