AI-isms — Ep05: Pam Baker, Coaching

In this episode of AI-isms I sit down with Pam, CEO and founder of Women's Coaching Alliance, the organization she built out of her husband's question. Young women get recruited, interviewed, trained, paired with a co-coach, given an adult mentor, paid, and then handed a team as head coaches. Not assistants. Head coaches. They run the practices, manage the parents, lose some games, and course correct with each other. It is leadership development disguised as a Saturday morning.

Then I did what I always do and dragged a beautiful human conversation into AI.

The idea that stayed with me came from one of Doug's friends. Two coaches at a sales meeting, and one asks the other how his season went. The answer: I don't know. I'll let you know in 30 years. Pam's point is that a coach is not looking at the scoreboard, she is looking at who these kids become. Sit with that next to every AI business case you have been asked to defend inside a quarter.

What we get into:

• Why the barrier every young woman named was not the game, it was the difficult parents, and why that is the same muscle you use on a difficult stakeholder at work
• Why Women's Coaching Alliance pays its teenage coaches, and how Pam connects that to the gender wage gap
• Where AI genuinely helps a small organization: synthesis, and turning long manuals into something a 16-year-old will actually read
• Why reading off an AI script makes you blind to the person in front of you, the eye contact, the shoulders, the thing that tells you it is working or failing
• Three real scenarios, Pam as coach: the 20-year-old who feels like she is reading someone else's words, the parent with the wearable data, the company swapping human mentoring for AI coaching
• AI is sycophantic. A real mentor tells you that will not fly here
• What happens when someone discovers a new AI capability and cascades it everywhere without asking whose job it was
• Why Pam does not use AI for parenting, and the line underneath it: there is no way AI knows more about my kids and me and our relationship than I do

Chapters

00:00 Where are all the women coaches?

01:10 Meet Pam Baker

01:52 The question Doug kept asking

02:44 Great coaching is great leadership

04:27 Eighteen young women raise their hands

06:09 She will not raise her hand on her own

10:17 Why every coach gets paid

10:35 A hard pivot into AI

11:38 How a coaching organization actually uses AI

13:05 No AI knows what is happening in front of you

13:33 "I'll let you know in 30 years"

16:13 Case 1: the coach who feels like she is reading a script

19:43 Case 2: the parent with the wearable data

21:58 An amazing player is not a great team

24:08 Snatching someone else's agency

25:44 Case 3: swapping human mentors for AI coaching

28:13 AI is sycophantic. A mentor is not.

33:08 What AI cannot know about your kid

39:09 The two things that stayed with me

This is AI-isms, human rhythms in a synthetic world. AI can build your practice plans. It is never going to be the person who believes in you before you believe in yourself. Ask yourself who that person was for you, and whether you are being that person for anyone.

Topics: coaching, leadership development, mentoring, AI coaching, AI mentoring, sycophancy, youth sports, women in sports, gender wage gap, culture, team culture, human-AI collaboration, AI adoption, parenting and AI, future of work, Women's Coaching Alliance, Pam Baker

Be your authentic self. We have spent years fighting the stories we were told in order to get there. And then the LLMs showed up, and a polished version of your work, your thoughts, even your introduction, became available at the click of a button. So what happens to the battle for authenticity now? It just got a lot harder.

In this episode of AI-isms I sit down with Lynn Perkins, co-founder and CEO of UrbanSitter. Lynn works in a category where authenticity matters more than almost anywhere else. You are letting a stranger into your home to care for the person you love most. So we set care aside and talked about the thing underneath it: trust, and what happens to it when a generation of people start using AI to rewrite who they are on paper.

The idea that stayed with me: imperfect human beats AI-generated polish, and it is not close. Lynn told me about a competitor that let care providers use AI to write their profiles. Two things happened. Everyone started to sound the same, and the platform's own fraud detection stopped working, because suddenly every profile sounded a little fake. Perfect turned out to be the tell.

What we get into:
• The science of trust (background checks, credentials) versus the art of it (values, connection, work ethic), and why AI can only fake one
• Why a too-polished proposal reads as less care, not more
• The fraud-detection paradox: when everyone sounds the same, the machine can't tell real from fake
• Four real scenarios, Lynn as coach: the non-native English job applicant, the exhausted sales rep, the 90%-automated inbox, the artist who refuses AI
• The pattern underneath all four: human-first for the deep and creative work, AI-first pass for the repeatable
• How UrbanSitter scaled AI by curiosity, not mandate, and why they share their failures out loud
• The one thing Lynn does to stay authentically herself

Chapters
00:00 The battle for authenticity just got harder
01:28 Meet Lynn Perkins
02:36 Trust has a science and an art
04:46 When the profile isn't the person
06:09 Why "too perfect" erodes trust
08:27 The AI proposal that felt like less effort
09:43 When everyone sounds the same, fraud detection breaks
10:54 Why competitors share their mistakes
13:07 Scenario 1: the non-native English job applicant
16:15 Scenario 2: the exhausted sales rep
18:47 Scenario 3: the 90%-automated inbox
21:45 Scenario 4: the artist who refuses AI
24:53 Adoption by curiosity, not mandate
30:43 Where UrbanSitter measures the outcome
33:11 Staying authentically Lynn: share the snafus
35:55 The AI-isms from this episode

This is AI-isms, human rhythms in a synthetic world. The next time you are tempted to run yourself through the machine until you sound perfect, ask what you are trading for it. Tell me what you find.

Topics: authenticity, trust, AI and hiring, resumes and AI, personal branding, fraud detection, human-AI collaboration, AI adoption, bottoms-up adoption, change management, marketplaces, childcare, UrbanSitter, Lynn Perkins, future of work

What am I role modeling today? Most of us think that question belongs to the most senior people in the building. This episode is here to convince you otherwise.

In this episode of AI-isms I sit down with Eli Potter, one of the most respected CIOs in Silicon Valley and an advisor to 125+ organizations. Eli was born in Cold War Bulgaria, arrived in the US with two suitcases, and learned English from the Beatles. Out of a lifelong love of words she coined one the moment needed, role modelship, and wrote the book by the same name.

The idea that stayed with me: AI can't be what it can't see. AI isn't just code and models, it's a mirror of our habits, our relationships, and our values, and it learns and unlearns the way we do. Which makes role modeling everyone's job, not just the CEO's. Every prompt is training the thing someone you love will one day ask for advice.

What we get into:
• Why the gap beyond leaders and managers is the who, and how role models "engineer identity"
• AI as a mirror, and why it's dangerous to treat it as an independent thing
• The five disciplines of role modelship, through real cases you're probably living right now
• Creator vs. consumer: why opting out of AI is a destiny choice, not a tech choice
• Mentorship vs. sponsorship, and the reputational chip nobody talks about
• What happens when a young employee asks the CEO, "what are you doing to learn AI?"

Chapters
00:00 What am I role modeling today?
01:34 Meet Eli Potter
02:13 The origin of role modelship
03:39 Managers, leaders, and the missing "who"
06:27 Role modelship in the age of AI
08:02 Why AI is a mirror
09:43 The five disciplines
10:24 Stewardship: the biased hiring AI
13:52 Fellowship: creator vs. consumer
17:25 Mentorship vs. sponsorship
22:25 Leadership: the town-hall question
27:12 The AI-isms Eli lives by
30:20 The five-year-old who got it
32:05 Finding your voice in a second language

This is AI-isms, human rhythms in a synthetic world. Next time you open a chat window, ask yourself: what am I role modeling today? Tell me what you see.

Topics: role modelship, AI and leadership, AI as a mirror, stewardship, fellowship, mentorship, sponsorship, creator vs consumer, AI-first vs human-first, future of work, CIO leadership, Eli Potter

At the heart of anxiety is uncertainty. There's research that when your brain has full certainty about what's coming, even if what's coming is an electric shock, you feel calmer than when you don't know. Now look at where we are. AI everywhere, no real playbook, a dozen versions of the future arriving at once. Of course we feel uncertain and anxious.

In this episode of AI-isms, I sit down with Liz Fosslien to talk about what AI is doing to how we feel at work. Liz studies human emotions at work. She's the author and illustrator of the bestsellers Big Feelings and No Hard Feelings, her work has run on NPR, TED, and Good Morning America. She and her co-author Molly Duffy recently concluded a study on how AI is making us feel at work.

The idea that stayed with me is ambivalent anxiety, two opposite emotions in the same body at the same time. I'm scared this ends my job. I'm relieved I might not have to do the part of it I hate. Both, at once. And most of us are performing 'calm' over the top of it.

What we get into:
• Why "uncertainty" has outgrown the word, and the research on why we'd rather know a bad thing is coming than sit with a fifty-fifty chance
• Ambivalent anxiety: holding fear and relief at once, and the quiet tax of performing calm at work
• The early-career squeeze, when the first rung is the first to get automated, and the human skills that travel across any industry
• Chasing curiosity instead of fear: peer learning, skills swaps, and how to move when nobody hands you a playbook
• Why pretending to have it figured out is the biggest mistake leaders make right now, and why the honest questions create more clarity than fake answers

Chapters
00:00 The uncertainty under the anxiety
01:36 Meet Liz Fosslien
02:36 We need a new word for uncertainty
03:26 Why humans hate uncertainty
07:25 Enter AI: ambivalent anxiety and performing calm
12:57 Case 1: early career and the "AI slop" moment
17:37 AI-isms for early career, and the mid-career squeeze
20:38 Chasing curiosity: peer learning and skills swaps
23:11 Not everything catastrophic will happen
26:35 Leading a transformation with no playbook
30:31 The illustration: ambivalent anxiety on a swing
34:04 How Liz processes her own emotions
35:54 The AI-isms I took from this one

This is AI-isms, human rhythms in a synthetic world. If you're leading people through this shift, what are you feeling underneath the calm? Tell me what you see.

Topics: AI and emotions at work, uncertainty and anxiety, ambivalent anxiety, the future of work, early career and automation, reskilling and peer learning, leadership in uncertainty, performing calm, Liz Fosslien, Big Feelings, No Hard Feelings

We spend our careers drawing lines. My team, my P&L, my data, my customers. Then agentic AI starts working across all of them, and the old question comes back sharper than ever: what happens to power?

In this first episode of AI-isms, I sit down with organization designer Amy Kates to unpack how AI is redrawing the lines of power inside organizations. Amy is the author of six books on org design and operating models and the founder of Kates Kessler.

Her core idea stayed with me - real power lives horizontally. That is where value gets created, and it is exactly where AI is now blurring the boundaries.

What we get into:
• Why organizational power is horizontal, not just vertical, and why the seams matter more as AI dissolves silos
• Designing for outcomes instead of headcount when agents do the work
• A real finance-versus-HR standoff over workforce planning, and how to redeploy capacity instead of just cutting it
• Why AI is often a red herring for collaboration problems, and how Jay Galbraith's STAR model diagnoses the real issue: trust and metrics
• Learning is greater than lurching: the honest answer to how long an org design should last

Chapters
00:00 Power in the age of agents
01:33 Meet Amy Kates
02:26 Why power lives in the horizontal seams
10:35 Enter AI: the end of people equals power
16:50 Case 1: Finance vs. HR and who owns workforce planning
24:43 Case 2: Two leaders who won't share the data
27:55 Why AI is a red herring for trust (the STAR model)
34:10 The shelf life of an org design: learning over lurching
37:25 How Amy actually uses AI in her life
40:26 The AIisms I took from this one

This is AI-isms, human rhythms in a synthetic world.

Intelligence now surrounds us in every dimension of our lives. It is rapidly evolving not just how we work, but our social norms and core behaviors. We are learning to think, team, lead, play, learn, and even sleep differently. 

Welcome to AI-isms. My name is Shulagna Dasgupta.

For over two decades, I’ve partnered with CXOs through their most audacious transformations. And this is what I can tell you: no matter how sophisticated the technology, at the end of the day, change is almost always human. AI is altering our behaviors and our state of being in the most profound ways - but are we being intentional about what we allow it to do to us? Are we using it in a way that unlocks our greatest potential or are we letting it into our subconscious in ways we didn’t intend to?

Join me and a guest as we bring some intentionality to AI-driven behavior change. In each episode we will talk through lived experiences of people who have used AI to better their lives and behaviors. Maybe, you could borrow an idea or two?

This is AI-isms. Let’s talk human rhythms in a synthetic world.