I built and sold a business to HubSpot in just 17 months.

I co-founded Mindstream, an AI newsletter that scaled to 150,000+ subscribers. Today, Mindstream is part of the HubSpot Media Network, where I lead brand strategy for the AI media products. 

Along the way, I’ve grown a 200,000+ following on LinkedIn, sharing real, unfiltered lessons on AI, entrepreneurship, and business growth. The wins and the mistakes.

I believe action beats hesitation. The biggest risk? Never starting.

Adam Biddlecombe

Helping Entrepreneurs Build with AI | Founder, Mindstream (Acquired by HubSpot)

Co-Founder & CEO 

AI is eating the world.
And you can’t even define “LLM”?
Yikes.

Don't stay clueless.
Don't stay behind.
Don't stay outdated.

These 40 AI terms explained easily will make you 10x smarter today.

1. Bias
When an AI unfairly prefers some answers over others, often because of the data it was trained on.

2. Label
A tag or answer given to data so AI knows what it is.

3. Model
The final program that can do tasks after learning from data.

4. Training
The process where AI learns from examples to get better at its job.

5. Chatbot
A computer program that talks to people in text or voice.

6. Dataset
A big collection of information that AI learns from.

7. Algorithm
Step-by-step instructions for solving a problem.

8. Token
Words or pieces of words AI uses to read and write text.

9. Overfitting
When AI learns the training data too well and can’t handle new, different examples.

10. AI Ethics
Making sure AI is used in ways that are right and fair to everyone.

If you want to know more AI terms, 
Check the infographic below 👇 

Have you started learning AI?
Comment below 👇 

Learn AI for free: https://lnkd.in/euYZeAdb 

♻️ Share this to help your network learn AI terms

AI is eating the world.
And you can’t even define “LLM”?
Yikes.

Don't stay clueless.
Don't stay behind.
Don't stay outdated.

These 40 AI terms explained easily will make you 10x smarter today.

Bias
When an AI unfairly prefers some answers over others, often because of the data it was trained on.

2. Label
A tag or answer given to data so AI knows what it is.

3. Model
The final program that can do tasks after learning from data.

4. Training
The process where AI learns from examples to get better at its job.

5. Chatbot
A computer program that talks to people in text or voice.

6. Dataset
A big collection of information that AI learns from.

7. Algorithm
Step-by-step instructions for solving a problem.

8. Token
Words or pieces of words AI uses to read and write text.

9. Overfitting
When AI learns the training data too well and can’t handle new, different examples.

10. AI Ethics
Making sure AI is used in ways that are right and fair to everyone.

If you're not using AI

You're not managing your growth wisely. 

(And no. I'm not talking about basic motivation) 

Personal development becomes efficient when humans and AI work together. 

✅ Write a prompt 
✅ Get a solution from ChatGPT 
✅ Follow up and keep brainstorming till you get the best solution 

 Self-improvement use cases on which you can collaborate with ChatGPT: 
✅ SMART Goal Setting
✅ Visualization Exercise
✅ Habit Formation Blueprint
✅ Growth Mindset Cultivation
✅ Personal Mission Statement
✅ Evening Wind-Down Routine
✅ Morning Routine Optimization
✅ Journaling for Self-Awareness
✅Weekly Time Management Plan
✅ Accountability Partner Simulation

And more covered in the infographic below. 

For what use cases do you use ChatGPT? 
Comment below 👇 

Learn AI for free 👉 https://lnkd.in/euYZeAdb 

♻️ Share this post to help your network use ChatGPT for personal growth

If you're not using AI

You're not managing your growth wisely. 

(And no. I'm not talking about basic motivation) 

Personal development becomes efficient when humans and AI work together. 

✅ Write a prompt 
✅ Get a solution from ChatGPT 
✅ Follow up and keep brainstorming till you get the best solution 

 Self-improvement use cases on which you can collaborate with ChatGPT: 
✅ SMART Goal Setting
✅ Visualization Exercise
✅ Habit Formation Blueprint
✅ Growth Mindset Cultivation
✅ Personal Mission Statement
✅ Evening Wind-Down Routine
✅ Morning Routine Optimization
✅ Journaling for Self-Awareness
✅Weekly Time Management Plan
✅ Accountability Partner Simulation

You waste time doing manual work. 

Even when you have AI now. 

Here's how to go from zero to pro in AI:

Core AI skills:
✅ Computer Vision
✅ Model Deployment
✅ Deep Learning (DL)
✅ Explainable AI (XAI)
✅ Generative AI (GenAI)
✅ Machine Learning (ML)
✅ Reinforcement Learning
✅ Model Monitoring & Versioning
✅ Natural Language Processing (NLP)

Top websites to learn AI:
✅ Fast AI
✅ Mindstream
✅ Kaggle
✅ Coursera
✅ Deeplearning
✅ Mygreatlearning

And a lot more covered in the infographic below

Have you started learning AI?
Comment below 👇

Learn AI for free 👉 https://lnkd.in/euYZeAdb 

♻️ Repost to help your network learn AI

You waste time doing manual work. 

Even when you have AI now. 

Here's how to go from zero to pro in AI:

Core AI skills:
✅ Computer Vision
✅ Model Deployment
✅ Deep Learning (DL)
✅ Explainable AI (XAI)
✅ Generative AI (GenAI)
✅ Machine Learning (ML)
✅ Reinforcement Learning
✅ Model Monitoring & Versioning
✅ Natural Language Processing (NLP)

Top websites to learn AI:
✅ Fast AI
✅ Mindstream
✅ Kaggle
✅ Coursera
✅ Deeplearning
✅ Mygreatlearning

You’re wasting hours doing tasks an AI Agent could finish in seconds.

The twist? You don’t even have to build them.

I’ve curated the 40 best plug-and-play Zapier Agent templates that do it all for you.

How do Zapier Agent templates help?
- Each template provides you a ready-to-use framework for a task.
- You get in-depth explanation about how the template works.
- You get to know why you should use a particular template.
- You get the perfect tool stack built in that template so you don’t need to juggle with random tools.

How to use Zapier Agent templates?
1. Go to the link below 
2. Click on any template 
3. Click ‘Use Template’
4. Check out the Agent
5. Connect your web apps
6. Click ‘Create’
7. Configure and use your Agent

Here are the Agent templates you should try:

Productivity:
- Automated Email Reply Drafts Agent
- Daily Outlook Event Reminder
- Google Calendar Zoom Meeting Link Adder
- Hackathon Event Notifier
- Follow-Up Reaction Notifier

Product Management:
- Product Expiry Management System
- PRD Document Creator
- Customer Call Summary and PRD Generator
- Weekly Project Status Update Email Generator
- Slack Thread Insights Extractor 

Project Management:
- Project Brief Generator 
- Slack to Asana Campaign Organizer 
- Google Doc to Asana Status Update 
- Project Update Notifier 
- Incomplete Jira Tickets Commenter 

To know more powerful Agent templates,
Check the infographic below 👇 

Try Zapier Agent templates here :https://lnkd.in/eBkNHjRz

What do you think about using Agent templates to automate manual work?

Comment below 👇 

♻️ Share this to help your network use the best Agent templates 

#ZapierPartner

Stuck staring at a blank page?
Ideas refusing to show up?
Brain fried from overthinking?

ChatGPT can spark it.
Prompts can fuel it.
Your creativity can explode.

Here are 10 ChatGPT prompts built to smash creative blocks in seconds.

Market Entry Strategy Brainstorm
- Act as a strategic business consultant and use chain-of-thought reasoning to brainstorm a step-by-step market entry strategy for [business type] targeting [specific market or region], including potential risks, competitive analysis, entry barriers, and actionable timelines; structure the output as a bulleted list with pros, cons, and recommended next steps, limited to 500 words.

Product Innovation Ideas
- Think step-by-step like an innovative product developer and generate 10 creative ideas for new products or features in [business type], focusing on [specific problem or customer need], incorporating market trends and feasibility analysis; format the response as a numbered list with brief descriptions, potential revenue impacts, and implementation challenges.

Competitive Analysis Framework
- Employ chain-of-thought to analyze [number] key competitors in [business type], evaluating their strengths, weaknesses, opportunities, and threats relative to my [specific business aspect]; output in a structured SWOT matrix table format, followed by 5 strategic recommendations for differentiation.

Growth Hacking Tactics
- As a growth expert, reason through innovative growth hacking tactics for [business type] aiming to achieve [specific growth metric, e.g., 20% user increase], drawing from data-driven examples; present as a prioritized list of 8 tactics with step-by-step execution plans and expected outcomes.

Business Model Canvas
- Use structured reasoning to create a customized Business Model Canvas for [business type] with [unique twist or focus area], detailing key partners, activities, value propositions, and revenue streams; format as a clear, bulleted section-by-section breakdown with visual descriptions for easy adaptation.

To get more ChatGPT prompts,
Check the infographic below 👇

For what other use cases do you use ChatGPT?
Comment below 👇

Learn AI for free: https://lnkd.in/euYZeAdb 

♻️ Repost this to help your network use AI to brainstorm better

Stuck staring at a blank page?
Ideas refusing to show up?
Brain fried from overthinking?

ChatGPT can spark it.
Prompts can fuel it.
Your creativity can explode.

Here are 10 ChatGPT prompts built to smash creative blocks in seconds.

Market Entry Strategy Brainstorm
- Act as a strategic business consultant and use chain-of-thought reasoning to brainstorm a step-by-step market entry strategy for [business type] targeting [specific market or region], including potential risks, competitive analysis, entry barriers, and actionable timelines; structure the output as a bulleted list with pros, cons, and recommended next steps, limited to 500 words.

Product Innovation Ideas
- Think step-by-step like an innovative product developer and generate 10 creative ideas for new products or features in [business type], focusing on [specific problem or customer need], incorporating market trends and feasibility analysis; format the response as a numbered list with brief descriptions, potential revenue

AI isn’t replacing jobs.
It’s upgrading people who use it.
But only if you know how to use it.

Most people are learning the wrong stuff.
The job market is shifting under your feet.
Smart learners will dominate.

These 15 AI skills will future-proof your career in 2025.

1. Prompt Engineering
2. Staying Updated
3. Retrieval-Augmented Generation (RAG)
4. AI Agents
5. Multimodal AI Application
6. Fine-Tuning and AI Assistants
7. Voice AI & Avatars
8. AI Tool Stacking
9. AI Video Content Generation
10. SaaS Development
11. LLM Management
12. AI Content Marketing
13. AI Chatbots
14. Website Creation
15. AI Workflow Automation

To know more about these skills,
Check the infographic below 👇 

Have you started learning how to use AI?
Comment below 👇 

Learn AI for free: https://lnkd.in/euYZeAdb 

♻️ Share this to help your network learn AI

AI isn’t replacing jobs.
It’s upgrading people who use it.
But only if you know how to use it.

Most people are learning the wrong stuff.
The job market is shifting under your feet.
Smart learners will dominate.

These 15 AI skills will future-proof your career in 2025.

Prompt Engineering
Staying Updated
Retrieval-Augmented Generation (RAG)
AI Agents
Multimodal AI Application
Fine-Tuning and AI Assistants
Voice AI & Avatars
AI Tool Stacking
AI Video Content Generation
SaaS Development
LLM Management
AI Content Marketing
AI Chatbots
Website Creation
AI Workflow Automation


Stop using ChatGPT the default way.

Most people don’t realise they’re blocking 80% of its power.

Here’s how the Personalization feature in ChatGPT helps you get 10x better outputs from AI.

What is the Personalization feature in ChatGPT?
1. Allows users to set how ChatGPT should respond and what it should know about them via custom instructions.
2. Enables the model to adapt tone, style, and focus based on those user-defined preferences.
3. Utilises memory of past interactions (if enabled) so responses become more context-aware over time.
4. Gives different users distinct experiences even with similar prompts, reflecting their unique guidance.

Follow these steps:
1. Go to chatgpt.com
2. Click on your profile picture on bottom left.
3. Click ‘Personalization’.
4. Select ‘Base style and tone’ from the available options. This helps you set the basic style and tone of how ChatGPT responds to you.
5. In ‘Custom Instructions’ add what you want ChatGPT to have and not have in its outputs.
6. Add your nickname ChatGPT can use in chats.
7. Add your occupation so ChatGPT understands you better.
8. Add more information about your interests, values, tone of voice, writing style, and preferences.
9. Enable or disable memory to let ChatGPT know more about you
10. Enable or disable record mode to enable or disable ChatGPT using previous chats as references to create new outputs

Dos:
- Do specify your role and goals clearly so the model knows your context.
- Do define how you prefer the tone, style, and format of responses.
- Do keep your instructions concise and manageable rather than overly long.
- Do update or refine your custom instructions as your needs or projects evolve.
- Do use custom instructions to reduce repetition and improve consistency across chats.

Don'ts:
- Don’t overload the instruction fields with every little preference; it can reduce effectiveness.
- Don’t assume the model will read between vague instructions; be explicit when necessary.
- Don’t include extremely sensitive or proprietary personal data if you’re unsure about usage/privacy.
- Don’t forget to review how the model is behaving; custom instructions aren’t a “set-it-and-forget-it” guarantee.
- Don’t expect custom instructions alone to solve all prompt-engineering issues; they complement good prompting, not replace it.

To know more about it,
Check the infographic below 👇

What do you think about the Personalization Feature In ChatGPT?
Comment below 👇

Learn AI for free: https://lnkd.in/euYZeAdb 

♻️ Repost to help your network use ChatGPT better

Stop using ChatGPT the default way.

Most people don’t realise they’re blocking 80% of its power.

Here’s how the Personalization feature in ChatGPT helps you get 10x better outputs from AI.

What is the Personalization feature in ChatGPT?
Allows users to set how ChatGPT should respond and what it should know about them via custom instructions.
Enables the model to adapt tone, style, and focus based on those user-defined preferences.
Utilises memory of past interactions (if enabled) so responses become more context-aware over time.
Gives different users distinct experiences even with similar prompts, reflecting their unique guidance.

Follow these steps:
Go to chatgpt.com
Click on your profile picture on bottom left.
Click ‘Personalization’.
Select ‘Base style and tone’ from the available options. This helps you set the basic style and tone of how ChatGPT responds to you.
In ‘Custom Instructions’ add what you want ChatGPT to have and not have in its outputs.

You’re analysing data wrong. 

Because you’re doing it manually. 

Here’s how ChatGPT helps you get hours of data analysis work done in minutes.

1. Go to chatgpt.com 
2. Add your dataset
3. Write your prompt.
4. Get the analysis done by ChatGPT.
5. Add a follow-up prompt if required

Prompt I've used: 
- Act as a professional data analyst. I've attached a dataset here of Uber rides. I want you to perform data cleaning of this data and spot inconsistencies, missing values, or duplicates in datasets & fix them. I also want you to highlight patterns, correlations, distributions & potential outliers in this dataset & create a report of takeaways from this dataset. 

After I got the output, I needed just concise key takeaways from the data. So, here's my follow-up prompt: 
- Write about 3 key takeaways from this data. 

Output I got:
- ChatGPT cleaned the data & gave me a good report, & then here are the key takeaways I got in an easy-to-understand way.

Do's of using ChatGPT for data analysis:
✅ Do prep your dataset.
✅ Do ask clear, specific analysis questions.
✅ Do validate AI results with real stats.
✅ Do use visualisations for better insights.
✅ Do cross-check with domain knowledge.

Don'ts of using ChatGPT for data analysis:
❎ Don't feed raw, messy data directly.
❎ Don't assume outputs are always accurate.
❎ Don't skip checking for bias or outliers.
❎ Don't use vague prompts like "analyse this".
❎ Don't rely on AI alone; always verify.

To get a more detailed explanation, 
Check the carousel below 👇 

What do you think of using ChatGPT for analysis?
Comment below 👇 

Learn AI for FREE: https://lnkd.in/eAAacxSK 

♻️ Share this to help your network use AI

How To Use ChatGPT For Analysis

I thought Google Nano Banana Pro was just another flashy update in AI image generation.

But I got a shock when I tried it for multiple business use cases.

Here’s the ultimate guide to creating mind-bending images with Google's Nano Banana Pro AI image generation model.

Prompt Template to create images:
Create a highly detailed image of [insert subject] set in [insert environment or setting], captured in [insert camera style or artistic style]. The scene should emphasize [insert key features, mood, or atmosphere], with lighting that enhances [insert lighting preference such as dramatic shadows, soft glow, neon reflections]. Include specific visual elements like [insert defining objects, textures, colors], and ensure the final image appears realistic, cinematic, and cohesive with strong composition.

How To Use It?
1. Go to gemini.google.com
2. Select the ‘Thinking’ model
3. Select ‘Create images’ in tools
4. Add your reference images if required
5. Add your prompt
6. AI generates the output in a minute

Do’s:
✅ Always use high-quality, well-lit reference images for crisp results.
✅ Specify subject, action, and setting clearly in each prompt.
✅ Refine with camera angle, lighting, and style details for better accuracy.
✅ Use constraints like aspect ratio or resolution for targeted results.
✅ Iterate by tweaking prompts for improved output quality.

Don’ts:
❎ Don’t use vague or generic prompts; results will be bland or off-target.
❎ Don’t overload with too many style or action requests in one prompt.
❎ Don’t use low-resolution or group photos as reference; AI struggles with blurry or crowded faces.
❎ Don’t forget identity markers when blending multiple people or scenes.
❎ Don’t expect perfect tiny text or intricate textures; these can render inaccurately.

To get more tips,
Check the infographic below 👇

Learn AI for free: https://lnkd.in/euYZeAdb 

What do you think about this new and trending AI image generation model?
Comment below 👇

♻️ Repost to help your network create better images with AI

I thought Google Nano Banana Pro was just another flashy update in AI image generation.

But I got a shock when I tried it for multiple business use cases.

Here’s the ultimate guide to creating mind-bending images with Google's Nano Banana Pro AI image generation model.

Prompt Template to create images:
Create a highly detailed image of [insert subject] set in [insert environment or setting], captured in [insert camera style or artistic style]. The scene should emphasize [insert key features, mood, or atmosphere], with lighting that enhances [insert lighting preference such as dramatic shadows, soft glow, neon reflections]. Include specific visual elements like [insert defining objects, textures, colors], and ensure the final image appears realistic, cinematic, and cohesive with strong composition. 

How To Use It?
Go to gemini.google.com
Select the ‘Thinking’ model
Select ‘Create images’ in tools
Add your reference images if required
Add your prompt

I used to end every day feeling like I worked all day but achieved less than my potential.

Then I added a new tool to my workflow, and the upgrade felt almost unfair.
I've added Google's Gemini 3.

Here’s the complete guide on how to use Google Gemini 3 to get more work done in less time.

Prompt framework you should use:
Role: Define who the AI should act as (e.g., expert, teacher, coach).
Task: Specify the exact job or output you want the AI to complete.
Context: Give details, boundaries, and exclusions to guide the AI properly.
Reasoning: Ensure logical steps, accuracy, and validation checks.
Output format: Tell ChatGPT the exact structure in which to return results.
Stop conditions: Clarify when ChatGPT should consider the task finished.

Prompt template:
Act as [Role] to [Task].
Begin with a checklist of 3–7 steps to plan how you’ll complete the task.
Ensure the content is accurate, unique, and excludes [things to avoid].
Prioritize clarity and practical value when presenting results.
Internally validate and cross-check information before finalizing.
Return the results in this format: [desired output format].
The task is complete when [stop condition].

Use cases of Google Gemini 3:
1. Do data analysis 
2. Create data visualisations
3. Create insane visuals for marketing, training, sales, and support
4. Create presentations
5. Create research reports
6. Create web apps
7. Create websites
8. Solve problems faster
9. Create games

To get more tips on how to use it better,
Check the infographic below 👇 

Learn AI for free: https://lnkd.in/euYZeAdb  

What do you think about Google Gemini 3?
Comment below 👇 

♻️ Repost to help your network be productive at work with Google Gemini 3

I used to end every day feeling like I worked all day but achieved less than my potential.

Then I added a new tool to my workflow, and the upgrade felt almost unfair.
I've added Google's Gemini 3.

Here’s the complete guide on how to use Google Gemini 3 to get more work done in less time.

Prompt framework you should use:
Role: Define who the AI should act as (e.g., expert, teacher, coach).
Task: Specify the exact job or output you want the AI to complete.
Context: Give details, boundaries, and exclusions to guide the AI properly.
Reasoning: Ensure logical steps, accuracy, and validation checks.
Output format: Tell ChatGPT the exact structure in which to return results.
Stop conditions: Clarify when ChatGPT should consider the task finished.

Stop writing random prompts.

Start writing structured ones that make AI work as your partner.

Here are 30 ChatGPT prompt templates to write better prompts. 

1. Detailed Instruction Template
"Give me detailed, step-by-step instructions on how to [perform a task]."
Example:
"Give me detailed, step-by-step instructions on how to set up a home Wi-Fi network."

2. Role Play Specialist Template
"Act as a [role/expert]. Help me with [problem/situation]."
Example:
"Act as a tax accountant. Help me with filing self-employment taxes."

3. Explain Like I’m Five (ELI5) Template
"Explain [concept] to me as if I were five years old."
Example:
"Explain blockchain technology to me as if I were five years old."

4. Comparison Table Template
"Create a table comparing [items or concepts] based on [criteria]."
Example:
"Create a table comparing electric vehicles, hybrids, and gasoline cars based on price, range, and emissions."

5. Pros and Cons List Template
"List the pros and cons of [topic/decision/technology]."
Example:
"List the pros and cons of working remotely full-time."

To know more prompt templates,
Check the infographic below 👇

What other prompt templates would you recommend adding here?
Comment below 👇

Learn AI for free: https://lnkd.in/euYZeAdb 

♻️ Share this to help your network use ChatGPT

Stop writing random prompts.

Start writing structured ones that make AI work as your partner.

Here are 30 ChatGPT prompt templates to write better prompts. 

1. Detailed Instruction Template
"Give me detailed, step-by-step instructions on how to [perform a task]."
Example:
"Give me detailed, step-by-step instructions on how to set up a home Wi-Fi network."

2. Role Play Specialist Template
"Act as a [role/expert]. Help me with [problem/situation]."
Example:
"Act as a tax accountant. Help me with filing self-employment taxes."

3. Explain Like I’m Five (ELI5) Template
"Explain [concept] to me as if I were five years old."
Example:
"Explain blockchain technology to me as if I were five years old."

4. Comparison Table Template
"Create a table comparing [items or concepts] based on [criteria]."
Example:
"Create a table comparing electric vehicles, hybrids, and gasoline cars based on price, range, and emissions."

5. Pros and Cons List Template
"List the pros and cons of [topic/decision/

30 AI algorithms that secretly run your life.

They choose what you watch.
They predict what you buy.
They know you better than you know yourself.

Here are 30 AI algorithms you can't miss.

1. Linear Regression
Predicts a number based on a straight-line relationship. Example: Predicting house prices from size.

2. Logistic Regression
Predicts a yes/no outcome (like spam or not spam). Despite the name, it’s used for classification.

3. Decision Tree
Uses a tree-like model of decisions with if-else rules. Easy to understand and visualize.

4. Random Forest
Builds many decision trees and combines their answers. More accurate and less likely to overfit.

5. Support Vector Machine (SVM)
Finds the best line (or boundary) that separates different classes. Works well for high-dimensional data.

6. K-Nearest Neighbors (k-NN)
Looks at the ‘k’ closest data points to decide what a new point should be. No learning phase, just compares.

7. Naive Bayes
Based on Bayes’ Theorem and assumes all features are independent. Fast and good for text classification.

8. Gradient Boosting
Builds models one after another, each fixing the previous one’s mistakes. Powerful but slow.

9. AdaBoost
Like gradient boosting, it combines weak models into a strong one using weighted votes.

10. XGBoost
A super-efficient version of gradient boosting, very popular in machine learning competitions.

To know more AI algorithms, 
Check the infographic below 👇

Have you started learning AI?
Comment below 👇

Learn AI for free: https://lnkd.in/euYZeAdb 

♻️ Share this post to help your network learn AI

30 AI algorithms that secretly run your life.

They choose what you watch.
They predict what you buy.
They know you better than you know yourself.

Here are 30 AI algorithms you can't miss.

Linear Regression
Predicts a number based on a straight-line relationship. Example: Predicting house prices from size.

2. Logistic Regression
Predicts a yes/no outcome (like spam or not spam). Despite the name, it’s used for classification.

3. Decision Tree
Uses a tree-like model of decisions with if-else rules. Easy to understand and visualize.

4. Random Forest
Builds many decision trees and combines their answers. More accurate and less likely to overfit.

5. Support Vector Machine (SVM)
Finds the best line (or boundary) that separates different classes. Works well for high-dimensional data.

6. K-Nearest Neighbors (k-NN)
Looks at the ‘k’ closest data points to decide what a new point should be. No learning phase, just compares.

Most people think they’re critical thinkers. 
They’re not.

They confuse skepticism with depth.
They confuse doubt with wisdom.
They confuse noise with thought.

These 25 ChatGPT prompts show you what real critical thinking looks like. 

1. First-Principles Deconstruction 
Act as a first-principles analyst. Decompose the problem: “{insert problem}” into its smallest assumptions, physical/financial/logical constraints, and causal mechanisms. For each assumption, evaluate its necessity (must-have vs convenience) and testability (how to falsify). Rebuild a solution from only the indispensable parts. Output in sections: Assumptions, Constraints, Mechanisms, Tests, Rebuild, Risks, Final Recommendation.
 
 2. Steelman the Opposition 
Act as an impartial philosopher. State my position: “{insert position}.” Identify the strongest possible opposing thesis that a highly intelligent critic would advance. Present the opponent’s best arguments, evidence, and counterexamples. Then respond by improving my position (not attacking a strawman) with revisions, concessions, and better evidence. Output sections: My Thesis, Steelman Opposition, Rebuttal, Revisions to My Thesis, Updated Thesis.
 
 3. Decision Tree with Expected Value 
Act as a decision scientist. For the choice “{insert decision},” enumerate mutually exclusive options. For each option, map a decision tree with key uncertainties and outcomes. Estimate probabilities, costs, and benefits; compute expected values and confidence ranges. Conclude with the dominant option, critical variables (by EV sensitivity), and what evidence would most change the decision. Output: Options, Trees, EV Table, Sensitivity, Value of Information, Recommendation.
 
 4. Red Team Pre-Mortem 
Act as a red-team strategist. Assume the plan “{insert plan}” failed catastrophically in 12 months. List the top 10 plausible failure modes, early warning signals for each, severity/likelihood ratings, and concrete countermeasures. Conclude with a prioritized mitigation roadmap and a monitoring dashboard (leading indicators, thresholds, and actions).
 
 5. Causal Map and Intervention Points 
Act as a causal modeler. Build a cause-effect map for “{insert outcome to improve},” identifying upstream drivers, mediators, and feedback loops. Label each link with direction and plausible strength. Propose 5 leverage points where small interventions could yield outsized effects, including intervention design, measurement, risks, and fallback plans.

To get more ChatGPT prompts, 
Check the infographic below 👇 

Have you tried using AI as your thinking partner?
Comment below 👇 

Learn AI for free: https://lnkd.in/euYZeAdb 

♻️ Share this to help your network use AI for critical thinking

Most people think they’re critical thinkers. 
They’re not.

They confuse skepticism with depth.
They confuse doubt with wisdom.
They confuse noise with thought.

These 25 ChatGPT prompts show you what real critical thinking looks like. 

1. First-Principles Deconstruction 
Act as a first-principles analyst. Decompose the problem: “{insert problem}” into its smallest assumptions, physical/financial/logical constraints, and causal mechanisms. For each assumption, evaluate its necessity (must-have vs convenience) and testability (how to falsify). Rebuild a solution from only the indispensable parts. Output in sections: Assumptions, Constraints, Mechanisms, Tests, Rebuild, Risks, Final Recommendation.

You give it a prompt.
It replies in seconds.
But here’s the part nobody tells you...

It doesn’t understand the way you do.
It’s not trained like humans.
It’s just... predicting?

Here’s the truth about how ChatGPT works and why it’s terrifyingly effective.

Step 1: You type a prompt.
Step 2: ChatGPT splits your input into smaller pieces called “tokens”.
Step 3: It converts these tokens into numbers (vectors) so the computer can understand them.
Step 4: ChatGPT checks where each token appears in your message using “positional encoding”.
Step 5: It uses powerful “transformer” neural networks to analyze all tokens at once, not just one by one.
Step 6: The model pays special attention to the most important words in your input using the “attention mechanism”.
Step 7: All this information passes through many transformer layers to understand deeper meaning.
Step 8: The model recalls patterns & knowledge learned from millions of examples in its training data.
Step 9: It predicts what word (token) should come next, aiming for the most likely and meaningful response.
Step 10: ChatGPT generates one token at a time, building its reply step by step. 

To know more about how ChatGPT works,
Check the infographic below 👇

Have you started learning AI?
Comment below 👇

Learn AI for free: https://lnkd.in/euYZeAdb 

♻️ Share this to help your network learn how ChatGPT works

You give it a prompt.
It replies in seconds.
But here’s the part nobody tells you...

It doesn’t understand the way you do.
It’s not trained like humans.
It’s just... predicting?

Here’s the truth about how ChatGPT works and why it’s terrifyingly effective.

Step 1: You type a prompt.
Step 2: ChatGPT splits your input into smaller pieces called “tokens”.
Step 3: It converts these tokens into numbers (vectors) so the computer can understand them.
Step 4: ChatGPT checks where each token appears in your message using “positional encoding”.
Step 5: It uses powerful “transformer” neural networks to analyze all tokens at once, not just one by one.
Step 6: The model pays special attention to the most important words in your input using the “attention mechanism”.
Step 7: All this information passes through many transformer layers to understand deeper meaning.
Step 8: The model recalls patterns & knowledge learned from millions of examples in its training data.

You're learning too slowly without realising it.

Here’s how to learn faster than ever with AI with Google NotebookLM.

About Google NotebookLM
- AI-powered “virtual research and learning assistant” that uses your uploaded content and prompt as a basis for conversation.
- Accepts diverse sources like PDFs, Google Docs, websites and Google Slides and can answer questions grounded in them.
- Generates summaries, explanations and even audio overviews (podcast-style) to help you understand learning materials quickly.

Follow these steps:
1. Go to notebooklm.google.com
2. Check any featured notebook if you want
3. Click ‘Create New Notebook’ to create a new one
4. Add the doc or link from which you want to learn
5. NotebookLM starts working on creating easy-to-digest content from your learning material.
6. You’ll get the study material summary prepared by NotebookLM.
7. Based on it, NotebookLM can create:
  - Audio podcast
  - Explainer video
  - Mind map
  - Reports
  - Flashcards
  - Quiz

How to use the output created by NotebookLM:
- You can share a custom link of your notebook for anyone to use it.

Do’s of using NotebookLM:
✅ Upload clean, relevant sources.
✅ Ask clear, specific questions.
✅ Organise into topic notebooks.
✅ Use summaries, quizzes, and overviews.
✅ Cross-check outputs with sources.

Don’ts of using NotebookLM:
❎ Don’t expect knowledge beyond uploads.
❎ Don’t dump irrelevant content.
❎ Don’t ask vague, broad questions.
❎ Don’t over-trust compressed summaries.
❎ Don’t share sensitive data.

To know more about NotebookLM,
Check the infographic below 👇

Have you tried using AI for learning?
Comment below 👇

Learn AI for free: https://lnkd.in/euYZeAdb 

♻️ Repost to help your network learn efficiently with AI

You're learning too slowly without realising it.

Here’s how to learn faster than ever with AI with Google NotebookLM.

About Google NotebookLM
AI-powered “virtual research and learning assistant” that uses your uploaded content and prompt as a basis for conversation.
Accepts diverse sources like PDFs, Google Docs, websites and Google Slides and can answer questions grounded in them.
Generates summaries, explanations and even audio overviews (podcast-style) to help you understand learning materials quickly.
Try here: notebooklm.google.com

Follow these steps:
Go to notebooklm.google.com
Check any featured notebook if you want
Click ‘Create New Notebook’ to create a new one
Add the doc or link from which you want to learn
NotebookLM starts working on creating easy-to-digest content from your learning material.
You’ll get the study material summary prepared by NotebookLM.
Based on it, NotebookLM can create:
Audio podcast
Explainer video
Mind map
Reports
Flashcards
Quiz

Anyone today can build an app.

With AI. In 5 minutes. From a single text prompt. Without any technical knowledge. 

Here’s the ultimate prompting guide to help you build apps with AI.

I use Replit.
It is a tool with which anyone can build mobile and web apps with AI from simple text prompts without writing a single line of code with Replit’s powerful AI agent.

How it works:
1. Go to replit.com 
2. Signup 
3. Describe the app or design idea in the build and design section
4. Select what you want to build
5. Add reference files or Figma designs or an existing project to build an app
6. Add your prompt
7. Get your prompt refined with AI if required
8. Submit your prompt
9. AI Agent starts building
10. You get the entire app built in minutes
11. Follow-up if you need any changes
12. Preview and test it
13. Invite your team to check and make any changes
14. Publish with Replit 

Do’s:
✅ Define a clear problem and user outcome before prompting.
✅ Start with a well-defined feature set and expand only after basic flow works.
✅ Provide concrete examples, data formats, and edge cases in your prompts.
✅ Test each generated feature manually in real user scenarios.

Don’ts:
✅ Do not expect the first generation to be production ready without review.
✅ Do not mix multiple unrelated features in a single long prompt.
✅ Do not skip security checks such as auth, validation, and rate limiting.
✅ Do not rely only on auto tests run by the tool without your own testing.
✅ Do not deploy to real users without monitoring, logging, and error alerts.

To get the complete guide to build apps with AI,
Check the infographic below 👇 

Try here: https://lnkd.in/e6Eua37q

What do you think about building apps with AI?
Comment below 👇 

♻️ Repost to help your network cook apps with AI

Every successful CEO has one secret weapon.

It’s not talent, money, or luck.
It’s the ability to make better decisions.

Here are 30 powerful ChatGPT prompts for efficient decision-making.

Strategic Business Decision Evaluation 
- I am deciding between [Option A], [Option B], and [Option C] for my business. Provide a structured decision matrix where you track each option against evaluation metrics such as long-term ROI, alignment, risks, and feasibility. Assign weights to each factor, rank for each score, and end with a scenario recommendation of the best choice along with possible trade-offs.
 
 Personal Life Choice Analysis 
- I need to decide between [Choice A], [Choice B], and [Choice C] for my personal situation. Consider life constraints like financial stability, budget, time, location, lifestyle impact, and weighted pros/cons to create a logical framework that concludes, supports, risks, and recommends the best direction based on what aligns with a clear justification.
 
 Career Path Decision Aid 
- I’m torn between pursuing [Career Path A] and [Career Path B]. Analyse both paths in terms of salary potential, required skills, long-term growth opportunities, and career stability. For each option note risks and rewards, highlight career paths made by others with similar roles, and decide which one is the best fit for my values [insert personal values].
 
Investment Opportunity Comparison 
- I am considering investing in [Investment A], [Investment B], or [Investment C]. Create a risk/return analysis, a timeline to liquidity, use of long-term potential gains, income stream potential, and which option is most aligned with my financial portfolio strategy. Provide a decision recommendation of which option is superior for my investor profile.
 
 Time Management Decision Support 
- I have [X hours] available per day and need to allocate them between [Activity A], [Activity B], and [Activity C]. Optimise the schedule with balances in effectiveness, rest, and long-term productivity. Recommend the best time allocation plan with practical proof and examples, and create a framework based on a sample day plan.

Note: AI can help you with fast decision-making, but take all important decisions only by adding your human thinking and intuition to it.

To get more ChatGPT prompts, 
Check the infographic below 👇 

Which one prompt did you like the most here?
Comment below 👇 

Learn AI for free: https://lnkd.in/euYZeAdb 

♻️ Share this post to help your network use AI for better decision-making

Every successful CEO has one secret weapon.

It’s not talent, money, or luck.
It’s the ability to make better decisions.

Here are 30 powerful ChatGPT prompts for efficient decision-making.

Strategic Business Decision Evaluation 
I am deciding between [Option A], [Option B], and [Option C] for my business. Provide a structured decision matrix where you track each option against evaluation metrics such as long-term ROI, alignment, risks, and feasibility. Assign weights to each factor, rank for each score, and end with a scenario recommendation of the best choice along with possible trade-offs.
 
 Personal Life Choice Analysis 
I need to decide between [Choice A], [Choice B], and [Choice C] for my personal situation. Consider life constraints like financial stability, budget, time, location, lifestyle impact, and weighted pros/cons to create a logical framework that concludes, supports, risks, and recommends the best direction based on what aligns with a clear justification.

You don't need to spend hundreds of hours on learning the old way. 

Perplexity can become your smartest tutor only if you know what to ask.

Here are 20 Perplexity prompts that will help you learn more in less time.

Here are the use cases for which you can use Perplexity for learning:
✅ Comprehensive Topic Overview
✅ Step-by-Step Learning Roadmap
✅ Concept Comparison Table
✅ Academic Paper Summary & Analysis
✅ Beginner-Friendly Theory Explanation
✅ Chronological Timeline of Key Events
✅ Case Study Breakdown
✅ List of Best Practices
✅ Technical Term Deep Dive
✅ Overview of Research Methodologies

And more use cases with prompts are covered in the infographic below 

Will you try using Perplexity for learning?
Comment below 👇 

Learn AI for free: https://lnkd.in/euYZeAdb 

🔁 Repost to help your network use AI for learning

You don't need to spend hundreds of hours on learning the old way. 

Perplexity can become your smartest tutor only if you know what to ask.

Here are 20 Perplexity prompts that will help you learn more in less time.

Here are the use cases for which you can use Perplexity for learning:
✅ Comprehensive Topic Overview
✅ Step-by-Step Learning Roadmap
✅ Concept Comparison Table
✅ Academic Paper Summary & Analysis
✅ Beginner-Friendly Theory Explanation
✅ Chronological Timeline of Key Events
✅ Case Study Breakdown
✅ List of Best Practices
✅ Technical Term Deep Dive
✅ Overview of Research Methodologies

It took them 9 years to hit $500,000,000 ARR.

Then just one year to 2x it.

This is what you get,
When infrastructure finally catches up with ambition.

Because scaling businesses globally shouldn’t feel like fighting gravity.

The fastest-growing companies, from Canva to Brex, aren’t waiting on legacy systems.
They’re growing with Airwallex

They’re moving money, 
Across 60+ currencies without friction.
Managing spend, 
And scaling financial operations for global businesses.

$1,000,000,000 ARR isn’t just a regular financial milestone.

It’s proof that borderless finance is becoming the default layer of global business.

And for founders building the next wave of AI, SaaS, or e-commerce,
This is the infrastructure quietly powering your growth behind the scenes.

The internet made business global.
Airwallex made finance global.

Why I like Airwallex 👇
→ Trusted by 150,000+ companies.
→ Built for scale, speed, and smarter cross-border ops.

Legacy banking had a good run.
But the next era of growth?
It’s built on infrastructure that moves as fast as you do.

Learn more here 👉 https://lnkd.in/e8KW2nv8

What do you think about using such a globally scalable financial infrastructure in businesses? 
Comment below 👇 

♻️ Share this to help your network use modern financial systems in their businesses 

#AirwallexPartner

You’re still doing manual work in 2025?

It’s draining profits.
It’s wasting hours.
It’s killing growth.

Here are 50 AI tools that will help you with your most painful tasks.

Marketing
- Writesonic
- Taplio

Sales
- Clay
- Apollo

Development
- Cursor
- Windsurf

Support
- Folk
- Tidio

Operations
- Notion
- Rows

If you want to know more tools,
Check the infographic below 👇

What other AI tools would you recommend adding here?
Comment below 👇

Learn AI for free: https://lnkd.in/euYZeAdb 

♻️ Share this to help your network use the best AI tools

You’re still doing manual work in 2025?

It’s draining profits
It’s wasting hours
It’s killing growth

Here are 50 AI tools that will help you with your most painful tasks.

Marketing
Writesonic
Taplio

Sales
Clay
Apollo

Development
Cursor
Windsurf

Support
Folk
Tidio

Operations
Notion
Rows

MMIND

MMIND ist dein Partner für die praktische Umsetzung von KI in KMUs und im Finanzsektor. Wir kennen die Herausforderungen von Unternehmen aus erster Hand und haben KI-Lösungen entwickelt, die genau dort ansetzen, wo der grösste Nutzen entsteht.

Unsere Mission: KI einfach und für alle zugänglich machen – mit konkreten Lösungen statt theoretischer Konzepte.

Während andere noch über KI-Potenziale diskutieren, haben wir bereits zahlreiche Innovationen erfolgreich umgesetzt:
• Nachhaltigkeits- und Projektberichte auf Knopfdruck
• Vollautomatisierte Marketingstrategien und -umsetzungen
• Vereinfachte Personalabläufe und -administration
• Automatisierte Offerten- und Buchhaltungsprozesse

Unser Angebot basiert auf drei Säulen:
(1) KI-Akademie: In praxisorientierten Workshops vermitteln wir KI-Kompetenzen für alle Unternehmensbereiche – von Grundlagen bis zu spezialisierten Anwendungen für den Finanzsektor. Unsere KI-Cafés bieten zudem eine Plattform für Austausch und Vernetzung.

(2) KI-Tools: Unsere sofort einsatzbereiten KI-Assistenten lösen konkrete Geschäftsprobleme ohne Programmierkenntnisse. Die Lösungen laufen auf unserer eigenen sicheren Serverinfrastruktur – Datenschutz garantiert.

(3) KI-Projekte: Wir optimieren Geschäftsprozesse durch massgeschneiderte KI-Lösungen und steigern so die Effizienz vieler KMU-Prozesse um 30-40 Prozent.

Was uns auszeichnet:
• Tiefes Verständnis der Bedürfnisse von KMUs und des Finanzsektors
• Praxisorientierung mit Fokus auf Wertschöpfung statt theoretischer Konzepte
• Befähigung zur eigenständigen KI-Nutzung durch einfache Tools
• Community-Ansatz mit regelmässigen Austauschformaten
• Expertise in KI-Compliance und Regulierung
• Innovative Preismodelle wie unsere KI-Flatrate

Bei MMIND lernst du nicht allein – sondern gemeinsam mit anderen KMUs auf Augenhöhe. Wir teilen Wissen, tauschen Erfahrungen aus und entwickeln gemeinsam Lösungen für die digitale Zukunft.

Co-Founder

Hübner Management GmbH

At Hübner Management GmbH, we empower SMEs and the public sector to harness AI and digital innovation for transformative results. With collaborative solutions in mind, we integrate a range of AI platforms—from Atlassian's advanced tools to platforms like OpenAI and LM Studio—to streamline processes, accelerate learning, and foster connected, future-ready organizations.

Our expertise lies in connecting intelligent assistants, optimizing stakeholder communication, and driving measurable outcomes that support business growth and resilience.

Discover how our tailored solutions can elevate your organization’s efficiency and impact. 

Get in touch at: hartmut@huebner.io

Meet Your Purpose

At Meet Your Purpose, our mission is to help organizations uncover and enhance the hidden dimensions of their corporate culture. Our main tool, the Culture Cockpit, accelerates problem identification and provides actionable insights that foster learning and growth. By promoting open dialogue and aligning team values with business objectives, we empower businesses to build resilient, purpose-driven cultures that are ready for the future.

Through our work as a project partner of digihub.li, the European Digital Innovation Hub for Liechtenstein, we support organizations in connecting strategy with purpose for measurable impact.

Association of British Cycling Coaches (ABCC)

ABCC Communications and Media | ABCC Coach

Ludwig-Maximilians-Universität München

Associate Lecturer @Department for Media and Communications (ifkw)

digihub.li

«𝐖𝐞 𝐦𝐚𝐤𝐞 𝐋𝐢𝐞𝐜𝐡𝐭𝐞𝐧𝐬𝐭𝐞𝐢𝐧 𝐚 𝐥𝐢𝐠𝐡𝐭𝐡𝐨𝐮𝐬𝐞 𝐟𝐨𝐫 𝐩𝐨𝐬𝐢𝐭𝐢𝐯𝐞 𝐝𝐢𝐠𝐢𝐭𝐚𝐥 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 𝐜𝐨𝐧𝐧𝐞𝐜𝐭𝐬 𝐡𝐮𝐦𝐚𝐧𝐬 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐭𝐫𝐮𝐬𝐭, 𝐭𝐫𝐚𝐧𝐬𝐩𝐚𝐫𝐞𝐧𝐜𝐲, 𝐚𝐧𝐝 𝐩𝐮𝐫𝐩𝐨𝐬𝐞-𝐝𝐫𝐢𝐯𝐞𝐧 𝐚𝐜𝐭𝐢𝐨𝐧.»

Co-Founder & Innovation Lead

ONE-X Purposeful Ecosystem

ONE-X Purposeful Ecosystem forms an open network of entrepreneurs who strengthen their mutual trust, communicate on an equal footing and use their energy to keep business and society in harmony. Within ONE-X we create an innovation space for corporate culture, which shows alternatives to "Business as Usual" and focuses on effectiveness and resilience.

Partner

Siemens

I have made a significant contribution to Siemens Financial Services, a leading global provider of B2B financial solutions in a growing position during the past years.

Key responsibilities 
•	Driving digital communications and implementing and agile working system for the global communication team
•	Planning and implementing corporate communications strategy so as to ‘mould’ a unique business brand and strategic development to drive change and position company in Europe, Asia and North America markets
•	Leading and managing the communications department in Munich plus a network communicators in North America and Asia 

Key achievements 
•	Empowering and developing staff from business start-up up to now utilizing a multitude of professional development opportunities so as to instil a high-performance culture 
•	Building strong rapport and collaboration with internal / external partners and key stakeholders to support ongoing projects and business operations
•	Conceptualizing and running thought leadership positioning programs in markets including financial services, energy, healthcare, industry and infrastructure. Received PR Report Award in the category of B2B communications  
•	Leveraging Social Media—Twitter, LinkedIn, Facebook—communications in B2B markets and pioneering lead management campaigns, outperforming benchmark competitors 
•	Running complex, cross-functional projects, covering marketing, business development and IT, to manage change, drive company recalibration and attain high employee engagement benchmarked against financial services competitors 
•	Management and moderation of press conferences, workshops, media days, road shows, town hall broadcasts and internal employee events

Head of Communications, Financial Services Division

Deutsche Börse

I was hired by one the largest exchange organisations worldwide which employed more than 4K personnel, responsible for Media, Public Relations, and Marketing Communications for global growth markets, delivering budget and agency management for international partners, among which the UK and US. 

Among my most notable contributions was the launch of “Vision and Money”, an international capital markets and investor relations magazine selling 35K copies worldwide, which constituted one of the few profitable company magazine at the time. 

I also administered communications projects and campaigns that addressed the requirements of capital market partners, e.g. to launch the stock exchange segment “Neuer Markt“ (“High-tech companies”).

Manager Public Relations

Genossenschaftsverband e.V.

Here, I offered communication consulting for member banks in the thematic areas of media relations, events, and annual reports. 

Key achievements: the launch of an annual report service for member banks and the addition of more than 15 new client banks; the development of a comprehensive inventory of external agencies and journalists to support design and editing services for the annual report service; the roll-out of media relations initiatives for member banks and the German Cooperative Federation.

Communications Consultant

AI innovation transforms businesses. Yet many struggle to harness its power effectively.

I help SMEs, financial services, and tech companies overcome daily business challenges, time constraints, and lack of AI support. My focus: finding real-world solutions that achieve growth and meaning at the same time.

My clients see an average 40% efficiency boost across projects, from marketing and communication to HR and admin tasks.

How do I achieve this? Through four key approaches:

1. Innovation projects: Step by step, we find the sweet spot of AI and ROI, and make your business ready for a future where humans and technology work hand in hand.
2. AI tools as a service: Cutting-edge technology delivered in an easy-to-use format, covering areas such as marketing, HR, compliance and admin.
3. AI Academy: Keynotes and practical skills training to empower your team.
4. Mastermind groups: Collaborative learning and problem-solving with industry peers.

With >20 years of experience helping businesses innovate, communicate and grow, I understand the unique challenges facing SMEs today. My straightforward, results-driven approach cuts through the noise, delivering clear value and measurable outcomes.

Ready to unlock the potential of AI for your business? 

Reach out to: hartmut@huebner.io

Hartmut Hübner, PhD

Microsoft Copilot ist gerade dabei, sich zu verändern. Aber nicht so, wie die meisten denken.

Letzte Woche: 3 Führungsworkshops, 3x die gleichen Erwartungen an Microsoft Copilot: Emails zusammenfassen. Dokumente durchsuchen. Mal eine Antwort draften.

Copilot als interne Suchmaschine. Nützlich. Aber nicht transformierend.

Dann habe ich den Teams Copilot Studio gezeigt.

Copilot Studio geht einen Schritt weiter. Du baust eigene Agents – spezialisierte 
KI-Begleiter, die:

➡️ Deine Abläufe verstehen
➡️ Mit deinen Tools sprechen (SharePoint, Teams, Outlook, APIs)
➡️ Aufgaben erledigen
➡️ Sich an Governance halten

Nicht mehr „Frag Copilot". Sondern „Baue einen digitalen Mitarbeiter für dein Team".

Ein Beispiel

Ein IT-Team hat einen Support-Agent gebaut – trainiert auf Policy-Docs, Email-Threads und Teams-Chats.

Vorher: Viele Stunden Support-Anfragen manuell beantwortet. 
Nachher: Agent beantwortet einfach Anfragen selbstständig. Das Team konzentriert sich auf die schwierigen Fälle.

Vier Agent-Typen, die ich sehe

1. Knowledge Hub 

Organisiert SharePoint, wendet Tags an, liefert Original-Quellen mit Zitat.

2. Meeting Facilitator 

Erstellt die Agenda, führt Live-Notizen, extrahiert Entscheidungen.

3. Autonomous Agent 

Wartet auf Events (Budget-Alert, monatliches Führungsmeeting) und reagiert autonom.

4. Multi-Agent Orchestration 

In Preview: Agents arbeiten zusammen – CRM-Daten → Word-Proposal → Outlook Follow-up.

Wo wir aktuell stehen

Die Technologie ist da. Wichtige Funktionen sind verfügbar.
Aber die meisten nutzen Copilot noch als Suchmaschine.

Warum?

- Das Setup innerhalb Microsoft ist nicht trivial (Graph-Anbindung, Admin-Setup)
- Die Kulturelle Bereitschaft fehlt
- Use Cases sind oft unklar

KI ist keine Tool-Einführung, sondern Transformation.

Die Frage ist nicht: „Kann KI das?" 

Die Frage ist: „Sind wir bereit, so zu arbeiten?"

♻️ Teile diesen Beitrag, um in deinem Netzwerk Innovation voranzubringen.
Und folge Hartmut Hübner, PhD, um informiert zu bleiben.

#KI #Führung #Copilot #Transformation #Leadership #mmindai

Empowering SMEs: AI-Driven Innovation, Simplified

Mit dem Microsoft Copilot in die Transformation gehen

Drei Geschäftsführer haben mich letzte Woche in Bern die gleiche Frage gestellt.

B Lab Switzerland hatte beim Swiss Impact Forum gerade die neuen Standards angekündigt. Jede B Corp braucht jetzt ein öffentliches Purpose Statement.

Die Frage: "Wie schreibe ich so ein Statement, ohne Wochen zu verlieren?"
Einer sagte: "Ich weiss, dass wir das brauchen. Aber ehrlich? Keine Ahnung, wo ich anfangen soll."

Verstehe ich.

Ein Purpose Statement ist nicht:

🚫 Ein Slogan für die Website
🚫 Eine Vision für die Investoren
🚫 Ein Marketingtext

Es ist die Antwort auf: Warum existieren wir wirklich?

Und B Lab meint es ernst. Das Statement muss: 

✓ Eine positive Wirkung auf Gesellschaft oder Umwelt beschreiben 
✓ Geschäftsrelevant sein und in die Strategie integriert 
✓ Vom höchsten Gremium genehmigt sein 
✓ Öffentlich zugänglich sein

Klingt nach Wochen Arbeit, oder?

Wir haben bei Meet Your Purpose und MMIND.ai einen Prozess entwickelt, der genau das Problem löst.

Ein Vormittag mit deiner Geschäftsführung.

Wir moderieren. Der AI Purpose Explorer gibt Impulse. Ihr reflektiert gemeinsam durch den Ikigai-Prozess:

Was liebt ihr? Worin seid ihr gut? Was braucht die Welt? Wofür werdet ihr bezahlt?

Am Ende des Vormittags steht euer Purpose Statement.

Die KI ist nicht der Ersatz für euer Denken. Sie ist der Beschleuniger.

Sie stellt die Fragen, die sonst in endlosen Diskussionen untergehen. Sie strukturiert und macht kritische Punkte besprechbar. Sie bringt eine neue Perspektive ein und macht sichtbar, was ihr bereits lebt.

Ein Teilnehmer aus dem letzten Workshop: "Ich dachte, wir brauchen drei Monate dafür. Nach einem Vormittag hatten wir ein Statement, hinter dem das ganze Team steht."

Der Unterschied zwischen drei Monaten und einem Vormittag?

KI-Begleitung + moderierte Reflexion + die richtigen Menschen im Raum.

Wenn du:
- B Corp bist und den neuen Standard erfüllen musst
- Als Geschäftsführung Klarheit über euren Purpose schaffen willst
- Den Prozess nicht in die Länge ziehen willst

Dann gibt's zwei Wege:

Weg 1: Buche einen KI-beschleunigten, moderierten Workshop mit deiner Geschäftsführung. Ein Vormittag. Am Ende habt ihr euer Statement.

Weg 2: Do It Yourself mit dem AI Purpose Explorer, den wir in einer Gratis-Version zur Verfügung stellen: https://my.purpose.li/ 

Purpose vor Tools. Immer.
Aber wenn Tools helfen, Purpose schneller zu klären – dann nutzen wir sie.

♻️ Teile diesen Beitrag, um deinem Netzwerk zu mehr Wachstum zu verhelfen.
Und folge Hartmut Hübner, PhD, um informiert zu bleiben.

8 Teilnehmer, 8 völlig unterschiedliche Projekte – und EINE KI-Methode, die für alle funktioniert.

Letzte Woche im Bildungszentrum stein egerta - bildet in Schaan, 🇱🇮.

Projektmanagement 4.0 mit KI.

Zwei Abende. Acht Menschen mit komplett unterschiedlichen Vorhaben:

→ Grosses Gemeindejubiläum planen
→ Weihnachtsferien in Südkorea organisieren
→ Maschinenteil für KMU-Produktion entwickeln
→ Natursport-Projekt im Team aufsetzen
→ Hilfsprogramm für junge Menschen in Liechtenstein starten

Was sie alle brauchten: Struktur. Klarheit. Einen Prozess, der trägt.

---

Wir haben Design Thinking mit einem Custom GPT kombiniert.

Double Diamond als Framework.
ChatGPT als Coach, der durch die einzelnen Schritte führt.

Am ersten Abend ging es um das Öffnen und Verstehen:

→ Was ist die Projektidee und das -ziel?
→ Wer sind die Stakeholder?
→ Warum ist dieses Projekt wichtig – und was wollen wir wirklich erreichen?

Auf dem Miro Board (siehe Bild) haben wir jede Projektidee durch den ersten Diamond (Discover/Define) geführt.

Von der ersten Idee bis zur klaren Problemstellung.

---

In der Mitte kam der technische Teil.

Wir haben einen Custom GPT gebaut.

Keinen generischen ChatGPT.
Sondern einen Projektmanagement-Coach mit Persönlichkeit.

Instructions: Unterhaltsam. Kreativ. Herausfordernd.
Knowledge Base: Open Strategy & Design Thinking Frameworks + projektspezifische Infos.
Canvas-Funktion: Jeden Schritt direkt formatiert und teilbar.

Die spannende Erfahrung:

Den GPT nicht einfach nur zusammenfassen lassen.
Sondern ihn herausfordern und innovativ werden lassen. Nachfragen. Neue Aspekte herauskitzeln.

Die neuen Reasoning-Modelle (in diesem Fall: GPT-5) nehmen die Herausforderung an.

---

Neben der Arbeit am Miro Board und digitalem KI-Coach kamen der persönliche Austausch und individuelle Kreativität nicht zu kurz.

Ein Teilnehmer hat seinen Canvas-Output noch während des Trainings an sein Team geschickt. 

Nicht fürs Feedback. Sondern direkt als Projektimpuls. 

--- 

Am Donnerstag kommt Teil 2. 

Dann geht es in den zweiten Diamond: 

→ Lösungsvielfalt aufzeigen 
→ Prototypen bauen 
→ Projektplan als Ergebnis 

Vom Problem zur konkreten Umsetzung. 

--- 

Was sich zunehmend zeigt: KI ist nicht mehr nur für Routine-Tasks. Sie wird zum Sparringspartner für kreative Prozesse. 

Das macht Projektmanagement nicht nur effizienter. 
Es bereitet Freude. 

♻️ Teile diesen Beitrag, um dein Netzwerk dabei zu unterstützen, Innovationen voranzutreiben. Und folge Hartmut Hübner, PhD, um informiert zu bleiben.

Philip E.C. Schädler, Nadine Altenöder, Gerhard Kieber, Gaston Jehle, Jill Vogt, Corinne Vogt, Jonas Vogt, Phi Yen Oehri, Zeno John, MMIND.ai

#KI #KMU #Projektmanagement #DesignThinking #CustomGPT #Liechtenstein #SteinEgerta #Innovation #KMUDigitalisierung

Wir optimieren unsere Maschinen bis zur Perfektion – aber führen wir unsere Menschen immer noch wie Roboter?

Diese Frage von Benjamin B. Bargetzi auf dem Digitaltag Vaduz hat gesessen.

Michael Reinhardt, Phi Yen Oehri, Anna Maciak, Géraldine D'Atria, Fabian Martin Sude und ich waren mit Meet Your Purpose und MMIND.ai am Stand des digihub.li vor Ort. Unser Thema: Wo trifft Technologie auf menschlichen Sinn?

Im Gepäck: Unser brandneuer „Culture Compass Analyst“ (Google Gemini). Wir füttern dieses Tool gerade mit Daten von rund 600 Unternehmen aus DACH und NL. Es erkennt Muster in Werten, Skills und Kultur-Metaphern.

In Vaduz haben wir erstmals die rohen Ergebnisse diskutiert. Was wir sehen, ist ein Spannungsfeld – ich nenne es die "Menschlichkeits-Lücke".

Hier ein Einblick in den Vergleich Schweiz vs. Deutschland:

1. Schweiz: Der Wunsch nach Schutz & Harmonie 🇨🇭 

Zwischen Wunsch (Gemeinschaft) und Realität klafft eine Lücke:

– Health und Happiness sind persönlich extrem wichtig, im Arbeitsalltag aber kaum präsent (nur ca. 4-5%).

– Das Bild: Metaphern wie "A comfortable bubble" oder "A well tended garden" dominieren. Man sucht die "Clan-Kultur" (gegenseitige Pflege), findet aber oft isolierte Strukturen.

2. Deutschland: Tradition vs. Aufbruch 🇩🇪 

Hier hat der Frust eine andere Farbe: Es fehlt der Mut zur Veränderung.

– Deutsche Arbeitnehmer vermissen vor allem Mut (Courage) und Bescheidenheit (Humility). Der "Gap-Score" bei Mut ist signifikant höher als in der Schweiz.

– Das Bild: Firmen werden oft als "Traditional family home looking to adapt" gesehen. Das Haus steht (noch), aber die Hierarchien bremsen. Der Transformationsschmerz ist greifbar.

Bargetzis Keynote hat diese Daten präzise bestätigt:

1. Kritisch denken ist der neue Code: In einer Welt voller KI-Antworten ist die Fähigkeit, die richtige Frage zu stellen, die wichtigste Währung.

2. Menschlichkeit ist nicht kopierbar: Das Unperfekte, Chaotische, Kreative – das ist unsere Nische in den "letzten 4%". KI kann Perfektion, wir können Originalität.

3. Führung braucht Freiraum: Wer micromanagt, degradiert Menschen zu schlechten Robotern. Echte Innovation entsteht nur in Autonomie ("im eigenen kleinen Garten").

4. Neuroplastizität als Skill: Unser Gehirn ist bis ins hohe Alter formbar. "Haben wir schon immer so gemacht" ist neurologisch gesehen Faulheit.

5. Purpose schlägt Prozess: Wenn die KI die Prozesse übernimmt, bleibt für uns das Warum. Ohne Purpose verlieren wir im digitalen Sturm die Orientierung.

Viele nicken bei solchen Sätzen – und verfallen morgen in alte Muster.

Mein Rat: Warte nicht auf den "Wandel von oben". Fang an, deinen eigenen "Garten" zu bestellen. Gib deinem Team Autonomie, fördere kritisches Denken.
Willst du wissen, wo dein Team kulturell wirklich steht?

Wir verlosen einen kostenlosen Culture-Workshop auf Basis unserer neuen Daten.

👉 Hier teilnehmen: https://lnkd.in/duip-XJc

#Digitaltag #Leadership #AI #Culture #NewWork #Purpose

63% of companies fail at the skills gap.
But the skills gap isn't the problem.

Last week, a CEO told me: "We depoliticized our leadership team with AI. For the first time in years, we get inputs that don't come from personal agendas."

Another said: "We now have transparency. Everyone sees what others are doing. It's uncomfortable at first, but it forces us to be honest."

68% of boardrooms report tensions from AI introduction.

Sounds bad? It's not. It's proof that transformation is beginning.

Here's what's happening:

The unexpected heroes: CFOs are leading the AI revolution. Not tech visionaries. Why? Because CFOs understand BOTH data AND people.

2024: 53% cited C-Suite as AI driver
2025: 81% cite C-Suite as AI driver

The pattern: We optimize what we see, not what works.

A hockey team thought ice cooling = biggest CO2 driver.

Data showed: ice = 3%, fans = 73%.

The formula: Data + Emotion = Transformation

37% success without AI strategy.
80% success with strategy.

The question isn't "Should we?"
It's "Can we afford not to?"

Because while others stare at numbers, winners understand: Data shows the way. Emotions walk it.

♻️ Repost to help your network accelerate transformation.
And follow Hartmut Hübner, PhD for more insights on AI transformation and cultural development.

Sources:
WEF Global Risks Report 2025: https://lnkd.in/dwpFVVFv
Future of Jobs Report 2025: https://lnkd.in/dBEBKyA7
HBR Pioneering Boards: https://lnkd.in/dEF_hsSK

#AI #Transformation #Leadership #SME #Culture

This week, one red thread ran through all my conversations: facts alone move little – momentum only emerges when data meets meaning. This is precisely

Open article: Data + Emotion = Transformation: Why 63% of Companies Fail at the Skills Gap – And How You Close It by Hartmut Hübner, PhD • 3 min read

Data + Emotion = Transformation: Why 63% of Companies Fail at the Skills Gap – And How You Close It

Many SMEs jump straight to technology or organisational charts, but skip the most critical question: Why are we doing this?

In this week’s Flow Factor newsletter, I share insights from the Swiss Impact Forum, our AI-driven workshops and the Digitalisation Guide for SMEs. From L’Occitane’s nature-positive purpose to Opaline’s reusable bottle initiative, and from design thinking sessions to new B‑Corp standards that require a public purpose statement – one message is clear: Purpose comes first.

We outline seven steps that start with clarifying purpose and end with anchoring change, explain how AI can help at each stage, and show why investing in sustainability pays off (double digit % market value growth over the last decade):

1️⃣ Clarify Purpose: Identify why the project matters and for whom.

2️⃣ Capture the Current State: Visualise processes and challenges.

3️⃣ Identify Potentials: Find bottlenecks and pain points.

4️⃣ Get Inspiration: Learn from other companies and AI tools.

5️⃣ Define the Desired State: Envision the ideal process without constraints.

6️⃣ Derive Measures: Select technologies that fit the purpose.

7️⃣ Anchor: Train, communicate and embed the change.

Curious how purpose, stakeholder governance and AI fit together? 

Read the full newsletter below 👇 

♻️ Repost to help your network accelerate growth.
And follow Hartmut Hübner, PhD for more.

#Purpose #Digitalization #Transformation #Bcorp #Governance #AI

This week I noticed the same pattern at four different events with four different groups: Organisations jump straight to the solution – picking a stru

Open article: From Problem to Solution – Why Purpose is (also) the Key to Successful Digitalisation by Hartmut Hübner, PhD • 5 min read

From Problem to Solution – Why Purpose is (also) the Key to Successful Digitalisation

Custom GPTs from OpenAI. Gems from Google. Projects from Claude. I spent three months testing all three platforms side by side.

Not in theory.
In real work with SMEs.

Same use case for all three: A project management coach that travels with you.

You're in a meeting. Pull out your phone. Record the discussion. Let the AI structure it. Send the output to your team.

Simple, right?

Here's what I learned.

The question isn't "which one is best?"

The question is: "What do you actually need it to do?"

Because the differences matter in daily use.

1️⃣ Audio Functionality:

All three have seamless audio integration and allow both conversational modes and recordings with transcription. Hands down.

Voice mode works seamlessly on mobile. You can have a real conversation. It understands context. It remembers what you said two minutes ago.

Custom GPTs? With Whisper integration in GPT-5, it is best for German and Swiss dialects.
Gems? Fast, reliable in English.
Claude Projects? Likewise, felt a bit tricky in German.

If you want to use AI in meetings or on the go – pick Custom GPTs for local language, Gems and Claude definitely for English.

2️⃣ Mobile Experience:

Gems surprised me here.

Google's mobile app is fast. Really fast.
And if you live in the Google Workspace (Drive, Docs, Gmail), the integration is brilliant.

OpenAI and Claude have great mobile apps, but Custom GPTs and Projects are a click further away than Gems.

3️⃣ Knowledge Base:

You can create a Gem, connect it to your Drive, and it has context from your documents without manually uploading anything.

In Custom GPTs and Claude Projects, you can build a proper knowledge base, too.

In Claude, there seemed to be a pretty limited capacity for these – lean text files preferred.

The knowledge base is an underused feature. 

Upload frameworks, processes, specific info. Answers will be grounded in that context.

4️⃣ Connectors:

Google wins this one. Gems + Google Workspace = seamless.

Create a Gem, give it access to your Drive and Docs, and it can pull information from across your workspace.

Custom GPTs can connect to some tools, but they are not reliable yet (eg. Outlook).

Claude Projects? Appears more desktop-focused, limited integrations on mobile.

Choose Gems if:
→ You live in Google Workspace
→ You need fast, integrated access to your Drive
→ You are a mobile-first user

Watch out for the Gemini Android Auto integration, as this will bring AI to your display: https://lnkd.in/dZDTngN7

Choose Custom GPTs if:
→ You need a mobile-first AI coach
→ You take lots of meeting notes on the go

Choose Claude Projects if:
→ You do deep analytical work
→ Smartphone is not your primary device.

The tool doesn't matter.
What you need it to do matters.

♻️ Repost to help your network accelerate growth.
And follow Hartmut Hübner, PhD for more.

#AI #CustomGPT #Gemini #Claude #mmindai #Productivity

The tools doesn't matter. What you need it to do matters.

Die meisten KMU behandeln KI wie ein Tool.
Ein weiteres Programm. Noch eine Lizenz. Etwas, das "die IT" einführt.

Aber KI ist keine Software. Es ist eine Transformation.

Die Realität der meisten Geschäftsführer:

- Tief im Hamsterrad. Keine Zeit für Tiefgang mit KI.
- Mal schnell ChatGPT abfragen – aber die Qualität stimmt oft nicht.
- Datenschutz-Bedenken. Unsicherheit.

Und dann kommen Berater. Mit Druck. Mit Maturity-Modellen. Mit "Ihr seid Stufe 2, müsst aber Stufe 4 sein."

Das verschlimmert alles.

Ich sehe es anders:

- Die Situation ist verständlich. 
- Ein Startpunkt, von dem aus wir arbeiten können.

Bei MMIND.ai machen wir drei Dinge anders:

✔️ Wir klären auf statt Druck zu machen.
✔️ Wir zeigen Möglichkeiten statt Stufen.
✔️ Wir beginnen konkret – bei dir persönlich.
✔️ Wir nutzen unsere Tools selbst. Jeden Tag. Das überzeugt mehr als jede Präsentation.
✔️ Wir bleiben tagesaktuell. Das Wissen geben wir weiter.

Erfolgreiche KI-Transformation folgt keiner Stufenleiter.

Sie bewegt sich in einem Dreieck aus drei Kräften:

1️⃣ DATEN – Strukturiert, zugänglich, vertrauenswürdig
2️⃣ KULTUR – Neugierig statt ängstlich, beweglich statt starr
3️⃣ TECHNOLOGIE – Tools, die wirklich verwendet werden

Diese drei wirken gleichzeitig. Zirkulär. Nicht linear.

Du musst alle drei gleichzeitig bewegen. In kleinen Schritten.

Und es hört nie auf. Es ist kein Projekt. Es ist eine Daueraufgabe.

Der zweite Rahmen: PERSÖNLICH → TEAM → SYSTEM

Wir beginnen mit DEINER Arbeit. Dein Kalender. Deine Strategien. Deine Entscheidungen.

Wir lösen Bedenken auf: 

✔️ Datenschutz? In Europa. 
✔️ Qualität? RAG statt Halluzinationen.
✔️ 10+ Stunden pro Woche gewonnen. Schnell.

Du erlebst die Erleichterung selbst.

Dann skaliert es. Und dabei bewegen sich immer alle drei: Daten, Kultur, Technologie.

Painkiller first. Dann Systemik.

Mit Klarheit statt Druck.

Mit Erleichterung statt Überforderung.

Mit echten Hebeln.

Die Frage ist nicht: "Auf welcher Stufe stehen wir?"

Sondern: "Bist du bereit, bei dir selbst anzufangen?"

Melde dich an zum nächsten KI-Café am 10. Dezember, um den Spirit live zu erleben:
https://lnkd.in/dT4JCZVZ

KI-Transformation braucht Leichtigkeit. So geht's.

Gestern Abend an der Universität Liechtenstein haben wir einen Mythos aufgelöst.

"KI-Antworten sind unzuverlässig. Man hat wenig Einfluss. Die Technologie ist unsicher."

Das hörten wir zu Beginn des Praxis-Moduls "Digitale Verantwortung". Acht Teilnehmer/innen, verschiedene Branchen, alle mit der Frage: Wie kann ich KI verantwortungsvoll einsetzen, wenn ich die Kontrolle nicht habe?

Drei Stunden später war das Feedback: Du hast mehr Kontrolle, als du denkst. Wenn du weisst, wo die Hebel sind.

Wir haben zwei Custom GPTs gebaut. Live. Als Deep Dive in verantwortungsvolle KI-Nutzung.

1️⃣ AI Governance Coach

Ein Custom GPT, der Unternehmen hilft, klare Regeln für den KI-Einsatz zu entwickeln. Er kennt den EU AI Act, OECD-Prinzipien und UNESCO-Empfehlungen. Aber er erklärt sie nicht nur – er führt dich durch einen Prozess, bei dem am Ende ein massgeschneiderter Verhaltenskodex steht.

Was dabei passiert ist genau das, was viele für unmöglich halten: Du kontrollierst, was die KI weiss und wie sie antwortet.

Durch Prompting definierst du die Rolle. Durch die Knowledge Base bestimmst du, auf welchen Quellen die Antworten basieren. Durch Sicherheitseinstellungen legst du fest, was die KI darf und was nicht. Web-Suche? Kannst du ausschalten. Halluzinationen? Minimierst du durch verbindliche Wissensbasis.

Die Canvas-Funktion macht das Ergebnis sofort nutzbar. Formatiertes Dokument, direkt teilbar. Als PDF. In drei Minuten.

2️⃣ CSR Risk Compass

Der zweite Custom GPT bewertet Lieferanten auf soziale und ökologische Risiken. Mit ISO 26000, UN Global Compact, OECD-Leitlinien als Fundament.

Auch hier: Vollständige Kontrolle über die Wissensbasis. Die KI antwortet nicht mit Google-Informationen, sondern mit den Standards, die du ihr gibst. Kein Rätselraten. Sondern Antworten, die auf verifizierten Quellen basieren.

Verantwortungsvolle KI-Nutzung ist kein Widerspruch. Es ist eine Gestaltungsfrage.

Du entscheidest:
→ Welche Quellen die KI nutzt (Knowledge Base)
→ Wie sie antwortet (Prompting & Instructions)
→ Was sie darf (Sicherheitseinstellungen)
→ Welche Funktionen aktiv sind (Web-Suche ja/nein)

Wir haben auch einen Google AI Studio Prototyp für Nachhaltigkeitsreports besprochen. Ein Liechtensteiner Bau-KMU hat damit in Minuten einen Bericht auf Knopfdruck erstellt. Manuell: viele Tage.

Heute bin ich am Swiss Impact Forum in Bern. B Corps, Tech for Good, die neuen B Lab Standards.

Das Thema von gestern Abend zieht sich durch: KI ersetzt Verantwortung nicht. Sie macht Verantwortung messbar. Und was messbar ist, wird steuerbar.

♻️ Teile diesen Beitrag, um dein Netzwerk dabei zu unterstützen, Innovationen voranzutreiben. Und folge Hartmut Hübner, PhD, um informiert zu bleiben.

#KI #KMU #Nachhaltigkeit #Governance #Verantwortung

Wie Nachhaltigkeit von der Pflicht zur Strategie wird

What if the tension you're trying to eliminate is actually your greatest leadership asset?

I keep hearing three patterns from CEOs around AI:

"We need to change fast – but how do we get the company to actually move?"
"We need more courage – but how do we make bolder decisions when the stakes are this high?"
"We're stuck in routine – how do we break out when our leadership team has worked together for years?"

Here's what surprised me:

They're all trying to solve the tension (which is what most of us naturally want to do).

Fast OR careful. Bold OR safe. Routine OR chaos.

And solving it might be the problem.

The Academy of Management just published new research on this.

One line stopped me:
"Paradoxes interlock and ripple across levels. Actors' moves on one paradox cascade into others, intertwining in vicious or virtuous cycles."

Translation: when you "solve" one tension, you don't eliminate it. You just push it somewhere else.

The CEO who chooses speed over alignment creates new tension in execution.

The leader who picks courage over caution creates new tension in risk management.

Solving isn't always the solution.

So what is?

The research says: "Learning requires courage to act without certainty, to experiment and fail, to teach and learn amid contradiction."

I've started testing this with leadership teams.

Not resolving tensions. Creating rhythms to live in them.

A few agile principles that help:

Openness – welcome ideas from people AND AI
Respect timeboxes – "good and done" beats "perfect and never"
Problem first, solution second
One person speaks, everyone thinks along
Work visibly together – create transparency

These aren't rules. They're rhythms.

They let you stay decisive AND reflective. Fast AND thoughtful. Bold AND grounded.

That courage to act amid uncertainty?

It starts with staying in the tension, not solving it.

♻️ Repost to help your network accelerate growth.
And follow Hartmut Hübner, PhD for more.

Here is more timely research on the topic:
Paradoxical leadership: https://lnkd.in/deM7sKKK
How to manage paradox: https://lnkd.in/dJ8u4_g3

Learning Through Paradox: A Timely Management Skill

I noticed something in three conversations this week.
All with smart people. All facing different challenges.

But underneath? The same pattern.

A manufacturing executive: "Friday's tariff deal – 15% instead of 39%. But it's not final. Parliament. Referendum. How do I plan supply chain for 2026?"

A finance director: "Our exports dropped 14% in May. Pharma down 19%. Plus new regulations. How do we stay fast enough?"

An IT head: "Monday was the Cybersecurity Conference. Tuesday new models launched. Wednesday three clients asked about AI integration. How do I keep up?"

The real question behind all three:
"How do we realign daily when everything is fundamentally changing?"

Here's what I've been noticing:

We're still trying to make choices.

Fast OR careful.
This model OR that one.
Plan ahead OR react quickly.

But the world isn't waiting for our decision anymore.

New AI models drop weekly.
Trade negotiations change daily.
Regulations shift monthly.

"Either-or" doesn't work anymore.

Then I came across new research from the Academy of Management.

One line stopped me:

"When you 'solve' one tension, you don't eliminate it. You just push it somewhere else."

That's exactly what I'm seeing in organizations right now.

The CEO who chooses speed creates new tension in execution.
The leader who picks courage creates new tension in risk management.

Solving isn't always the solution.

So what is?

Three things I've learned in recent weeks:

1. Strategy isn't a 3-month project anymore

A client developed their 5-year strategy with AI as sparring partner.
Draft done in 2 weeks instead of 3 months.

But here's the shift: They now adjust it quarterly.

Not as weakness. As feature.

2. Test both new models

When Gemini 3 launched, one team asked: "Switch now? What about GPT-5.1?"

We tested both for 90 minutes.
Kept GPT for that use case.

But learned how Gemini works.

That's not paralysis. That's flexibility.

3. Start with yourself

Don't transform the organization first.
Start with your own way of working.

Analyze your calendar. 
Test Deep Research. 
Structure voice notes.

Several hours gained per week.

Then scale it.

The pattern I'm seeing:

It's not about choosing anymore.
It's about creating a rhythm to live in both.

The question isn't: "What do I decide?"

The question is: "What rhythm do I create to handle contradictory developments?"

Read the full story in the new Flow Factor newsletter 👇

And, if you're in the region 🇱🇮 and 🇨🇭 don't forget to register for the next MMIND.ai Café on December 10 in Schaan: https://lnkd.in/dT4JCZVZ

Together with Manuel Pfiffner (CEO sl.one AG), we'll show:

1️⃣ How to set up AI securely
2️⃣ How cloud solutions integrate into your data world
3️⃣ How to work with Microsoft Copilot Studio.

#AI #Transformation #Leadership #Paradox #Strategy #Flexibility

Three days last week. Three new topics: "Friday brought the tariff deal. 15% instead of 39%. But it's not finalized yet. Parliament. Referendum. How d

Open article: From Either-Or to Both-And – What AI Is Teaching Us Right Now by Hartmut Hübner, PhD • 3 min read

From Either-Or to Both-And – What AI Is Teaching Us Right Now

Letzte Woche habe ich in drei Workshops dieselbe Beschwerde gehört:
"Die neuen KI-Modelle sind schlechter geworden."
"ChatGPT liefert nur noch oberflächlichen Schrott."
"Man kann sich auf die Ergebnisse nicht verlassen."

Jedes Mal habe ich dasselbe gefragt: "Zeig mir deinen Prompt."

Und jedes Mal: Ganz kurzer Prompt, meist eine Frage. Keine Struktur. 
Erwartung: Perfekte Antwort.

Das hat bei ChatGPT 3.5 noch funktioniert. Die Modelle waren simpel.

Heute funktioniert es nicht mehr.

Nicht weil die Modelle schlechter wurden. Sondern weil sie anspruchsvoller sind.

So haben wir das Problem gelöst:

Die Führungskraft tippt: "Strategie für 2025 erstellen."

Das Ergebnis? Generischer Fluff. Austauschbare Inhalte, die auf jedes Unternehmen passen.

Dann haben wir umformuliert:

"Denk intensiv darüber nach und wäge drei strategische Optionen für 2025 gegeneinander ab. Berücksichtige: Marktdynamik in der Fertigungsindustrie, unsere begrenzten Ressourcen (20 Mitarbeiter, kein Vertriebsteam), und das Cashflow-Risiko im Q3."

Dasselbe Modell. Derselbe Mensch. Anderes Briefing.

Das Ergebnis war nicht wiedererkennbar.

Plötzlich konkret. Nutzbar. Relevant für genau diese Firma.

Der GF sagte: "Moment. Das ist ja wie mit einem echten Senior Consultant zu sprechen."

Was den Unterschied gemacht hat:

Erstens: Sag dem Modell, dass es nachdenken soll.

"Denk intensiv darüber nach" aktiviert den Thinking Mode. Das Modell nimmt sich Zeit. Prüft Alternativen.

Ohne diesen Trigger? Es liefert die wahrscheinlichste Antwort. Nicht die beste.

Zweitens: Gib Kontext, der fokussiert.

Nicht "Fass zusammen". Sondern "Identifiziere die drei kritischsten Risiken für unsere Liquidität im Q3. Basierend auf den Zahlen auf Seite 47-52."

Das Modell weiss, wo es hinschauen soll.

Drittens: Definiere die Rolle präzise.

Nicht "Agiere als Berater". Sondern "Du bist unser CFO mit 15 Jahren Fertigungserfahrung. Du kennst unsere Cashflow-Probleme. Keine generischen Empfehlungen."

Das Modell schneidet Floskeln weg.

Was in diesem Workshop passiert ist:

Fünf Leute haben ihre eigenen Prompts umgebaut. Mit Struktur. Mit Kontext.

Die Ergebnisse? Nicht wiederzuerkennen.

Ein Feedback war: "Ich dachte, das Modell ist schlechter geworden. Aber eigentlich bin ich nicht mitgewachsen."

Genau.

Die neuen Modelle sind wie hochqualifizierte Experten, Koryphäen. Sie können brillant sein.

Aber sie sind nicht telepathisch.

Sie brauchen ein gutes Briefing.

♻️ Teile diesen Beitrag, um anderen zu zeigen, wie sie aus ChatGPT mehr rausholen können. Und folge Hartmut Hübner, PhD, um informiert zu bleiben.

Zum Weiterlesen:
GPT 5.1 Prompting Guide: https://lnkd.in/d5EsU_iV
Prompting Guide für Gemini 3: https://lnkd.in/dsVdB78f
Microsoft Model Choice Guide: https://lnkd.in/d2h_RvMN

I spent last week testing GPT 5.1 with real business queries.

One pattern stopped me cold.

I asked the model to analyze a manufacturing cost problem. Clear question. All the data provided.

Perfect answer.

Then I ran the same question again. But this time I added an extra sentence of background context. Relevant, but not critical to the analysis.

The answer quality dropped at least by 40%.

Same question. Same model. Same data. Just one extra sentence.

I thought it was a fluke. So I tested it a dozen more times with different queries.

Same pattern. Add noise? Performance collapses.

This is what researchers call the "Fragility Paradox."

These models score 92% on PhD-level scientific benchmarks. They can reason through complex logic chains. They're genuinely brilliant.

But add one unnecessary clause to your prompt? They stumble.

Here's why this matters for your business:

You can't assume the model will filter out irrelevant information.

It won't.

The model isn't reasoning like a human consultant who knows what to ignore. It's pattern-matching at massive scale. And extra context confuses the pattern.

The irony? We finally have models that can process million-token contexts.

But the best results come from precision, not volume.

Three things changed in how I design prompts:

Every sentence I add is now a risk. Cleaner prompts win.

High-stakes decisions need verification checkpoints. I tell the model to validate its own logic at key steps.

The "throw everything at it" approach is dead.

Your competitive advantage isn't having access to Gemini 3 or GPT 5.1.

It's knowing when to use 100 tokens instead of 10,000.

What's been your experience? Are you seeing the same brittleness?

Read more on:
Limitations of Mathematical Reasoning in Large Language Models: https://lnkd.in/dtjM2Z7V
Effective context engineering for AI agents: https://lnkd.in/d7W5d4z3
Prompt Engineering 3.0 and the Strategic Deployment of Gemini 3, GPT 5.1, and Copilot Agents: https://lnkd.in/d-v3w4ys

#AI #GPT5 #MachineLearning #Prompting #Business #Strategy

"We thought distributed leadership means less leadership. We were completely wrong. It requires MORE leadership from everyone."

That hit me Yesterday at yesterday's Swiss Impact Forum, the big community event by B Lab Switzerland. 

The speaker described the two-year transformation journey at Alternative Bank Schweiz (ABS). 

And it's very similar to what we're seeing with AI at MMIND.ai. "Self-organization means no bosses." "AI means no managers needed."

Both are wrong in the same way.

Alternative Bank tried something different. About 80 people. They spent two years moving from hierarchy to distributed leadership inspired by sociocracy.

What made it work wasn't eliminating structure. It was clarifying it.

They documented every role. Not just job descriptions. Actual clarity: What's the purpose of this role? What decisions can this person make? What can others expect from them?

Most organizations have vague responsibilities. Few have this precision.

They also developed working principles. Not three-page mission statements. Four simple principles:

Sustainability before profit.
Trust before participation.
Honesty before harmony.
Impact before growth.

These aren't abstract values. They're decision tools. When someone orders coffee for the cafeteria, "sustainability before profit" means choosing expensive sustainable beans. Clear. Actionable. No committee needed.

The speaker was emphatic about one thing: "You need someone with final accountability. Always."

This connects directly to what we built on Tuesday night with SMEs – the AI Governance Coach.

Not rules. Principles. Decision frameworks that work in the moment.

The bank's showed something else: "Structure follows process, follows strategy, follows purpose."

Most organizations start with structure. They draw org charts. They implement tools.

That's backwards.

You start with purpose. Then strategy. Then process. Structure comes last.

It's the same pattern with AI. Organizations that start with "let's implement ChatGPT" struggle. Organizations that start with "what problem matters and why" succeed.

The speaker also said: "You can only transform as far as the least developed member of your leadership team has evolved."

We've seen it, too. If your leader believes AI is uncontrollable, your entire organization hits that ceiling.

Therefore, it's also helpful in any transformation to start with myth-busting. Not with tools.

Technology doesn't replace responsibility. It makes responsibility transparent.

♻️ Repost to help your network accelerate growth.
And follow Hartmut Hübner, PhD for more.

#SwissImpactForum #FutureOfWork #AI #Governance #Leadership #Development

I spent last weeks mapping every major enterprise AI framework.
Microsoft's Cloud Adoption Framework. Google's maturity models. Atlassian's playbooks. EU AI Act guides.

Why? Every SME leader asks: "Where do we actually start?"

The frameworks are scattered. The jargon overwhelming. So I synthesized it.

Six actionable steps.

Step 1: Build a Business-First AI Strategy

Not a technology-first experiment.

Microsoft's AI maturity model is clear: business objectives before implementation.

Three questions that clarify everything:

Which repetitive tasks can we automate for speed and cost?
Where can predictive AI reduce errors and risk?
How can generative AI accelerate knowledge work?

Full framework: https://lnkd.in/ds2nABHR

Step 2: Establish a Scalable Data Foundation

You can't build skyscrapers on swampy ground.

Inadequate data structure is the biggest SME blocker.

Solution: Data Lakehouse architecture.

Unifies data lakes + warehouses
Eliminates silos
Creates single source of truth

Result: AI trains on consistent, reliable data. No hallucinations.

Technical breakdown: https://lnkd.in/djajt2hQ

Step 3: Launch Targeted Pilots and Upskill Teams

Pilots de-risk investment and solve the skills shortage.

Two-track approach:

Run small-scale pilots on high-impact use cases
Build internal AI champions through targeted upskilling

Atlassian's playbook: https://lnkd.in/dsC8PpgK

Step 4: Choose Your Technology Model

Smart SMEs use both paths:

SaaS (Copilot, Atlassian Intelligence): Quick wins. Immediate ROI.
PaaS (Azure, Vertex AI): Custom solutions. Competitive edge.

BMW example: IoT system on Azure analyzing 10,000+ vehicle signals in real-time.

Step 5: Operationalize with Governance by Design

See the EU AI Act is a differentiator, not a burden.

European customers value trust. Trust sells.

Core obligations for high-risk systems:

Formal risk management throughout lifecycle
High-quality, bias-free training data
Robust oversight, security, traceability

Summary: https://lnkd.in/dVFaJUBV
Governance = customer trust = market advantage.

Step 6: Leverage the Partner Ecosystem

You don't build this alone.

Cloud partners bridge skills gaps, accelerate implementation, ensure governance.

They transform theory into value-generating solutions.

Microsoft's European model: https://lnkd.in/dP4yR-n6

The Shift

From ad-hoc experiments to structured execution.

From "let's try AI" to "we're leading our market."

Want the complete implementation framework?

📥 Download the MMIND.ai take on SME Digitalization: https://lnkd.in/dqyJ37mh

Simplified checklists. Relevant frameworks. Ready to use.

Most executive teams start AI transformation in the wrong place.
They hand it to IT. Or assign a "digital lead." Or hire consultants to run pilots.

Then nothing scales.

Here's what I've learned from 50+ talks with SME leaders:

AI adoption works when it starts at the top. 

Not because executives are smarter. 

But because the organization watches what leadership does, not what they say.

Three reasons why executive teams must go first:

1. Your meetings are the highest-leverage use case

A Swiss manufacturing CEO told me last month: "We spend 12 hours a week in leadership meetings. Half of that is status updates and data gathering."

We built a Custom GPT trained on their processes, KPIs, and decision history.

Now their weekly leadership meeting prep takes 10 minutes instead of 2 hours.
The leadership team discusses outliers and strategic decisions. Not "Where do we stand?"

After 6 months: Meetings 30% shorter. 8 hours back per week for the leadership team.

That's the business case. Right there.

2. You can't delegate what you don't understand

One CEO asked me: "Should we use GPT 5.1 or Gemini 3 for our customer service team?"

I asked: "Have you tried both with a real customer query?"

He hadn't.

You can't guide AI investment if you've never written a structured prompt.
You can't evaluate vendors if you don't know the difference between RAG and fine-tuning.
You can't set governance frameworks if you haven't seen how these models actually behave.

Walk the talk means: Train your skills. Use them yourself first.

Then you know what you're talking about when you scale it.

3. You control the infrastructure decisions

AI doesn't run on local servers. Not productively.
It needs cloud infrastructure. Microsoft 365. Google Workspace. Atlassian.

That's not an IT decision. That's a strategic investment decision.

And it sits with you.

Without cloud foundation, AI is an experiment that never scales.
With cloud foundation, AI becomes operational infrastructure.

The sequence that works:

→ Cloud foundation (you approve the investment)
→ Leadership team uses AI in core meetings (you develop the skill)
→ Team sees it working (you model the behavior)
→ Organization adopts (because you went first)

In that order.

Not the other way around.

This week's Flow Factor breaks down the full framework:
→ Why cloud platforms are non-negotiable
 → Three prompting rules that changed executive decision-making
 → The 7 leadership meetings where AI has the biggest impact

Read the full guide: [Link to newsletter]

===

If you're in Liechtenstein or Switzerland:

Join us for AI Café on December 10.

Manuel Pfiffner (CEO, sl.one) shows why cloud is the AI foundation. We demonstrate Microsoft Copilot live in three executive use cases.

See it working. Ask questions. Take it back to your team.

→ Register here: https://lnkd.in/dTgYjaZ6

Artificial Intelligence is rapidly shifting from trend to competitive necessity. Yet most SMEs still hesitate with concrete implementation. High compl

Open article: Cloud First, Then AI – Why Sequence Matters by Hartmut Hübner, PhD • 4 min read

Cloud First, Then AI – Why Sequence Matters

68% der C-Suites berichten: KI spaltet unser Unternehmen. Hier ist, warum das ein gutes Zeichen ist.

Letzte Woche sass ich mit einem CEO zusammen. Er erzählte mir: "Wir haben die Geschäftsleitung mit KI entpolitisiert. Zum ersten Mal seit Jahren bekommen wir Impulse, die nicht aus persönlichen Agenden kommen."

Das hat mich nicht überrascht. Denn während viele KMU noch überlegen, ob KI ein Thema fürs Board ist, läuft die Transformation bereits im Hintergrund.

2024 nannten nur 53% der IT-Leader die C-Suite als Treiber der KI-Adoption. Heute sind es 81%. Und 35% der grossen Unternehmen haben bis Ende 2025 einen Chief AI Officer – Tendenz steigend.

Die Vorreiter sind nicht die Tech-Visionäre. Es sind die CFOs.

Ja, richtig gelesen. Die traditionell konservativste Rolle im Board führt die KI-Revolution an.

Warum? Weil die Zahlen für sich sprechen:

➡️ Fanatics Betting & Gaming: AP-Workflows von 20 Stunden auf 2 Stunden reduziert
➡️ McKinsey UK: 21% schnellere M&A-Entscheidungen durch KI-Dashboards
➡️ Bain & McKinsey: 20%+ EBITDA-Verbesserung durch KI-Transformation

Und dann gibt es Geschichten wie diese: IHC aus den UAE – das grösste börsennotierte Unternehmen der Region – ernannte "Aiden Insight", einen virtuellen Menschen, als Board Observer. Er nimmt an Diskussionen teil, das Protokoll wird offiziell geführt.

Science Fiction? Nein. Business-Realität 2025.

Aber zurück zu den 68%, die von "Spannungen" berichten.

Ein CEO formulierte es so: "Wir haben jetzt Transparenz. Jeder sieht, was die anderen tun. Das ist anfangs unbequem, aber es zwingt uns, ehrlich zu werden."

Genau das ist der Punkt: KI im Boardroom ist kein Komfort-Tool. Es ist ein Wahrheits-Tool.

CEOs nutzen es für Echtzeit-Competitive Intelligence. CHROs für Bias-Erkennung im Recruiting. CROs für proaktives Risikomanagement. 

Und CFOs? Die automatisieren Forecasting, simulieren Szenarien und reduzieren Compliance-Aufwände auf einen Bruchteil.

Die unbequeme Wahrheit:

Nur 37% der Executives ohne formale KI-Strategie sagen, sie seien erfolgreich. Mit Strategie? 80%.

Die Frage ist nicht mehr: "Sollen wir KI ins Board bringen?"

Die Frage ist: "Können wir uns leisten, es nicht zu tun?"

♻️ Teile diesen Beitrag, um dein Netzwerk dabei zu unterstützen, Innovationen voranzutreiben. Und folge Hartmut Hübner, PhD, um informiert zu bleiben.

Zum Weiterlesen:
Deloitte Boardroom Study: https://lnkd.in/dVnCyk6v
McKinsey AI Workplace Report: https://lnkd.in/d_KQDZ2R
HBR Pioneering Boards: https://lnkd.in/dkTz6Bed

Mit KI im Führungsteam für Transparenz sorgen

In Minute 47 einer GL-Sitzung passierte etwas Unerwartetes.

Der Leiter Produktion eines Schweizer Industriebetriebs mit 100 Mitarbeitern stellte eine Frage zum Thema Lieferketten-Engpässe. Wir fütterten seine Frage in einen Custom GPT – trainiert auf die internen Prozesse, Lieferantenverträge und Produktionsdaten des Unternehmens.

Was normalerweise eine Woche Analyse durch den Einkauf gekostet hätte, war sofort da. Mit drei konkreten Optionen. Inklusive Kostenvergleich. Die GL entschied noch im selben Meeting.

Der CEO sagte später: "Das hat uns nicht nur Zeit gespart. Es hat uns gezwungen, anders zu denken."

Hier ist, was wir in den letzten Monaten bei KMU-Geschäftsleitungen gesehen haben:

KI funktioniert nicht als "Hintergrund-Tool". Sie funktioniert als zusätzliche Stimme im Raum. Eine Stimme, die sich traut, unbequeme Fragen zu stellen oder Denkmuster aufbricht.

Das Problem: Viele GL-Sitzungen laufen seit Jahren gleich ab. Gleiche Struktur. Ähnliche Themen.

Ein Custom GPT bringt Impulse und frische Ideen.

Die 7 Kern-Sitzungen, in denen KI den grössten Hebel hat:

1️⃣ Weekly 1:1 
Der Custom GPT bereitet jedes Gespräch vor: Offene Commitments, letzte Entscheidungen, kritische Themen. 
Resultat: Kein "Was haben wir letzte Woche besprochen?"

2️⃣ Führungsteam 
Meeting 10-Minuten KPI-Review automatisch vorbereitet. Die GL diskutiert nur noch Ausreisser und strategische Weichen. Nicht mehr: "Wo stehen wir eigentlich?"

3️⃣ Operating Review 
Der GPT markiert Anomalien in Produktionsdaten, Lagerbestand, Lieferterminen. Das Team fokussiert auf Massnahmen, nicht auf Datenbeschaffung.

4️⃣ Quartalsplanung (OKRs) 
Vorquartal wird automatisch ausgewertet. Die GL plant die nächsten 3 Monate.

5️⃣ Voice-of-Customer 
Kundenfeedback aus CRM, Support-Tickets, Verkaufsgesprächen wird gebündelt. Der GPT identifiziert Muster. Die GL sieht die Pain Points der Kunden.

6️⃣ VR Update 
Prägnante Unterlagen in 15 Minuten erstellt. Der GPT formuliert die Kernbotschaften. Die GL ergänzt, verfeinert, versendet.

7️⃣ All-Hands 
Das "Warum" von Veränderungen wird klar kommuniziert. Der GPT hilft, Entscheidungen verständlich zu machen. Q&A werden vorbereitet.

Wie das funktioniert:

Wir bauen einen Custom GPT mit Wissensbasis. Relevante Dokumente, Prozesse, Entscheidungshistorie.

Optional: Anbindung an Microsoft Copilot, Google Workspace oder Atlassian – je nach Reifegrad und Sicherheitsanforderungen.

Mobil nutzbar. Per Sprache oder Eingabe. Im Büro, Homeoffice, unterwegs.

Der Schweizer Industriebetrieb nach 6 Monaten:
Die GL gewinnt 8 Stunden pro Woche zurück. Meetings dauern 30% weniger lang. Entscheidungen sind dokumentiert und nachvollziehbar.

Das ist keine Science Fiction. Das ist heute verfügbar.

Zusammen mit dem KMU-HSG | Institut für KMU und Unternehmertum haben wir einen Leitfaden Digitalisierung entwickelt: Sicher, pragmatisch, ohne technische Hürden.

→ Hier downloaden: https://lnkd.in/dqyJ37mh

After analyzing insights from over 50 interviews with entrepreneurs, I've noticed something profound. A silent shift is happening in leadership teams everywhere: the role of the entrepreneur is being suffocated by the duties of the manager. 

The core mission, for many, has become blurred, leading to a single, powerful goal that resonates across industries: to be an entrepreneur again, not just a manager.

This erosion isn't a failure of vision; it's a consequence of today's business complexity. Leaders are drowning in the day-to-day operational "firefighting." 

Strategic foresight is being displaced by the relentless pressure of administrative tasks, endless meetings, and bureaucratic demands. 

This constant operational overload has become the single biggest barrier to the very innovation and growth that entrepreneurs are meant to drive.

When new solutions like AI are proposed, the hesitation isn't about the technology itself. 

The real fear, one that is rarely spoken aloud, is the loss of control. It’s the deep-seated concern that introducing AI will become another massive, energy-draining project that pulls focus from an already overstretched team. 

This fear is valid, which is why the right approach isn't about handing over the reins, but about augmenting your control.

The solution is not another IT project. It’s a paradigm shift: treating AI as a core leadership function. 

Imagine a "digital chief of staff" that integrates seamlessly into your existing workflows, not as a complex new system, but as a supportive partner. This isn't about feeding your strategy into a public model; it's a private, secure system with data sovereignty built-in, integrating directly into your existing Microsoft or Google environment. 

Imagine your weekly leadership meeting automatically prepped with a 10-minute KPI review, or your 1:1s concluding with auto-generated notes and tracked commitments. 

As Dr. Alexander Fust of the University of St. Gallen states, "Digitalization is not a question of company size—it is a question of attitude and approach." 

It reduces your operational load immediately because it works within the tools you already use—no disruptive IT overhaul required.

How much of your week is spent managing versus creating? 

At MMIND.ai, we help leadership teams win back 5-10 hours per week through a practical, guided process. 

The first step is a complimentary 30-minute 'Focus Call'—a brief strategic exchange to identify the biggest levers for reclaiming your time. 

Let's find your path back to being an entrepreneur. 

👉 Check in right here: https://lnkd.in/dqyJ37mh

#AI #Leadership #SME #Entrepreneurship #Digital #Transformation

taplio

The Battle

Adam Biddlecombe

Hartmut Hübner, PhD

Followers

Posts

Engagements

Likes

Performance ScoreAdam Biddlecombe's LinkedIn Performance

Performance ScoreHartmut Hübner, PhD's LinkedIn Performance

Ready to grow your LinkedIn presence?

taplio

The Battle

Adam Biddlecombe

Hartmut Hübner, PhD

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);}Adam Biddlecombe's LinkedIn Performance

Performance ScoreHartmut Hübner, PhD's LinkedIn Performance

Ready to grow your LinkedIn presence?

Performance ScoreAdam Biddlecombe's LinkedIn Performance