Practical AI
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Your snips
[02:00] Begin With A Needs Assessment
[06:06] Make Events Hybrid And Inclusive
[07:46] Openness Accelerates Learning
[09:27] Sharing Reduces Imposter Syndrome
[11:50] Use Case Studies And Small Groups
[16:41] Listen First, Then Double Down
[19:14] AI Is Becoming Democratized
[21:24] Adoption Creates Workforce Tension
[24:06] Train Industry-Specific AI Skills
[25:11] AI Skills Spread Into Existing Roles
[32:19] Mid-Levels Regret Losing Junior Devs
[34:51] Know How To Code First
[35:41] Reported Use Outpaces Successful Deployment
[39:06] Set Policies For Shadow AI Use
[41:39] Data Centers Spark New Innovations
What I learned from 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗔𝗜 Today 🎙️
I just finished listening to:
𝗣𝗼𝗱𝗰𝗮𝘀𝘁: Practical AI with Dan Whitenack and Chelsea Linder
𝗘𝗽𝗶𝘀𝗼𝗱𝗲: The impact of AI on the workforce: A state-level case study
𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆
Businesses are reporting AI adoption at around 10%, but 99% of AI pilots fail. There's a gap between saying you're using AI and actually using it successfully. Most organizations are still experimenting, not deploying at scale.
𝗪𝗵𝘆 𝗜𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀
In finance and data roles, we're under pressure to show AI wins fast. But this data reminds us that adoption isn't the same as impact. Real value comes from solving the right problem with the right tool, not just checking the AI box.
𝗥𝗲𝗳𝗹𝗲𝗰𝘁𝗶𝗼𝗻 🧠
This hit home for me. I've seen teams rush to pilot AI without clear goals or governance. The result? Wasted budget and lost trust. The lesson: start small, measure outcomes, and build guardrails early. AI tools are powerful, but only if you know how to use them and what problem you're solving.
To get the full insight, check out the podcast!
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