The Hidden Economics of AI

AI’s Hyped Excitement Secret: Are We Deploying Systems We Can’t Cost Properly

The Development Bill I Struggle to Get Anyone to Talk About

When organisations discuss their AI journey, the conversation typically centres on development costs: data scientists’ salaries, cloud infrastructure for training, consultancy fees, and software licences. These figures are tangible, budgetable, and relatively straightforward to calculate. A six-month AI project with a team of three might cost £250,000 to £500,000 – painful, perhaps, but knowable.

But what happens after you press “deploy“?

After the Launch Party:
The Real Cost of Keeping AI Alive

Here’s where the conversation becomes remarkably quieter. Whilst businesses are increasingly deploying AI into production – from chatbots handling customer enquiries to predictive maintenance systems monitoring manufacturing equipment – few seem willing or able to discuss the ongoing operational costs with any precision.

The challenge is that production AI costs are fundamentally different from traditional software. They’re:

• Variable and unpredictable: Unlike a standard application that costs roughly the same whether one person or one thousand use it simultaneously, AI systems often charge per API call, per token processed, or per inference made. Your monthly bill can fluctuate wildly based on user behaviour you can’t always forecast.
• Model-dependent: A customer service chatbot using GPT-4 might cost 20 times more per interaction than one using GPT-3.5, whilst a fine-tuned open-source model running on your own infrastructure presents entirely different economics again.
• Hidden in complexity: The true cost involves not just inference charges but also vector database hosting, monitoring systems, quality assurance sampling, model versioning, fail-safes, and the engineering team maintaining it all.

The Questions That Need Answering

Consider a mid-sized retailer deploying an AI-powered customer service assistant. Development might have cost £300,000. But what does it cost to run each month?

• Is it £500? £5,000? £50,000?
• How does that scale with customer growth or usage?
• What happens when API providers decide to change their pricing (as OpenAI did in mid-2023)?
• How much do you spend on monitoring quality versus the core service?
• What’s the cost when things go wrong and you need to roll back?

Or take a manufacturer using computer vision for quality control on the production line. Once deployed:

• What’s the per-item inspection cost?
• How does that compare to human inspectors?
• What about the GPU infrastructure costs if you’re running models locally?
• How much buffer capacity do you need for peak production periods?

The Reason I Think This Matters

We’re entering a critical phase where AI is moving from proof-of-concept to business-as-usual.

Finance directors want to know: what’s our run rate?

Can we forecast this? What’s our cost per customer interaction or per transaction?

What savings would the business get from people re-alignment post AI production?

Yet anecdotal evidence suggests many organisations simply don’t know their true production AI costs with any confidence. Costs are scattered across different budget lines (cloud services, API subscriptions, DevOps teams), making the total picture murky. Some discover their pilot project economics don’t scale linearly, or at all.

This opacity creates risk. It makes ROI calculations unreliable, it complicates build-versus-buy decisions, and it prevents organisations from optimising their AI spending effectively.

Call-to-Action: Share Your Reality

Have you deployed AI into production in your organisation?

We’re keen to hear from practitioners who can speak frankly about the economics:

• What are your actual monthly running costs?
• How do they break down (infrastructure, API calls, monitoring, maintenance)?
• Were there any surprises compared to your initial projections?
• Can you confidently forecast your costs for the next 12 months?
• What’s your cost per use/transaction/customer, and how does that compare to your previous solution?

The industry needs honest conversations about AI economics. Development costs are well-documented; production costs remain largely hidden.

I am looking to build a report that will remain anonymous for any of those that don’t want to share the company of course. If you’re willing to share your wider experience – even anonymously – please get in touch.

Let’s bring some transparency to the true cost of putting AI to work.

Email me Here:  or WhatsApp me on +97158537-six-3-one-one
(number changed with text to stop BOT scraping)

Are you running AI in production? What’s it really costing you? Share your insights in the comments below or contact us directly. All contributions can be made anonymous upon request.

by Craig Ashmole, Fractional CIO, Dubai UAE