Lean AI Blueprint

5 Biggest Mistakes AI Teams Make In Their Data Initiatives That Lead To Reputational Damage, Regulatory Penalties, And Wasted Resources (And How To Avoid Them)

  • A step-by-step guide to understanding the hidden pitfalls of AI projects.

  • The 5 biggest mistakes AI teams make (and how to avoid them).

  • Why these mistakes happen to even the most experienced data professionals.

  • 5 actionable strategies to ensure your AI efforts stay on track and deliver value—without compromising compliance or efficiency.

My name is Prashant Bansod, a Data Scientist with 7+ years of experience in AI and data strategy. I help data teams in startups and big tech navigate the complexities of data initiatives to drive sustainable growth and minimize risk.

"This is essential reading for any AI or data team aiming to build a future-proof data strategy!"

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Here's a sneak peek of everything you're going to learn inside this email course:

Day #1. Chasing the Shiny Tech Without Solid Data And Why following the latest tech trends without a solid data foundation leads to unreliable AI, project failures, wasted time, and financial losses.Day #2. Excluding Data Teams from the Product Design Stage And why it results in Misaligned teams, broken data pipelines, and delayed feature performance insightsDay #3. Lack of collaboration between data producers and consumers And why it results in constant data pipeline issues, ad-hoc fixes, and growing data debtDay #4. Publishing Data Without a Contract and why it results in a chaotic mix of data sources with inconsistent quality and unclear ownershipDay #5. Overlooking Data Governance and Compliance and why it results in hefty fines and damage to your company’s reputation.

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