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 ⬆️

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 playbook

The 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 automate

Reclaim hundreds of hours across teams

Scale without adding headcount

That’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 systems

I’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 out

If you’re done with the AI noise and ready for results that stick, let’s talk.

Ranjana Sharma

We all walk around with a pocket full of beliefs.

Stories we inherited.
Stories we lived.
Childhood lessons. Culture. Wins we carry. Wounds we hide.

Over time, they harden.
What began as perspective starts to feel like fact.

Then someone enters our life with a different map.

They see complexity where we see certainty.
They interpret the same moment through a completely different lens.

Not because they are wrong.
Because their life shaped them differently than ours shaped us.

And that is where so many relationships begin to strain.

Not always from lack of care.
Often from lack of space.

Space to let another person see the world differently without treating it as a threat.
Space to stay curious when disagreement would be easier.
Space to remember that difference is not always disrespect.

There are greys.
More than we like to admit.
And they do not show up the same way for each of us.
That is where tension begins.
Voices harden. Hearts close.
And we do not just lose connection with the person.
We lose what understanding might have made possible.

Maybe you have been there too.

There is a small moment, right before judgment takes over,
when we can pause.

Not to abandon who we are.
But to make space for another human being beside our truth.

To ask:
What shaped them?
What pain, hope, or history am I not seeing?

Understanding is not agreement.
It is humanity with a little more room in it.

Relationships rarely break because of difference.
They break because no one paused long enough to understand it.

In a world obsessed with being right,
the real power is this:

Listen without armor.
See without assumption.
Care without needing to win.

That does not make you weak.
It makes you someone people feel safe being honest with.

And that changes everything.

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

Burnout rarely comes from one big thing.
It comes from a hundred small “sure”s that quietly steal your day.

And the frustrating part is this: you can be busy all day and still feel like you did nothing that actually mattered.

Here’s why we waste time doing things the wrong way (even when we’re competent, thoughtful people) and the small tweaks that protect your health and your sanity:

1) We confuse “being available” with “being valuable”
So we respond fast, hop on calls, keep Slack open, say yes to “quick questions”…
…and quietly train everyone that our attention is a public utility.

Tiny tweak: decide your “availability hours” like you’d set store hours. 
If it’s not open, it’s not open.

2) We treat everything as urgent because we’re afraid to disappoint
A lot of us would rather overwork than risk being misunderstood.

Tiny tweak: when a new task lands, ask:
“What should I deprioritize for this?”
If no one can answer, the request isn’t real. It’s just loud.

3) We use meetings to avoid making decisions
Meetings feel productive because they’re social proof: “Look, we’re working.”
But without an agenda, a meeting is just… structured wandering.

Tiny tweak: no agenda = no meeting. Or convert it to:
a 5-line doc
a Loom
a decision in writing

4) We keep checking because we’re addicted to “tiny certainty”
Inbox zero. Notifications. The little hit of “I’m on top of it.”
It’s not focus. It’s anxiety management dressed as responsibility.

Tiny tweak: batch replies into 2–3 windows a day.
Your brain stops living in “reaction mode” and starts doing actual work again.

5) We sacrifice recovery and then wonder why we’re fried
Working through lunch, powering past a break, scrolling late at night…
That’s not discipline. 
That’s interest payments on your nervous system.

Tiny tweak: protect one anchor each day:
lunch outside, no screen
first hour phone-free
hard stop at a set time
Pick one. Keep it sacred.

6) We do other people’s jobs because it’s faster… today
You can always rescue the situation yourself.
But over time you become the bottleneck and call it “high standards.”

Tiny tweak: delegate anything that doesn’t need your strategic input.
If it can be taught, it shouldn’t live in your head forever.

These aren’t “productivity hacks.”
They’re health decisions.
Because burnout isn’t just tired. It’s:
shorter patience
worse judgment
lower creativity
more mistakes
more self-doubt

The fix is rarely a big life overhaul.
It’s usually 9 small boundaries that stop your day from bleeding out.

What’s the one boundary you know would change your week… if you actually held it? 👇

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Image and Idea credit Monica Federico. Give her a follow.

Imagine getting put on a performance improvement plan by a bot because you missed quota while attending your grandmother’s funeral.

Absurd?
A little.
Possible?

More than most companies want to admit.

We keep talking about AI for performance optimization, productivity, and efficiency like those are neutral words.

They’re not.

Because the moment AI starts managing work, it is not just helping with tasks.
It is shaping judgment.

It decides what counts.
What gets flagged.
What looks like “low accountability.”
What gets rewarded.
What gets punished.

And that is where this stops being a tech conversation and becomes a leadership one.

Most companies are not inserting AI into some pristine, thoughtful system.

They are dropping it into workplaces that already struggle with:
context
empathy
bad management habits
weak HR processes
obsession with metrics over reality

So what happens?

The old dysfunction gets a shiny new operating system.

Now the pressure is faster.
The language sounds cleaner.
The decision feels more “objective.”
And the human being on the other side has even less room to explain what actually happened.

That should concern all of us.

Because if AI is only trained to optimize output, it will miss the very thing good leadership is supposed to protect:
context, nuance, and humanity.

The real question is not:
Can AI manage performance?
It’s:
Should it?
And if it does, who is designing the rules, the thresholds, the appeals, and the human override?

If we are going to use AI to move the needle at work, we better be very clear on which needle we are moving.

Efficiency without judgment is not innovation.

It’s just automated coldness dressed up as progress.

Curious how others see this:
Where should AI support performance...
and where should it never be allowed to replace human judgment?


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Video Credit :dom Odoguardi (IG)

AI doesn’t have to “change everything.”
I’d settle for it changing the part where nothing moves unless I nag.

Because that’s the real drain.

Not the big, important work.
The after work:
turning a meeting into decisions
chasing owners
writing the follow-up
updating the status doc
reminding people (politely) for the third time

All we need is one tiny agent that gives us our time back.

Because right now, a lot of us are using AI like a fancy search bar:
Type. Read. Copy. Paste. Repeat.

That’s not transformation. That’s just… faster typing.

The shift is simple:

Stop asking AI to answer questions.
Start asking it to run a small part of your day.

That’s what an AI agent is in real life:
A helper that takes an input (email, calendar, notes), does the boring middle, and produces an output (a draft, a list, a plan).

No cape. No sci-fi. Just relief.

Here are a few “starter agents” from the list that are almost unfairly useful:
✅ Daily Priority Compass: Reads your calendar + inbox and tells you the 3 moves that matter today.
✅ Meeting-to-Momentum: Turns a recording into decisions, owners, and next steps.
✅ Follow-up Finisher: Drafts the email you keep meaning to send.
✅ “What Did We Decide?” Finder: Pulls the latest decision from docs/chats with the source.
✅ Weekly Wrap-Up: Creates your update so you’re not writing it Sunday night with one eye twitching.

And the best part?

You don’t need to build “the perfect system.”

You just need to pick one thing you do every week and say:

“AI, you own this now.”

Try this today (10 minutes, max):
Choose one pain you repeat weekly (updates, follow-ups, meeting notes, research).
Write the simplest version of the agent: input → steps → output.
Run it manually for a week.
Only then automate.

That’s how real change happens: not in grand announcements…
but in small, repeatable wins.

If you want, reply with the part of your workday you hate most:
meetings, email, admin, planning, or research
…and I’ll suggest the best “starter agent” to try first.

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You do not need to “catch up on AI.” 
You need a better way to classify it.

A lot of professionals feel overwhelmed by AI for one simple reason:

We keep talking about it like it is one thing.

It is not.

“AI” is an umbrella term for very different tools that do very different jobs.

That is why the conversation gets confusing fast.

In one meeting, people are talking about:
→ automation
→ machine learning
→ generative AI
→ computer vision
→ agents
→ agentic AI
→ multimodal systems

That is like calling a bicycle, a bus, and an airplane the same thing because they all move.
Technically not wrong.
Practically very unhelpful.

Most teams do not need the most advanced tool.

They need the right tool for the actual problem.
Examples:
✅ Repetitive admin tasks → rules + automation
✅ Forecasting and planning → regression / machine learning
✅ Drafting content or summaries → generative AI
✅ Reading customer messages at scale → NLP
✅ Image tagging / inspection → computer vision
✅ Multi-step workflows across tools → agents (sometimes)

Different vehicle. Different trip.

This is where a lot of AI projects go sideways:
Teams start with the shiny thing, not the real bottleneck.

The result?
More demos. Less value.
AI gets much easier to use when you stop asking:
“What is the best AI tool?”

And start asking:
“What kind of problem are we solving?”

That question saves time, money, and a surprising amount of internal chaos.

You do not need to master every corner of AI this year.

You just need a better map.

What type of AI is actually helping your team right now, and where are people still getting lost?


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Image Credit : Clare Kitching.

I think a lot of people are trying to make sense of AI while the language around it keeps getting bigger, faster, and harder to pin down.

Terms like agents, agentic systems, and agentic AI get used so loosely that it can start to feel like everything is the same thing.

What I like about this visual (thanks to Chris Donnelly) is that it shows AI more like a progression of capability than one giant bucket.

You can see how we moved from:
👉 Machine Learning learning patterns from data
👉 to LLMs handling language and reasoning
👉 to Generative AI creating content
👉 to AI agents that can take actions
👉 and then toward more coordinated, goal-driven systems

For me, that’s the useful part.

Not because every business needs the most advanced version.
But because it helps explain why so many conversations about AI feel confusing.

Sometimes people are talking about a chatbot.
Sometimes they mean automation.
Sometimes they mean agents using tools.
Sometimes they mean systems making decisions across workflows.

Those are not small differences.

I also appreciate that this graphic ends with governance, safety, and control.

Because in real life, that part matters just as much as the capability itself.

It’s one thing to ask what AI can do.
It’s another to ask what it should do, where humans need to stay involved, and what has to be in place before any of this works inside an actual business.

That’s the part I find most important right now.

Not racing to sound advanced.
Not labeling everything “agentic” because it’s the word of the month.
Just getting clearer about what we’re really building, what stage we’re actually in, and what kind of support people need to use it well.

A lot of the noise around AI gets quieter when you can see the layers.



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What we take for granted, someone else is still praying for.

A healthy body.
A job.
A peaceful home.
A second chance.
One person who truly cares.

We move through life so quickly that even the meaningful things can start to feel ordinary.

But ordinary is often a matter of perspective.

The life that feels routine to one person may look like relief, hope, or answered prayer to someone else.

It is a quiet reminder that people are carrying battles we cannot see.
And it is also a reminder to notice the life we already have while we are busy reaching for more.

Gratitude does not mean you stop wanting better.
It just means you stop treating what is precious like it is guaranteed.

Some things in our lives may feel small now only because we have grown used to them.

That does not make them small.

Maybe today is a good day to be a little more thankful, a little less entitled, and a little more aware of the unseen weight other people may be carrying.

Most business users do not need to understand AI like an engineer.

They need one simple aha:

What kind of business problem does each layer of AI actually solve?

That is why I made this visual.

Because for most leaders, teams, and operators, AI still shows up as one giant fog bank called “advanced technology.” Useful in theory. Murky in practice.

But when you break it into layers, it gets much easier to see:

1. Rules-Based & Knowledge AI (Classic AI)
This is where AI follows logic, rules, and workflows.
Think: approvals, routing, compliance checks, business rules.

2. Predictive & Optimization AI (Machine Learning)
This is where AI helps you forecast and improve outcomes.
Think: demand forecasting, fraud detection, dynamic pricing, segmentation.

3. Decision Core AI (Neural Networks)
This is where AI gets better at handling more variables and more complexity.
Think: finding patterns humans would miss in messy business data.

4. Pattern & Relationship AI (Deep Learning)
This is where AI starts making sense of unstructured information.
Think: documents, conversations, images, trends, signals across channels.

5. Content & Insights AI (Generative AI)
This is the layer most people know now.
Think: drafting, summarizing, product copy, knowledge discovery, campaign ideas.

6. Business Agent AI (Agentic AI)
This is where AI starts taking action across workflows.
Think: orchestrating tasks, using systems, carrying work forward, not just generating output.

So the real shift is this:
AI is not one thing.
It is a ladder of capability.

From:
following rules
to predicting outcomes
to understanding complexity
to creating content
to taking action

That matters because too many businesses are trying to jump straight to the top layer while the foundations underneath are still held together with spreadsheets, tribal knowledge, and polite panic.

No judgment. Just facts in uncomfortable shoes.

If you are a business leader, this is the better question to ask:

Which layer do we actually need for the problem in front of us?
Not every problem needs an agent.
Not every team needs a custom model.
Sometimes the win is much simpler:
better rules
better predictions
better knowledge access
better execution

That is usually where the real ROI starts.

The best AI strategy is not the most impressive one.
It is the one your business can actually use.


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A lot of people think getting better at AI means learning secret prompts.

It usually means learning how to think more clearly.

That’s why frameworks like these are helpful.

Not because AI needs fancy acronyms to function.
But because we often give vague instructions, missing context, and then act surprised when the output comes back looking like beige porridge.

Most bad AI results are not a model problem.
They’re a clarity problem.

These prompt frameworks are useful because they force you to answer a few basic questions before you hit enter:
→ What exactly am I asking for?
→ What role should the AI play?
→What context does it need?
→ What does “good” look like?
→ What format do I want back?

That is valuable far beyond ChatGPT.

It sharpens your thinking.
It improves delegation.
It exposes fuzzy goals.
And it makes you realize how often “help me with this” is not actually a brief. It is a cry for help wearing office clothes.

A simple example:
Instead of:
“Write me a customer email.”

Try:
“You are a customer success manager. Write a short email to inactive customers who have not logged in for 30 days. The goal is to bring them back with a helpful tone, not a sales tone. Keep it under 150 words and include one clear call to action.”

Same tool.
Different outcome.
Because the instruction got smarter.

That is the real lesson here:
Prompting is not about sounding technical.
It is about being specific enough that the machine can actually help you.

You do not need to memorize all 8 frameworks.
Pick one. Use it for a week. Notice what changes.

My advice? Start simple:
Role + Task + Format

That alone will make most people dramatically better at using AI for real work.

The future does not belong to the people with the most tools.
It belongs to the people who can ask better questions. This has never changed - with or without AI.

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We are not just building intelligence. We are modeling values.

Most people hear AI risk and picture a movie.

That is not the part keeping me up.

Short term, the danger is our incentives. Incentives we cannot walk away from.
Long term, the danger is our arrogance.

The first danger is not some futuristic machine deciding to overthrow humanity.

It is us.
Our incentives.
Our impatience.
Our talent for calling something “innovation” when it mostly means fewer people, more pressure, and the rewards flowing upward.

That is the short-term risk.

AI can already help people work faster.
But faster for what?
To create better outcomes?
Or to cut costs, shrink teams, flood the internet with fake trust, and widen the gap between the people who benefit and the people who absorb the damage?

That is not science fiction.
That is strategy, policy, leadership, and human choice.

Then there is the longer-term risk.

Not just intelligence.
Arrogance.

The quiet assumption that because we can build something more capable, we will automatically know how to control it.
That because we made it, we are above it.
That scaling power is the same as understanding power.

It isn’t.

So no, I do not think this conversation should be driven by fear.

But it does need to be driven by reality.

Because whether we like it or not, AI is already changing how trust works, how jobs work, how information spreads, how decisions get made, and who gets left behind.

We do not get to vote on whether change arrives.
Only on whether we meet it half-awake or clear-eyed.

Short term, the danger is our incentives.
Long term, the danger is our arrogance.

And if we cannot handle the very human misuse happening now, we should be far less casual about what comes next.


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The biggest AI battle is not model vs. model. It is guardrails vs. incentives.

That is the real story.

Not Anthropic.
Not OpenAI.
Not which CEO moved faster when the opening appeared.

The real question is much bigger:

When AI becomes powerful enough to shape security, surveillance, and warfare, who gets to decide where the line is?

Because once we move past chatbots, meeting notes, and productivity demos, the conversation changes.

It is no longer about what AI can do.

It becomes about what powerful institutions, companies, and governments decide AI should do.

And that is where things get uncomfortable.

One company may say, “These are the boundaries.”
Another may say, “We can work within those requirements.”
And suddenly the market does what the market always does:

It tests whether principles are real... or just expensive preferences.

That is why this moment matters.

Safety is no longer living in research papers and keynote slides.
It is being negotiated in contracts, power structures, and high-stakes deals.

That means this is not just a tech story.

It is a preview of how AI norms will be set for the rest of us.

How much surveillance becomes acceptable.
How much autonomy we hand to machines.
How much human oversight remains when speed, profit, and national interest all enter the room wearing expensive shoes.

So yes, pay attention.

Because the issue is not simply who wins the deal.

It is whether guardrails hold when incentives get loud.

And history suggests incentives rarely use an inside voice.

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Image Credit: Dennisgoris (IG)

Leaders keep telling me, “We’re using AI… but the output is still kinda meh.”

That’s not an AI problem.
That’s a leadership problem.

Because most teams treat AI like a magic vending machine:
insert vague prompt → expect brilliance

AI is more like a very fast new hire.
It needs a good brief, clean inputs, and a way to check its work.

Here are 5 “hidden” AI skills that separate leaders who use AI from leaders who get outcomes:

1) Pack the Context (like you’re briefing a human)
Before you ask for “a strategy,” give:
→ Role: “You’re my eCommerce Ops lead”
→ Goal: “Reduce returns without killing conversion”
→ Audience: “CFO + Head of CX”
→ Success criteria: “3 options, each with effort/impact/risk”
Prompt starter:
“Act as ___. We’re trying to ___. Constraints: ___. Success looks like ___. Output format: ___.”

2) Decompose the Ask (stop asking for the whole elephant 🐘)
Big asks produce fluffy answers.
Break it into 3–5 steps:
→ Research options
→ Outline recommendation
→ Draft the output
→ QA for gaps and assumptions
This is how you get signal instead of word salad.

3) Edit the Signal (garbage in, confident garbage out)
Before you hit enter:
→ Remove the “nice-to-have” backstory
→ Standardize dates, terms, units
→ Keep only decisions, data, and constraints
If you wouldn’t put it in an exec email, don’t put it in the prompt.

4) Pair and Verify (AI is not your source of truth)
Use a second pass to critique:
→ “What’s missing?”
→ “What assumptions did you make?”
→ “What would a skeptic push back on?”
Then cross-check anything important against your docs/data.

5) Capture the Workflow (the real ROI move)
The win isn’t the one good answer.
The win is turning it into a repeatable play:
→ Save the prompt
→ Turn the steps into a 5-step SOP
→ Store examples in a shared playbook

That’s how AI becomes an operating advantage, not a party trick.

If you only adopt one this week:
Start decomposing your asks. It’s the difference between “AI helped” and “AI changed how we work.”

👇 Which one will you practice on your next task: Context, Decompose, Edit, 

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Image Credit Alex Miguel Meyer. Give him a follow.

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The Battle

Andy ⬆️

Ranjana Sharma

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The Battle

Andy ⬆️

Ranjana Sharma

Followers

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Likes

Performance Score.css-goub6s{font-size:var(--chakra-fontSizes-md);font-weight:500;color:var(--chakra-colors-gray-600);display:block;margin-top:var(--chakra-space-1);}Ranjana Sharma's LinkedIn Performance

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