Bahar Selman, PCC

You look successful on paper.

Senior title. Strong salary.
Respect inside your company.

But your career is more fragile than it looks.
AI is compressing senior roles.
Restructuring doesn’t care about loyalty.
And your 15–25 years of expertise is invisible outside your employer.

If corporate ended tomorrow, most leaders at your level have:
• No external personal brand
• No consulting or advisory options
• No income diversification
• No real Plan B

That’s not a talent problem.
It’s a career longevity problem.

Career longevity isn’t about staying in one job longer.
It’s about staying relevant, visible, and in demand across decades—
regardless of age, AI disruption, or employer dependency.

That’s where career autonomy comes in.

Career autonomy means:
• Your expertise is portable
• Your reputation exists outside one company
• You have options before you need them
• One corporate decision can’t derail your future

I work with senior corporate leaders (Directors, VPs, C-suite) who:
• Have built impressive careers inside multinationals
• Feel the growing risk of invisibility and replaceability
• Want to protect their income, identity, and legacy
• Need a strategic way to build visibility without harming their corporate role

This is not about quitting your job.
And it’s definitely not about becoming an “online influencer.”

It’s about strategic positioning.

I spent over 15 years inside global corporations, building brands, leading teams, and operating at senior levels.

I’ve seen firsthand how quickly:
• Roles disappear
• Experience gets discounted
• High performers become “too expensive”
• Loyal leaders get caught unprepared

Since then, I’ve worked with 500+ senior professionals to help them:
• Turn corporate experience into visible authority
• Build reputation-safe personal brands
• Create consulting, advisory, and portfolio options
• Design exit strategies on their own terms

Through The Career Autonomy Blueprint™, I help experienced leaders:
• Clarify their next chapter
• Position their expertise for the external market
• Build LinkedIn visibility that attracts opportunities
• Create a strategic safety net before they’re forced to use it

The outcome isn’t noise or vanity metrics.

The outcome is power:
• Options
• Confidence
• Leverage
• Control over your future

If you’re still employed but quietly thinking:
“Something needs to change but I don’t know where to start”
You’re exactly who this work is for.

DM me on LinkedIn or email info@baharselman.com when you’re ready to explore your options

Consultant

Bahar Selman

I was invited to deliver the "Speak Up, Step Forward" communication workshop for the TAQA Group Junior Sustainability Program, led by New River Consulting, at Khalifa University

We moved beyond the basics of public speaking to tackle the real-world challenges of influence:
✅ Mastering active listening and social styles
✅ Navigating generational communication gaps
✅ Crafting compelling narratives that drive change

We spent the day diving deep into the art of communication.

These students are not just future engineers and scientists; they are future change-makers. 

The energy in the room was electric. 
It proved that when you meet students where they are, the learning goes deeper. 

They walked away not just with "tips," but with a full toolkit for managing anxiety, projecting confidence, and owning the room.

Special thanks to Dilara Özmeriç for the invitation and for championing this world-class program.

#CommunicationSkills #Uphuman #CorporateWorkshops #Sustainability

Bahar Selman,PCC

Career Longevity & Personal Branding Strategist for Senior Corporate Leaders │ Executive Coach │ Corporate Trainer │ Corporate Workshops & Keynotes @ Uphuman │ Ex–Diageo, Danone, Red Bull

5 signs your current career strategy is a ticking time bomb:

	1.	You haven’t updated your LinkedIn in 6+ months
	2.	No one outside your company knows what you do
	3.	You’re waiting for “the right time” to build your personal brand
	4.	You think loyalty will protect you from restructuring
	5.	You have no Plan B if your job ends tomorrow

If you see #3, stop what you’re doing immediately.

Because “the right time” never comes.

The right time is NOW. While you still have leverage.


How many of these apply to you?

No alternative text description for this image

Let me guess.
You're reading this because part of you is thinking, "This sounds great, Bahar. But is it too late for me?"

You're in your mid-40s/50s. Everyone else started years ago. Haven't you missed your window?

Here's what I want you to know.

You haven't missed your window. 
You've been building credibility while others have been building followers. Now it's time to combine both.

Your 15-25 years of experience is not a liability. 
It's your unfair advantage.
But only if people know you exist.

That's the difference between professionals who thrive and professionals who survive. 

Between those who have options and those who feel trapped.

The best time to build your brand was 5 years ago. 
The second best time is NOW.

Because every week you wait, your expertise becomes less relevant. 
Not because you're getting older. 
But because the market is moving forward without you.

It’s About Not Putting Your Head in the Sand.
One line in this conversation stopped me cold.

Jamie Dimon doesn’t dramatize AI.
He doesn’t soften it either.
He says it plainly:
“Don’t put your head in the sand.”
That sentence alone is a career warning.

Because when asked whether AI will wipe out jobs, he doesn’t hedge:
“Will it eliminate jobs? Yes.
 Will it change jobs? Yes.
 Will it add some jobs? Probably.”

This isn’t panic.
This is realism.

And then comes the line every professional should sit with:
“You can hope for the world you want, but you’re going to get the world you got.”

Hope is not an employability strategy.
What matters now is how fast change happens, and whether people are ready for it.

Dimon makes a critical distinction most career conversations miss:
The danger isn’t AI itself — it’s speed without preparation.
“If it goes too fast for society… that’s where government and business need to step in together.”

Retraining.
Relocation.
Income support.
Phasing change instead of detonating it.

His example is blunt:
“You can’t lay off two million truckers tomorrow.
 You phase it in. You retrain.”

Now zoom out and make this personal.
This is no longer just a policy issue.
It’s a career design issue.

Because a few minutes later, he’s asked the question everyone avoids:
“In five years’ time, will JP Morgan have fewer employees than it has now?”

His answer:
“My guess is there’ll be fewer employees.”
Not no employees.
Fewer.

Which means employability is shifting from:
role ownership → value ownership
job security → skill mobility
experience alone → adaptable relevance

AI won’t replace professionals.
It will replace professionals who can’t clearly explain why they still matter.

Career longevity now belongs to those who:
Reposition early
Learn continuously
And stop waiting for certainty before acting

Because as Dimon puts it:
“You’re not going to slow it down.”

The question is not if your work changes.
It’s whether you change before the market decides for you.

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

Alex Miguel Meyer

Everyone wants AI like a magic pill.
No one wants to do the cold, unsexy work that makes it real.

Most organisations are chasing outcomes fast:

• More productivity.
• Lower costs.
• Better decisions.

So they jump straight to AI tools and use cases.

But here’s the part that gets missed:
AI doesn’t fix the fundamentals. It amplifies them.

Strong data becomes a superpower.
Messy data becomes a liability at scale.

Good workflows get faster.
Broken workflows get automated.

The stuff that actually determines success isn’t glamorous:

→ Getting the data foundations right.
→ Redesigning workflows end-to-end.
→ Defining ownership and accountability
(if “everyone” is responsible, no one is).

That’s the difference between a great pilot deck and a real capability that sticks.

What is your starting point to get AI-ready?
⬇️ Let me know in the comments.

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♻️ Repost to remind your network of the fundamentals to make AI work.

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

Top 6 AI Trends That Will Define 2026
(backed by data)

Most AI predictions are noise.
Here's what's actually shifting,
and what you should do about it:

1. The model wars are over
GPT, Claude, Gemini: they're all getting similarly good and similarly cheap.
The new battleground?
→ The app layer:
Integrations. Workflows. Distribution.

If you're building anything AI-related, stop obsessing over which model to use.

Start asking: "What can I build on top that people actually need?"

2. Forget autonomous agents. Build workflows.
Everyone wants AI to "do it all."
But the real value isn't full autonomy. It's redesigned workflows where AI does the grunt work and humans validate.

Think:
• AI drafts, you edit
• AI analyzes, you decide
• AI suggests, you choose

That's where ROI lives in 2026.

3. The technical divide is closing
You no longer need to code to build technical things.
Non-technical people are now doing what used to require engineers.

If you've been waiting for "the right time" to try something hard, this is it.
Attempt the thing you thought you couldn't do.
You'll be surprised how far you get.

4. Context beats prompting

Stop spending hours perfecting your prompts.
Start organizing your files, your notes, your knowledge base.

AI in 2026 isn't about clever commands.
It's about feeding it the right context, fast.

→ Clean up your digital environment.
That's the new competitive edge.

5. Ads are coming to your chatbot

Free AI tools need a business model.
Advertising in ChatGPT and others is inevitable.
But done right, it won't ruin the experience.
→ Think: subtle, contextual, non-intrusive.

The upside?
You keep getting world-class models for free.

6. AI is moving into the physical world

Robots aren't science fiction anymore.
Warehouses. Delivery. Construction.

AI is turning physical assets into software platforms that improve over time.
Blue-collar work is being redefined.
And it's happening faster than most people realize.

The Bottom line:

The AI landscape is shifting from models to applications.
From autonomy to workflows.
From technical gatekeeping to democratization.

What trends in AI are you expecting?
⬇️ Let me know in the comments

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Most AI strategies fail before they even start.

Not because of the tech. 
But because leaders pick tools, not playbooks.

The result?

Zero scale.
Endless pilots.
No measurable impact.

That’s why I ranked
12 AI Strategy Playbooks for 2026
by real enterprise impact.

1. McKinsey: The State of AI in 2025
• Focuses on enterprise adoption ROI and execution.
• Snapshot of what enterprises do with AI.

2. Deloitte - "Tech Trends 2026"
• Great guide to scale pilots practically
• Shows AI woven into fabric of lives

3. Accenture - "The Art of AI Maturity 2025"
• Moves from pilots to enterprise-wide AI capability
• Provides structured roadmap for AI growth

4. BCG - "The Widening AI Value Gap: Build for the Future 2025"
• Reveals exact roadblocks in scaling AI effectively
• Explains why AI fails to deliver

5. Microsoft - "CIO GenAI Playbook 2025"
• Operationalizes GenAI with control and trust completely
• Essential for CIOs balancing AI adoption

6. Stanford - "AI Index Report 2025"
• Includes global benchmarks trends and trajectories today
• Best macro-level view of AI progress

7. IBM - "2025 CDO AI Multiplier Effect"
• Turns data leadership into competitive advantage successfully
• Shows how strong data drives results

8. Bain - "Transforming Your Business With AI 2025"
• Focuses on how AI drives revenue growth
• Covers customer experience improvements and innovation

9. Amazon - "AI Value Gap Report 2025"
• Helps bridge execution gaps in enterprises today-
• Moves from experimentation to business impact

10. Google - "State of AI Infrastructure 2025"
• Provides foundations for scalable AI systems successfull
• Explains what building scalable infrastructure takes

11. NIST - "AI Risk Management Framework 2025"
• Leading framework for governing AI risk properly
• Covers trust and responsible AI deployment

12. WEF - "Advancing Responsible AI Innovation: A Playbook 2025"
• Drives ethics-led innovation at global scale worldwide
• Guides leaders aligning AI with ethics

Which playbook are you using to guide your AI strategy?
⬇️ Let me know in the comments

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12 AI Strategy Playbooks For 2026

Most people suck at critical thinking.

We’re drowning in information, opinions, noise, and decisions.
But most of our thinking happens on autopilot.

That’s no better than letting AI think for us.

Critical thinking is not a talent.
It’s a skill set.

Here are 8 key elements of critical thinking:

1. Observe
Notice patterns, anomalies, inconsistencies.
Most breakthroughs start with seeing what others ignore.

2. Wonder
Ask “what if?” and “why?”
Curiosity is the gateway to original thinking.

3. Gather Information
Actively seek diverse perspectives.
If your data is one-sided, your conclusions will be too.

4. Analyze
Break arguments apart.
Spot assumptions.
Challenge your default interpretations.

5. Synthesize
Connect dots others don’t even see.
This is where creativity meets logic.

6. Reflect
Audit your own thinking.
Where are your biases pulling you? What’s shaping your conclusions?

7. Identify
Define the real problem.
Most people solve the visible symptom and wonder why nothing changes.

8. Decide
Choose with intention, not impulse.
If you had to defend your decision publicly, would your reasoning hold?

Critical thinking isn’t slow.
It’s smart.

It saves time, reduces rework, and improves every decision you make.

Which discipline of critical thinking resonates most with you?
⬇️ Let me know in the comments

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If your company does anything with AI,
you must know ISO42001.

Not because it’s trendy.
Because of the EU AI Act.

In simple words:
EU AI Act = the rules of the game
ISO 42001 = your operating system to play it

The AI Act answers one question:
“Does this AI system meet legal requirements?”

ISO 42001 answers a different one:
“Does this organization have control over how it uses AI?”

That gap is where most companies are exposed.

The AI Act tells you WHAT you must do:

• classify AI by risk
• ensure human oversight
• prove compliance when asked
• manage data, models, documentation

ISO 42001 gives you the HOW:

• clear AI governance
• roles and accountability
• structured risk management
• controls, audits, escalation paths
• continuous improvement

ISO 42001 does not make you AI-Act compliant by default,
But without it you’re managing AI with
ad-hoc decisions, slides, and good intentions.

That’s risky.

Especially if:

• you have multiple AI use cases
• you operate across teams or countries
• you sell B2B and face audits or RFPs
• you already run ISO 9001 or 27001

In short:
The EU AI Act protects society.
ISO 42001 protects your company.

If AI touches your business, governance is no longer optional.

Need help scaling AI securely and legally compliant?
I’m just one DM away.

Want to succeed with AI?

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Where AI Actually Creates Leverage
5 overlooked processes AI can fix today 
(Easy setup)

Most AI initiatives fail.

High expectation.
Low readiness.

Here are 5 simple workflows that are easy to improve with AI.

1. Reporting
The problem: Reports are slow and backward-looking. What AI should fix Summaries, insights, variance explanations.
→ Without fast insights, strategy becomes opinion

2. Planning
Too many priorities. No real outcomes. What AI should fix Option comparison and impact vs effort.
→ AI should reduce noise, not create more

3. Decisions
Decisions get made and then forgotten. What AI should fix Decision memory and assumption tracking.
→ Teams repeat mistakes faster

4. Knowledge
Information exists but can’t be found. What AI should fix Reuse answers and guide to sources.
→ Wasted time kills momentum

5. Learning
Lessons stay in people’s heads. What AI should fix Fetch patterns and reusable lessons.
→ Learning speed is a real advantage

Real AI ROI lives in boring workflows. 
Not shiny strategies.

Which workflow is breaking your AI efforts? 
⬇️Let me know in the comments

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Where AI actually Creates Leverage 

The future of automation isn't drag-and-drop.
It's plain language.

I just built a Reddit news scraper:
complete with AI-analyzed posts and auto-generated infographics in minutes.

No nodes.
No workflows.
No code.

Just plain English instructions to Google's Anti-Gravity platform.

Here's what's changing:

The Old Way: Manual Configuration
• You design every step.
• You connect every node.
• You debug every failure.

The New Way: Agentic Workflows
• You describe what you want.
• The AI figures out how to build it.
• The system debugs itself.

This is the shift from "automation" to "autonomy."
It works with the DOE Framework in 3 layers:

1. Directive — You state the goal in plain language
2. Orchestration — AI breaks it into logical steps
3. Execution — The system runs and self-corrects

Think of it as:

• Strategy consultant (understands intent)
• Project manager (organizes tasks)
• Execution team (does the work)

All automated.

→ Most people don't fail at automation because they lack ideas.
They fail because execution is too hard.

Remove the technical barrier, and you unlock:

• Faster iteration
• More experimentation
• Less time stuck in troubleshooting

I'm skeptical of hype.
But watching AI read documentation, write code, and fix its own mistakes?
That's not flashy. That's functional.

If you could describe any workflow in plain language and have it built for you...
What would you automate first?
⬇️ Let me know in the comments

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If your team can’t explain a workflow in 5 steps,
AI won’t fix it.

Most “AI adoption” fails for a boring reason:
Nobody can clearly describe what happens today.
So teams automate chaos.

Here’s a practical way to turn any messy process into an AI-ready SOP.
You can do it in 15 minutes.

Pick one workflow that hurts weekly.
Examples: sales follow-up, client onboarding, invoice approval, support triage.

Then write it in 5 steps.
Not 20.
Five.

Why?
Because AI doesn’t need more complexity.
It needs a clear target.

Try this now:
1. Open a blank doc.
2. Write the workflow in 5 steps.
3. Circle the step that is “mostly text”.
4. That step is your first AI wedge.

Once you have that wedge, you can ship a small pilot in 7 days.
And learn with real data.

If you want, DM me “SOP” and I’ll share the prompt I use to convert a workflow into a checklist + quality bar.


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AI Terminology is a big mess.
Most people don’t know what is what anymore.

I evaluated dozens of AI vendors.

Every demo looked impressive.
Every marketing sounds revolutionary.
Every product claimed to be "next-generation."

But underneath the buzzwords,
Most are just recombining the same 20 building blocks.

Here's the mental model that changed how I think about AI:

→ Think of AI like chemistry.

You don't need to know every molecule.
You need to know the elements.

1. The Structure

4 rows. 5 families. 20+ elements.

• Row 1 = Primitives 
The foundational capabilities. You can't build AI without these.

• Row 2 = Compositions 
How primitives combine to create functional systems.

• Row 3 = Deployment 
What it takes to ship AI into production.

• Row 4 = Emerging 
The bleeding edge. Still evolving.

2. The 5 Families (Columns)

A. Reactive 
How AI responds to inputs. 
Elements: prompts, function calling, agents.

B. Retrieval 
How AI accesses external knowledge. 
Elements: embeddings, vector databases, RAG.

C. Orchestration 
How AI coordinates multiple steps. 
Elements: chains, multi-agent systems, frameworks.

D. Validation 
How AI ensures quality and safety. 
Elements: guardrails, red teaming, interpretability.

E. Models 
The engines that power everything. 
Elements: LLMs, fine-tuning, thinking models.

→ How This Helps:

When someone demos a "revolutionary chatbot," I now see:

• Embeddings (retrieval primitive)
• Vector database (retrieval composition)
• RAG (retrieval composition)
• LLM (model primitive)
• Guardrails (validation deployment)

Not magic. Just elements reacting.

This lens reveals:
• What's genuinely novel vs. recombined
• Where the complexity actually lives
• What's missing or overengineered

Next time you see an AI product, ask:
→ Which elements is this using?
→ Which row does the core innovation sit in?
→ What reactions are happening between elements?

If the vendor can't answer clearly, they probably don't understand their own architecture.

⬇️ How do you keep an overview?

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Which AI model should you actually use?

Here's the truth most "AI experts" won't tell you:

The model doesn't matter. The match does.

I have rigorously tested every major AI.
Across business and private scenarios.

Writing, research, automation, and strategy.
Not theory. Actual work.

Here's what I've learned:

1. Claude 4.5 - The thinking partner

Both Opus & Sonnet Models are strong.
Opus 4.5 being the top choice for complex tasks.

Claude is the one I trust most for serious work.
Excellent for structured writing, reasoning, and complex multi-step problems.
Handles massive context without breaking.
Too cautious for creative exploration.

2. ChatGPT-5 - The daily driver

The most versatile option.
Strong across content, research, and automation.
Good balance of creativity, reasoning, and usability.
Still prone to hallucinations in niche domains.

3. Perplexity - The research engine

When accuracy matters.
Fast, web-grounded answers with sources.
Ideal for founders who need reliable data quickly.
Limited flexibility beyond research.

4. Grok 4 - The real-time radar

Not fully there yet, but very interesting.
Strong for trend detection and social sentiment.
Direct access to live X data is its edge.
Weaker for technical depth and precision work.

5. Gemini 3 - The ecosystem play

Best inside Google’s world.
Useful for Workspace, SEO, and multimodal tasks.
Smooth handling of text, images, and video.
Output often feels generic and mechanical.

6. DeepSeek V3.1 - The efficiency machine

Excels at value for price.
Very capable for engineering and technical workloads.
Lightweight and affordable.
Poor at narrative thinking and human-style writing.

Founders and operators who understand this don’t pick one tool.
They switch models intentionally,
and that’s where the next 20% come from.

What’s your favorite model?
⬇️ Let me know in the comments

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Top 6 AI Models Right Now

95% of AI pilots fail.
Not because the technology doesn't work. 
Because companies keep making the same six mistakes.

MIT, RAND, BCG, and S&P Global all point to the same pattern:

→ Chasing hype over outcomes.
→ Demo addiction.
→ Data neglect.

Here's what the research shows:

1. Solving non-problems
Companies buy tools before defining the business need

2. Pilot purgatory
Dozens of proofs-of-concept, zero production systems

3. Data denial
80% of AI work is data prep; most budgets ignore this

4. Platform obsession:
Building "enterprise brains" before winning a single vertical

5. Human blindness:
Only 37% invest meaningfully in change management

6. Budget misalignment:
Money flows to visible marketing tools;
ROI hides in boring back-office automation

The 5% that succeed…

…start with problems, not tools. 
…treat AI as a product, not a project. 
…invest 70% of resources in people and process.

Which mistake have you seen most often?

Want to avoid these traps?

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♻️ Repost to help your network build AI that actually ships

Why 95% of AI pilots fail?

ChatGPT is just the tip of the iceberg.

Most companies never look below the surface.
And that’s why they’re not building real advantage.

1. Above the surface

Chatbots like ChatGPT, Claude, Copilot.
Nice demos. Low cost. Everyone has access.
→ Useful? Yes. Differentiating? No.

2. Shallow waters

Automation tools.
Zapier, Make, Power Automate.
Quick efficiency wins. Faster emails. Cleaner workflows.
→ Good hygiene, but still easy to copy.

3. Mid-depth

This is where things get interesting.
Custom agents. RAG systems. Orchestration.
AI connected to your data, your processes, your decisions.
→ Now AI starts to reflect how your business actually thinks.

4. Deep waters

Enterprise AI systems.
Proprietary data. Custom tools. Governance, monitoring, evaluation.
This is where strategy meets engineering.
→ This is where markets are shaped, not followed.

Most AI conversations stop at prompts.
Serious value starts with strategic thinking + system design.

The future isn’t built at the surface.
It’s built below it.

At what level is you company?
⬇️ let me know in the comments

Want to know if AI is worth it?

Use my ROI calculator. It’s free.
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♻️ Repost to help your network to understand the AI-universe.

The most successful people I observe,
focus more on giving, not taking.

And it’s not just a feel-good idea. It’s a real advantage.

Genuine generosity:

• Builds trust faster than any resume
• Creates stronger relationships
• Brings a calm confidence when things get chaotic

The funny part?

When you give without keeping score, the “selfish” upside shows up anyway.

Kindness at work isn’t just nice. It’s strategic.

3 ways it helps you:

1. Trust becomes your currency

• Small helpful moments add up
• People remember who showed up
• Your reputation travels when you’re not in the room

2. Your network compounds

• Today’s peers are tomorrow’s decision-makers
• The relationships you invest in early matter
• Tiny favors can turn into lifelong allies

3. You grow faster

• Teaching forces clarity
• Mentoring builds leadership
• Thoughtful feedback sharpens communication

Best part: you rarely know which moment will matter most.

The person you helped today might be the person who opens a door later.

The most successful people I know — the ones who are both happy and successful — are usually the most generous.

Do you agree?
⬇️ Let me know in the comments

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This tool has been going viral on social.
Let’s unpack it.
→ Clawdbot.

Sounds like Claude from Anthropic?
Not quite.
Here’s what it is — and isn’t.

Clawdbot ≠ Claude.

Clawdbot is a third-party open-source agent.
Claude is a language model.
Big difference.

Clawdbot is the “hands.”
Claude (or GPT) is the “brain.”

You can run Clawdbot locally and wire it to messaging apps like WhatsApp or Telegram.
It listens. Thinks. Acts.

And it doesn’t just chat. It does:
File stuff. Calendar stuff. Real-world automation.

So what happens when you say:
“Make a dinner reservation at 8 pm”

Here’s what goes down:

1. Message hits Clawdbot.
2. Clawdbot calls the LLM (e.g. Claude or GPT) to figure out what you want.
3. The model replies: “This looks like a calendar + browser task.”
4. Clawdbot runs the tools locally.
5. You get a reply: “Booked. 8pm. Italian.”

Behind the scenes, your own device (e.g. Mac Mini) just ran a full AI-powered assistant loop locally.

Why this matters:

Clawdbot gives you the power of LLMs without giving away your data.

You choose what stays local.
You choose what gets called.
You choose what it can access.

This flips the control dynamic.
You’re not just chatting with a black box.
You’re running the box.

The future isn’t “chat with AI.”
It’s “live with AI that works for you.”
Clawdbot is an early glimpse of that.

Have you tried setting one up yet?
⬇️ Let me know in the comments

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

Sign up here 👇 
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The 3 reasons why most AI projects die.
(Here's how to make sure yours doesn't)

Only 25% of AI projects deliver their expected ROI.
Even worse? Just 16% scale enterprise-wide.

The pattern is familiar:

•  Big launch energy
•  Promising pilot results
•  Then… silence

It's the corporate version of a New Year's resolution.

3 reasons that kill most AI projects:

1. The Technology Trap

Teams fall in love with the model before defining the problem.
They build impressive demos that solve nothing important.

→ The fix:

•  Start with the business problem, not the algorithm.
•  Define success metrics before writing a single line of code.
•  Ask: "What decision will this help us make better?"
•  If you can't answer clearly, stop.

2. The Stakeholder Desert

You can't build AI in a vacuum.
Yet most teams try.
They work in isolation, then wonder why nobody adopts their solution.

→ The fix:
Secure buy-in early. Speak the language of your stakeholders:

•Finance cares about cost reduction.
•Operations cares about efficiency.
•Sales cares about revenue.

Technical excellence means nothing if stakeholders don't see value in their terms.

3. The Deployment Blindspot

The biggest mistake?
Treating deployment as an afterthought.
Teams celebrate when the model works in a notebook.

They forget the model needs to:
• Integrate with existing systems
• Run reliably at scale
• Be monitored for drift
• Actually get used by humans

→ The fix:

• Plan deployment from day one.
• Build monitoring into your roadmap.
• Track model performance in production.
• Measure business impact repeatedly, not just once.

The returns often grow exponentially as you scale.
But you'll only see them if you're measuring.

The Pattern That Works:

1. Start with a clear business problem 
2. Define success metrics upfront 
3. Secure stakeholder alignment early 
4. Plan deployment before building 
5. Monitor and measure continuously

AI projects don't fail because of bad technology.
They fail because of bad planning.
Don't add yours to it.

What's been your biggest challenge in scaling AI projects?
⬇️ 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
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Let’s clean up a popular myth about AI:
“ChatGPT gets 40% of its answers from Reddit…”

I’ve seen posts claiming:

“ChatGPT gets 40% of its answers from Reddit.
26% from Wikipedia.
24% from YouTube.
23% from Google.”

Sounds alarming.
It’s also misleading.

Those numbers don’t describe how AI is trained.
They come from third-party studies that counted which websites AI sometimes links to in its answers.

That’s very different from “where it learns from.”

Here’s the uncomfortable truth:

No major AI provider publicly shares exact training data percentages (so far).
Not OpenAI.
Not Google.
Not Anthropic.

We know models are trained on:

• books
• licensed data
• human feedback
• large web crawls
• academic material

But we don’t know the precise mix.

So when you see neat percentages?
Be skeptical.

AI isn’t a search engine.
And it’s not “asking Reddit” in real time either.

It’s generating text based on patterns learned from massive datasets.
That has a real downside:

It doesn’t label confidence:

A viral thread
A random opinion
A peer-reviewed paper

→ Same tone. Same certainty.

No warning sign.
That’s the real risk.
Not “40% Reddit.”
But unexamined trust.

If we want people to use AI well, we should teach better questions:

• What’s missing?
• Who would disagree?
• What evidence supports this?
• What would change my mind here?
• Is this fact, interpretation, or guess?

AI is powerful.
But Critical thinking is still non-negotiable.

How are you scrutinizing AI output?
⬇️ Let me know in the comments
→ 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/eUmy2Bdp

The Skill Gap That Will Separate AI Winners from Everyone Else

Your AI Chief of Staff Arrives in 2026

Here's why:
We've had AI tools for 2+ years.
But we're still using them like interns, not executives.

That changes next year.
Not because the models get 10x better. 
Because three things finally align.

1. Hardware catches up

New consumer GPU chips hit laptops in 2026.
Translation:

• Local AI processing becomes seamless
• Cloud + device work together
• No more lag, no more friction

You won't think about "running an AI." 
It'll just run.

2. Agents learn to stay on task

Current AI has the attention span of a goldfish.
2026 agents run perpetually:

• They hold context across days, not minutes.
• They remember what you delegated last week.
• They follow through without you checking in.

New scaffolding techniques solve the memory problem.
The agent doesn't forget.
It builds on what it knows.

3. The interface problem gets solved

Here's the real bottleneck: 
We don't know how to talk to AI like a chief of staff.
Most people still use AI like Google.
One prompt. One answer. Done.

But a chief of staff needs:

• Clear priorities
• Structured delegation
• Ongoing access

The UX breakthrough isn't flashy. 
It's an always-on interface where you can:

• Assign tasks clearly
• Check in when needed
• Let it run in the background

→ Think: a dedicated space that lives alongside your work.

What this means for you:

By early 2026, your AI won't just write.
It will:
• Manage your calendar
• Build decks and spreadsheets
• Track long-term goals
• Synthesize updates across projects
• Remind you what matters

Not in theory. In practice.

Here is what this means for YOUR skills:

You'll need to learn how to delegate to machines.

That means:
• Defining tasks clearly
• Organizing your own thinking first
• Trusting the agent to execute

Most people aren't ready for this.
Not because the AI isn't capable.
Because they haven't structured their work for delegation.

Bottom line:
2026 is the year the pieces come together. 
Anyone can have a functional AI chiefs of staff.
The question isn't whether this happens. 
It's whether you'll be ready to use it.

What's the first thing you'd delegate to your AI chief of staff?
⬇️ 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 👇
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Google released 101 architectural blueprints
for the most impactful Gen AI use cases.

Each blueprint shows a design pattern and a tech stack to solve real-world challenges.

Here are 5 realistic blueprints to start working on this week:

1. Retail: Create personalized product descriptions in seconds

• Use Vertex AI to generate unique, SEO-friendly content
• Reduce manual writing time by 90%
• Boost product discoverability

2. Healthcare: Build an AI radiology assistant

• Use computer vision to detect potential areas of interest
• Help radiologists work faster and more accurately
• Improve patient outcomes through intelligent technology

3. Manufacturing: Automate industrial safety audits

• Use AI to analyze equipment photos for compliance
• Reduce audit time from weeks to hours
• Ensure workplace safety with intelligent monitoring

4. Financial Services: Create an AI mortgage quote generator

• Instantly compare and generate personalized loan offers
• Cut application processing time dramatically
• Provide a seamless customer experience

5. Logistics: Develop a digital twin of your distribution network

• Track real-time package movements
• Predict and prevent potential bottlenecks
• Optimize routing and delivery efficiency

These aren't just concepts
They're actionable blueprints ready to revolutionize your industry!

Want to dive deeper?
Check out the full Google Cloud blog post. 👇
https://lnkd.in/euwDvGNu

Want to know if AI is worth it?

Use my ROI calculator. It’s free.
⬇️ Sign up here
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♻️ Repost to help your network find AI use cases.

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