Top 5 “Don’t Be That Guy” Moves in the AI Gold Rush
Here’s How to Not Embarrass Yourself (or Your Org)
For years, companies treated data like that extra sauce packet in a takeout bag — tossed aside, forgotten, maybe stepped on in a corner of the org. But suddenly, AI is the hot new air fryer — shiny, overhyped, and somehow now essential to every digital transformation plan… by next quarter.
“Quick, get us some of that data stuff! Plug it into AIGPT or DeepSick, slap an ‘Agent’ label on it, and boom — innovation!”
Except… that’s not how this works.
You can’t skip the boring, foundational part (you know, the decade of infrastructure, collection, cleaning, labeling, and security) and expect to waltz into AI greatness. Agents aren’t magic wands.
If your company couldn’t find value in its data last year, slapping a chatbot on top of chaos isn’t going to fix it. It’s just dressing up a dumpster fire in a suit and calling it “transformational.”
So sure, welcome to the AI party. But maybe next time, don’t show up late, wearing VR goggles, asking where the data snacks are.
Top 5 ‘Don’t Be That Guy’ Tips for Starting with Data
Since you’re here — possibly panicking, definitely scrambling — let’s at least help you fake like you’ve been doing this all along. Here’s your crash course in not embarrassing yourself:
1. Hire Real Data Engineers — Not “Python on My Resume” People
You need folks who actually know how to build pipelines, not just someone who took an online course and remembers how to import pandas. Data engineers are the plumbers of AI — and if you don’t have them, your “agent” is going to end up drinking raw sewage and hallucinating product recommendations.
2. Stop Hoarding. Start Organizing.
Having a petabyte of data doesn’t mean you’re ready for AI — it means you probably need an intervention. If your data warehouse looks like your inbox (10 million unread rows and zero structure), you’re not building anything smart. You’re just collecting digital lint.
3. Treat Data Governance Like a Grown-Up
Governance isn’t optional. It’s not just for compliance people with very dry slides. It’s how you avoid leaking customer info, tanking trust, or showing your CEO’s browser history to the ML model. Own your data, label your data, secure your data — or enjoy being on the front page of TechCrunch for all the wrong reasons.
4. Measure What Actually Matters
Not every KPI deserves to be carved in stone and mounted on a dashboard. Focus on the data that actually drives outcomes. Nobody cares about how many users clicked a button if none of them converted or understood what the button did. Vanity metrics aren’t just misleading — they’re a great way to look busy while staying lost.
5. Invest in Data Culture Before AI Hype
If your decision-making process still involves “what feels right” or “what Larry from Sales dreamed about,” stop everything. Build a data-literate culture first. If your org doesn’t trust data or understand how to use it, AI will just make your dysfunction faster and more expensive.
You’re Not Late — You’re Just Unprepared
Yes, the AI gold rush is on. Yes, you missed a few memos while chasing Web3 or whatever blockchain pitch deck was hot last year. But it’s not too late — if you’re willing to do the unsexy work of treating data like the strategic asset it actually is.
Want to build great AI? Start by not treating your data like leftovers. Otherwise, all you’re doing is automating bad decisions — faster, louder, and probably with a voice interface.
And no, an Agent won’t fix it. Not until you do.
Get in touch with me, and let’s protect you and your investors from costly mistakes that could drain hundreds of thousands of dollars.
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