Your First AI Hire Shouldn’t Code—They Should Clarify

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As a founder who lives in the small-to-midsize business world, here’s the most counter-intuitive AI hire I recommend: don’t start with a machine-learning engineer. Start with a Documentation Coordinator.

Why? Because AI only performs as well as the information it’s fed. Most of the SMB’s I run across have an information problem, it’s just scattered and incomplete. Procedures live in someone’s head, in half-finished SOPs, or across five tools. When you ask an AI assistant to “help,” it drinks from that firehose and spits out inconsistent results. A Documentation Coordinator fixes the root cause: they capture how your business actually runs, standardize naming, define fields, and keep processes current. That turns your tribal knowledge into clean company data that allows your AI to work from.

The data backs this up. McKinsey’s 2024–2025 research shows organizations seeing the most AI value are redesigning workflows and putting structure and governance around AI from the top down. They’re organizing knowledge, not just tinkering with models.  Poor data quality is also expensive; Gartner has long estimated it costs organizations an average of $12.9 million per year. Even if your SMB scales that number way down, the principle holds: bad inputs create real waste. 

There’s also a compliance and risk angle. NIST’s AI Risk Management Framework highlights documentation, data provenance, and governance as core to responsible AI. You don’t need a lawyer to see how clean, traceable procedures reduce risk and speed up adoption across your team. 

And let’s talk productivity. Decade after decade, studies show knowledge workers burn huge chunks of time hunting, cleaning, and reconciling information before they can do real analysis. Different surveys put “data prep” at 45–80% of effort. A Documentation Coordinator cuts that waste by building a shared source of truth.  Harvard Business Review has been preaching a similar lesson for years: the companies that codify and share knowledge build durable advantage. 

What does this role do in the first 90 days?

1. Map reality. Sit with sales, service, accounting, and operations to document how work really moves—fields used, handoffs, exceptions, and “shadow spreadsheets.”

2. Name things once. Establish simple taxonomies and data definitions: what we call a “lead,” how we record a “ticket,” what “closed-won” means.

3. Write clear SOPs. Short, step-by-step guides that match the screens people see.

4. Make it searchable. Centralize in a knowledge base, tag it, and keep revision history.

5. Prepare AI hooks. With clean SOPs and fields, you can safely connect an AI assistant to draft emails, update CRM records, generate job posts, or summarize tickets.

Here’s my personal experience: when we invested in documentation first, communication in my company started to improve and soon, our knowledge was usable across departments. Training became easier, and customer experience improved. I built a knowledge asset that can now be mined by AI for faster responses and deeper insights.

If you’re an SMB leader choosing your first AI hire, first build your knowledge assets. Hire a Documentation Coordinator to capture how your business runs today. Then, and only then, layer on the tech. You’ll spend less, move faster, and sleep better, because your AI will finally have something worth learning from.

Until next week,

 —Jared 

Text Me: 314.806.3912

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