Every company in 2025 claims to be “AI-powered.” Investor decks are littered with generative buzzwords. Product managers name-drop LLMs before they mention what their product does. Board slides reference “predictive intelligence” with alarming frequency.
But under the hood? Most so-called AI strategies are nothing more than basic statistical modeling with better branding.
Let’s be clear: there’s nothing wrong with regression. The problem is pretending it’s something it’s not.
AI Theater: Now Playing in Every Boardroom
If your company’s AI strategy involves:
- AutoML forecasts from a SaaS tool,
- a few heuristic-based scoring models in Google Sheets,
- or a dashboard filter called “Smart Insights,”
You’re not doing AI.
You’re running a slightly fancier analytics pipeline -and probably mislabeling it for stakeholders who don’t know the difference.
This isn’t pedantry. It’s a credibility issue.
Executives who frame statistical reporting as machine learning inevitably lose the trust of their data teams, and worse, their investors. Everyone says they want explainability in AI models -but they’re not even asking for transparency in internal labeling.
If your “churn prediction model” is a logistic regression trained on 18 months of usage data, that’s fine. Just don’t pretend it’s OpenAI-grade infrastructure.
What’s Actually Under the Hood?
The typical “AI-powered” feature set in 2025 looks like this:
- Regression or decision trees wrapped in a product UI
- Scikit-learn models deployed via batch scripts
- Some marketing copy that swaps “algorithmic” for “artificially intelligent”
- One LLM integration -often an API call that summarizes user comments
What’s missing?
- Interpretability
- Domain-specific tuning
- Performance monitoring
- Feedback loops
- Embedded ML teams
In short: a strategy.
What we have instead is a thin veneer of AI aesthetics applied to mostly deterministic logic.
Why This Still Happens
There are three reasons most “AI” strategies are still regression under the hood:
- It’s Good Enough to Fool the Room
A simple model that works 80% of the time will outperform a half-baked neural net with no guardrails. It’s easier to build. Easier to explain. And in many cases, it works. But rather than say that plainly, teams wrap it in AI buzz to impress leadership. - No One Wants to Maintain a Real Model
Machine learning requires iteration, retraining, validation, and ownership. And most companies aren’t staffed for that. It’s easier to ship a one-off model and pretend it’s a product. - Budgets Go Further With Pretend AI
You can justify bigger budgets and higher valuations if you frame your analytics team as an AI team. The incentives to exaggerate are structural, and most stakeholders don’t know enough to call it out.
But It’s Not All Bad News
Here’s the upside: basic models still move the needle -when they’re actually aligned to business goals.
Companies that skip the hype and focus on practical, interpretable models tend to see better adoption, better retention, and cleaner user feedback. In fact, some of the most successful AI feature launches we’ve audited in 2024–2025 used:
- Time-series forecasting with seasonality adjustments
- ElasticNet regression to rank prospects by engagement
- Simple clustering to personalize onboarding flows
None of these would impress a Stanford ML PhD. But they were business wins -and they didn’t require a “prompt engineering” title to execute.
The lesson? You don’t need to fake AI sophistication to extract value from your data.
So What Does a Real AI Strategy Look Like?
If you want to move past regression theater and into actual applied AI, here’s what it takes:
- Dedicated ML ownership -not just a side project for your lone data scientist
- Retraining pipelines and performance monitoring baked into the product lifecycle
- Model governance so legal, product, and engineering all know who’s responsible
- Clear evaluation criteria tied to business outcomes, not just accuracy scores
- Buy-in from product and design to actually build the UI around intelligent systems
It doesn’t have to be generative. It doesn’t have to be bleeding-edge.
It just has to be real, accountable, and useful.
Bottom Line
In 2025, your AI credibility is worth more than your AI ambition.
There’s no shame in building with regression. But there’s a cost to pretending you’re doing something you’re not. AI theater erodes trust, creates inflated expectations, and clogs up product strategy with misaligned incentives.
If you want to build intelligence into your product, start with a question that matters -not with a model you think sounds impressive.
And please -if it’s a logistic regression, just call it one.