- Eric D. Brown, D.Sc.
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- Don't "AI Wash" Your Strategy
Don't "AI Wash" Your Strategy
Why rebranding existing tools as "AI" creates strategic debt that compounds faster than you think—and what forward-thinking leaders do instead
Eric Brown
July 02, 2025
Sponsored by
When companies slap "AI" labels on existing tools without building genuine capabilities, they're merely adding new labels to old problems.
Last week, a CEO told me his board demanded "AI in everything" by year-end. His response? He renamed the analytics dashboard to "AI Insights Platform."
He's not alone.
Companies everywhere are slapping AI labels on existing tools. Basic automation becomes "machine learning." Excel macros get rebranded as "AI-powered solutions."
This rebadging creates problems you won't see coming until it's too late.
The AI-Washing Playbook
Boards pressure leadership for quarterly wins. Someone suggests adding "AI capabilities" to the roadmap. Marketing loves it. Sales need it. The tech team knows better, but they play along.
Within months, you've got:
A chatbot that's just a decision tree
Predictive analytics ,that's linear regression from 2015
An AI strategy that's 90% PowerPoint, 10% proof-of-concept
The stock price bumps. Analysts nod approvingly. Competitors scramble to match your "AI leadership."
But you've just created a problem that compounds over time.
Strategic Debt Compounds Faster Than Technical Debt
When you AI-wash, you train your organization to value appearance over function.
Your teams learn that looking forward-thinking beats being forward-thinking. They stop pushing for real change because fake change gets rewarded faster. The gap between claims and reality becomes normal.
Then reality shows up.
A competitor launches something genuinely transformative. Regulators start asking about your "AI" decision-making. Your best technical talent leaves for companies doing real work. You need actual AI capabilities, but you've spent years building nothing.
1. Trust Erodes
When employees see leadership celebrating fake wins, they stop believing in the direction. I've watched engineering teams turn cynical, assuming every project is just another rebrand.
Once that trust is gone, implementing real change becomes nearly impossible.
2. Skills Don't Develop
Real AI needs specific capabilities: data engineering, model validation, ethical frameworks, and change management. When you AI-wash, you don't build these. You build presentation skills. Marketing skills. Not what you'll need.
3. You Stop Seeing Real Opportunities
You become blind to actual value creation. When everything already has an "AI" label, why invest in machine learning? When your metrics show success, why question what those metrics measure?
What This Looks Like
A financial services firm I know spent two years AI-washing their risk assessment. Impressive demos. Awards from industry publications. Board satisfaction.
Reality check:
Zero ML models in production
No data pipeline for actual AI
No team with real ML expertise
No governance for algorithmic decisions
When regulators asked about algorithmic bias, they had nothing. When a fintech launched a genuinely predictive risk model, they couldn't respond. The fake AI had prevented them from building real capabilities.
The fix will take three years and cost eight figures.
The Alternative Path
What if you just told the truth?
For your next board presentation, say, "We're not ready for AI in most areas. Here's our plan to build real capabilities where they matter."
Let your tech team know it's acceptable to say: "That's not AI, it's a rules engine." Call it what it is and make it excellent.
Some companies do this. They move more slowly at first. They face more complex questions.
But when they implement AI, it works. It scales. It creates value.
They build organizations that can transform, not just pretend to.
Breaking the Pattern
If this sounds familiar, start here:
Audit the gap. List every "AI" initiative. Ask each team to explain how their solution uses machine learning. If you have more than three or four real AI projects, you're moving too fast.
Speak clearly. Yes, some stakeholders won't like it. But problems compound. Stop adding to them.
Build one thing right. Pick one area where real AI creates measurable value. Build it properly. Let success replace fiction.
Reward honesty. Celebrate the team that says "this doesn't need AI" as much as successful model builders. Make truth profitable.
What Matters
Companies winning with AI aren't using it everywhere. They use it precisely where it helps, built on solid operational foundations.
They know the difference between automation and AI. They understand when human judgment wins. They know transformation comes from capability, not technology labels.
They also know that strategic debt always comes due. Pay it on your terms or the market's.
Your next move matters more than your next announcement.
If you're cutting through the AI noise to build real capabilities, you don't have to figure it out alone. That's the work I do. Let's talk: ericbrown.com
If you found this post helpful, consider sharing it with another executive grappling with AI, technology, and data. If you want to explore AI and other Technology strategies, grab some time on my calendar, and let's chat.
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