Using AI daily doesn't mean you're good at it
About the Practical AI Fluency Framework (PAFF) for mid-career PMS & professionals
Every PM I talk to asks the same question: “Am I using AI enough?”
Wrong question.
The real question is: “Am I using AI well?”
There’s a difference between touching AI tools daily and actually being fluent. Most frameworks confuse activity with capability. They measure how often you prompt ChatGPT, not whether you’re making better decisions because of it.
I built the Practical AI Fluency Framework (PAFF) after watching dozens of mid-career professionals—PMs, operators, founders—struggle with this gap. They weren’t beginners. They’d tried the tools. But they had no way to know if they were actually good at this, or just going through motions.
The problem with existing frameworks
Academic frameworks are too theoretical. They teach you what transformers are. You don’t need to know how an engine works to drive a car well.
Tool-focused frameworks are too narrow. They teach you ChatGPT tricks. But fluency isn’t about memorizing prompts—it’s about knowing when AI helps and when it doesn’t.
Generic frameworks miss context. A marketer’s AI fluency looks different from a PM’s. Same tools, different judgment calls.
What was missing: a framework that measured real-world capability for people who need to use AI to do their jobs better, not become AI engineers.
What AI fluency actually means
After building AI workflows for LexiMoney, automating parts of my own product work, and helping other PMs integrate AI into their processes, I kept seeing the same five capabilities separate effective AI users from people just experimenting:
1. Strategic Delegation – Knowing what to give to AI vs. what to keep human. This isn’t “can AI do this?” It’s “should I let it?”
A beginner delegates obvious tasks: “write this email.” An expert designs hybrid workflows where AI handles research synthesis while they make the strategic call on what it means.
2. Effective Communication – Getting quality results from AI. Not memorizing prompt templates. Understanding how to articulate requirements, provide context, and iterate when the first output misses.
I’ve seen PMs write 50-word prompts that get garbage. I’ve seen others write 10 words that get exactly what they need. The difference isn’t prompt engineering knowledge—it’s clarity of thinking.
3. Critical Discernment – Spotting when AI is wrong. Every AI output contains decisions about what to include, emphasize, or skip. Can you catch when it hallucinates a data point? When it misses a critical edge case? When “good enough” isn’t actually good enough?
This is where most people fail. They treat AI like Google: if it returned a result, it must be right.
4. Workflow Integration – Building AI into daily work, not just using it occasionally. Do you have repeatable processes? Can you measure the impact? Have you documented what works so you’re not reinventing it every time?
The difference between someone who “uses AI” and someone who’s actually fluent: workflows. Documented, repeatable, measured.
5. Responsible Practice – Using AI ethically and safely. Do you know when you’re about to paste sensitive customer data into ChatGPT? Do you disclose AI assistance when it matters? Do you catch bias in outputs?
This matters more as AI becomes infrastructure, not experiment.
Why scoring matters
Most frameworks give you concepts. PAFF gives you a score: 0-100 across these five competencies.
Not because scores are inherently valuable, but because you can’t improve what you don’t measure.
When someone scores 45/100, they’re not “bad at AI.” They’re probably an Intermediate user—using AI daily, seeing some value, but without systematic approach or optimization. That’s the middle 40% of professionals.
The score tells them exactly where the gaps are:
Weak at delegation? You’re probably doing too much manually.
Weak at discernment? You’re shipping AI errors you don’t catch.
Weak at workflow integration? You’re not capturing the full productivity gain.
And because it’s benchmarked against peers in your role and experience level, you know if you’re behind, average, or ahead.
The insight that changed how I built this
Initially, I was building separate frameworks for PMs, marketers, sales folks. Different assessments, different competencies.
Then I realized: the competencies are universal. Strategic delegation, effective communication, critical discernment—these apply across roles.
What changes is the application.
A PM delegates user research synthesis. A salesperson delegates prospect research. Different tasks, same judgment: knowing what AI should handle vs. what needs human expertise.
So PAFF uses one framework, five competencies, role-specific examples. You get scored on universal capabilities, benchmarked against your specific peer group.
What this means for your AI decisions
If you’re a PM wondering “should I be using AI more?”—wrong frame.
Better questions:
Can you identify which of your recurring tasks AI should handle?
When AI gives you output, do you catch the errors?
Have you built any repeatable AI workflows, or are you winging it every time?
Can you measure how much time AI actually saves you?
These aren’t tool questions. They’re judgment questions.
AI fluency isn’t about touching more tools. It’s about making better decisions about when and how to use the tools you already have.
Most professionals are stuck at Intermediate (36-60 points): using AI daily, but without strategy, verification, or measurement. They know they should be getting more value. They don’t know how.
The framework gives them a map. Not to learn AI. To use it well.
Dipender
P.S. — I’m opening early access to PAFF this week. If you want to see where you actually stand (not where you think you stand), reply to this email. I’ll send you the assessment link when it’s live.