Javier Llerena 🇺🇸🇪🇸

Distinguished Fellow Coach 

I empower leaders to cultivate team dialogues that foster confidence, collaboration, and psychological safety.

Is your team feeling disconnected, stuck in silos, or playing it too safe to innovate?

You’re not alone—and you don’t have to fix it alone.

I’m Javier Llerena, Executive Facilitator and Leadership Coach at Re-Invent Consulting. I help leaders like you build teams that trust each other, communicate openly, and innovate with purpose.

With 30+ years of executive leadership experience, I know how hard it is to align people, manage change, and keep teams engaged—especially in hybrid or high-growth environments. That’s why my workshops are designed with you and for you, blending proven frameworks, real connection, and powerful facilitation that move teams from friction to flow.

Here’s what we’ll achieve together:

🔹 Build trust and open communication
🔹 Strengthen collaboration and decision-making
🔹 Ignite creativity and innovation
🔹 Align your team around a shared vision and clear goals

From nonprofits to Fortune 500s, I’ve helped organizations transform their culture—creating spaces where people feel safe, heard, and motivated to lead boldly.

If you’re planning a leadership retreat, launching a new team, or need to reset your culture, let’s create a transformational experience that sticks.

📩 jllerena@re-invent.consulting
🌐 www.re-invent.consulting
📅 Book a Clarity Call: https://meetings.hubspot.com/llerena/discovery-call-

Professional Development Facilitator ►Leadership and Mental Fitness Coach► Workshop Creator

WHAT I DO: As a Transformational Leadership Development expert and a virtual facilitator experience maker, I help leaders and organizations create positive change! It’s that simple. My objective is to help leaders optimize their mission and maximize their growth by increasing profit, employee retention, ongoing sustainability while creating a positive company culture shift.

WHY IT MATTERS: 💥 Positive organizational change creates internal best practices, bringing together resources that initiate change within an organization. Improving these processes can be the key to unlocking the potential for your organization.

Such changes improve staff morale, levels of engagement, and communication, which all lends itself to a positive environment of productivity, effective processes, and higher retention of employees. This can be applied for the individual level all the way to the system (organization,)

 MY STORY: 👤 I am a passionate and resilient entrepreneur with 25 years of experience in bringing out the best in individuals and driving organizations to exponential growth. I thrive on coaching and facilitating inquiry-based conversations with individuals and groups that lead to endless positive changes in their organization. I am always learning new techniques and partnering with fellow consultants, coaches, and transformational facilitators to create a movement of cutting-edge organizations.

HOW IT WORKS: We start with a complimentary strategic session via Zoom to discuss your needs. We provide a detailed review of your current challenges, opportunities, and aspirations. We offer strategies for exponential outcomes regarding identity, resilience, and peer and systems change.

READY TO TALK? 

☎ Reach out to me directly on LinkedIn or email me at jllerena@re-invent.consulting

Javier Llerena 🪄

Helping growing companies with human-centered AI solutions for improved efficiency.

Starting a (AI) project is easier said than done.

➜ (You need to understand what AI can do for your organisation)
➜ You need to align with colleagues. 
➜ You need to gather input and feedback. 
➜ You need to know your next steps to get things moving.

And that’s without even mentioning all the different possibilities, as well as the doubts and confusion that AI brings.

I’ve been active in software product development for years and have had the chance to fill various roles in the process. I’ve upskilled from a software developer to a functional and business analyst, and eventually to a scrum master, project manager, and AI advisor.

After more than 7 years of bridging the gap between business and IT, the conclusion was clear...

Starting and successfully completing projects is no easy task.

The rise of AI has created new opportunities, but also new challenges. Many companies know they want to do something with AI but have no clear idea of where to start or how AI can improve their business processes.

And that’s exactly where I focus as an "AI business architect." Starting and bringing AI projects to a successful conclusion.

I help companies on their "AI journey."

➜ From idea to implementation. 
➜ From ambition to adoption. 

My primary focus is on the business side:

• Developing an AI strategy aligned with other business goals 
• Listing all AI opportunities within the company 
• Assessing whether AI is the answer to the problem we want to solve or the goal we want to achieve 
• Building an AI business case 
• Training and adoption processes 
• ...

All of this is done in close collaboration with the company’s employees, without losing the company’s identity.

At ID Volk, I use the tools and techniques I’ve learned to help companies successfully start and deliver their AI projects. From idea to innovation.

Michiel Hauwaert

You think working harder is the answer.

But every hour you add, the ceiling stays the same.

Here's what's happening.

Your brain sees work as effort.
More output means more hours.
More clients means more chaos.
More growth means more people.

So you keep pulling the cart alone.

The problem?
Effort doesn't scale. Systems do.

Here's the shift:

→ Stop doing repetitive work (automate the loops)
→ Let AI handle coordination (not your brain)
→ Build process once (run it forever)
→ Free your mind for decisions (not data entry)

I used to think being solo meant staying small.

Then I watched someone handle 50 clients with zero team.
Another launch products while traveling.
Another write, edit, and publish in half the time.

None of them worked harder. They worked through systems.

Your brain is for thinking, not tracking.
Your time is for judgment, not repetition.

Most people add hours to grow. Smart people add leverage.

The introvert with AI doesn't need a bigger team.
They need better systems.

This isn't about personality.
It's about multiplication.

♻️ Repost if you've been pulling the cart solo for too long.
🏷️ Follow Michiel Hauwaert for insights on turning effort into leverage.

Everyone's asking "Can we use AI here?"

Almost nobody's asking "Should we?"

That gap is where time gets wasted. And where trust quietly disappears.

I've seen many teams experiment with AI over the past year.

The ones that succeed don't just look at the capabilities of AI.
They also look at the consequences.

Here's a framework that could help you take your first steps:

The Should-We Matrix
(A simple way to decide where AI makes sense. And where it doesn’t.)
 
1️⃣ High Impact + Low Risk

↳ Customer support triage
↳ Meeting summaries and action items
↳ First-draft content creation
↳ Data entry and cleanup

Start here. These use cases save time fast. 
They’re easy to explain. 
And they help teams build confidence instead of resistance.

2️⃣High Impact + High Risk
↳ Customer-facing responses
↳ Legal document review
↳ Medical diagnosis support
↳ Financial advice generation

Here, AI supports. It does not decide.
A human stays responsible. Always.
 
3️⃣ Low Impact + Low Risk

↳ Email subject line testing
↳ Image background removal
↳ Calendar scheduling

Fine to automate.
Just don’t sell this internally as “AI transformation”. It’s hygiene, not strategy.
 
4️⃣ Low Impact + High Risk

↳ Automated hiring decisions
↳ Performance review generation
↳ Sensitive data analysis

Don’t.
If the upside is small and the downside is big, the answer is always no.
  
The real question isn't technical.

It's strategic: Does this make us better at what matters?
And ethical: Could this harm someone if it fails?

Most AI projects fail because teams skip these questions.

They see a demo. Get excited. Deploy fast.
Then wonder why adoption is low or trust is broken.

The companies winning with AI?
They say no more than they say yes.

What's your filter for deciding when AI makes sense?
Drop your framework below.

📌 Want to build AI strategy that actually works?

Follow Michiel Hauwaert for practical AI workflows.

Everyone's asking "Can we use AI here?

This mistake killed 6 months of progress for me.

And I see 90% of people making it right now.

The mistake?

Chasing motivation instead of building systems.

I used to think:
→ "I need to feel inspired to take action"
→ "Discipline is about willpower and grinding"
→ "Big goals require massive overnight changes"

My results stayed flat. Started strong. Quit by week three.
I was fighting against my own nature.

Then I learned something.
Now habits run on autopilot.

Here's the framework:

H - Harness: Capture what already works for you
A - Align: Match habits to your actual identity
B - Build: Stack small wins into bigger patterns
I - Iterate: Test and adjust without judgment
T - Transform: Let repetition reshape who you are
S - Sustain: Design systems that outlast motivation

The real insight nobody talks about:

Transformation doesn't come from one big decision.
It comes from six small ones repeated daily.

Stop waiting for motivation.
Start designing better systems.

Save this post.

Companies buy AI, skip the groundwork, and then blame employees when adoption drops.

The problem isn’t the tool.
It’s the order.

Most companies roll out AI like this:
Buy the software → announce it → expect behavior change.

That approach fails almost every time.

The framework that actually works looks boring, but it’s reliable.

𝗣𝗲𝗼𝗽𝗹𝗲 → 𝗣𝗿𝗼𝗰𝗲𝘀𝘀 → 𝗔𝗜

Not the reverse.

𝗣𝗲𝗼𝗽𝗹𝗲 come first.
Start with the people doing the work every day.
What slows them down.
What are their painpoints.
What are their frustrations.


A sales team doesn’t want another tool.
They want fewer clicks between lead and close.

𝗣𝗿𝗼𝗰𝗲𝘀𝘀 comes next.
Map the workflow as it actually exists, not how leadership thinks it works.
Find the handoffs, delays, and manual steps.

If the process is broken, AI just moves the chaos faster.

𝗔𝗜 comes last.
Not because it’s unimportant.
But because the tool should be the answer to a real problem.
Once you know the goal and the friction points, picking AI gets easy:
You choose the tool that removes that bottleneck.
Not the tool you hope people will “figure out.”
When AI is chosen in function of the work, adoption stops being a push.
People use it because it helps.


If your AI initiative isn’t working, don’t blame the team.
Check the order.

A sales team doesn’t want another tool.

Most AI pilots die within 6 months.

Not because the tech is bad. 

Because teams treat adoption like a software install.
Here’s what usually kills it:

𝟭) 𝗧𝗼𝗼𝗹-𝗳𝗶𝗿𝘀𝘁 𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 

People start with demos and features. 
Then they’re surprised nobody uses it.

What’s missing:

• a simple habit plan 
• clear “when do we use this?” moments 
• small weekly wins Result: adoption dies around week 3. 


𝟮) 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝘁𝗵𝗲𝗮𝘁𝗲𝗿 

A one-hour demo doesn’t create users. It creates: 
“Ok… but what do I do with this on Monday?”

What works better:

• practice on real tasks 
• short weekly prompts people can copy 
• someone to ask when it goes wrong Muscle memory takes time. 

𝟯) 𝗡𝗼 𝘀𝗵𝗮𝗿𝗲𝗱 𝗿𝘂𝗹𝗲𝘀 

If there are no basic agreements, everyone improvises.

Then you get:

• messy output
• trust drops fast
• no reuse of good prompts
• different quality per person

𝟰) 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝘀𝗲𝗲 𝗽𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹, 𝘁𝗲𝗮𝗺𝘀 𝗳𝗲𝗲𝗹 𝗲𝘅𝘁𝗿𝗮 𝘄𝗼𝗿𝗸 

Leaders think: “This could save hours.”
Teams think: “Great, another thing to learn.”

If the “why now” isn’t clear and personal, resistance shows up quietly.

𝟱) 𝗧𝗲𝗰𝗵 𝗺𝗼𝘃𝗲𝘀 𝗳𝗮𝘀𝘁. 

𝗣𝗲𝗼𝗽𝗹𝗲 𝗺𝗼𝘃𝗲 𝘀𝗹𝗼𝘄 Tools change weekly. 
Habits change slowly. 

That gap creates frustration. 
People stop trying.

𝟲) 𝗪𝗿𝗼𝗻𝗴 𝗺𝗲𝘁𝗿𝗶𝗰𝘀 

Many pilots track logins. 
That’s not adoption.

Track things like:

𝟳) 𝗢𝗻𝗲 𝗰𝗵𝗮𝗺𝗽𝗶𝗼𝗻 𝗶𝘀 𝗻𝗼𝘁 𝗲𝗻𝗼𝘂𝗴𝗵 

One excited person can’t carry the whole team.

You need:

• visible quick wins
• peer examples people can copy
• a few early adopters in different roles
  
AI adoption isn’t a tech problem.

It’s a habit problem.
So stop “rolling out tools”. 

Start building simple habits that make work lighter

AI isn't breaking your processes. It's showing you they were already broken.

Better than any consultant. 
Better than any audit. 
Better than any retrospective.


Here are 5 truths about what AI actually exposes in your work
(and why most teams blame the wrong thing)

𝗔𝗜 𝗿𝗲𝗺𝗼𝘃𝗲𝘀 𝘁𝗵𝗲 𝗯𝘂𝗳𝗳𝗲𝗿.

You used to have time to fix things.
The "I'll clean this up later" step.
The "someone will catch this" safety net.

AI removes all of that.

When the work is clear, AI looks smart.
When the work is fuzzy, AI looks random.

Teams say AI is unreliable.
But AI is just fast. And speed exposes what was always there.

𝗚𝗮𝗿𝗯𝗮𝗴𝗲 𝗶𝗻, 𝗴𝗮𝗿𝗯𝗮𝗴𝗲 𝗼𝘂𝘁.

This rule never changed.
AI doesn't fix unclear processes.
It produces faster, more confident mess.

If your input is vague, your output will be vague.
If your process has gaps, AI will find every single one.

And it will do it at scale.

𝗧𝗵𝗿𝗼𝘄𝗶𝗻𝗴 𝗔𝗜 𝗼𝗻 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴 𝗳𝗮𝗶𝗹𝘀.

Most teams skip the hard part.
They add AI before fixing the foundation.

The better order:
Improve the process first. Make the work clear.
Automate what's stable and repetitive.
Use AI only if it makes sense.

Otherwise you're not adopting AI.
You're scaling chaos.

𝗖𝗹𝗮𝗿𝗶𝘁𝘆 𝗳𝗶𝗿𝘀𝘁, 𝘁𝗼𝗼𝗹𝘀 𝘀𝗲𝗰𝗼𝗻𝗱.

AI works when the work is defined.
When you know what good looks like.
When the steps are documented.

If you don't have that, AI won't save you.
It will just show you what you've been avoiding.

𝗧𝗵𝗲 𝗿𝗲𝗮𝗹 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻.

Is AI unreliable?
Or was your process already unreliable?

AI doesn't create chaos.
It makes visible what was always broken.

And that's not a bug.
That's the feature.

I'm not saying AI is perfect.
(It's not. And it won't be.)

But most AI failures aren't AI failures.
They're process failures that finally got exposed.

The teams winning with AI?
They fixed their processes first.

🔄 Repost this if you've blamed AI for something that was broken before AI arrived.
➡️ Follow Michiel Hauwaert for AI insights that separate tool problems from process problems.

𝗔𝗜 𝗿𝗲𝗺𝗼𝘃𝗲𝘀 𝘁𝗵𝗲 𝗯𝘂𝗳𝗳𝗲𝗿.

Your AI is only as good as what you feed it.

Garbage data in.
Garbage decisions out.

Most companies rush to implement AI.
They want the speed.

The automation.
The efficiency gains.

But they skip the foundation.

They feed their systems messy data.

Incomplete training sets.

Zero human oversight.
Then wonder why the output misses the mark.

Here's what works:

→ Start small, refine, then scale
→ Clean your data before you train
→ Build feedback loops with real users
→ Test with humans in the loop, not after

AI doesn't fix broken processes.
It amplifies them.

If your input is rushed, your output will be rushed.
If your data is biased, your decisions will be biased.
If your training is shallow, your results will be shallow.

The companies winning with AI aren't the ones moving fastest.

They're the ones building right.

They treat AI like a team member.
Not a magic button.

They invest in the input.
The output takes care of itself.

Your AI reflects your standards.
Feed it something worth repeating.

➡️ Follow Michiel Hauwaert for human-centered AI insights that drive real efficiency.

Everyone loves the idea of saving time.

Until they automate the wrong thing and create more work than they started with.

On paper, automation sounds great.
Less manual effort.
Faster output.
More leverage.

In reality, most people skip the thinking part.

They jump straight to tools and wonder why things feel messier than before.

What actually helps is having a filter.

Before automating anything, I run it through 4 simple questions.

Nothing fancy. Just practical checks.

First, does this task repeat?
If it’s a one-off, it’s not worth touching.
Weekly and daily tasks are the sweet spot.
Monthly can work too.
Volume matters more than complexity.

Second, is the output clear?
Can you explain what “done” looks like without guessing?
Does it follow the same structure every time?
If the format keeps changing, skip it.
Consistency is what makes automation useful.

Third, are the rules simple?
If you can’t explain the logic in 3 steps or less, it’s a bad candidate.
Tasks that need judgment, nuance, or context tend to fall apart fast.
Simple rules hold. Complex ones don’t.

Fourth, will a human review it?
Nothing should run unchecked.
Always add a review step before anything goes live.
The outcome still reflects on you.

If a task doesn’t pass all 4, I leave it alone.

Most failures happen because people chase speed without structure.
They automate first and think later.

That’s how you end up spending more time fixing outputs than doing the original task.

Used the right way, automation supports your work.
It doesn’t replace your judgment.

And that difference matters more than the tool itself.

You memorized atomic numbers for a year.

Never once saw what those elements actually look like.

AI changed that in 30 seconds.

Here's what traditional chemistry education gets wrong:

𝟭) 𝗪𝗲 𝘁𝗮𝘂𝗴𝗵𝘁 𝘀𝘆𝗺𝗯𝗼𝗹𝘀, 𝗻𝗼𝘁 𝘀𝘂𝗯𝘀𝘁𝗮𝗻𝗰𝗲

Students memorize "Cu 29" on charts.
They recite "Li 3" for tests.

But they never see:

• what copper looks like when you cut it open
• why lithium powers their phones
• how gold conducts electricity differently than silver

Result: 72% of students say chemistry feels abstract.

𝟮) 𝗔𝗯𝘀𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 𝗸𝗶𝗹𝗹𝘀 𝗰𝘂𝗿𝗶𝗼𝘀𝗶𝘁𝘆

We ask: "What's the atomic number of iron?"

We should ask: "Why does iron rust but gold doesn't?"

The difference:

• one tests memory
• one builds understanding
• one dies after the exam
• one sticks for life

𝟯) 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗲𝘃𝗲𝗿𝘆𝘁𝗵𝗶𝗻𝗴

AI shows the grain structure of titanium that makes airplane wings strong.
The crystalline lattice of silicon that powers every chip.
The ductility of aluminum that shapes car frames.

Students can now explore:

• why potassium explodes in water while sodium just fizzes
• how copper's conductivity connects to circuits in their phones
• why carbon makes both diamonds and graphite

Real-world stakes. Not abstract symbols.

𝟰) 𝗧𝗵𝗲 𝗴𝗮𝗽 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗸𝗻𝗼𝘄𝗶𝗻𝗴 𝗮𝗻𝗱 𝘀𝗲𝗲𝗶𝗻𝗴

We memorized orbital configurations.
Never understood why lithium batteries hold charge differently than alkaline.

Teachers can replace static Mendeleev charts with simulations showing reactivity in real-time.

Students stop memorizing. Start exploring.

Education is about to get visceral again.

🔄 Repost this if you learned chemistry from symbols, not substances.
➡️ Follow Michiel Hauwaert for insights on how technology transforms education.

5 AI trends that will shape how teams work in 2026. 👇

🤖 Agentic AI
↳ AI that acts, not just answers.

What it means:
- AI systems that complete multi-step tasks independently
- They book meetings, draft proposals, manage workflows
- No human needed for every micro-decision

Real impact:
- Your team's role shifts from "doing" to "directing"
- Focus moves to setting guardrails, not executing tasks
- Companies using this cut admin time by 60%+

Strategy:
- Start with one repeatable workflow (scheduling, data entry)
- Define clear boundaries for what AI can decide
- Train your team to manage AI agents, not replace them

👤 Human-in-the-Loop
↳ AI suggests. Humans decide.

What it means:
- AI handles speed and scale
- Humans handle judgment and context
- The loop keeps quality high and risk low

Real impact:
- Faster decisions without losing control
- AI drafts, humans refine
- Trust builds when people stay in charge

Strategy:
- Use AI for first drafts, research, and options
- Keep final approval with your team
- Track where human input adds the most value

⚖️ Governance Frameworks
↳ Rules before problems.

What it means:
- Clear policies on what AI can and can't do
- Who owns decisions, data, and outcomes
- Compliance built in, not bolted on later

Real impact:
- Reduces legal and reputational risk
- Builds trust with clients and teams
- Speeds up AI adoption (no constant "can we do this?" debates)

Strategy:
- Document your AI use cases and approval process
- Assign ownership for AI decisions
- Review quarterly as tools and risks evolve

📦 Small Language Models
↳ Faster, cheaper, more private.

What it means:
- Smaller AI models trained for specific tasks
- Run on your own servers, not in the cloud
- Lower cost, faster response, better data control

Real impact:
- No need to send sensitive data to third parties
- Tailored AI that fits your exact workflow
- Cuts AI costs by 70%+ for focused use cases

Strategy:
- Identify repetitive, high-volume tasks
- Test small models for internal tools first
- Keep proprietary data in-house

⚡ Edge AI
↳ AI that works offline, on-device.

What it means:
- AI runs locally (phones, laptops, machines)
- No internet required
- Instant responses, zero latency

Real impact:
- Field teams work without connectivity
- Real-time decisions in manufacturing, logistics, healthcare
- Data never leaves the device

Strategy:
- Pilot with mobile or remote teams
- Focus on speed-critical workflows
- Combine with cloud AI for hybrid setups

AI isn't slowing down.

But you don't need to chase every trend.

Pick one. Test it. Build trust with your team.

That's how you lead through change.

Which trend are you watching closest?

- - - -

Found this useful?
→ Repost to help other leaders stay ahead
→ Follow Michiel Hauwaert for practical AI insights

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Michiel Hauwaert

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The Battle

Javier Llerena 🪄

Michiel Hauwaert

Followers

Posts

Engagements

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);}Michiel Hauwaert's LinkedIn Performance

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