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Don't stay outdated.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Don't stay outdated.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 AI randomly, and that’s why you’re not improving fast.

You need a structured path that removes the guesswork.

Here are the 28 levels you should go through to go from zero to the best in the world in AI.

Level 0: 
AI Awareness: Learn what AI is and what it is not, and understand basic terminology.

Level 1: 
Digital Literacy: Master computer fundamentals, file systems, basic coding concepts, and internet research skills.

Level 2: 
Programming Foundations: Learn Python, control structures, functions, data types, and basic scripting.

Level 3: 
Math for AI Foundations: Understand linear algebra basics, probability fundamentals, and essential calculus concepts.

Level 4: 
Data Handling Basics: Learn data cleaning, preprocessing, visualisation, and working with datasets using Python libraries.

Level 5: 
Machine Learning Fundamentals: Understand supervised, unsupervised, and simple model training workflows with scikit-learn.

Level 6: 
Practical ML Application: Learn model evaluation, cross validation, feature engineering, and real-world ML pipelines.

Level 7: 
Deep Learning Foundations: Understand neural networks, backpropagation, activation functions, and basic model architectures.

Level 8: 
Applied Deep Learning: Learn to build and train CNNs, RNNs, LSTMs, and transformers and run experiments with PyTorch or TensorFlow.

Level 9: 
Natural Language Processing: Understand tokenisation, embeddings, sequence models, and transformer-based NLP workflows.

Level 10: 
Large Language Models: Basics Learn how LLMs function, including attention mechanisms, pretraining, fine-tuning, and inference concepts.

To know about the next levels,
Check the infographic below 👇 

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

Do you invest time in learning AI every day?
Comment below 👇 

♻️ Repost to help your network learn AI

You’re learning AI randomly, and that’s why you’re not improving fast.

You need a structured path that removes the guesswork.

Here are the 28 levels you should go through to go from zero to the best in the world in AI.

Level 0: 
AI Awareness: Learn what AI is and what it is not, and understand basic terminology.

Level 1: 
Digital Literacy: Master computer fundamentals, file systems, basic coding concepts, and internet research skills.

Level 2: 
Programming Foundations: Learn Python, control structures, functions, data types, and basic scripting.

Level 3: 
Math for AI Foundations: Understand linear algebra basics, probability fundamentals, and essential calculus concepts.

Level 4: 
Data Handling Basics: Learn data cleaning, preprocessing, visualisation, and working with datasets using Python libraries.

Level 5: 
Machine Learning Fundamentals: Understand supervised, unsupervised, and simple model training workflows with scikit-learn.

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

I help enterprises cut through AI hype and turn it into business value: measurable improvements in cash flow, risk reduction, and stronger customer outcomes.

For 15+ years I’ve led operational change across Fortune 500s and scaleups in Quote-to-Cash, Customer Service, Shared Services, and Data Governance.

Companies call me when processes stall, teams drown in manual workarounds, and leadership needs outcomes they can trust without losing compliance or control. My role is to simplify complexity, automate where it creates value, and ensure governance and results go hand in hand.

I believe AI isn’t just about faster processes. It’s about shifting human effort to what matters: customer outcomes, financial performance, and strategic decision-making.

My background blends Lean Six Sigma discipline with applied AI, RPA, and operational analytics. I’ve delivered multi-region programs that improved cash flow, service levels, and working capital. Every project has been grounded in governance and compliance.

Core focus areas
• AI-enabled Quote-to-Cash optimization
• Governance-ready automation and compliance
• SSC → IIntelligence Hub operating models
• KPI-driven AI strategy

Currently completing an MSc in Applied AI, I take on executive and fractional transformation roles where accountability and delivery matter more than theory.

If you’re looking to modernize workflows, scale AI responsibly, and turn transformation into lasting business value, let’s connect.

Joanna Miler

Over the years, I’ve joined many transformation meetings that started the same way – long lists of tools, vendors, modules, and integrations.

And every time, I found myself asking the same question:

What are we really building: a system or a setup?

In most transformation discussions, the focus drifts quickly toward tools.

Which vendor to choose? Which module to deploy?

But the real foundation, the design logic that defines how people, data, and processes connect, often gets overlooked.

Here’s the reality: Most modern platforms share the same core ingredients.

1/. Machine learning for pattern recognition.
2/. OCR for document extraction.
3/. RPA for reconciliation.

Different logos. Same chemistry.

What separates success from stagnation isn’t the brand name.

It’s how well the architecture aligns with business goals, ensuring traceability, scalability, explainability, and compliance.

Transformation should begin with logic, not licensing.

Define the data flows, governance model, and control points that serve your purpose.

Once that foundation is clear, choosing a platform becomes a strategic step, matching the vendor to the architecture, not the other way around.

Think of it like pharmacology.

The packaging doesn’t heal, the active ingredient does.

In transformation, that active ingredient is architectural coherence.

It decides how components integrate, how data is governed, and how accountability is maintained.

Too many programs rush into deployment or follow brand familiarity. That’s when ROI fades, integration breaks, and ownership blurs.

Security becomes symbolic rather than structural.
Real transformation rests on disciplined architecture —

Traceable processes.
Auditable decisions.
Curated data pools.
Debiased models.

And alignment with frameworks like the EU AI Act or ISO/IEC 42001, or the growing demand for AI sovereignty.

Security, scalability, and sustainability are not optional add-ons.
They are architectural outcomes.

When architecture leads, tools stay interchangeable.

Teams adapt faster. Compliance becomes a catalyst for innovation, not a constraint.

Transformation success depends on one thing: the blueprint beneath the brand.

When your organisation begins its next transformation, what will you design first, the interface or the architecture?

AI Transformation Executive | Quote-to-Cash, Customer Operations & Data Strategy | Driving Scalable, Compliant & Outcome-Focused Change Across Enterprises

You can have 98% accuracy and still miss the KPI that actually matters.

Because most AI training pipelines stop at prediction, not performance.

Think about it.

We train models to classify, segment, detect...but rarely does it influence the levers that impact business outcomes.

That’s where the real gap lies, between data science success and business success.

The Core Problem

Teams define metrics like:

🔻 Precision
🔻 Recall
🔻 F1 Score

…but the CEO is looking for:

👉 Faster collections
👉 Higher conversion
👉 Lower churn
👉 Better NPS

Different languages.
Same data.
Zero alignment.

The Fix: Bridge AI Training to KPIs

1️⃣ Start from the business goal, not the dataset.

Ask: “What are we trying to improve?” before “What can we predict?”

2️⃣ Map every model metric to a business outcome.

Precision ↑ → fewer false escalations → lower cost per collection.

3️⃣ Close the loop.

Feed real business results back into model retraining.

4️⃣ Make explainability non-negotiable.

AI should show why it made a call, not just what it predicted.

When you align AI metrics with KPIs, data science stops being a lab exercise and becomes a growth engine.

Train for impact, not just intelligence.

Because models don’t create value, outcomes do.

If you’re building AI for real-world business performance, not just dashboards, let’s talk.

Every transformation looks flawless on a slide,

Until the human factor starts asking questions.

In large-scale transformation projects, we spend months mapping systems, workflows, and integrations.

Yet, when the architecture is finally ready, the rollout faces resistance, not because of poor design, but because people process change differently.

We’ve seen perfectly built systems slow down because teams weren’t emotionally ready for new ways of working.

The blueprint explained how things would work: not how humans would adapt.

The human side of transformation doesn’t fit neatly into boxes and arrows.

But it determines whether those boxes ever connect in real life.

Here are the key dimensions we often miss while focusing on system design:

1️⃣ Psychological Safety

People adopt change faster when they feel safe to fail, question, and learn.
Governance can’t enforce it, leadership must model it.

2️⃣ Change Energy Mapping

Every organisation has “change accelerators” and “change resistors.”
Mapping emotional energy early helps leaders pace the rollout intelligently.

3️⃣ Communication Rhythms

Transformation fails in silence.
Regular, transparent communication builds alignment faster than dashboards or reports ever could.

4️⃣ Behavioural Feedback Loops

Technology gives visibility, but people need feedback.
Real transformation happens when systems and behaviours evolve together.

5️⃣ Culture by Design

No architecture is complete without cultural touchpoints: rituals, recognition, and reflection mechanisms that sustain momentum after launch.

Results from Experience:

In one client program, integrating these human factors reduced transition friction by 40% and increased adoption velocity across departments.

The architecture didn’t change, the approach to people did.

When you think about transformation, do you design for systems first or with people in mind?

Because the real architecture of change often begins where the diagram ends.

The Human Side of Transformation

Most companies don’t struggle with AI adoption, 

They struggle with scaling it.

Because they treat intelligence as a project, not a platform.

Teams often start with a pilot: one use case here, one tool there.

Each delivers value in isolation, but they don’t connect or compound.

That’s when leaders realise local success ≠ enterprise intelligence.

Real scalability begins when organisations shift from project thinking to platform thinking.

A project delivers outputs.

A platform delivers capabilities: reusable data pipelines, governance layers, standardised APIs, and cross-team interoperability.

Here are some data-backed signs of what scaling looks (and what failing to scale feels) like:

- 72 % of organisations report using AI in at least one business function.

- Less than one-third of companies report following the majority of best practices required for scaling AI. 

- Leading companies that scale AI effectively embed technical enablers: e.g., feature stores, standardised protocols, MLOps platforms.

Here are a few examples:

1/. A major manufacturer moved beyond isolated use cases to automate entire production lines, treating each line as part of one scalable platform.

2/. A global bank used a centrally led operating model for generative AI, then rolled it out across risk, compliance, operations, not just marketing.

When every team plugs into the same intelligent backbone, AI stops being a bunch of siloed projects, it becomes a strategic advantage.

That’s how leaders turn isolated wins into enterprise-wide intelligence.

It’s the difference between having ten smart models and one smart organisation.

Also, platform thinking reduces redundant work and shortens time to value because teams don’t rebuild core capabilities from scratch.

What does your AI foundation look like right now, a collection of pilots, or a platform built to scale?

Because in the next decade, the winners won’t just use AI. 

They’ll scale it.

Your data doesn’t lie. 

But can you prove who last told the truth?

Most data challenges don’t begin with privacy, they begin with ownership.
When no one can trace who changed what or why, governance loses its foundation.

Strong governance starts when every decision, edit, and update has a clear origin.

Clarity creates trust and trust accelerates decision-making.

At Procter & Gamble, AI mapped data lineage across the organization.

Every update carried a timestamp, user ID, and reason. 

Audit cycles that once took three days now complete in four hours: a clean, traceable audit trail.

At Unilever, the same principle scaled across 140 markets.

Real-time data quality monitoring gave every regional team visibility into accuracy and consistency.

The result: a unified global system that improved compliance and decision-making speed.

According to Gartner, poor data quality costs organizations $12.9 million annually, proof that visibility isn’t optional. 

It’s operational strength.

Governance today works like version control for business logic.

Every model, metric, and dataset carries accountability and history.

When teams see data lineage clearly, they stop auditing for reassurance, because they already know what’s real.

The next generation of governance won’t be about checklists or controls.

It will be about systems that understand themselves, data that explains its own journey, and leaders who can trace a decision back to its source in seconds.

That’s how governance evolves from compliance overhead to strategic advantage.

Modern governance is not about control.

It’s about clarity: transforming inspection into insight, and compliance into confidence.

When organisations know the story behind their data, they move faster, decide better, and lead with assurance.

How much of your data can explain itself without an audit?

From Cloud Chaos to Controlled Intelligence. 

Why AI-ready architecture is the next C-suite priority.

Most enterprises still treat the cloud as storage.

But in the age of AI, it’s the backbone of intelligence.

The challenge isn’t adopting AI tools. It’s designing systems that can scale, stay secure, and remain compliant under constant change.

Without disciplined architecture: modular, observable, and governed- AI becomes expensive experimentation, not enterprise transformation.

In reality, many firms are investing heavily: global public cloud spending is forecast to hit $723 billion in 2025, up from $595.7 billion in 2024. 

Yet, 32 % of cloud budgets go to waste due to poor visibility, under-utilisation and lack of governance. 

That gap between rapid spend and mediocre ROI signals one thing: architecture discipline matters more than ever.

Most organisations are stuck between agility and governance.
Teams launch AI pilots faster than ever.

But their infrastructure still lacks lineage, versioning, and auditability. And when regulatory or cost pressure hits, they struggle.

In my latest article, I break down how global companies evolve from fast, lean cloud setups to Fortune-500-grade intelligence fabrics,  balancing agility, compliance, and cost control at every stage.

You’ll discover:

- The 4 architectural maturity stages of AI-driven enterprises.
- The DNA of trusted AI systems, from micro-modularity to continuous governance.
- A full checklist for CEOs, CFOs, and CAIOs to design scalable, secure, and auditable cloud systems.

If you’re leading technology, operations or finance, this is your roadmap to build AI on foundations you can trust.

🔗 Read the full piece: https://lnkd.in/dx3d9kZh

After reading, share your thoughts in the comments. 

How ready is your cloud for intelligence?

Before AI can transform business,

It has to pass a simpler test of credibility.

Every organisation is accelerating its AI agenda.

Yet progress depends on one invisible factor: trust.

Trust is the layer that converts automation into adoption.

It ensures every prediction, recommendation, and decision is understood, explainable, and reliable.

When teams understand how an AI system thinks, they move from using it occasionally to relying on it consistently.

That shift: from curiosity to confidence is what defines sustainable adoption.

The trust layer transforms AI from a technical tool into a decision partner that people can depend on.

The trust layer is built through:

1/. Transparent logic: Decisions that can be traced back to data and reasoning.
2/. Accountable data pipelines: Every transformation is documented and reviewable.
3/. Explainable outcomes: Users understand why an output was generated.
4/. overnance by design: Compliance, traceability, and oversight built into workflows.

Here’s how it plays out across industries:

Finance:

☞ Credit models that display input factors behind each approval.
☞ Dashboards that allow auditors to verify model behaviour.
☞ Risk teams using explainability as part of due diligence.

Healthcare:

☞ Diagnostic systems built on traceable clinical data.
☞ Validation logic visible to medical experts.
☞ Transparent models that enhance patient confidence.

HR:

☞ Algorithms evaluated for bias before deployment.
☞ Scoring frameworks that remain consistent across reviews.
☞ Continuous feedback loops that align fairness with performance.

Social proof reinforces credibility.

When teams see audited models, validated results, and trusted outcomes in practice, adoption accelerates naturally.

The more AI proves itself under real-world pressure, the stronger its cultural acceptance becomes.

The trust layer is not a feature, it is a governance mindset.

It combines clarity, accountability, and validation to make AI a dependable business partner.

Transformation scales when confidence scales.

How is your organisation strengthening the trust layer in its AI systems?

The Trust Layer Building Confidence

Most enforcement actions don’t fail because of bad lawyers.

They fail because we don’t read the data right.

At a receivables conference in Poland, Marcin Świtkowski broke down MS-KOM23, a national dataset tracking enforcement outcomes across methods.

The numbers were plain.
The implications weren’t.

They expose what most recovery strategies get wrong.

If you build AI automation, O2C decision engines, or collection strategies,

This is the dataset your models should learn from.

1/. Enforcement Effectiveness = Strategy Intelligence

Bank account garnishment → ~60 % success.

Everything else (asset seizures, repossessions, etc.) → < 20 %.

👉 AI logic: No active income or bank signal = low recovery probability.

Don’t escalate blindly. Route it to write-off or human negotiation.

2/. Average duration: 9–12 months.

Successful ones close in 3–6 months.

👉 AI logic: If no progress by 180 days → auto-deprioritise or close.

Use decay curves in your routing logic.

3/. 40 %+ of cases → zero recovery.

👉 AI logic: Stop “court-by-default.”

Use enforcement scoring before you escalate.

4/. Non-cash enforcement (repossessions, evictions) → almost no impact.

👉 AI logic: Route for human judgment or creative settlement.

5/. Average recovery: < €350 per case.

👉 AI logic: Compare recovery floor vs cost-to-collect.

If it’s not profitable, don’t escalate.

-------------

Every country’s enforcement data hides the same levers:

- Legal success rates by method
- Time-to-resolution by channel
- Cost-per-action vs yield
- Outcome distribution by debtor profile

Feed that data into your AI.
Let models think like strategists, not clerks.

Not everything should escalate.
Not everything is recoverable.
Smart O2C = Knowing when to stop.

Because automated doesn’t mean mindless.

Thanks to Marcin Świtkowski for the clarity.

Now, let’s turn public data into decision intelligence.

SaaS isn’t always the smartest cloud move, 

→ Especially for CFOs who value control over convenience.

For a decade, Software-as-a-Service has been sold as the default answer, fast to deploy, low cost, and maintenance-free.

But in the world of Order-to-Cash, compliance, and AI-driven operations, that convenience often hides a serious governance cost.

Cloud decisions now sit on a spectrum between control and convenience:

→ On-Premise gives full control but limits agility.
→ IaaS and PaaS balance ownership with flexibility.
→ SaaS hands everything, infrastructure, data, and risk, to the vendor.

And that’s where many CFOs lose visibility.

Key realities most leaders overlook:

👉 You don’t control where or how your data is stored.
👉 Customization stops at configuration, not at process redesign.
👉 Integration friction with ERP systems leads to fragmented reporting.
👉 Vendor lock-in and OPEX creep turn early savings into long-term costs.
👉 “Black-box” AI modules raise audit and explainability risks under SOX and the EU AI Act.
👉 Security failures on the vendor side can paralyse receivables and billing without internal failover options.

The numbers confirm it: Gartner estimates global SaaS spending will hit $300B by 2025, yet 53% of licenses in large enterprises remain unused, creating millions in wasted OPEX.

Smaller firms churn faster, up to 20% annually, as convenience gives way to cost, complexity, and compliance challenges.

The smarter CFO play:

1/. Use SaaS for tactical workflows like reminders or front-end automation.
2/. Retain control of core systems in PaaS, IaaS, or DBaaS environments.
3/. Prioritize open standards (REST, GraphQL, CSV) to preserve portability and governance.

Before your next subscription renewal, ask one question:

Do I want convenience, or do I want control?

Read the full article: SaaS Isn’t Always the Smartest Cloud Move

Read here: https://lnkd.in/dFY3u2vN

Because in finance transformation, control isn’t resistance, it’s ROI.

Every CFO is moving to the cloud 

Not everyone is in control of it.

Cloud adoption in finance has crossed the line from an IT decision to a strategic governance question.

CFOs today are not just funding infrastructure they’re designing the architecture that defines compliance, resilience, and accountability.

Every model: Public, Private, Hybrid, and Community, carries a unique trade-off between speed, scalability, and control.

Understanding that balance has become a core CFO competency.

1/. Public Cloud: Speed over control

Fast, flexible, and cost-efficient, perfect for analytics or AI pilots.

Yet shared infrastructure and external data residency can create compliance and governance challenges.

2/. Private Cloud: Control over convenience

Exclusive infrastructure with full visibility and auditability, ideal for ERP and financial systems.

The trade-off is higher cost and slower scalability, but it remains essential for regulated industries.

3/. Hybrid Cloud: Governance meets innovation

Critical workloads stay private, experimental or high-volume ones move public.

Integration becomes complex, but CFOs gain both agility and compliance.

4/. Community Cloud: Shared risk, shared compliance

Organisations with similar obligations, like banks or government networks, share infrastructure governed by common standards.

It moves more slowly but strengthens trust and consistency.

According to Gartner’s 2025 Finance Cloud Market Report:

→ 68% of financial institutions now operate in hybrid or multicloud setups.
→ 47% still rely on private clouds for sensitive workloads.
→ 53% of subscriptions remain underutilised, inflating the total cost of ownership.

These numbers reveal a key insight: speed without structure leads to inefficiency.

The right cloud model isn’t the one that scales fastest,
It’s the one that sustains compliance while enabling innovation.

CFOs must now act as Cloud Governors, asking:

- Do our cloud contracts guarantee portability and exit readiness?
- Are compliance and audit frameworks applied across all environments?
- Can our data and automation pipelines withstand regulatory review?

Because cloud decisions are governance decisions.

And in finance, control is not resistance, it is protection for growth.

Key takeaway:

Public Cloud = Speed
Private Cloud = Control
Hybrid Cloud = Balance
Community Cloud = Trust

For detailed frameworks, data, and real CFO case studies, read the full article: Cloud Models in Finance: Why Control Still Matters

Read here- https://lnkd.in/dgT4exMK

The right architecture is not just a technical foundation. 

It is the backbone of financial integrity.

AI systems scale faster than laws and oversight can adapt.

The rules meant to keep enterprise AI honest are falling behind – widening the friction between speed and accountability under new pressures such as the:

EU AI Act and rising demands for sustainability and radical transparency.
Governance isn’t just a compliance checklist.

It’s becoming the operating layer – the connective tissue linking innovation to integrity.

As automation and AI expand across every enterprise function, systems modernise while decision-making often lags.

Security and compliance frameworks exist, yet accountability and traceability still depend on manual oversight – leaving gaps where innovation and trust quietly erode.

The next stage is Governance as a Service (GaaS): not another tool, but an architectural rethink – a foundational layer that makes governance real-time, modular, and measurable.

Five Principles Defining GaaS

1️⃣ Modular Governance Frameworks 

Each domain finance, data, operations, HR – owns its own micro-layer, evolving autonomously while staying aligned with enterprise policy.

2️⃣ Role-Based Accountability Grids

Every decision maps to a clear owner and measurable outcome, shifting governance from retroactive control to continuous responsibility.

3️⃣ Dynamic Compliance Dashboards 

Governance becomes live and visual. Leaders track audit readiness, policy adherence, and risk anomalies in real time – not months later.

4️⃣ AI-Assisted Policy Enforcement

Machine learning surfaces bias patterns, anomalies, and breaches early, enabling preventive loops that reduce audit friction and strengthen controls.

5️⃣ Behavioural Enablement 

Governance endures only when culture does. 

Embedding intent documentation, context sharing, and bias reviews into daily routines turns governance from rule to reflex.

The Payoff:

Organizations that treat governance as an operating layer report faster decisions, higher audit readiness, and a culture of ownership that accelerates innovation.

1/. Estonia delivers transparency through AI-integrated governance, reducing audit closure time by 35%.

2/. Telstra operationalized AI governance, lowering policy exceptions by 20%.

3/. Mastercard aligned innovation with ethical responsibility and compliance, improving policy coverage metrics by 30%.

The real evolution isn’t from non-compliance to compliance – it’s from control to confidence.

Governance as a Service is moving from concept to execution: a structural imperative and the bridge between trust and transformation as enterprises scale AI at speed.

Is your governance keeping pace or slowing down your innovation?

Your forecast doesn’t fail because of data. 

It fails because of behaviour.

Forecasts are like weather reports.

Everyone reads them. Everyone depends on them.

But their accuracy fades when the right signals are missing.

Artificial Intelligence doesn’t make forecasting smarter by default.

It does when it’s trained on clean, connected, and behavioural data, the kind that captures how people and processes truly operate.

AI learns who pays late, under what conditions, and why those patterns repeat. That understanding turns projections into precision.

At BNY and Falconi Consulting, data from CRM, contracts, and payment history was unified into a single system.

When AI analysed those records, it uncovered a consistent pattern, 5 sales representatives were over-promising payment terms to close deals faster.

Once those commitments were aligned with real payment behaviour, forecast accuracy improved by 20%.

The breakthrough didn’t come from retraining the model.
It came from retraining behaviour.

Reliable forecasting begins with ownership.

When teams take responsibility for the data patterns they create, models become smarter and decisions become sharper.

AI reveals where attention is needed. 

Leadership decides what happens next.

When both align, forecasts stop being assumptions and start becoming advantages.

Today’s CFOs and finance leaders don’t just need predictive power.

They need behavioural visibility, knowing not just what will happen, but why it keeps happening.

That’s the new edge in financial transformation.

If your forecasts still depend more on confidence than evidence,
It’s time to rebuild them around truth, not tradition.

Are your forecasts driven by patterns you understand or behaviours you haven’t yet seen?

Let’s discuss how AI-led visibility can make your next forecast your most reliable one yet.

Customer experience weakens long before a complaint appears.

It usually starts with operational friction.

A wrong invoice.
A missed shipment.
A delayed credit note.

Each small delay creates confusion at the front end and frustration for the customer.

AI brings value when it links those front-line signals to the root causes behind them,, where real problems begin.

1/. At DHL Supply Chain, a rise in the phrase “Where is my invoice?” inside customer emails caught attention. 

- AI helped identify a flaw in the batch billing logic.

The issue was corrected once, and ticket volumes dropped by 40%.

No extra tools.
No workflow redesign.
Just clear visibility across systems.

2/. At Amazon and Telefónica, real-time sentiment models now monitor live calls. They detect tone shifts and hesitation patterns that indicate frustration.

Teams act before the emotion turns into escalation.
Customer trust stays intact.

The principle is simple: Strong customer experience depends on operational awareness. 

You can’t fix what you can’t see.
And when you see early, you prevent, not repair.

AI enables that awareness. It connects timing, data, and decisions across departments so that every team acts on context, not noise.

When systems talk to each other, customers experience consistency. 

When operations stay aligned, service feels reliable.

That is how intelligent operations create confidence, not through apology scripts or dashboards, but through timing that never misses.

How operational visibility can make customer experience proactive, not reactive.

Control + Alt + Disrupt: We’re asking one bold question:  

What if progress did not have to hurt?

For too long, modernization has been treated like a demolition project.

Full ERP migrations. Global rollouts. Endless reboots and millions lost to “transformation fatigue.”

But what if real advantage came not from tearing systems down, but from building intelligence around them?

In this episode, Mark Harrison and I unpack how finance and shared-services teams can modernize without drama and without waiting for a 2028 S/4HANA deadline.

Key ideas:

 👉 Using modular cloud layers (AWS Lambda, Azure NLP, Snowflake) to automate what matters now.
👉 Why “wait until migration” is the costliest phrase in operations.
👉 Why optimization is not revolution, it’s friction control.

Because modernization is no longer about massive overhauls — it is about precision.
 
Small, data-driven steps that create measurable momentum.

The future will belong to organizations that learn to layer intelligence, not rebuild everything.

Transformation fatigue is real, but so is transformation agility.

This is not another “AI will change everything” talk. 

It is where execution meets realism, for leaders with deadlines, not dreamboards.

🎧 Spicy AI: Modernization Without Migration
 
Listen now → https://lnkd.in/dN4w6b4D

What do you think: can modernization happen without migration?

Drop your thoughts below I woud love to hear real-world examples and opinions.

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