Founder, The Career Accelerator I Strategy & Leadership in the AI Age I 1000+ trained and coached I 10M+ Views I Follow for daily strategies to advance your career with AI

AI is changing 99% of businesses.

But AI without critical thinking is a Ferrari without a driver.

Most teams have ChatGPT and Copilot. They ship faster emails, basic summaries, and generic content.

The top 1% use AI to solve strategic problems.

MY APPROACH

Critical Thinking + AI as one integrated skill.

Not just prompts. Strategic thinking with AI to drive real decisions.

WHAT I DO

AI Strategy Consulting for leaders and teams:

- Develop comprehensive AI strategies tied to business outcomes
- Find and rigorously assess high‑impact AI use cases
- Conduct AI readiness audits across people, process, data, and tech
- Implement AI solutions from prototype to production
- Train teams in AI and critical thinking so they become AI‑empowered power users

Some testimonials from my signature AI & Critical Thinking Training:

“Alexander has crafted a truly innovative training program… empowering consultants to tackle complex challenges with confidence… I highly recommend his training.”

- Rainer Große, Principal & Learning Hub Lead, Allianz Consulting

“The AI component was a highlight—it improved my productivity and performance… I became the go‑to person for AI‑based problem‑solving.”

- Christian Sting, Senior IT Project Manager, ProSiebenSat.1 Media

Ready to go from hype to real AI ROI?

Let's talk.
-> Book a call in my featured section

AI can now pass bar exams and diagnose diseases.
But it still can’t think.

Yet, too many people treat its output like gospel.
That’s the problem.

When we stop thinking critically, we outsource judgment.
And blindly follow predictions, summaries, strategies—
Without checking if they make sense.

This leads to:

Unquestioned biases baked into systems
Wrong decisions with real-world consequences
Teams confused by perfect-sounding nonsense

This is happening in meetings, documents, and dashboards—right now.

The worst part?
It looks convincing.

A graph. A headline. A full paragraph in perfect grammar.
It’s easy to mistake polish for truth.

So what’s the fix?

Don’t hand over your thinking.
Use AI as a tool, not a brain.

When you get an output, pause.
Ask:
• “Is this actually true?”
• “What’s missing here?”
• “Would I stake my name on this?”

Critical thinking isn’t optional anymore.
It’s the last human edge in a world of machine-generated everything.

You don’t need to be a genius.
You just need to stay curious, ask questions, and trust your own reasoning.

Your job isn’t to be faster than AI.
It’s to be smarter than those who stopped thinking.

Let’s keep our brains switched on.

What’s one AI output you’ve questioned lately?
⬇️ Let me know in the comments.


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♻️ Repost to help others think critically before trusting AI outputs.

Alex Miguel Meyer

AI Strategy Advisor I Trainer, Coach & Speaker I AI + Critical Thinking I 2000+ trained | Founder of AI Empowered Leaders

Will you let your critics’ opinions stop you?

When I started posting on LinkedIn, I felt unsure.

“Am I oversharing?”
“What will people think?”
“Will they take me seriously?”

It didn’t take long for the first negative feedback to arrive.

Diplomatically phrased, a colleague told me:
“Your content sucks.”
But who was that colleague, really?

Someone who never posted anything.
Someone who complains about their job—but never leaves.
Someone who keeps talking about starting a business—but never does.
Someone who complains about people posting on LinkedIn—while scrolling endlessly through their feed.

My point:
Your critics are rarely the people you want to impress.

It’s just noise.
From people who aren’t doing the work.

Think about it:

→ Athletes don’t laugh at you for going to the gym.
→ Millionaires don’t shame you for learning new skills.
→ Founders who’ve built something real don’t mock your ideas.

Because they’ve been there.
It’s not the builders who criticize.
It’s the bystanders.

The people with the loudest opinions?

They’re usually not even in the arena.
They’re not experimenting.
They’re not shipping.
They’ve either never started—or they quit too early.

So here’s your reminder:
Don’t let them stop you from—

• Taking the leap
• Launching your work
• Betting on yourself
• Starting your thing (even if it’s small)

In the early days, failure isn’t the biggest risk. 
Quitting is.

Keep going.
Because the people you admire most?
They’d tell you to try.

What’s a moment you’re proud you didn’t quit?
⬇️ Let me know in the comments

Want to scale your business with AI?

Join AI-Empowered Leaders.
A weekly note with AI and strategy you can use.
From my work as AI advisor, trainer, and coach.
Sign up here 👇
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♻️ Repost to remind someone to keep going.

7 Signs you need to hire a Fractional Chief AI Officer.

AI is moving fast.
Leadership must keep pace.

Not every firm needs a full time CAIO.
A fractional CAIO can bridge the gap.

Clear signs you need a CAIO:

1. Data is messy, and risks slow work.
2. Your AI talk outpaces shipped value.
3. Buy vs build choices stall for months.
4. Teams test tools, but no roadmap exists.
5. You face vendor FOMO and random spend.
6. Your board expects clear AI communication.
7. You need near term wins and a 12‑month plan.

What a fractional CAIO does:

• Up-skill leaders and hire key roles.
• Stand up a lean AI operating model.
• Track ROI with clear, simple metrics.
• Align strategy with revenue and risk.
• Set guardrails, policy, and LLM safety.
• Size bets and pick the right use cases.
• Build a data plan that products can use.

When fractional beats full time:

• You want impact in 90 days, not a hire cycle.
• Scope is spiky across weeks and quarters.
• Budget fits days per month, not a salary.
• You need senior talent, not headcount.

Signs you are not ready:

• No access to data or tech leaders.
• No exec sponsor or budget owner.
• You want magic, not change management.

What would a CAIO solve first in your org?
⬇️ Let me know in the comments

Want to have your own AI Strategy Consultant?

Try my free AI Strategy Bot:
👇 Sign up here:
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♻️ Repost to help your network decide when to hire a CAIO

If your company uses AI, you need an AI Management System.
Start with ISO42001.

AI is moving fast.
Risk and waste can move faster.

An AI MS gives speed with guardrails.
It aligns people, process, and tech.

Without it you get shadow AI and drift.
You pay in rework, fines, and lost trust.

What an AI MS delivers:

• A clear scope and AI use case tiers.
• Roles, ownership, and decision rights.
• Data governance and model hygiene.
• Controls across the AI lifecycle.
• Monitoring, incidents, and feedback loops.
• Supplier and model inventories.
Human oversight and usable docs.
Continuous learning and improvement.

Why ISO 42001:

• It is the global AI MS standard.
• Built on PDCA, like ISO 9001 and 27001.
• Risk based and lifecycle wide.
• Works for ML and GenAI systems.
• Maps to EU AI Act and client due diligence.
• Audit ready and procurement friendly.
• Scales as your AI portfolio grows.

What it looks like in practice:

• An AI policy and a risk register.
• Use case reviews and approvals.
• Data sheets and model cards.
• Eval gates and red teaming for high risk.
• Traceable logs, KPIs, and alerts.
• User notices and human in the loop.
• Vendor checks and contract clauses.
• Training, internal audits, and reviews.

Start in 30 days:

• Name an accountable owner.
• Inventory AI apps, models, and vendors.
• Tier use cases by impact and risk.
• Set simple guardrails and checklists.
•Define 3 KPIs to track performance.
• Pilot 42001 controls on one use case.
• Close gaps, then roll out to others.
• Plan your audit path and timeline.

Do you already have an AI Management System?
⬇️ Let me know in the comments

Want to scale your business with AI?

Join AI-Empowered Leaders.
Weekly AI and strategy insights from my work.
I advise, train, and coach leaders on AI.
Sign up here 👇
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♻️ Repost to help your network govern AI with confidence

The smartest people aren't grinding harder.
They're quietly mastering skills that most people ignore.

While everyone's chasing the same old playbook:
• Work harder
• Get more degrees
• Follow the hustle culture

The top 1% are building unfair advantages.
Here's what they're learning:

1. AI Mastery Beyond Prompts

Stop using AI like a caveman with fire.
Layer multiple tools strategically, not desperately.

2. Value-First Negotiation

Make your value so obvious there's nothing to argue about.
When they see what's in it for them, they stop pushing back.

3. Strategic Prioritization

CEOs don't grind harder - they decide what NOT to do.
Separate what feels urgent from what moves the needle.

4. Digital Leverage Without Creating

One well-placed Forbes article > 1000 viral TikToks.
Build reputation with the right people, not all people.

5. Emotional Bulletproofing

High-stakes situations separate leaders from followers.
Train emotional control like a muscle - it pays $29k more annually.

Notice what's missing?
Not one skill is about working harder.

Success shifted from sweat to strategy.
From effort to edge.

The funny thing? None of these feel urgent until it's too late.

Which skill are you quietly mastering?
⬇️ Let me know in the comments

Want to scale your business with AI?

Join AI-Empowered Leaders:
My weekly newsletter with actionable AI and strategy insights from my work as AI-advisor, trainer & coach.
Sign up here 👇
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♻️ Repost to help your network build unfair advantages

5 Skills the Smart People Are Quietly Learning

“We’re trying to roll out AI.
But we’re getting zero results."
A CIO told me a few days ago.

I asked:
"Can you trust your data?"

Silence.

That silence?
It's costing companies billions.

The unsexy truth:

AI isn't failing you.
Your data is.
And throwing more money at AI tools won't fix it.

Think about it:

You can't build a skyscraper on a cracked foundation.
You can't run a Formula 1 car on contaminated fuel.
You can't deploy AI on messy data.

Yet companies do it every day.

Here's what happens when you skip data readiness:

• AI models learn from garbage—produce garbage results
• You make million-dollar decisions based on wrong numbers
• Projects run 6-12 months over timeline (and over budget)
• Your team loses faith in "digital transformation"
• Competitors who did it right leave you behind

The companies winning with AI?

They didn't start with AI.
They started with data:

📊 Organizations with strong data foundations deploy AI 3x faster.
📊 Clean data reduces AI project failure rates by 70%.
📊 Companies that fix data first see ROI 3x faster.

But here's what "data readiness" actually means:

→ Knowing where your data lives. 
→ Having only one version of truth. 
→ Someone who owns it and keeps it clean.

It's not sexy.
It's not what the vendors sell you.
But it's what works.

The most dangerous phrase in business today:
"We'll clean up the data later."
Later never comes.
The projects pile up.
The mess gets bigger.
And AI becomes impossible.
Here's what the best companies do differently:

1. They audit their data before buying AI tools
2. They assign owners—real people who are accountable
3. They fix one data source at a time, prove it works, then scale
4. They build dashboards that expose problems immediately
5. They treat data quality like a product, not a project

Excellence in AI doesn't start with algorithms.
It starts with asking:

"Can I trust the numbers I'm seeing right now?"

If the answer is no, that's your starting point.
Not AI.
Data.

How are you approaching AI-readiness?
⬇️ Let me know in the comments.

Want to get my proven Data-Readiness Checklist?
DM me “DATA”.

♻️ Repost to help your network get AI-ready

I have consulted and trained 20+ companies on AI.
This is what 90% businesses get wrong
(and how to fix it)

Most of businesses are implementing AI completely wrong.

• They're obsessed with the shiny stuff.
• Building chatbots that nobody uses.
• Automating tasks that don't matter.
• Chasing AI trends instead of business results.

Meanwhile, your competitors are quietly using AI to:

→ Cut admin time by 70%
→ Generate months of content in hours
→ Automate their most expensive processes

The window is closing fast.
Every day you wait, someone in your "dinosaur industry" is gaining an unfair advantage.
Insurance, real estate, law, oil & gas - these sectors are ripe for AI disruption.

The companies moving now will dominate the next decade.

Here's how to build an AI strategy that actually works:

1. Start With Pain, Not Possibilities

Find your biggest time-suckers first.
What takes 3-4 hours daily that shouldn't?

2. The Consistency Test

If inputs change constantly (PDF → video → brief), AI fails.
Pick tasks with predictable patterns.

3. The 70/30 Rule

70% AI efficiency + 30% human oversight.
Avoid the "AI microplastics" flooding the internet.

4. Data Strategy Before AI Strategy

Clean, structured data in consistent formats.
No data strategy = guaranteed AI failure.

5. Security First, Features Second

HIPAA? GDPR? SOC compliance?
Your security needs determine your platform choices.

6. Culture Beats Technology

Foster AI adoption, don't force it.
Most jobs need human-in-the-loop, not replacement.

The math is simple:

• Small changes → massive leverage.
• One well-automated workflow → 10x efficiency gains.
• Early adoption → competitive moats.

But only if you start with strategy, not shiny objects.

How far along your AI journey is your business?
⬇️ Let me know in the comments

Want to know if an AI Automation is worth it?

Use my ROI calculator. It’s free.
⬇️ Sign up here
https://lnkd.in/dKNuKHza
♻️ Repost to help your network build winning AI strategies

6 Steps to Actually Get ROI from AI

I have trained 2000+ professionals on AI & Problem-Solving.
Here are 13 AI success factors leaders urgently need to know.

AI is not a tool. It is a new way to run.
Leaders must reshape how work gets done.

These 13 factors will make (almost) guarantee AI ROI.

1. Set a clear north star. Tie AI to revenue and risk.
2. Start with 3 to 5 use cases. Rank by value and ease.
3. Fund teams, not pilots. Ship in 90-day cycles.
4. Appoint an AI owner. Give them budget and teeth.
5. Build a data spine. Govern for quality and access.
6. Upskill managers first. Then the rest of the org.
7. Pair domain experts with ML and product.
8. Write guardrails. Privacy, bias, and safety.
9. Measure impact weekly. Adoption, cost, and lift.
10. Change incentives. Reward outcomes, not decks.
11. Simplify the stack. Fewer tools, deeper skill.
12. Design for control. Human-in-the-loop by default.
13. Plan the path to scale. Integration beats demos.

New technologies require a new way of leading.

What is the one barrier you must remove this quarter?
👇 Let me know in the comments

Want to scale your business with AI?

Join AI-Empowered Leaders.
A weekly note with AI and strategy you can use.
From my work as AI advisor, trainer, and coach.
Sign up here 👇
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♻️ Repost to help your network lead AI with clarity

Most meetings are useless. 
No clarity. No decisions. No progress.

And yet, we keep doing them.
Sound familiar? Be honest.

Here’s how to fix it: 
A 4-step AI-powered method to run focused, outcome-driven sessions.

1. Clarify the Agenda
Goal: Everyone knows why we’re here. 
What to do: Ask each person to share the goal. 
Prompt to use: “Summarize goals into one sentence.”

2. AI-Supported Brainstorm
Goal: Generate ideas fast without groupthink. 
What to do: Let GPT spark the first ideas fast. 
Prompt to use: “Give 5 ideas ranked by impact level.”

3. Filter the Options
Goal: Move from ideas to viable options. 
What to do: Use GPT to rank the solutions. 
Prompt to use: “Sort by ROI and feasibility.”

4. Decide and Assign
Goal: Leave with a clear action plan. 
What to do: Summarize the outcome & next steps. 
Prompt to use: “List actions, owners, and deadlines.”

You don’t need another chaotic meeting. 
You need a system.

Clear goals. Bold ideas. Real outcomes.

What would change if your meetings were 3x more effective?
⬇️ Let me know in the comments

Want to scale your business with AI?

Join AI-Empowered Leaders.

Weekly AI and strategy insights from my work.
I advise, train, and coach leaders on AI.
Sign up here 👇
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♻️ Repost to help your network end bad meetings with AI.

How To Run Fast & Focused Meetings with ChatGPT

Most AI failures aren’t model problems.
They’re management failures.

Boards ask for ROI.
Teams ship pilots.
Risk teams panic.
Value dies in the gap.

Unmanaged AI slows you down,
raises risk, and erodes trust.

Here’s the fix leaders need now:
A simple AI Management System (AIMS) that scales.

Use this to align execs, reduce risk, and ship value with control.

1. Context — Know Your Terrain
• Why: Win by seeing the whole chessboard.
• Do: Run a 60‑min context workshop. Pick 3–5 use cases.

2. Leadership — Own the AIMS
• Why: Accountability beats committees.
• Do: Name one exec owner and a cross‑functional board.

3. Planning — Turn Risk to Roadmap
• Why: Translate uncertainty into delivery.
• Do: Set KPIs and a 12‑month roadmap with go/no‑go gates.

4. Support — Equip People & Tools
• Why: Resourcing is strategy in disguise.
• Do: Fund data + MLOps. Train leaders on AI risk now.

5. Operation — Run with Control
• Why: Standardize the lifecycle end‑to‑end.
• Do: Gate changes, vet suppliers, design HITL (human‑in‑the‑loop) + rollback.

6. Evaluation — Measure What Matters
• Why: Reality beats opinions.
• Do: Track drift, fairness, latency. Audit quarterly.

7. Improvement — Fix & Evolve
• Why: Incidents are inputs, not embarrassments.
• Do: Run RCAs fast. Close actions. Prioritize learning.

Steal this framework.
Adapt it to your context.
Make AI safer, faster, and accountable.

Where is your biggest gap today?
Leadership, process, or metrics?
⬇️ Let me know in the comments

Want to know if an AI Use Case is worth it?

Use my ROI calculator. It’s free.
⬇️ Sign up here
https://lnkd.in/dKNuKHza
♻️ Repost to help your network build an AI Management System

7 Key Components of an AI Management System

Most people skip the fundamentals of AI.

That's why they make costly mistakes:

• Dropping $5,000 on software that sits unused
• Handing tasks to AI, then getting frustrated with poor output
• Automating the wrong things while ignoring what actually slows them down

Then AI gets the blame.

Here's how to avoid that trap.
Learn the essentials before you pay a $50k agency to do it for you.

How many of these concepts do you recognize?

1. AI Agent
A system that takes action on its own, not just spits out answers.

2. Multi-Agent Collaboration
Several AI systems working together to solve bigger problems.

3. Agent Persona
AI trained for a specific role like strategist, writer, or data analyst.

4. Tool Use
AI that performs actions: scheduling, emailing, updating systems.

5. Goal-Driven Automation
Manages full workflows to hit outcomes, not just one-off tasks.

6. Workflow Orchestration
Links processes together so nothing slips through the cracks.

7. Adaptive Action
Adjusts based on performance without manual intervention.

8. Feedback Loops
Improves by learning from your edits and input.

9v Self-Healing Systems
Detects problems and corrects them before you notice.

10. Prompt Engineering
Crafting clear, precise instructions that get better results.

11. Retrieval-Augmented Generation (RAG)
Combines AI reasoning with live data from reliable sources.

12. Reasoning
The logic engine that helps AI think through problems.

13. Chain of Thought
Breaks down how the AI arrived at its answer.

14. Context Awareness
Remembers your goals and past interactions to stay on track.

15. Memory
Recalls previous conversations to deliver personalized responses.

16. Task Decomposition
Splits large projects into smaller, manageable actions.

17. Workflow Automation
Transfers data across tools automatically so you don't have to.

18. Hallucinations
When AI invents facts. Always verify what it tells you.

AI isn't some distant future.
It's the operating system you should be using right now.

Which of these do you already rely on?
⬇️ Drop it in the comments

Want to scale your business with AI?

→ Join AI-Empowered Leaders.
My weekly newsletter with practical AI insights from my work as advisor, trainer, and coach.

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Most teams use AI for shortcuts. 
Only a few use it for real advantage.

That’s the problem. 

AI isn’t just a tool, it’s a multiplier. 
But only if you evolve how your team uses it.

Here’s the 4-Stage roadmap to go from efficiency to intelligence.

1. The Curiosity Stage

What it is: Teams use AI for summaries, drafts, ideas. 
How to level up: Pick one repeatable task & document it.

Prompt to use: “List 10 repeatable tasks my team could automate or accelerate with AI.”

2. The Efficiency Stage

What it is: AI drive reports, templates, notes. 
How to level up: Redesign one 5-hour task using prompts.

Prompt to use: 
“Summarize key patterns or insights from these reports. What decisions do they suggest?”

3. The Thinking Partner Stage

What it is: Use AI as a co-strategist for options & outcomes. 
How to level up: Start meetings with AI-prepared analysis, not blank slides.

Prompt to use: 
“Evaluate the pros, cons, and likely outcomes of these 3 strategic options.”

4. The Intelligence Stage

What it is: AI is built into leadership rhythms, reviews, & forecasts. 
How to level up: Create an AI Code of Practice with ethical rules.

Prompt to use: 
“Audit how aligned our AI use is with our values, data standards, and leadership goals.”

AI maturity isn’t about more tools. 
It’s about better thinking.

These are the 4 stages of maturity I typically see in teams. 
At what stage is your team?
Let me know in the comments

Want to scale your business with AI?

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♻️ Repost to help your network build unfair advantages

The 4 Stages of AI Maturity

90% of AI agent projects fail,
because developers confuse workflows with agents

The hard truth:
Most "agents" aren't agents at all.
They're just fancy workflows pretending to be autonomous.

The problem?
You're building the wrong thing.
You're creating rigid, step-by-step processes when you think you're building intelligent systems.

The result?
Frustrated users, wasted resources, and AI that can't adapt.

But there's a better way.
Here's how to build AI agents that actually work:

1. Know the Difference

Workflows = Fixed steps, predictable paths
Agents = Autonomous loops, unknown endpoints
Stop calling everything an agent when it's just chained prompts.

2. Think Like the Model

Close your eyes for a minute, blink at the screen
Ask: "What would I do with this context?"
Empathy with your AI beats fancy architecture every time.

3. Write Tools Like Documentation

Your agent can't read your mind
Name parameters clearly, add descriptions, explain everything
If an engineer couldn't use it, neither can Claude.

4. Start Stupidly Simple

One LLM call + good orchestration beats complex multi-agent systems
Simple scales, complexity breaks
Build the minimum that works, then add complexity.

5. Measure Everything

No feedback = no improvement
Build verification into every loop
You can't optimize what you can't measure.

6. Pick Your Battles

Best agents: High value + Low error cost
Avoid: Consumer booking tasks (too many preferences)
Target: Coding, search, repetitive business tasks
Choose problems where iteration actually helps.

In reality,
most production systems don't need agents yet.
They need better workflows.

But when models get smarter,
your simple system should get better too.
Not obsolete.

What's your biggest challenge building with AI agents?
⬇️ Let me know in the comments

Want to know if an AI Use Case is worth it?

Use my ROI calculator. It’s free.
⬇️ Sign up here
https://lnkd.in/dKNuKHza

♻️ Repost to help your network build better AI systems

Why 90% of AI agents fail and how to avoid it

95% of companies are “doing AI.”
Only 24% are getting real results. Why?

You don’t have an AI problem.
You have a data problem.

Most companies do.
That’s what 205 C-level executives and data leaders told the MIT Technology Review.
And it’s what I keep seeing in practice.

The real bottlenecks are:
• Poor data quality
• Scattered infrastructure
• Unclear governance

So while AI pilots are everywhere, production use cases are rare.
Not because AI doesn’t work but because the foundations aren’t there yet.

Before chasing the next model release, fix what matters first:

→ Data hygiene
→ Infrastructure
→ Governance
→ ROI logic

Deploying one chatbot is easy.
Scaling AI across the enterprise? That’s the hard part.

Here are 5 lessons that stood out from the MIT report:

1. Everyone’s testing AI. Almost no one is scaling it.
• 95% of companies use AI
• 76% are stuck with just 1–3 use cases
→ Execution, not exploration, will define the next wave of winners.

2. Bad data kills great models.
→ Without curated, accessible, high-trust data, your AI is dead on arrival.

3. Governance and risk management are slowing AI down, and that’s a good thing.
→ 98% of leaders say they’d rather be safe than first.
→ Trust > speed in this phase of AI maturity.

4. Custom AI > generic GenAI.
→ The real value comes from business-specific applications, not chatbots.
→ Think domain expertise + AI, not just another shiny tool.

5. Legacy systems are dead weight.
→ Retrofitting AI into outdated infrastructure often costs more than rebuilding from scratch.

So if want to get ahead in AI,
Don’t just look at what the tech can do.

Look at what’s holding it back.
Start there.

How is your company using AI?
⬇️ Let me know in the comments

Want to succeed with AI?

→ Join AI-Empowered Leaders: My weekly newsletter with actionable AI insights from my work as AI-advisor, trainer & coach.

Sign up here 👇 https://lnkd.in/dar5M9p7

♻️ Repost to help your network succeed with AI.
➡️ Follow Alex Miguel Meyer for more on AI, Critical Thinking & AI-Leadership.

If your AI doesn't pay back in 90 days, it's a hobby. Not a strategy.

I've watched teams give up on AI after flashy demos that didn’t deliver ROI.

The truth? Most AI failures aren't technical.
They're strategic.

You're solving the wrong problems.

Here's the 6-step playbook I use with clients to find AI wins that actually move the needle:

1. Map Your Biggest Time Drains

• List 10 workflows your team does weekly.
• Track time spent, error rates, and handoffs.
• The boring, repetitive stuff? That's your goldmine.

2. Calculate What You're Losing

• Model current costs: time spent & mistakes made.
• Calculate what 30-70% improvement looks like in dollars.
• Get specific.

3. Rank by Speed to Value

• Focus on 6-12 month payback projects first.
• Pick 1-2 quick wins over the "moonshot" everyone's excited about.
• Momentum beats perfection.

4. Audit Your Reality

• Check your data quality, system access, and compliance requirements before you build anything.
• Most AI projects die here, not in the algorithm.

5. Start Simple

• Use the simplest tool that solves the problem.
• Sometimes it's basic automation, not generative AI.
• Fewer moving parts mean faster results.

6. Prove It Works

• Run a 2-4 week pilot with clear metrics
• Keep humans in the loop. Measure everything
• Scale only what delivers.

This approach has helped my clients generate measurable ROI within quarters, not years.

Stop chasing AI for AI's sake.
Start chasing problems worth solving.

Which workflow will you quantify and pilot this week?
⬇️ Let me know in the comments
—
Want to scale your business with AI?

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