It’s simple Watson!!

Here’s the truth about “AI success”

Most teams end with a demo.
Few go to production.
That gap kills real ROI.

The top pie wins applause.
The bottom pie wins adoption.

If your roadmap is “pick a model and prompt it,”
you’ll get a great screenshot,
a nice video.

𝐖𝐡𝐚𝐭 𝐚𝐜𝐭𝐮𝐚𝐥𝐥𝐲 𝐬𝐡𝐢𝐩𝐬 𝐯𝐚𝐥𝐮𝐞 𝐢𝐬 𝐬𝐲𝐬𝐭𝐞𝐦 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠:

→Data that’s fresh, governed, findable.
→Evals that catch regressions before customers do.
→Security/Guardrails that manage failures.
→Tool Integration so agents can do work.
→UI/UX people love (and can escalate when it’s wrong).
→User Training so the org actually adopts it.
→Prompting tuned to your constraints.

And the Model?
Yeah, that’s important.
But not as much as you think.

𝐓𝐫𝐲 𝐭𝐡𝐢𝐬 𝐰𝐢𝐭𝐡 𝐲𝐨𝐮𝐫 𝐧𝐞𝐱𝐭 𝐛𝐮𝐢𝐥𝐝:

✅ Define the right-pie slices for your context.

✅ Set 2–3 measurable SLOs per slice
(e.g., p95 latency, task-success, jailbreak rate).

✅ Invest in the slices, not the demo.

✅ Gate release on the composite score.

Looking at your current AI program, which slice is most underfunded:
Data, Evals, Security, Tooling, UX, or Training?

What’s the one fix that would move the needle this quarter?

𝑁𝑜𝑡𝑒: 𝑆𝑙𝑖𝑐𝑒𝑠 𝑜𝑓 𝑡ℎ𝑒 𝑝𝑖𝑒 𝑎𝑟𝑒 𝑓𝑜𝑟 𝑖𝑙𝑙𝑢𝑠𝑡𝑟𝑎𝑡𝑖𝑜𝑛 𝑜𝑛𝑙𝑦. 𝑇ℎ𝑒𝑠𝑒 𝑣𝑎𝑟𝑦 𝑤𝑖𝑡ℎ 𝑢𝑠𝑒-𝑐𝑎𝑠𝑒𝑠 𝑎𝑛𝑑 𝑡𝑦𝑝𝑒 𝑜𝑓 𝑏𝑢𝑠𝑖𝑛𝑒𝑠𝑠.