Hi, I’m Andy, a Change Leadership Consultant based in Montreal, originally from France, now helping organizations across Canada and beyond to turn transformation into a lasting success.

For over 12 years, I’ve worked with executives, HR leaders, and transformation teams to strengthen their change maturity, the mindset, capability, and culture needed to lead change effectively.

Many organizations struggle with transformation because people, culture, and leadership aren’t ready and lack capability for it. 

That’s where I come in.

Here’s what I do:

→ Design tailored change strategies and transformation roadmaps.
→ Coach leaders and sponsors to drive change with confidence.
→ Assess impact, resistance, and risk to ensure smooth transitions.
→ Build internal change capability through hands-on training.
→ Drive clear, 360° communication across all levels, using existing channels effectively.
→ Measure engagement, readiness, and behaviors to demonstrate ROI and lasting impact.

At Elevation Groupe Conseil, my goal is simple: make change your competitive edge.

If you’re ready to make your change initiatives stick, let’s connect.

Send me a DM or email me at andy.lauret@elevationgc.ca

Andy ⬆️

Everyone wants transformation to go faster.

Almost no one counts the damage speed creates.

I’ve seen rushed transformations burn teams, miss decisions, 
and stall adoption months later.

I’ve felt that pressure too.

“Go faster” sounds productive.

Until energy drops and shortcuts multiply.

Speed without clarity isn’t progress.
Speed without capacity creates workarounds.
Forced pace looks efficient, but it quietly sabotages outcomes.

Real acceleration comes from alignment, not pressure.

What’s one change you slowed down to move faster later?

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from the change frontline.

Andy Lauret ⬆️

Change management partner | Building Change-ready Organizations by helping People-First leaders and employees | Founder @Elevation Groupe Conseil

Change management does not need a seat at the table.
It needs its own table.

For years, change has been treated like support work.

Brought in after decisions are made.

Asked to help people “align” once the hard parts are over.

That is not change management.
That is damage control.

Change work shapes the decision, not the announcement.
- It challenges timelines.
- It questions readiness.
- It slows things down when speed would break trust.

If change leaders only react, they will always be ignored.
If they only facilitate, they will never influence.

Owning the table means saying no.

It means pushing back on leaders who want buy in without ownership.

It means being accountable for outcomes, not just adoption plans.

P.S. Is change invited early or only when things start breaking?

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from the change frontline.

Leaders accidentally kill engagement.

They broadcast instead of dialogue.
Present instead of co-create.
Celebrate go-live instead of measuring what sticks.

Here’s the truth:
Engagement is not about making people excited.
It’s about making them feel involved, equipped, and heard.

This framework shows:
→ The 5 things that kill engagement (even when leaders mean well)
→ Why each shift matters

One column builds compliance.
The other builds ownership.

Same intentions but very different outcomes.

Worth asking: which one are you designing?

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from the change frontline

People support what they help shape.

Consultation creates compliance.
Co-creation creates care.

One informs people.
The other gives them authorship.

Change doesn’t need more direction.
It needs shared ownership.

And you, are you designing for control...
or for commitment?

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from the change frontline

Saying ‘I support this change initiative’ is not leadership. 

It’s distance.

Support without visibility is abandonment.

It tells teams the work matters, but not enough for leadership time.

Change does not need cheerleaders.
It needs sponsors who are willing to be seen and challenged.

Here’s what sponsorship actually looks like:

1/ Publicly own the change.
Say “I am accountable for this” instead of “I support this.”

2/ Show up where the tension is.
Join the meetings where people disagree.

3/ Repeat the message often.
If you are tired of saying it, people are just starting to hear it.

4/ Make one visible decision early.
Cut a process, fund a priority or stop an old habit.

5/ Protect the work publicly.
When resistance shows up, address it out loud.

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from the change frontline.

Same goal, different approaches.

And… it’s OK!

Company A: top-down, directive driven.
Company B: co-created, Participative-driven.

We often search for *the* right way to lead change.

Truth is: there isn't one!

Both succeeded, because their strategies fit their culture, pace, and people.

The danger? Forcing a framework that doesn’t fit your DNA.

Yes my preference goes for a more co-created path… 
but different doesn’t mean wrong!

Context is king.

Custom beats copy.

And you, what’s the boldest change path you’ve seen work?

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from the change frontline.

For a long time, I thought leading change required authority.

A role, title or mandate.

Then I watched most change actually happen.
It didn’t come from the people with the biggest say.
It came from the ones who stepped in early.

The ones who noticed friction and removed it.
Who helped others make sense of what was shifting.
Who stayed calm when things got uncomfortable.

They didn’t “drive” change, they made it easier to move.

What I’ve learned is this:

Influence doesn’t come from position.

It comes from usefulness, trust, and timing.

– When you create value under pressure, people listen.
– When you follow through, people rely on you.
– When you model the change before it’s popular, people follow.

That’s how change gets led long before it’s approved.

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How to Lead Change Without Authority

Kotter 8-step Framework was meant to guide, not dictate.

It’s not the framework that fails.

It’s how rigidly we apply it.
The problem is using a copy-paste approach to a personalized challenge.

Kotter’s 8 Steps?
Still one of the best frameworks out there.
If you treat it as a compass not a checklist.
Change isn’t linear.

Sometimes urgency comes later.

Sometimes coalitions form mid-flight.

And sometimes empowerment needs to start before the vision is clear.
You can adapt the order.

You can tailor the approach.

But you can’t ignore the conditions.

Frameworks don’t fail.
Misusing them does.

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from the change frontline.

You didn’t lose value when the project failed.
You lost it when people stopped using it.

On time.
On budget.
Zero value...

Not because of resistance.
But because adoption was assumed, not managed.

Every hour of low adoption is a hidden cost.
And it adds up faster than you think.

Delivery is an event.
Adoption is the investment.

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cost of low adoption

Every conversation about AI sounds exciting -until you try to make it work inside a real business.Most leaders see the potential.What they don’t see is a clear starting point.So they stay stuck - not because they’re doing nothing,but because they’re doing too many things that don’t move the needle.→ Teams wasting hours on repetitive work that should’ve been automated months ago→ Customer support drowning in simple questions while real issues wait in line→ Sales reps managing CRM chaos instead of relationships→ Marketing pushing out content that doesn’t convert→ Projects that “pilot forever” but never scale→ Systems that don’t talk to each other - so people become the integration layer→ Leaders flooded with vendor pitches, buzzwords, and no clear playbookThe result?Rising costs. Missed opportunities.And that quiet, uncomfortable feeling that competitors are moving faster.It doesn’t have to be this way.With the right AI and automation strategy, you can:Pinpoint the highest-impact areas to automateReclaim hundreds of hours across teamsScale without adding headcountThat’s where my work begins - with an AI Automation Audit.A hands-on, practical look at your workflows, tech stack, and decision loops to uncover what’s possible right now.No jargon. No hype. Just a clear starting point and a roadmap that actually works.Because AI only matters if it improves judgment, strengthens teams, and removes friction from work.That’s been my practice for over 25 years - scaling businesses, repairing broken processes, and delivering change that lasts.Here’s what that looks like in real life:👉 Replacing fragile systems that can’t support growth👉 Scaling untapped opportunities hiding in plain sight👉 Applying AI and data to sharpen decisions, not decorate dashboards👉 Leading transformations that make daily work simpler for the people who actually use the systemsI’ve done this across the spectrum - from shoestring budgets to million-dollar programs.What I bring:✅ Calm and clarity when things grow complex✅ A focus on value, not features✅ Data that informs, never dictates✅ Teams that perform - without burning outIf you’re done with the AI noise and ready for results that stick, let’s talk.

Ranjana Sharma

Tech didn’t invent new human behaviors.
It just found a way to scale the old ones… beautifully, efficiently, profitably.

Human drives → Monetized at scale

There are seven drives baked into being human.
Not learned. Not taught. Wired.
Each one of us carries them.

Lust became swipe velocity with Tinder.
Gluttony became frictionless consumption Uber Eats and Door Dash.
Greed got wrapped in “democratization” with Robinhood.
Sloth learned autoplay with YouTube.
Wrath found comment sections in Reddit.
Envy got an algorithm with TikTok.
Pride? Well… we optimized that one for engagement in Linkedin.

Tech didn’t create these behaviors.
It built business models around them.

Apps didn’t just serve our drives.
They packaged them.
Gamified them.
Optimized them.

And the better they got, the more balance we lost.

Endless scrolling.
Frictionless spending.
Outrage on demand.
Comparison as a daily habit.

Not because people are weak.
Because the systems are designed to win.

Now add AI to the mix.

AI doesn’t introduce new incentives.
It removes the last bit of resistance.

What already worked will work faster.
Cheaper.
At scale.
And harder to escape.

We’re already seeing it.
From hyper-personalized manipulation
to tools that normalize deepfake sexualization.

This isn’t a distant future.
It’s a direction we’re already moving in.

And that’s the real risk.

The most dangerous thing about AI isn’t intelligence.
It’s efficiency pointed in the wrong direction.

Which means the real question isn’t
Can we build it?

It’s
What parts of human nature are we choosing to accelerate… and who’s responsible for the brakes?

Because technology reflects us.
AI just removes the delay.


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Image Credit : Michael Lee

Turning AI Hype Into Results That Stick | AI Strategy • Automation Audits • Scaling Smarter, Not Louder

Most of us don’t know what we’re good at
until we notice what comes easier to us than to others.

Everyone has strengths.
Some are obvious.
Others stay hidden because they don’t look like the “standard” version of success.

You can call them natural gifts.
Talent.
A unique kind of intelligence.

I think of it as your inner wiring.

It took me years to recognize mine.

It was always there -
in how I approached work,
how I framed problems,
how I connected ideas.

People often called me “creative.”
I didn’t fully own it.
It felt vague. Almost uncomfortable.

What I didn’t see then was this:
my upbringing, experiences, and life choices had shaped how I think.
They wired me to see patterns others missed.

For a long time, that difference frustrated me.
I felt like I had to work harder -
to bring people along to where my thinking already was.

But that tension changed me.

It sharpened my ability to persuade.
To communicate clearly.
To translate ideas so others could act on them.

Somewhere along the way, I stopped resisting it.
I leaned into it.
And it became a strength I could rely on.

Here’s what I’ve learned:
Every one of us has a uniqueness-
a way of seeing the world,
approaching problems,
and making sense of complexity.

That difference isn’t something to fix.
It’s something to cultivate.

The goal isn’t to blend into the mainstream.
The goal is to grow into the version of yourself
that only you can be.

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Knowing your value changes everything.
But first, you have to know it.

A shoe is just a shoe
until my son steps into it.

Then it becomes confidence.
Posture.
A quiet belief that he belongs wherever he’s about to walk.

That’s how value works.
A role is just a role.
A paycheck is just a number.
A “final offer” is usually just a pause.

Until you know what you bring.

Most people wait for permission that never comes.
They negotiate from fear.
Of being difficult. Of losing the deal. Of asking one question too many.

But no real change happens until you realize
you’re holding more cards than you think.

So how do you actually know your value?

• Track outcomes, not effort
Effort feels noble. Outcomes get paid.
What changed because you showed up?

• Follow the pain
Value lives where problems are expensive, loud, or avoided.

• Replace yourself on paper
If you vanished tomorrow, what would it cost to replace you
in money, time, momentum?

• Notice where you’re pulled in
Unplanned meetings. Sticky problems. Decisions waiting on you.
That’s the market voting quietly.

• Separate value from approval
Being liked doesn’t create leverage. Being useful does.

• Name it before someone else does
If you don’t articulate your value, someone else will
and they’ll do it in budget language.

Negotiation isn’t aggression.
It’s awareness.

Sometimes growth doesn’t require a louder voice.
Just a steadier one.

Know your value.
Then step in like the fit was never in doubt.

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I stopped hiring for skills long time ago.
Started hiring for character.

Now, when everyone has access to the same AI tools…
when technical work gets automated…
character becomes the only moat.

Here's what I look for:

1️⃣ Curiosity over certainty
They ask "what are we missing?" instead of "I know this."
They wonder about edge cases.
They question assumptions others accept.

2️⃣ Judgment when data isn't enough
AI can process information.
It can't weigh competing values.
They make calls when perfect answers don't exist.

3️⃣ Comfort being wrong publicly
They say "I was wrong" without deflection.
They update their thinking in real time.
They see pivots as progress.

4️⃣ Empathy in system design  
They ask "who gets hurt if this breaks?"
They consider second-order effects.
They design for humans, not just efficiency.

5️⃣ Clarity under complexity
They translate technical depth into business impact.
They find the signal in organizational noise.
They explain "why this matters" to any audience.

6️⃣ Resilience without rigidity
They adapt strategy while keeping principles.
They evolve methods without losing direction.
They stay flexible on tactics, firm on values.

7️⃣ Ownership beyond their code
They care about what happens after deployment.
They follow up on promises made in meetings.
They measure success by outcomes, not output.

Skills → teachable
Tools → learnable  
Character → everything else

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Image credit: Mike Leber

Skip the TL;DR trap
Ask what actually matters

Most people aren’t bad at using AI.
They’re just asking it to do the wrong job.

“Summarise this.”
“Give me the key points.”
“TL;DR please.”

That’s not thinking.
That’s outsourcing reading.

AI isn’t valuable because it shortens text.
It’s valuable because it changes how you think about the work.

What actually gets results 👇

Instead of asking AI to summarize, ask it to:

👉 Extract the decisions hiding in the noise
“What are the 5 insights here that would change a real decision?”
“What should we stop doing based on this?”
“If I had to decide today, what would matter most?”

👉 Turn information into action
“Translate this into a 5-step plan with owners and measurable outcomes.”
“What should happen in the next 30, 60, 90 days?”
“What can be done this week vs what needs leadership approval?”

👉 Surface what everyone is assuming
“What must be true for this to work, and what breaks if it’s wrong?”
“Where are we relying on best-case scenarios?”
“What are we assuming about customers, teams, or timelines?”

👉 Compare opposing views
“Where do these perspectives align, conflict, and when is each valid?”
“What would a skeptic say about this plan?”
“How would this look if I were responsible for cost instead of growth?”

👉 Filter through a role lens
“What matters here to a leader vs a marketer vs an engineer?”
“What would success look like to finance vs operations?”
“What would my boss care about that I might be missing?”

👉 Pull out the contrarian take
“What would smart people disagree with, but shouldn’t ignore?”
“What part of this feels uncomfortable but true?”
“If this fails, what will we wish we had questioned earlier?”

👉 Find leverage points
“Where could small actions create outsized impact?”
“What’s the one change that would unblock three others?”
“What’s the lowest-effort move with the highest downstream effect?”

This is the shift most professionals miss.

AI isn’t here to save you time.
It’s here to upgrade the quality of your thinking.

The teams getting real value aren’t faster typists.
They’re better question-askers.

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Image Credit : Charlie Hills 🦩

Everyone wants “AI agents.”
What business owners need is confidence.

Confidence that:
decisions are owned
risk is contained
governance isn’t an afterthought

That’s why Agentic AI isn’t about intelligence.
It’s about architecture.

This diagram isn’t showing tools.
It’s showing operating models.

And one pattern consistently reduces risk and builds trust:
Hierarchical agents with explicit Human-in-the-Loop gates.

Why?

Because:
one planner owns the goal
specialists execute
humans decide where it matters

No diffused accountability.
No runaway autonomy.

Most AI projects don’t fail because models aren’t weak.

They fail because no one decided:
who owns decisions
where judgment stays human
what earns autonomy over time
The safest path forward isn’t maximum automation.

It’s earned autonomy.

Start with hierarchy.
Add human gates.
Expand deliberately.

Agentic AI doesn’t remove responsibility.
It forces clarity.

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If you’re building AI for the real world, 🔔 follow along.

Most leadership playbooks were built for a world that moved predictably.

Clear hierarchies.
Stable markets.
Enough time to analyze, decide, and correct.

That world rewarded certainty.

This one doesn’t.

Today, leaders are navigating:
• Faster cycles
• Noisier signals
• AI-shaped decisions
• Ambiguity that doesn’t wait for consensus

And experience alone no longer guarantees good judgment.

The shift isn’t about becoming more technical.
Or chasing the latest AI tool.

It’s a mindset shift.

From having the answers
→ to asking better questions

From control
→ to clarity

From authority based on tenure
→ to trust built through judgment

The real work of leadership has moved upstream.

It now lives in:
• Designing decision environments, not just making decisions
• Choosing where speed helps and where it harms
• Creating systems where people can think, not just execute
• Using AI to extend judgment, not outsource it

Tools don’t make leaders faster.
They make judgment visible.

And that visibility is uncomfortable for outdated models of leadership.

But essential for the future.

Yesterday’s leaders were rewarded for certainty.
Today’s leaders are rewarded for sense-making.

Not because leadership got harder.
But because the world got faster, noisier, and less predictable.

Those who adapt don’t just survive change.
They shape it.

If this resonates, Part 2 goes deeper into governing AI without slowing innovation.

Save this if you’re rethinking what leadership actually requires now.

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Your energy is finite.
Your worry is infinite.

Most people spend their days fighting battles they can't win.
Wrestling with forces beyond their control.
Then wonder why they're exhausted before lunch.

The discipline of letting go isn't surrender.
It's strategy.

Every minute you spend obsessing over uncontrollables is energy stolen from what actually moves you forward:

What you can't control:
↳ Politics and regulatory shifts
↳ Market volatility and economic turbulence  
↳ Your competitor's next move
↳ Industry disruption timelines

What you absolutely can:
↳ Your authenticity in every interaction
↳ The quality standards you maintain
↳ How you respond to uncertainty
↳ Where you direct your mental energy
↳ The relationships you choose to build

Energy management is decision management.

Every worry you release creates space for focus.
Every uncontrollable variable you stop wrestling with frees up bandwidth for impact.

The noise never stops.
Your participation in it is optional.

What uncontrollable are you ready to stop fighting?

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Most AI failures aren’t technical.
They’re conceptual.

This graphic shows the pattern I see over and over again in boardrooms, steering committees, and “AI task forces.”

Leaders aren’t getting AI wrong because they’re careless.
They’re getting it wrong because the language sounds familiar… and behaves very differently.

A few of the most expensive misunderstandings I see 👇

👉 AI Strategy isn’t buying the right tool.
It’s redesigning how decisions get made.
Miss that, and pilots stall quietly after launch.

👉 Automation isn’t replacing people.
It’s removing friction.
Focus on replacement and you miss augmentation. And morale takes the hit.

👉 AI Agents aren’t smarter chatbots.
They’re autonomous digital workers.
Treat them like Q&A tools and you cap their value before they start.

👉 Accuracy isn’t certainty.
It’s probability with confidence.
Expect perfection and you introduce risk where judgment should live.

👉 Data readiness isn’t volume.
It’s cleanliness, access, and trust.
Dirty data doesn’t just slow AI down. It teaches it the wrong lessons.

👉 Adoption isn’t people logging in.
It’s people changing how they work because the tool exists.
Without behavior change, AI is just a shiny new cost center.

👉 ROI isn’t only cost savings.
It’s better decisions, new revenue, and strategic advantage.
Efficiency alone is table stakes.

None of this is academic.
Every one of these misunderstandings shows up later as:
– “Why isn’t this scaling?”
– “Why didn’t this move the needle?”
– “Why are people frustrated?”

AI doesn’t fail because models are dumb.
It fails because expectations are misaligned.

If you’re leading AI right now, the real work isn’t choosing tools.
It’s choosing the right definitions before the organization builds around the wrong ones.
Most AI failures aren’t technical.
They’re conceptual.

♻️ Repost if this helps put words to what you’re seeing inside your organization.
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Most AI strategies don’t fail because the models aren’t powerful enough.
They fail because ownership never shifts.

In most initiatives, we built AI like a factory line.
Input goes in.
A model runs.
An output comes out.

It’s efficient. Predictable. Safe.
And it’s where most businesses still operate.

But Agentic systems change the game.

They don’t just suggest.
They decide.
They act.
They observe outcomes and adjust.

That’s not optimization.
That’s execution.
Ownership changes and trust becomes THE problem.

And execution introduces a question most strategies quietly avoid:
Who owns the decision when AI takes action?

Without clear ownership, autonomy isn’t innovation.
It’s RISK. 

That’s why the conversation can’t stop at agents.
It has to include control, governance, memory, and learning.

Not to slow things down.
But to make autonomy safe.

This is the shift most AI strategies miss.
Not because it’s hard to build.
But because it’s hard to lead.


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Most goals don’t fail because they’re unrealistic.
They fail because the ground underneath them is soft.

We set ambitious targets.
Revenue. Growth. Transformation. Even “do better this year.”

The ladder is strong.
The intent is clear.

But the footing?

Processes are unclear.
Decisions depend on who’s in the room.
Knowledge lives in people’s heads.
Progress requires heroics instead of flow.

So every step up feels unstable.
Not because people aren’t capable -
but because the system underneath them can’t carry the weight.

This isn’t just about AI.
It’s about leadership.

Goals are directional.
Systems are structural.

Goals tell people where to climb.
Systems determine whether the ladder stands or sinks.

If you want progress that doesn’t wobble under pressure,
stop asking people to climb harder.

Build firmer ground:
– Clear ownership
– Repeatable workflows
– Decisions that survive turnover
– Standards that don’t bend with stress

Because when things break,
it’s rarely the ambition that fails.

It’s what it was standing on.

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Clarity doesn’t slow progress.
It prevents the wrong kind of speed.

Most AI conversations don’t fall apart because people aren’t smart.
They fall apart because people are using the same words to mean different things.

When someone says “we’re using AI,” that could mean:
– a chatbot drafting copy
– a model answering questions on company data
– an agent taking actions across systems
– or a half-built experiment someone tried once and abandoned

Same label. Very different realities.

That’s what this reference is meant to help with.

These 12 essential AI terms aren’t here to impress anyone or turn leaders into technologists.
They’re here to give teams a shared starting point - so discussions about risk, cost, capability, and value don’t drift into assumptions.

This is the kind of material that helps when:
• you’re evaluating vendors and everyone sounds convincing
• a team says “the model can do that” but can’t explain how
• decisions are being made faster than understanding is catching up

It’s not about knowing everything.
It’s about knowing what question you’re actually asking.

If AI is now part of how work gets done - even indirectly - having a common reference like this reduces friction, rework, and misalignment.

📌 Save this so it’s easy to come back to when a term shows up in a meeting or deck.
♻️ Repost if you think shared language is a prerequisite for good decisions, not a nice-to-have.
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Most companies don’t fail at AI because of the tech.
They fail because they skip the middle.

They buy tools.
Run a few pilots.
Announce they’re “AI-first.”

And then… nothing really changes.

This infographic from Alex Barády  nails what actually works.
Becoming AI-native isn’t a single leap. It’s a sequence.

Here’s the path most leaders underestimate 👇

Step 1: Understand the new paradigm
AI today isn’t just automation.
It spans:
• Vibe-coding for fast experimentation
• AI-assisted work for productivity gains
• Agentic systems that plan, act, and improve

Different levels. Different risks. Different use cases.
Treating them as “the same thing” is how confusion starts.

Step 2: Train internal AI champions
This is where transformation quietly succeeds - or dies.

Not everyone needs to code.
But everyone needs:
• The right mental model
• Clear KPIs for AI value
• Guidance on when to trust AI vs when to pause

AI maturity is a people problem before it’s a platform problem.

Step 3: Apply AI to every role (not just engineering)
AI belongs across the full product and operations cycle:
• Planning
• Research
• Prototyping
• QA
• Delivery

When AI stays siloed with “the tech team,” scale never happens.

Step 4: Change the way work actually flows
This is the hardest part and the most ignored.

AI doesn’t just speed up tasks.
It changes:
• Who does what
• When humans decide
• Where agents act autonomously

New workflows. New handoffs. New accountability.

That’s what “AI-native” really means.

Not more tools.
Not louder announcements.
A different operating model designed on purpose in 2026.

📌 Save this if you’re building an AI roadmap
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It starts innocently.

Someone has a deadline.
A deck which needs to prepared and delivered before weekend.
A doc that won’t write itself.
A spreadsheet that’s staring back… unblinking.

Upstairs, the conversation sounds like this:
“We should pause.
Run surveys. Evaluate.
Make sure everyone feels comfortable with AI.”

Downstairs, someone thinks:
“I just need this done before dinner.”

So they open ChatGPT.
No announcement.
No transformation program.
Just… help.

Ten minutes later, the work is finished.

That’s Shadow AI.

Not secrecy.
Not defiance.
Just humans doing what humans have always done:
finding the fastest way through friction.

While leadership debates readiness, surveys sentiment, and plans “Phase 1”…
the work doesn’t wait.

So teams quietly:
• Write drafts faster
• Analyze data without asking
• Automate the boring parts
• Ship work that would’ve taken days

Not because they’re reckless.
Because friction has a cost.

Here’s the uncomfortable truth:
AI adoption isn’t failing because people resist change.
It’s failing because governance is moving slower than gravity.

And when that happens, AI doesn’t disappear.
It goes underground.

If you want AI to be safe, scalable, and aligned:
Stop treating it like a rollout.
Start treating it like a work behavior that already exists.

The choice isn’t:
Adopt AI vs don’t.

It’s:
Lead it intentionally
or inherit it accidentally.

♻️ Repost if you’ve seen this play out.
🔔 Follow Ranjana for practical thinking on leading with clarity, empathy, and impact in an AI-powered world.

A concept everyone should know about:
The Paradox of Knowledge.

For a long time, I believed knowledge was the barometer for success.
The more I knew,
the better I’d become,
the more valuable I’d be.

That hasn’t quite turned out to be true.

After spending nearly two years going deep into AI, I feel like I have more to learn than ever.
It feels unsettling sometimes. 
And that’s ok. 

When you start, everything feels simple.
Tools look straightforward. Concepts feel manageable.
But the deeper you go, the more you realize how much sits beneath the surface.

That feeling didn’t last.

As I went deeper, AI stopped being about models and tools and started becoming about systems.
Data quality. Trade-offs. Incentives. People. Decisions.

The work didn’t get louder.
It got more thoughtful.

That’s the paradox of knowledge.

Early learning brings confidence.
Deeper learning brings humility.
And somewhere in between, real capability starts to form.

From the outside, expertise looks like clarity.
From the inside, it’s mostly learning how to navigate higher-quality confusion.

Here are a few mindset shifts that changed how I approach gaining new expertise - especially in fast-moving fields like AI:

First: learning isn’t about collecting information.
It’s about repetition, refinement, and reflection.
Understanding fewer things more deeply and applying them in real situations.

Second: expertise looks boring up close.
It’s built by making the same decisions, just a little better each time.
Most people chase variety. The best ones chase precision.

Finally: stay a student, even when you’re teaching.
The moment you think you’ve mastered something, growth slows down.

Mastery, I’ve learned, is a bit of a myth.

You often have to act without full clarity.
That’s just reality.

In fast-moving fields like AI, this matters.

The moment you think you’ve mastered it, you stop seeing what’s changing.

The leaders who do well aren’t the ones with all the answers.
They’re the ones willing to keep learning, even when they’re already teaching.

Just remember: The more you learn, the less certain you may feel but the more capable you become.

Happy learning.


♻️ Repost if this made you uncomfortable (in a good way)
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Credit for original idea and Carousel Chris Donnelly

Mastery is a bit of a myth.

At first glance, this project looked like it failed.

Nothing launched.
Nothing was announced.
Nothing visibly changed.

From the outside, it felt like time was lost.

But below the surface, something very different was happening.

The work that took the longest wasn’t the model.
It wasn’t the tool.
It wasn’t even the use case.
It was the unseen work.

Data issues that would have broken the system later were uncovered early.
Bad automation was avoided before it became expensive.
Teams aligned on what was real, not what was hoped for.
False certainty was challenged.
Rework was never created.
Trust stayed intact.

None of this was in the original project plan.
All of it became the foundation.

This is where many AI projects get mislabeled as “failures.”

Because we measure progress by launches.
And ROI by what’s visible.

But in reality, the most valuable work often happens below the waterline.

The real return wasn’t speed.
It was stability.
It was future scale without fragility.

Instead of asking, “Why do AI projects fail?”
A better question might be:

Are we planning for the foundational work that determines whether they ever succeed?

That’s where the real ROI lives.

♻️Repost if you believe this shift will change how we plan for AI projects

🔔 Follow Ranjana Sharma for reflections on growth, leadership, and evolving in the age of AI

🔹 Leadership didn’t stop evolving.

It got more consequential.

From certainty
→ to sense-making
From control
→ to clarity

But that was only the visible part of the transition.

This is the harder one.

Because once AI enters decision-making, leadership stops being about what you decide
and starts being about what you allow, encourage, and reward.

That’s governance.
Not the bureaucratic kind.
The leadership kind.

Here’s where many organizations stumble.

They set goals.
But forget guardrails.

They optimize outcomes.
But skip the conversation about values.

They move fast.
Without deciding what must never be broken along the way.

So Part 2 zooms in on what leadership actually requires in an AI-augmented world.

1️⃣ Set guardrails, not just goals
Ambition without boundaries doesn’t scale innovation.
It scales risk.

Guardrails clarify what success cannot cost.
Trust. Privacy. Fairness. Human recourse.

2️⃣ Decide justice upfront
What feels “fair” in a meeting room
can feel very different to someone affected by an automated decision.

If AI gets it wrong:
– Who owns the error?
– Who can challenge it?
– Who fixes it?

Those answers shouldn’t come after deployment.

3️⃣ Use AI to extend thinking, not outsource it
The best leaders aren’t faster because of tools.
They’re clearer because judgment becomes visible.

AI exposes assumptions.
It doesn’t replace responsibility.

4️⃣ Lead through systems, incentives, and trust
Culture won’t save you if incentives reward speed over sound decisions.
And policy won’t help if people don’t feel safe raising concerns.

True leadership hears.
But it also steers.

The goal isn’t to control every AI outcome.
That’s impossible.

The goal is to shape the systems and incentives guiding those outcomes.

Yesterday’s leadership experience taught us how to decide.
Today’s leadership requirement is knowing how decisions happen at scale.

And that’s the real transition map.

• If Part 1 (in the link below) was about mindset, Part 2 is about responsibility.
• Save this if AI governance is now on your leadership agenda.
• Repost if leadership needs updating, not defending.



♻️ Repost if you believe leadership needs updating, not defending.
🔔 Follow Ranjana Sharma for practical insights on leading with clarity, empathy, and impact in an AI-powered world.

Forget the $5,000 AI strategy bootcamps.
Forget the exclusive executive roundtables.
Forget waiting for your board to approve consultants.

The frameworks that matter most are already public.
Most leaders just don't know where to look.

Save this for the future reference.

Strategy isn't about having insider access.
It's about knowing which questions to ask.

Here are 9 resources that will sharpen how you think about AI leadership:

1. Stanford's AI Index Report → the data that shapes Silicon Valley's long-term bets
Link: https://lnkd.in/e9SjAx5y

2. MIT's AI Readiness Assessment → diagnostic questions most organizations skip
Link: https://lnkd.in/ecTJDtuY

3. BCG's AI Governance Templates → what board oversight actually looks like in practice
Link: https://lnkd.in/eDPcNfmq

4. McKinsey's AI Transformation Cases → pattern recognition across failed and successful deployments
Link: https://lnkd.in/e3iEU2qd

5. Harvard Business Review's Change Management Series → why 70% of AI initiatives stall at adoption
Link: https://lnkd.in/edW_FxHb

6. World Economic Forum's Responsibility Frameworks → the compliance landscape before it becomes regulation
Link: https://lnkd.in/ePVBrcCK

7. Brookings Institution's Policy Research → early indicators of what's coming for your industry
Link: https://lnkd.in/ep9dSDsW

8. IEEE's Ethical AI Standards → technical guardrails translated for business decisions
Link: https://lnkd.in/eYYGvDrg

9. Partnership on AI's Enterprise Library → candid post-mortems from companies that got it wrong first
Link: https://lnkd.in/efVrWskc

These aren't just reports.
They're intelligence that shapes decisions at the highest levels.

___

If I had 30 minutes to prepare for an AI strategy discussion:

• Stanford's executive summary → understand the competitive landscape
• MIT's readiness questions → audit where you actually stand today
• BCG's governance checklist → know what sophisticated oversight requires
• McKinsey's failure patterns → anticipate where resistance will emerge
• HBR's adoption research → plan for the human side of transformation

The difference between AI leaders and followers?
Leaders know what questions to ask before problems become crises.

___

♻️ Repost if this changes how you think about AI strategy.

🔔 Follow Ranjana for practical thinking on leading with clarity, empathy, and impact in an AI-powered world.

🚨 Breaking: Salesforce just stepped inside ChatGPT.

Pause on that for a second.
Not connected to.
Not integrated with.

Inside.

This isn’t a shiny feature drop.
It’s a line in the sand for where enterprise AI is actually going.

Now picture a day in the life.

8:37 AM.
A seller opens ChatGPT. Not Salesforce.

They ask:
“What needs my attention today?”

The answer isn’t generic advice or motivational fluff.
It’s grounded in live Salesforce data.

Deals slipping.
An account heating up.
A renewal quietly at risk.

ChatGPT isn’t guessing.
It knows the business.

10:12 AM.
Before a customer call, the seller asks for a quick account brief.

Not a template.
Not best practices.

Actual context:
• Past conversations
• Pipeline history
• Open risks
• Next-best actions

They walk into the call prepared without touching five different tools.

11:04 AM.
The call ends.

“Update the opportunity.
Log notes.
Adjust the forecast.”

Done.
Inside the conversation.

No tab switching.
No CRM clean-up guilt at 6 PM.

This is the breakthrough.

Not because ChatGPT got smarter.
But because it finally knows where work lives.

Enterprise leaders have said:
“AI is impressive… but it doesn’t understand our data, our rules, our reality.”

That was the blocker.

Large language models are great at language.
They are terrible at governance, workflows, and accountability.

Salesforce stepping inside ChatGPT collapses that gap.

It turns a consumer assistant into an enterprise-grade, action-taking agent.

From a bigger lens, this is the real signal:
Salesforce is positioning itself as the last mile of enterprise AI.

As models become interchangeable, value shifts to the layer that connects AI to:
• Trusted data
• Business logic
• Real outcomes

That’s the layer that wins.

This also tells us something important about the agentic era.

It’s not about picking the “best” model.
It’s about how fast insight turns into action, safely.

And distribution matters.

Agentforce doesn’t force teams into new tools.
It shows up where work already happens.
Slack. Apps. Voice. And now ChatGPT.

That’s how adoption actually scales.

Net-net?

This is how AI stops being a demo.
And starts being how revenue teams actually work.

Quietly seismic.
And we’re going to feel the aftershocks.

♻️ Repost if this feels like a turning point for enterprise AI.
🔔 Follow Ranjana Sharma for reflections on growth, leadership in the age of AI

Claude can now read your entire Excel file formulas, tabs, dependencies and answer questions about it.

This changes who can do serious spreadsheet work.

Most of the world still runs on spreadsheets.
Budgets. Forecasts. Pricing. Headcount. Planning your kid's soccer fundraiser.

Excel isn't glamorous but it's where decisions actually happen.

Now Claude (Anthropic's AI) works 𝘪𝘯𝘴𝘪𝘥𝘦 Excel.
Not next to it.
Not pasted in from a chat window.
Inside the file you're already using.

That changes how people can use AI, especially non-technical folks.

What this means in real life

Claude can now:
• Understand how tabs relate to each other
• Follow formulas across cells
• Help build or explain models without breaking them
• Spot issues when numbers don't add up

Translation:
You don't need to be an Excel wizard to do serious work anymore.

You can ask things like:
• "Explain what this sheet is actually doing"
• "Build a version of this model with different assumptions"
• "Where could this logic break?"
• "Turn this mess into something clean and readable"

And it answers 𝘸𝘪𝘵𝘩 𝘢𝘸𝘢𝘳𝘦𝘯𝘦𝘴𝘴 𝘰𝘧 𝘵𝘩𝘦 𝘴𝘱𝘳𝘦𝘢𝘥𝘴𝘩𝘦𝘦𝘵, not generic advice.

Why this matters (beyond productivity hype)

The real shift isn't speed.
It's that AI is moving into 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀, not just conversations.

When AI lives where work happens in Excel, email, docs, tools. It becomes useful, not distracting.

One important reality check

Claude is powerful.
It's not magic.

You still need humans to:
• Decide what assumptions matter
• Check logic before trusting outputs
• Apply judgment at the "last mile"

AI accelerates thinking.
It doesn't replace responsibility.

Who should pay attention

If you:
• Work in business, ops, finance, marketing, or planning
• Spend time cleaning spreadsheets instead of thinking
• Feel like Excel runs your life (because it does)

This is worth learning.

Not to become "AI-driven."
But to spend less time wrestling cells and more time making better decisions.

If you want, I can break down simple ways to start using Claude in Excel no finance degree required. Drop a comment - EXCEL or DM me.

📌 Save this for future reference
♻️ Repost if this reframed how you think about AI at work.
🔔 Follow Ranjana Sharma  for practical thinking on systems, leadership, and sustainable progress.

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