July 14, 2026
We’ve all heard the “we need AI” corporate mandate in some variation. But that's not a strategy, a vision, or useful. Somewhere between the executive proclamation and the actual agent doing work is a real gap where a leap of faith is taken.
There's plenty written about AI governance (e.g., risk, access, responsible use). There's plenty written about AI tooling (e.g., LLMs, agents, harnesses, applications). What's missing is the bridge between the two. This three-part article (Part 2, Part 3) is that bridge based on a B2B SaaS agile product operations model that’s also extensible with other business and operating models. Warning: I’m not going to talk about AI much. This is about good business practice, which AI happens to reward.
Let’s dig in!
The goal, stated plainly: I will build an AI agent/workflow to do X, for Y, using context from 1, 2, and 3, to achieve A, which will impact B.
If you can't fill in this sentence yet, you're not ready to pick a tool. You're ready for this article. The diagram below is where we’re headed over the next 3 articles. I’ll break down each step, but the culmination will help you think about how and why AI agents and workflows can drive results towards your goals.
Steps in the articles denoted by the circled numbers.
Here's where planning exercises can lose touch with reality: they jump straight to KPIs (that’s hard for me to say because I live for a solid KPI). I'd argue you should start somewhere softer with the emotional state you'd be proud of. Culture and efficiency are experienced emotionally before they're measured logically, if ever measured (a.k.a. the eye test). If the feeling isn't there, no amount of dashboard green status will convince you it's working. (For anime fans, there's a series where 2 scientists try to reduce love to a formula: Science Fell in Love, So I Tried to Prove It. Spoiler: formulas don't do feelings justice. Neither do metrics, until you've named the feeling first.) State that feeling without overthinking it.
Take this example:
"Cross-functional PDLC stakeholders need to feel confident in what we launch without burdening the PM team with constant hand-holding."
These aren’t metrics… yet. Break this down into its components:
Notice what this example outcome is not about: delivery speed. Rather, it's about accountability and predictability; what goes into the PM function, what comes out, and whether PM can handle that flow without becoming a chokepoint. Don’t try to force something in if it didn’t come naturally in your original outcome.