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AI can make the call. It can’t take the fall.

Pav Lertjitbanjong is a decision scientist, former corporate strategy and analytics leader, and founder of PAVNESS.

After more than 20 years inside Fortune 500 and global organizations, she became obsessed with a problem that is getting harder as AI gets better:

When a machine materially shapes a consequential decision, what is the human still responsible for?

That is the work Pav studies and teaches.

Through PAVNESS, she helps leaders establish and practice a human standard for consequential decisions made with AI in the room — using decision science, live simulations, and a practical thinking routine rather than another lecture about prompts.

WHY THIS MAKES A GOOD EPISODE

Your audience probably does not need another conversation about prompts, productivity hacks, or which AI tool launched this week.

They are already using AI.

The harder problem is what happens after the output looks good.

A recommendation arrives polished, specific, quantified and confident. Someone reviews it. Someone forwards it. Someone else approves it.

But who actually made the decision?

Pav explores the increasingly important gap between:

AI-assisted work that a human has seen

and

a consequential decision a human can actually explain, challenge and defend.

The conversation is practical, provocative and useful whether your audience consists of executives, managers, entrepreneurs, consultants, analysts, or ambitious professionals.

They leave thinking differently about every AI-assisted recommendation that lands in front of them.

WHAT PAV BRINGS

She has lived inside consequential decision environments.

Pav spent more than two decades working inside large, complex organizations, including strategic analytics leadership. Her work put her close to executives, restructurings, major operating decisions, and the uncomfortable reality of organizations.

She comes at AI through decision science.

Pav holds a BBA in Decision Science and a Kellogg MBA. Her work focuses less on what AI can produce and more on what happens to human judgment after AI enters the decision process.

She gives people something they can use immediately.

Her THINK routine is:

Tune In · Hypothesize First · Interrogate Everything · Narrow to the Call · Know How to Land It.

It gives professionals a way to use AI aggressively without quietly surrendering authorship of consequential decisions.

STRONG EPISODE ANGLES

1. AI Can Make the Call. It Can’t Take the Fall.

Who actually owns a decision once AI has materially shaped it — and why “human in the loop” is often far less meaningful than companies think.

2. Is AI Making Smart People Worse Thinkers?

Not because AI is bad, but because a polished recommendation can collapse the distance between receiving an answer and forming a judgment.

3. Your Career Advantage When Everyone Has the Same AI

When everyone can generate competent analysis, the differentiator becomes being the person trusted to question it, make the call, and defend why.

QUESTIONS PAV IS ALWAYS READY TO ANSWER

1. “AI can make the call. It can’t take the fall.” What does that actually mean?
2. If AI becomes better than humans at analysis, what judgment should humans still own?
3. How can you tell whether you're using AI as a thinking partner or simply outsourcing your thinking?
4. Why is a confident AI answer sometimes more dangerous than an obviously bad one?
5. What is the most important assumption to identify before approving an AI-assisted recommendation?
6. How do you use AI aggressively without becoming intellectually dependent on it?
7. What does good judgment look like when the machine may be better at the analysis than you are?
8. What is the THINK protocol, and how can someone use it before their next consequential decision?
9. When everyone has access to powerful AI, what becomes the new career advantage?
10. Why do you believe a machine will never be brave?