I’ve told this story before, but it keeps applying to new things, so here we go again.
A few years ago, we built a duplex. In-laws moving into the downstairs units, my office going upstairs. About a month out from moving in, I called the cable company to get service set up. Figured a couple of weeks, no big deal.
That’s when I learned about the pole permit...
Turns out, the cable was already on the shorter telephone pole. It just needed to be attached to the new taller one right next to it. Simple, right? Nope… Minimum 90 days to get approval from the power company. For a cable that needed to move about 2 feet, two companies that already worked together… Who’da thunk.
I didn’t know what I didn’t know. And I found out way too late.
I’m watching companies make the same kind of mistake with AI right now, except the price tag is a lot bigger than a cable bill.
The $250K Discovery Project Disguised as a New Hire
Everyone is rushing to hire a Head of AI, called by different titles; it makes sense and can be a good hire for many. I’m seeing these postings everywhere. $200K-$300K salary, VP or C-level title, and a job description that reads like someone fed ChatGPT the phrase “we need AI” and asked it to write a wish list. SQL Server, Fabric Databricks, PySpark, SQL, PostgreSQL, ETL pipelines, Delta Lake, Spark optimization, Data Engineering – all in one person. That’s not a job description. That’s a unicorn wishlist, and it signals something important: the company doesn’t actually know what they need yet.
They’re listing every data and AI buzzword they’ve heard, maybe even all the data sources and sinks they use and know about, and hoping someone shows up who can figure it out for them.
Here’s the thing. Gartner predicted that through 2026, 60% of AI projects will be abandoned because the data isn’t AI-ready. Not because the models are bad. Not because the team isn’t talented. Because nobody did the homework on what data they actually have, where it lives, whether it’s governed, and whether any of it is connected to anything else.
Your shiny new Head of AI isn’t going to walk in and start building machine learning pipelines on day one. They’re going to spend their first six months asking “where does this data actually live?” and “who owns this?” and “why is this table called sales_rollup_final_v2_REAL_final?”
Six months of a $250K salary is $125K. Spent mapping your data landscape. Before a single AI solution gets built.
Before You Hire Your Head of AI, Consider This
Before you write that job description, try answering a few questions first:
- What data do you actually have? Not what you think you have. What’s really there. What’s in the warehouse, what’s in the ERP, what’s in Julie’s “CFO actual data” spreadsheet on the shared drive?
- Where does it live? On-prem? Cloud? Hybrid? Bob’s laptop?
- What’s connected and what’s siloed? Can your systems actually talk to each other, or are you moving data between them on thumb drives and email attachments? Don’t laugh. I’ve seen it at companies with nine-figure revenue.
- What’s governed and what’s a mess? Do you have data ownership? Retention policies? Access controls? Or is it the wild west? What will the [insert regulatory or audit framework people of choice here – NCUA examiner, State examiner, SOC2 auditor, HIPAA auditor, etc.] say when they realize that we don’t even know who owns these key data sources?
- What’s your actual business problem? “We need AI” is not a business problem; it’s sort of a panicked plea to not miss out. “We’re losing 15% of customers in the first 90 days and we don’t know why” is a business problem. “The monthly financial snapshot we deliver to the board each month isn’t trusted by anyone because the data in it keeps changing.”
What Changes When You Do the Homework First
When you can answer those questions, three things change.
Your job description gets specific and realistic instead of being a buzzword buffet. Maybe you need a data engineer before you need a data scientist. Maybe you need a data governance framework before you need either one.
Your hiring priorities shift. You stop looking for a unicorn who can do everything and start looking for the right person for the actual problem you have.
And your odds of success go way up. Because your new hire walks into clarity instead of chaos, and they can start building on day one instead of spending six months figuring out what century your data infrastructure is in.
Our CDO, Buck Woody, does exactly this kind of work. He calls them Data Estate Audits, and they’re designed to give companies a clear picture of what they actually have before they start writing big checks for AI strategy. It’s the kind of thing that turns a six-figure guessing game into an informed decision.
Whether you work with someone like Buck or figure it out internally, just do the work first. Map what you have. Name what’s broken. Understand the connections and the gaps. Then hire.
The companies that will win with AI aren’t the ones that hired a Head of AI the fastest. They’re the ones who gave that person something real to work with when they showed up.