Who is actually accountable?

Not who is excited about it.

Not who is experimenting with the tools.

Not who has attended a masterclass, read the latest report, or asked ChatGPT to summarise the risks.

Who is accountable?

In my conversation with Shefaly Yogendra, this question kept returning.

Shefaly is a portfolio non-executive director, board adviser, author of Uncharted Spaces: Reset the Agenda. Reimagine the Boardroom, and someone who has spent years inside boardrooms across regulated sectors including financial services, energy, technology, and higher education.

She brings a rare combination to this conversation: engineering, governance, technology policy, decision-making, and the lived reality of boardrooms under pressure.

And her view is clear.

AI is not just another item for the board agenda.

It is changing the agenda itself.

Listen here

[EMBED EPISODE LINK]

Why this episode matters

For a long time, boards could treat technology as something to be overseen, reviewed, governed, or periodically challenged.

That is no longer enough.

Generative AI entered organisations in an unusual way. It was supposed to be enterprise technology, but it reached individual users first. Employees could use it before many organisations had policies. Leaders could experiment before governance had caught up. Proprietary data could be copied into public tools before anyone had fully understood the risk surface.

The smart organisations, Shefaly says, moved quickly to put guardrails in place.

But guardrails are only the beginning.

The deeper question is not simply, “Are people allowed to use this tool?”

It is:

What does this technology make possible?
What does it make fragile?
What does it expose?
What does it demand of leadership, governance, and decision-making?

And perhaps most importantly:

Who is willing to ask those questions before the consequences become obvious?

The big idea

Shefaly uses two words that I think belong at the centre of every boardroom conversation about AI:

Bravery and imagination.

Imagination, because boards need to be able to ask:

If we had to design this organisation again, with what we now know, what would we build differently?

What work exists because it creates value?

What work exists because nobody has yet had the time, courage, or clarity to question it?

What processes are meaningful?

What processes are fluff?

Bravery, because these questions do not stay theoretical for long.

They affect people.

They affect roles.

They affect younger generations entering the workforce.

They affect senior professionals whose experience has lived largely in their heads for decades.

They affect the way organisations decide what can be automated, what should not be automated, and what happens when one task is connected to ten other invisible tasks.

This is where AI governance becomes human.

Because automation is not only about breaking work down into tasks.

It is about understanding the connections between tasks.

It is about asking what gets lost when tacit knowledge is not surfaced.

It is about making visible the invisible judgment inside experienced people’s work.

That takes more than technical fluency.

It takes metacognitive reflection.

People must understand not only what they do, but how they do it.

And boards must be brave enough to ask for that work.

What does AI-savvy actually mean?

One of the most useful moments in the episode comes when Shefaly questions a phrase that is now being used casually:

“We need more AI-savvy board directors.”

It sounds sensible.

But what does it mean?

Does AI-savvy mean someone who uses the tools?

Someone who understands how models are changing?

Someone who can govern AI transformation?

Someone who can help redesign organisational processes?

Someone who understands risk, regulation, data, culture, and second-order consequences?

Or someone who can ask the right questions when everybody else is seduced by speed?

Shefaly’s point is sharp: asking for AI-savvy board directors without defining what that means is asking for a lot without being specific.

And that lack of specificity matters.

Because AI is not one thing.

It is a technology question.
A governance question.
A risk question.
A talent question.
A culture question.
A decision-making question.
A relevance question.

And if boards are not precise about what capability they need, they may end up confusing tool familiarity with judgment.

That would be a serious mistake.

The danger of cognitive outsourcing

There was another moment in this conversation that stayed with me.

Shefaly spoke about people using ChatGPT as a substitute for their own thinking.

Not as an assistant.

As a replacement.

She described the discomfort of hearing people say, “ChatGPT says this,” and thinking: but you are a grown, experienced human being. What do you think?

That distinction matters.

Because the danger is not that leaders use AI.

The danger is that leaders outsource the very cognitive work they are there to do.

Judgment is messy.

Thinking is often chaotic before it becomes clear.

Human reasoning does not arrive perfectly formed.

But if we keep replacing the roughness of our own thought with the neatness of machine-generated articulation, what happens to our ability to judge?

What happens to discernment?

What happens to the responsibility of independent directors to think independently?

In Shefaly’s words, context matters. Advice does not transfer neatly. Best practice does not travel unchanged from one context to another.

That is precisely why judgment matters.

And why boardrooms cannot afford cognitive dumping disguised as efficiency.

Relevance is no longer static

One of the most powerful ideas in this episode is Shefaly’s point that relevance is now dynamic.

For years, many senior leaders and board directors built credibility through accumulated experience.

They knew the industry.

They knew the regulator.

They knew the patterns.

They had seen cycles before.

That still matters.

But it is no longer enough.

The question now is not simply: what do you know?

It is:

Which parts of what you know still apply here?

Which assumptions need to be questioned?

Which models are useful?

Which models are outdated?

What do you know from the past that helps you see the future more clearly?

This is where Shefaly’s thinking becomes both practical and generous.

She is not arguing that experience has become irrelevant.

Quite the opposite.

In regulated industries, for example, someone who understands why regulation took the shape it did, how regulators think, and where the trajectory might go still has real value.

But that value does not remain alive automatically.

It has to be worked.

It has to be connected to scenarios.

It has to be projected forward.

It has to be re-examined in light of what is changing.

Relevance is not a possession.

It is a practice.

The boardroom kaleidoscope

Shefaly offered a beautiful metaphor.

She said our jobs have become like a kaleidoscope.

The pieces may be static, but every pattern that emerges is different from the one before.

That is what AI is doing to leadership and governance.

The pieces may be familiar:

Risk.
Talent.
Regulation.
Technology.
Strategy.
Culture.
Decision-making.
Accountability.

But the patterns keep changing.

And the speed at which they change is increasing.

By the time a board has made sense of one pattern, the next one may already be forming.

This is why curiosity is no longer a pleasant leadership trait.

It is survival.

What you’ll learn in this episode

• Why generative AI became a boardroom issue differently from earlier waves of enterprise technology.

• Why guardrails matter, but are only the beginning of AI governance.

• Why “AI-savvy board director” is an incomplete phrase unless organisations define what they actually need.

• How AI exposes the difference between individual adoption and organisational transformation.

• Why cognitive outsourcing may weaken the judgment leaders most need.

• Why relevance is dynamic, not static.

• Why good chairing may matter more than ever when boards are facing rapid change.

Best moments

00:28
Shefaly introduces her boardroom experience, her work across regulated sectors, and the thinking behind Uncharted Spaces.

02:47
Why generative AI captured everyone’s attention, even though predictive AI had already been embedded in business for years.

04:48
Why generative AI is transformational rather than merely additive.

05:30
The early organisational risk: employees using proprietary data in public tools before governance had caught up.

07:49
The accountability rupture: boards point to the C-suite, while the C-suite points in multiple directions.

08:45
Why boards need bravery and imagination.

12:19
What does “AI-savvy board director” actually mean?

17:45
Why it may be safer to say “I don’t know” than to perform knowledge in a rapidly changing environment.

18:30
The danger of cognitive outsourcing and leaders relying on machine-generated articulation instead of their own judgment.

21:48
Why relevance is now dynamic.

23:30
The kaleidoscope metaphor for work, relevance, and changing patterns.

26:56
Why good chairs can make or break how boards serve companies.

29:05
Why individual AI adoption and organisational AI adoption are not the same thing.

31:37
The closing note: optimism grounded in realism.

Memorable lines

“AI is not just another tool. It gives you the chance to ask what you would design differently if you had a blank sheet of paper.”

“What does AI-savvy mean?”

“It is safer to say I don’t know than to say I know and then be found out.”

“I want to know what you think.”

“Best practice doesn’t travel. It is specific to context.”

“Relevance is now not static. It is dynamic.”

“The pieces are static inside the kaleidoscope, but every pattern that gets made is different.”

“Good chairing will probably never go out of fashion.”

Resources and ideas mentioned

• Uncharted Spaces: Reset the Agenda. Reimagine the Boardroom by Shefaly Yogendra

• Generative AI

• Predictive AI

• Explainable AI

• AI guardrails

• Board accountability

• Independent non-executive directors

• Metacognitive reflection

• Cognitive outsourcing

• Dynamic relevance

• Governance in regulated sectors

• The role of the chair

• Organisational risk appetite

• Second and third order consequences

The question I am left with

What if the most important AI question for boards is not:

“Do we understand the technology?”

But:

“Are we still capable of independent judgment?”

Because in this moment, the boardroom does not need more people who can repeat what the tools say.

It needs people who can think clearly when the tools sound convincing.

It needs people who can ask better questions.

It needs people who know when to say, “I don’t know.”

And it needs people who are willing to do the harder work of staying relevant while the pattern keeps changing.

Listen to the episode

If this conversation made you think of a board member, CEO, founder, or senior leader who is trying to make sense of AI beyond the hype, forward this to them.

And I would love to know:

What does “AI-savvy leadership” actually mean to you?

Leave a comment, reply, or share this with someone sitting in the room where these decisions are being made.

Shivangi

P.S.

One of Shefaly’s lines stayed with me:

“I want to know what you think.”

That may be the quietest and most important leadership challenge in the age of AI.

Leaders talk about speed as if speed is the only variable that matters.

How fast can we automate?
How fast can we redesign roles?
How fast can we upskill people?
How fast can we get the organisation to adopt the tools?

But human beings do not transform at the speed of software releases.

A model can improve overnight.
A workflow can be redesigned in a week.
A role can be rewritten in a slide deck before lunch.

But identity does not move like that.

Trust does not move like that.

The feeling of being useful, valued, needed, respected, and seen does not move like that.

That is where my conversation with Abhijit Bhaduri begins.

Abhijit is a former GM Global Learning and Development at Microsoft, the first Chief Learning Officer at Wipro, author of six books including Career 3.0, and an advisor to organisations on leadership, talent, and culture.

When I asked him what leaders say they are ready for with AI but are actually not ready for, he did not hesitate.

“The human side of transformation.”

That sentence is the whole episode.

Why this episode matters

There is a version of AI transformation that sounds clean.

Technology changes.
Roles change.
Skills change.
People are retrained.
The organisation moves forward.

It is neat. It is rational. It is also incomplete.

Because work is not just a collection of tasks. Work is identity, rhythm, belonging, status, predictability, usefulness, and human contact.

Abhijit makes this point beautifully.

When we say machines may do more of the work, many people jump to the obvious economic question: what will people earn?

But that is not the only question.

The deeper question is: who will people be?

If a copywriter with 20 years of experience is told that a young trainee using AI can produce work just as good, the wound is not only professional. It is existential.

The question becomes:

Am I more experienced?

Or just more expensive?

That is the kind of line that should make every leader pause.

Because if you are leading AI transformation only through tools, platforms, workflows, and productivity metrics, you may be missing the place where the real resistance lives.

Not in the technology.

In the human meaning attached to the work.

The big idea

Abhijit draws a distinction that I think every leader needs to understand now:

AI time and human time are not the same.

AI development is accelerating. The gap between one model and the next can feel enormous. Capabilities shift quickly. What seemed impressive last month may feel ordinary now.

But human learning does not work that way.

You cannot send people to a workshop, expose them to a tool, and expect their identity, habits, judgment, confidence, and trust to shift at the same speed.

This is why so many transformation efforts feel strangely hollow.

The technology moves.

The slide decks move.

The dashboards move.

But people lag behind, not because they are lazy or resistant, but because humans need time to integrate change into who they are.

That is not inefficiency.

That is being human.

What you’ll learn

• Why the real AI transformation gap is not only technical, but psychological.

• How AI reshapes identity, especially for experienced professionals whose value has been built over decades.

• Why work is not just about money, and why that matters when leaders talk casually about automation.

• Why relationships are not built through efficiency.

• What leaders misunderstand when they try to teach human skills through online formats.

• Why the office may need to become a place for trust-building, not merely task completion.

One of the strongest moments

Abhijit says something that sounds simple, but is quietly radical:

“Human relationships are never built on efficiency. They are built on inefficiency.”

I loved that.

Because so much of modern work has been designed around removing friction.

Shorter meetings.
Sharper agendas.
Async updates.
Fewer conversations.
More dashboards.
Less wandering.
Less waiting.
Less small talk.

And yes, some of that is useful.

But the friction we removed was not always waste.

Sometimes it was where trust was being built.

The few minutes before a meeting.
The walk to coffee.
The conversation that had no obvious agenda.
The glance, the pause, the humour, the irritation, the repair.

This is not nostalgia for office life.

It is a reminder that human beings do not build trust like machines process instructions.

We build trust in context. In proximity. In repetition. In the unplanned spaces between tasks.

When leaders forget this, they may create workplaces that are efficient and brittle.

Best moments

• Why this wave of change feels different from previous technology shifts.

• The widening gap between technology change and human adoption.

• The identity question: “Am I more expensive, or am I more experienced?”

• Why good AI transformation happens at the level of tasks, skills, activities, and role redesign.

• What younger professionals need now: complex problem solving, orchestration, communication, and working across difference.

• Why work is not just about money.

• The commute as a transition ritual between home identity and work identity.

• Why human relationships are not built through efficiency.

• How leaders should rethink the office as a space for trust-building.

• Why people-related skills cannot simply be built through Zoom.

• The closing insight: human time and AI time are different.

Memorable lines

“The human side of transformation.”

“The gap between the human side of change and the tech side of change is becoming larger and larger.”

“Adoption is a very different challenge.”

“Am I just more expensive, or am I more experienced?”

“Work is not just about earning money.”

“Human relationships are never built on efficiency.”

“Use the office to really get people to build trust with each other.”

“Human time and AI time are different.”

Resources and ideas mentioned

• Career 3.0 by Abhijit Bhaduri

• SCARF model by David Rock

• Predictably Irrational by Dan Ariely

• Large language models

• Forward deployed product manager

• AI adoption

• Psychological adoption

• Role redesign

• Human skills and trust-building

• Hybrid work and office purpose

• Storytelling for sales

• Talent strategy in the AI economy

The question I am left with

What if the future of work is not simply about what machines can do faster?

What if it is about what humans need more slowly?

That may be the leadership question hiding underneath all the AI noise.

Because if leaders only optimise for speed, they may miss the thing that makes transformation possible in the first place:

trust.

And trust does not move at AI speed.

Listen to the episode

If this conversation made you think of a leader who is redesigning work faster than people can absorb it, forward this to them.

They will know why you sent it.

And I would love to know:

Where are you seeing the biggest gap between AI time and human time?

Leave a comment, reply, or share this with someone leading through this shift.

Shivangi

P.S.

One line from Abhijit keeps coming back to me:

“Work is not just about earning money.”

That sounds obvious.

But most AI transformation conversations are still designed as if it is not true.

The First Thing He Said Was: “I Was Scared.”

Cem Ozenc of Novo Nordisk on AI, uncertainty, and why the leaders who survive this moment may not be the ones with the best answers.

There is a kind of sentence senior leaders are trained not to say.

“I don’t know.”

“I’m not sure.”

“I’m scared.”

Not because they never feel those things. Of course they do. But most leadership rooms still reward the appearance of certainty. So the uncertainty gets managed. Polished. Translated into language that sounds more acceptable.

“We are navigating complexity.”

“We are learning as we go.”

“We are staying agile.”

All true, perhaps. But also safer than the real thing.

When I invited Cem Ozenc onto the first episode of Leadership at the Edge of AI, I asked him what went through his mind when I said I wanted to speak with him about what AI is doing to leaders.

His answer was not polished.

He said he was scared.

Not because he is inexperienced. Cem has spent two decades at Novo Nordisk, lived and led across six countries, and is now working inside one of the most watched companies in the world as healthcare, technology, consumer behaviour, and AI collide in real time.

And still, his first honest response was fear.

“I don’t see myself as an AI expert,” he told me. “It kind of humbles you.”

That sentence stayed with me.

Because this is the part of the AI conversation I do not hear enough.

We talk endlessly about tools, workflows, productivity, automation, risk, regulation, and strategy. All of that matters. But underneath it is something far more intimate.

What happens to leadership when the leader can no longer credibly pretend to know?

That is the real edge.

Listen here

[EMBED EPISODE LINK]

Why this episode matters

Cem is not speaking about AI from a conference stage with a clean framework and a neat conclusion.

He is speaking from inside the change.

Inside a company whose industry has already been reshaped by GLP-1 medicines, changing patient behaviour, new consumer expectations, digital health, and the rise of AI agents that are influencing how people search for medical information, make decisions, and arrive at conversations with clinicians.

He describes a healthcare model being inverted.

In the past, pharmaceutical companies generated clinical data, educated doctors, and waited for that knowledge to travel to patients.

Now patients arrive already informed. Sometimes already decided. Sometimes guided by AI before they ever speak to a doctor.

That is not a small shift.

It changes communication. It changes trust. It changes power. It changes what leaders inside these organisations must understand, decide, and explain.

And Cem’s point is not simply that AI is moving fast.

It is that leaders are being asked to lead in systems they cannot fully predict.

He says his team can spend weeks trying to understand an algorithm, only to find that it has changed again.

So what do you tell your team?

How do you give direction when the ground itself keeps moving?

His answer is not louder certainty.

It is more honest leadership.

The big idea

Cem said something I think every senior leader should sit with:

“I can’t lie to them. If I tell them I know what I’m doing with AI, that will be a lie. I am constantly learning.”

That is not a throwaway line.

It is a leadership philosophy.

There is still a very old idea embedded in many organisations: authority comes from knowing. You lead because you have answers. You are credible because you see further than others. You are followed because you can reduce ambiguity.

That model is under pressure now.

Not because expertise no longer matters. It does.

But because expertise alone is no longer enough when the thing you understood two weeks ago may already be outdated.

Cem offers a different model.

The leader as learner.

Not the most informed person in the room.

Not the most certain.

Not the person performing calm while privately panicking.

The person willing to ask better questions, make learning visible, and bring the organisation into the work of sense-making.

That sounds simple. It is not.

Because most leaders have been rewarded for the opposite.

They have been rewarded for confidence, clarity, control, speed, and answers.

AI is now exposing the fragility of that performance.

What you’ll learn in this episode

• Why AI may be forcing leaders to unlearn more than they need to learn.

• How patient behaviour and AI agents are changing the healthcare model from the outside in.

• Why “not knowing” may be the leadership capacity most executives are least prepared for.

• How Cem thinks about honesty, fear, and credibility with his own team.

• Why the future of leadership may belong to the best learners, not the smartest experts.

Best moments

Cem names his first reaction to being invited onto the podcast: fear.

He explains why AI feels different from previous waves of disruption.

The “best learner in the room” idea emerges.

Cem describes how AI and GLP-1 medicines are changing the healthcare model.

The leadership challenge: algorithms change just as teams begin to understand them.

Cem explains why he tells his team what he does not know.

A practical example: using AI to support better performance feedback and reduce recency bias.

The thing leaders are not ready for: not knowing.

Memorable lines

“I was at first probably a bit scared.”

“I don’t see myself as an AI expert.”

“It kind of humbles you.”

“What do I need to unlearn as a leader?”

“You will not be the smartest. You will not be the most knowledgeable.”

“What you can do is aim to be the best learner.”

“I can’t lie to them. If I tell them I know what I’m doing with AI, that will be a lie.”

“Most leaders are not ready for not knowing.”

Resources and ideas mentioned

• Novo Nordisk

• GLP-1 medicines

• Ozempic and Wegovy

• AI agents in healthcare information-seeking

• Large language models

• AI-supported performance management

• Recency bias in feedback

• Adam Grant, mentioned as a possible feedback/persona inspiration

• OpenAI collaboration, mentioned by Cem in the context of company learning and model training [verify exact public framing before adding link]

The question I am left with

What if the leaders who thrive in the age of AI are not the ones who know the most?

What if they are the ones who can stay credible while learning in public?

That is a very different kind of leadership.

And it is much harder than having answers.

Listen to the episode

If this conversation made you think of a leader who is carrying uncertainty but not saying it out loud, forward this to them.

They will know why you sent it.

And if you are navigating this yourself, I would love to know:

Where are you still pretending to know more than you actually know?

Leave a comment, reply, or share this with someone who is leading at the edge of AI.

Shivangi

P.S.

At the end, I asked Cem for one word to describe what AI is doing to leadership.

He said:

“Unlearn.”

Not learn.

Unlearn.

There is a whole leadership curriculum hidden inside that distinction.

Most conversations about AI and leadership ask the wrong question.

They ask: what should leaders do with AI?

I’ve been asking something different: what is AI doing to leaders?

Because what I keep seeing in my work with senior executives isn’t a capability gap. It’s an identity gap. When the environment rewrites its own rules faster than experience can keep up, titles stop working as anchors. Frameworks stop working as maps. And the leaders who are struggling most are often the ones who have been most successful.

That’s the territory Leadership at the Edge of AI is built to explore.

Not the polished keynote version. The honest one.

Season 1 opens with three guests who have each stood at a different edge.

Cem Ozenc is Group Vice President at Novo Nordisk, based in Copenhagen, leading a newly created team sitting at the intersection of digital innovation, omnichannel, and DTP. Before this, he led the historic Wegovy launch in Australia through active supply constraints, across six countries and a career that has never stopped moving. He understands what it means to build while the ground is still shifting.

Abhijit Bhaduri was Chief Learning Officer at Microsoft during the period when the company was rebuilding its global learning strategy around its own AI offerings. He has watched from the inside as one of the world’s most powerful organisations tried to figure out what human development means when the technology is learning too. Author, educator, advisor. He is someone who thinks about learning as survival, not upskilling.

Shefaly Yogendra sits on boards across industries, advises FTSE 100-350 CXOs on GenAI, and is the author of Uncharted Spaces. She was also COO of Ditto AI. She brings the rare combination of governance-level perspective and deep operational experience with the technology itself. She asks the questions that boards are not yet asking out loud.

Three different arenas. Three different edges. One consistent thread: the human experience of leading when the rules keep rewriting themselves.

Episode 1 with Cem Ozenc drops on 22 May 2026.

If you’re a senior leader who has felt the ground move under something you thought was settled, this show is for you. Not to give you the answers. To give you the honest conversation you can’t always have in your official role.

Subscribe so you don’t miss the first episode.

And if this resonates with someone in your network who is navigating the same territory, share it with them. The best conversations start when the right people find each other.

Shivangi

The question nobody is asking in your all-hands.

What a whisper in a corner office revealed about the real conversation on AI and why almost everyone is ignoring it.

He had a corner office, twenty years of results, and the kind of presence that changes the temperature of a room the moment he walks in.

And then he leaned forward and whispered.

“What if AI makes me irrelevant?”

There was no framework to reach for. No model that fit the moment. So I told him the truth.

I’ve had that exact fear about my own work. About whether what I built still means something in a world that’s rewriting the rules faster than we can read them.

We sat in that together. And what came next wasn’t clarity. It was something rarer.

Recognition.

This fear isn’t a personal failure. It’s the most important signal a leader can receive right now. And almost everyone is ignoring it.

Here’s what I know after years of coaching leaders through transformation:

The AI disruption conversation is lying to you.

It’s dressed up as a strategy problem. A skills problem. A change management problem. There are frameworks for it, five-step models, roadmaps with colour-coded swim lanes.

But that’s not what’s happening in the corner offices. That’s not what leaders are sitting with at 11pm before a board review. That’s not what surfaces when the room gets quiet enough for the real question to come through.

At its core — underneath all the strategy language and transformation decks — this is a question of identity.

Who am I if the thing I was certain about no longer holds?

That question is terrifying. It’s also the only one worth asking.

I’ve spent the last year having a version of this conversation over and over — with SVPs navigating restructures, with CHROs trying to train leaders for a world that’s changing faster than their curriculum, with recently exited CEOs who finally feel free to say what they couldn’t say when they were still in the seat.

What I kept noticing: the most important part of every conversation happened after the official agenda ended. In the lift. Over a second coffee. In the pause before someone said “I probably shouldn’t say this, but...”

That’s the conversation I want to put on record.

Introducing: Leadership at the Edge of AI — the podcast

This isn’t a podcast about AI. It’s a podcast about what AI is doing to the people in charge — and what they’re choosing to do about it.

Every week I sit down with leaders who are willing to speak without the corporate filter. No press-release answers. No techno-optimism scripts. Just experienced leaders being honest about the most complex leadership moment of a generation.

The conversation your peers are too afraid to have publicly? We’re having it here.

This newsletter is where the thinking lives between episodes. Each week: one idea, one tool, one question worth carrying into your next conversation — drawn from coaching work, from the podcast, and from the leaders who are generous enough to think out loud with me.

I won’t publish unless I have something worth saying. And I’ll never tell you everything is fine if it isn’t.

The question I’m sitting with this week:

“What are you not saying in your all-hands that you need to be?”

Hit reply and tell me. I read every response — and your answer will shape what I write next.

Shivangi Walke

Executive Coach · Leadership Trainer · TEDx Speaker

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