Why Most AI Initiatives Stall (and What You Can Do Differently)


​ Reader, ​

I just wrapped up a great conversation with Clemens Adolphs, co-founder of AIce Labs, for the Scaling With Agility podcast.

We dug into a question we both see more and more leaders wrestling with:

Why do so many AI proof-of-concept projects look promising on paper, but never scale past the pilot?

Clemens and I discussed some emerging practices for dealing with the pace and uncertainty of developing internal AI capabilities. Here are a few of the ideas we unpacked:

— How to avoid the proof-of-concept graveyard by designing for internal market validation from the start
— Why “metrics theater” can be worse than no metrics at all
— Ways to leverage agile principles and practices to manage AI experimentation effectively
— Common anti-patterns that slow (or sink) AI initiatives

If you’re leading (or planning) AI investments and don’t want to end up with another expensive prototype gathering dust, I think you’ll find this episode useful, Reader.

🎧 Clemens Adolphs on the Role of Agility in Getting AI Investments Right

When you’ve had a listen, I’d love to hear your take. Are you seeing the same pitfalls in your world?

Scaling w/ Agility

Are You Struggling to Scale Your Organization ? Need agility but dubious of process BS/dogma? I share reflective, pragmatic, principled takes on how to approach scaling your organization leveraging the essence (rather than theater) of product operating models, agile practices and frameworks, and business operating systems such as EOS and OKRs.

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