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AI Anti-Patterns

Matthew Shump

For operators and early data leaders who want to scale Data and AI right—the first time.

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Substack
PricingOnly free issuesPublishesDaily
Issues45Foundeda year agoLast Issuea year ago
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Latest Issues

🚫 Data & AI Anti-Pattern #25: Treating Model Development as a One-Time Project

Models aren't set-it-and-forget-it—they're products. And products need maintenance.

📉 Performance drifts, but no one’s watching
📉 No retraining, so relevance fades over time
📉 Bugs, bias, and blind spots go undetected

Ask yourself...

a year ago

🚫 Data & AI Anti-Pattern #26: Neglecting Explainability and Interpretability

If people can’t understand how a model works, they won’t trust—or use—it.
And if you can’t explain it, you can’t defend it when it matters.

📉 Decisions get questioned or blocked
📉 Models fail audits or risk reviews
📉 Consumers avo...

a year ago

🚫 Data & AI Anti-Pattern #24: Starting with the ML/AI Model, Not the Problem

Just because you can build a model doesn’t mean you should.

📉 Fancy prototypes solve imaginary problems
📉 Success gets defined post-hoc—or not at all
📉 Business partners lose trust when nothing ships (or makes an impact)

Ask yourse...

a year ago

🚫 Data and AI Anti-Pattern #23: Waiting Too Long to Invest in Data & Analytics Engineering

If your analytics team is drowning in ad hoc, manual tasks—the team isn’t the problem, the foundations are.

📉 Brittle pipelines can’t keep up with growing demand
📉 Firefighting replaces value delivery
📉 Frustration grows, trust erod...

a year ago

🚫 Data and AI Anti-Pattern #22: Over Engineering Infrastructure Too Early

Big dreams don’t need big infra—at least not right away.
Overbuilding before you've nailed the basics slows you down and bloats your budget.

📉 Complexity creeps in before it’s needed
📉 Costs scale faster than value
📉 Agility takes...

a year ago

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  • Matthew Shump

    Data, Analytics and AI Leader, Founder, Investor. Posting about Data and AI Anti-patterns. Useful for all leaders and practitioners, but with especially for first data hires and early data leaders.

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