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10/11/2026

๐—š๐—ฒ๐˜๐˜๐—ถ๐—ป๐—ด ๐—ฏ๐—ฒ๐˜๐˜๐—ฒ๐—ฟ ๐—ฟ๐—ฒ๐˜€๐˜‚๐—น๐˜๐˜€ ๐—ณ๐—ฟ๐—ผ๐—บ ๐—”๐—œ ๐˜€๐˜๐—ฎ๐—ฟ๐˜๐˜€ ๐—ฏ๐—ฒ๐—ณ๐—ผ๐—ฟ๐—ฒ ๐˜†๐—ผ๐˜‚ ๐—ฝ๐—ฟ๐—ฒ๐˜€๐˜€ ๐—˜๐—ป๐˜๐—ฒ๐—ฟ.

A vague prompt leaves more room for interpretation. A well-framed prompt gives AI clearer direction by defining the context, identifying the information that matters, and explaining what kind of answer you need.

For businesses, prompt quality can affect how useful AI outputs are for research, communication, analysis, and everyday decision-making.

But better prompts don't eliminate AI errors. Human oversight and verification remain essential, especially when the stakes are high.

Visit our YouTube channel to learn how to frame better AI prompts and get more useful results from generative AI.

Governance

10/11/2026

๐—ช๐—ต๐˜† ๐—ฐ๐—ฎ๐—ป ๐—”๐—œ ๐—ด๐—ถ๐˜ƒ๐—ฒ ๐˜†๐—ผ๐˜‚ ๐—ฎ ๐—ฑ๐—ถ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐˜ ๐—ฎ๐—ป๐˜€๐˜„๐—ฒ๐—ฟ ๐—ฒ๐˜ƒ๐—ฒ๐—ฟ๐˜† ๐˜๐—ถ๐—บ๐—ฒ ๐˜†๐—ผ๐˜‚ ๐—ฎ๐˜€๐—ธ ๐˜๐—ต๐—ฒ ๐˜€๐—ฎ๐—บ๐—ฒ ๐—พ๐˜‚๐—ฒ๐˜€๐˜๐—ถ๐—ผ๐—ป?

Unlike a calculator, generative AI doesn't simply return one fixed result. It uses statistical patterns and probabilities to generate responses, which means the same prompt can produce different answers.

Understanding this variability is essential for businesses adopting AI. A confident response doesn't automatically mean an accurate one, and important decisions require human judgment, verification, and accountability.

Knowing how AI works helps you use it more effectively and responsibly.

Visit our YouTube channel to learn more about AI behavior, its limitations, and what businesses should understand before relying on its answers.

Governance

10/10/2026

๐—ช๐—ต๐—ฎ๐˜ ๐—ถ๐—ณ ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐—”๐—œ'๐˜€ ๐—ด๐—ฟ๐—ฒ๐—ฎ๐˜๐—ฒ๐˜€๐˜ ๐˜€๐˜๐—ฟ๐—ฒ๐—ป๐—ด๐˜๐—ต๐˜€ ๐—ถ๐˜€ ๐—ฎ๐—น๐˜€๐—ผ ๐—ผ๐—ป๐—ฒ ๐—ผ๐—ณ ๐—ถ๐˜๐˜€ ๐—ฏ๐—ถ๐—ด๐—ด๐—ฒ๐˜€๐˜ ๐—ฐ๐—ต๐—ฎ๐—น๐—น๐—ฒ๐—ป๐—ด๐—ฒ๐˜€?

Generative AI can respond to open-ended questions and produce different answers because it relies on statistical patterns and probabilities. That flexibility makes it useful for exploring ideas and solving problems, but it also means its outputs can vary.

For organizations, this creates an important responsibility: understand where AI can add value, recognize where it can fail, and establish appropriate human oversight.

Responsible AI adoption requires more than experimenting with new tools. It requires understanding the technology and managing the risks that come with it.

Visit our YouTube channel to explore AI's unpredictability and what it means for businesses using generative AI.

10/10/2026

Can saying โ€œpleaseโ€ and โ€œthank youโ€ improve your AI prompts?

The way you communicate with generative AI can influence the responses you receive. Ken Fanger explores why polite language may help guide AI toward certain responses, while highlighting a bigger lesson: the way you frame a prompt matters.

For businesses using AI, effective prompting starts with understanding how the technology works, providing clear context, and recognizing its limitations.

Better AI interactions begin with better questions.

Visit our YouTube channel to learn more about AI, prompting, and how to get better results from generative AI.

10/10/2026

Are you learning from business books, or are you trying to follow someone else's formula for success?

Books can introduce valuable ideas, but applying every lesson exactly as written can keep you from finding what works for you.

Ken Fanger shares an important perspective on reading self help and business books: question the advice, consider your own circumstances, and pay attention to what happens when things go wrong.

Failure can reveal lessons that success stories leave out. Knowing where an approach might fail helps you evaluate ideas more critically and make smarter decisions.

The best lessons are the ones you understand, test, and adapt to your own goals.

Visit our YouTube channel to learn more about business books, personal growth, and practical lessons for entrepreneurs.

10/09/2026

Big goals can inspire you. Small steps can help you get there.

What makes the difference between reading about success and actually creating change in your own life?

Ken Fanger compares the perspectives behind Atomic Habits and 10X, highlighting an important distinction between learning from someone else's journey and learning from practical experience.

One approach focuses on building progress through small, consistent changes. Another encourages ambitious goals that challenge you to think bigger. Both offer useful ideas, but neither approach automatically works for everyone.

Ken also shares how starting small helped him build up to a daily four mile walk. Progress did not happen overnight. It developed one step at a time.

The lesson for business owners is to choose strategies that fit their circumstances, stay committed to their goals, and recognise that meaningful progress can happen through different paths.

Visit our YouTube channel to learn more about business growth, habits, leadership, and practical lessons from Walk to Business.

10/09/2026

What if the business advice you follow is only telling you half the story?

Successful people are often praised for the traits and decisions we believe made them successful. But how do we know those were the real reasons behind their success?

In Walk to Business, Ken Fanger explores Winner Bias and why business owners should look beyond success stories when learning from business books.

A strategy that worked for someone else may not work in your business. Understanding the mistakes, setbacks, and circumstances behind a result can help you make better decisions and avoid repeating someone else's failures.

The goal is to learn from other people's experiences while recognising what actually works for your situation.

Visit our YouTube channel to learn more about business strategy, leadership, and lessons for entrepreneurs.

10/03/2026

๐—ช๐—ต๐—ฎ๐˜ ๐—ต๐—ฎ๐—ฝ๐—ฝ๐—ฒ๐—ป๐˜€ ๐˜„๐—ต๐—ฒ๐—ป ๐—”๐—œ ๐—บ๐—ฎ๐—ธ๐—ฒ๐˜€ ๐—ฎ ๐—ฑ๐—ฒ๐—ฐ๐—ถ๐˜€๐—ถ๐—ผ๐—ป ๐—ณ๐—ผ๐—ฟ ๐˜†๐—ผ๐˜‚๐—ฟ ๐—ฏ๐˜‚๐˜€๐—ถ๐—ป๐—ฒ๐˜€๐˜€ ๐—ฎ๐—ป๐—ฑ ๐˜๐—ต๐—ฎ๐˜ ๐—ฑ๐—ฒ๐—ฐ๐—ถ๐˜€๐—ถ๐—ผ๐—ป ๐—ด๐—ผ๐—ฒ๐˜€ ๐˜„๐—ฟ๐—ผ๐—ป๐—ด?

Imagine an AI system giving away your services for free because someone manipulated it into doing so.

Who is responsible?

That is one of the questions businesses need to consider as AI systems become capable of making decisions and taking actions on their own.

Ken Fanger explains why AI liability will become an increasingly important part of AI governance and why organizations need to know whether their AI systems are safe, reliable, and operating as expected.

Trusting AI does not mean giving up accountability.

Businesses need to understand what their AI is doing and make sure the results match what they expect their customers to receive.

10/02/2026

๐—ง๐—ฎ๐˜†๐—น๐—ผ๐—ฟ ๐—ฆ๐˜„๐—ถ๐—ณ๐˜ ๐—ต๐—ฎ๐˜€ ๐—ฎ ๐—ฑ๐—ถ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ฒ๐—ป๐˜ ๐Ÿฎ๐Ÿฐ ๐—ต๐—ผ๐˜‚๐—ฟ๐˜€.

That was the response Ken Fanger got when he said that everyone has the same 24 hours in a day.

But the real lesson is not about Taylor Swift.

It is about how much we try to do ourselves.

Small business owners often find themselves handling everything at once: marketing, sales, operations, and delivering the actual service.

The problem is that you cannot be everywhere and do everything.

Ken connects this idea to Who Not How and the importance of moving the right things off your plate so you can focus your time where it creates the most value.

We all have 24 hours. The difference is how we choose to use them and what we decide does not need to be done by us.

Watch the full episode on YouTube to hear Kenโ€™s perspective on time management, delegation, small business growth, and making better use of your time.

10/02/2026

๐—”๐—œ ๐—บ๐—ฎ๐—ฑ๐—ฒ ๐˜๐—ต๐—ฒ ๐—ฑ๐—ฒ๐—ฐ๐—ถ๐˜€๐—ถ๐—ผ๐—ป. ๐—ฌ๐—ผ๐˜‚๐—ฟ ๐—ฐ๐—ผ๐—บ๐—ฝ๐—ฎ๐—ป๐˜† ๐—บ๐—ฎ๐˜† ๐˜€๐˜๐—ถ๐—น๐—น ๐—ฏ๐—ฒ ๐—ฟ๐—ฒ๐˜€๐—ฝ๐—ผ๐—ป๐˜€๐—ถ๐—ฏ๐—น๐—ฒ.

As businesses increasingly use AI to write contracts, make decisions, and automate business processes, liability becomes an important part of the conversation.

You cannot simply say AI made the mistake and walk away from the consequences.

If your organization uses AI, you need to understand what the system is doing, what decisions it can make, and where responsibility ultimately falls.

AI governance is not only about adopting new technology. It is also about understanding the risks that come with it.

Ken Fanger explores why AI liability is becoming a growing concern and why businesses need to think about accountability before putting AI into action.

Watch the full video on YouTube to hear the complete conversation about AI liability and governance.

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