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🤖 Ghostwritten by Claude · Curated by Tom Hundley
This article was written by Claude and curated for publication by Tom Hundley.
The API bill is the tip of the iceberg. Here is what lies beneath.
When we audit AI pilot budgets for mid-market clients, we almost always see the same line item: OpenAI API Costs: $500/month.
They assume the cost of AI is the cost of the intelligence.
This is like assuming the cost of a Ferrari is the cost of the gas.
In a production AI system, the Model Inference Costs (the tokens) usually represent only about 20% of the Total Cost of Ownership (TCO).
So, where does the other 80% go?
Your data is messy. Its in PDFs, its in a legacy SQL database with cryptic column names (col_a_1), or its trapped in SharePoint.
How do you know if the bot is right? You cant just vibe check it.
The model outputs JSON. Your ERP needs XML. The model outputs a summary. Your Slack bot needs a formatted block.
The best AI tool is useless if your team ignores it.
Unlike traditional software, AI is probabilistic. You dont write the code once and run it. You write the prompt, test it, realize it fails on edge cases, rewrite it, test it again...
You are not paying for software development. You are paying for experimentation cycles.
For a typical mid-market internal tool (e.g., Sales Knowledge Assistant), stop budgeting $5k. Start budgeting $50k for the MVP and $20k/year for maintenance.
The Breakdown:
AI is cheaper than a human employee, but it is much more expensive than a SaaS subscription. If you go in with a SaaS mindset ($20/user/month), you will under-resource the project, and it will fail.
Go in with an Infrastructure Mindset, and you will build an asset that pays dividends for a decade.
This article is a live example of the AI-enabled content workflow we build for clients.
| Stage | Who | What |
|---|---|---|
| Research | Claude Opus 4.5 | Analyzed current industry data, studies, and expert sources |
| Curation | Tom Hundley | Directed focus, validated relevance, ensured strategic alignment |
| Drafting | Claude Opus 4.5 | Synthesized research into structured narrative |
| Fact-Check | Human + AI | All statistics linked to original sources below |
| Editorial | Tom Hundley | Final review for accuracy, tone, and value |
The result: Research-backed content in a fraction of the time, with full transparency and human accountability.
Were an AI enablement company. It would be strange if we didnt use AI to create content. But more importantly, we believe the future of professional content isnt AI vs. Human—its AI amplifying human expertise.
Every article we publish demonstrates the same workflow we help clients implement: AI handles the heavy lifting of research and drafting, humans provide direction, judgment, and accountability.
Want to build this capability for your team? Lets talk about AI enablement →
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