Teaching Doesn't Change People; Understanding Does

Personal context first: Maya's story

The first time Maya fired a high-performer, she had three scripts printed out and still ended up crying in the bathroom.

On paper, she had done everything the "right" way. She read the management books, highlighted the frameworks, and practiced the exact words she was supposed to say.

The problem wasn't that she lacked teaching. It was that none of it fit her.

Teaching doesn't change people; understanding does.

Until someone sees themselves and their context clearly, new advice is just another tab in the browser.

The missing piece in most coaching

When people talk about coaching—especially in leadership and performance—they default to content:

• 10 principles of great managers
• 5 feedback scripts that always work
• The frameworks you're supposed to use in every 1:1

I've used a lot of these. They're not wrong. They can be inspiring. They just don't always fit the nuance.

I was lucky to have had several great mentors and coaches. The conversations that actually changed my trajectory didn't start with a framework. They started with someone understanding me:

• Where I was in my career and life
• What I was afraid of losing
• What "great" looked like to me, not in a book
• The constraints I was pretending didn't exist

Once that was on the table, the "teaching" landed fast. Until then, it bounced.

Maya didn't need better advice. She needed better context.

Back to Maya.

Her situation looked simple from the outside:

• Brilliant engineer, toxic behavior
• Team trust degrading
• Leadership already frustrated

Ask a room of experienced managers and you'll get mostly the same plan: clarify expectations, document clearly, give a path, hold the line.

That's what her mentor did. The plan wasn't bad.

What it missed:

• Maya was a first-time manager in a post-reorg environment.
• Two senior architects quietly sided with the engineer because they worshipped output.
• Her VP was in "prove we can execute" mode and wanted visible decisiveness.
• Maya's wiring was conflict-averse but deeply principled.

The advice didn't fail because it was wrong. It failed because it was generic.

The cold open, revisited

In that bathroom, Maya realized her real fear wasn't the conversation—it was losing the respect of the architects who valued raw output over trust. Naming that fear let her prepare differently. She brought one of them into the pre-brief with her VP to align on the principle: "We protect how we work, not just what we ship."

The 1:1 didn't become easy. It became clean.

She was direct. She tied examples to impact. She offered a narrow, time-boxed path that honored the engineer's talent without tolerating the behavior. She set a date to review and defined what "better" meant in observable terms.

Understanding as a precondition for change

When we finally put the frameworks aside, we did three things before talking tactics:

1. Named the fear
   Not generically—specifically. She was less afraid of the conversation and more afraid of losing the respect of people who valued raw output over trust.

2. Mapped the constraints
   Time, political capital, team health, business expectations. What could actually move in the next 30 days?

3. Defined "great" in her own words
   Not "what would a textbook manager do?" but "what decision would I be proud of in a year?"

After that, the plan almost wrote itself. The "right" script wasn't a mystery anymore. It was a natural extension of who she was and what the situation demanded.

You can borrow someone else's script. You can't borrow their conviction.

Understanding gives you that. It aligns the tactic with the person using it. When it clicks, the conversation isn't louder. It's cleaner.

The day Maya made her call, she didn't deliver a perfect speech. She delivered a decision she believed in—and a team she could lead.

Two weeks later, the engineer opted out. The team's output dipped for a sprint and then climbed—fewer escalations, faster approvals, clearer ownership.

Understanding is not soft. It's surgical.

There's a belief that "understanding people" is the soft part and "teaching them what to do" is the hard part.

My experience as an operator and founder has been the opposite:

• Understanding is the hard part because it forces you to face reality—motivation, energy, politics, trade-offs.
• Once you see that reality clearly, the next action is usually obvious, even if it's uncomfortable.

Finding their FIT

What actually changes behavior is a plan that fits the person's wiring and the moment's constraints.

I call this pre-coaching move "FIT":

• Feel: Name the real stakes and feelings without flinching. (Anxiety is data, not an enemy.)
• Inventory: Get to ground truth—facts, constraints, decision rights, timing.
• Target: Define one observable behavior that would count as progress this week.

Do FIT first. Then pick tactics.

A simple loop that actually sticks

The work is less about downloading more information and more about tightening the feedback loop between:

• Who this person is
• What situation they're in
• What one behavior will move them forward this week

The pattern I come back to looks like this:

1. See the person, not the role.
   What do they actually want in the next 3–6 months? What scares them? What gives them energy?

2. See the situation, not the theory.
   What's really constraining them—time, skills, stakeholders, money, politics, risk appetite?

3. Pick one behavior, not a new identity.
   Something observable in the next 7 days, not "be more strategic" or "act like a leader."

4. Close the loop quickly.
   Review what happened, what surprised them, and what changed. Then notch difficulty or scope up by just one.

If you get 1 and 2 wrong, 3 and 4 don't matter.

Where AI gets this backward

Most AI-powered "coaching" today is an advanced teaching engine:

• It can summarize books.
• It can generate frameworks.
• It can give you 10 ways to handle a hard conversation.

All useful. None of that by itself changes behavior.

The real unlock is AI that starts the way a great human coach does: by understanding the person in front of it and the context they're actually operating in. It doesn't just learn the big moments—it pays attention to the small routines, the timing of your days, and the tiny nudges that actually change what you do next.

And here's where AI can go beyond humans: it can quietly synthesize patterns across hundreds of signals every day for every person—calendar, conversations, commitments—at a scale no manager or coach could ever keep up with, and it can do it with a more objective lens, less polluted by recency bias, politics, or who spoke the loudest in the room.

That's the bet we're making at Ariso.

Personal context first: the foundation of Ari

When we talk about "AI partners" at Ariso, we don't mean a smarter content library. We mean systems that:

• Build a deep model of how you work—your rhythms, your communication style, your current priorities.
• Respect your boundaries and consent around what they see and use.
• Use that context to suggest one or two precise moves, not a wall of generic advice.
• Show up at the right moment (before the 1:1, ahead of the performance review, when a tough email hits your inbox).

In other words:

personal context first, content second.

And we're not just building a coach who talks at you—we're building a coach who is also your copilot: a partner that doesn't just preach or nudge, but rolls up their sleeves with you, helps draft the hard message, structures the conversation, closes the loop, and quietly does pieces of the work alongside you.

That is the foundation of how we design our AI partners. If they don't understand you, if they can't actually jump in to help, we don't trust them to "teach" you.

The shift I care about

I don't want a future of work where we're surrounded by smarter teaching machines shouting better advice at us.

I want a future where our tools understand us well enough that the next step feels obvious, small, and deeply ours.

That's where change actually happens.