Human–AI collaboration for real teams doing real work.

I’m building at the intersection of product leadership and applied AI — integrating AI into real teams and systems already in motion, in ways that increase creativity and impact without deceleration. The goal isn’t “more AI.” It’s better work: clearer decisions, stronger judgment, less busywork, and learning that compounds over time.

How we stress-test frameworks → Explore AI Literacy → Browse Playbooks →

Why this matters now

Modern knowledge work is overloaded. Teams are buried in coordination, context switching, and “work about work,” leaving less time for the thinking that actually moves products forward.

60%

of time spent on “work about work”

68%

say they don’t have enough focus time

73%

of business leaders say success is at risk

64%

struggle to find the time and energy to do their job

Sources: Asana “Anatomy of Work” (as referenced across site)

How we work: stress-testing AI, not just shipping it

Most AI guidance assumes the framework is correct — and the problem is just adoption. We don’t make that assumption. We stress-test models, metaphors, and methodologies before we recommend or build around them.

We ask questions like:

• Where does human judgment actually live?
• Who is accountable when something goes wrong?
• What breaks under ambiguity or complexity?
• What looks helpful in theory but fails in practice?

The outcome:

Not a single “right” answer — but clarity about roles, limits, responsibility, and the right model for the job.

Read: HITL vs Loop → Prompt vs Collaboration Engineering → Not every AI is a teammate →