GenAI pilots keep failing. Day 1 explained why. Day 2 and 3 focus on how to fix it.

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Welcome to BIPro 106.

The Machine Learning Summit opened yesterday by tackling the question a lot of teams are quietly asking:

Why do so many GenAI projects stall out after the pilot phase?

Stephen Klein (Curiouser.ai founder, AI ethics expert) laid it out clearly:

Most failures aren’t technical. They happen because of misaligned goals, poor handoffs, and internal gridlock.
The real differentiator isn’t just model performance. It’s how clearly your team can think, how quickly they can act, and how much your organization actually trusts the output.
His line summed it up: “We can’t automate trust. We can’t automate imagination. That’s where the value is.”

This week’s sessions are built to help you avoid those traps. Ticket holders get full access to all session replays and condensed highlights.

Register for the ML Summit

Here’s what’s coming up:

Day 2 (today): Practical playbooks for scaling ML beyond the prototype: LLM fine-tuning, tabular and time series use cases that actually deliver value, and getting model interpretability right.
Day 3: How to operationalize GenAI properly: LLMOps, causal AI, synthetic data pipelines, plus a hands-on workshop to build working AI agents.

If you want frameworks and examples you can apply directly, especially for getting past stalled pilots and messy deployments, this is worth your time.

Register for the ML Summit

Cheers,

Merlyn Shelley

Growth Lead, Packt