On Technology Partners

𝗪𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝘀 𝘄𝗵𝗲𝗻 𝗔𝗜 𝗺𝗮𝗸𝗲𝘀 𝗮 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗮𝗻𝗱 𝘁𝗵𝗮𝘁 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗴𝗼𝗲𝘀 𝘄𝗿𝗼𝗻𝗴?

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.

𝗧𝗮𝘆𝗹𝗼𝗿 𝗦𝘄𝗶𝗳𝘁 𝗵𝗮𝘀 𝗮 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁 𝟮𝟰 𝗵𝗼𝘂𝗿𝘀.

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 Ken’s perspective on time management, delegation, small business growth, and making better use of your time.

𝗔𝗜 𝗺𝗮𝗱𝗲 𝘁𝗵𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻. 𝗬𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝗺𝗮𝘆 𝘀𝘁𝗶𝗹𝗹 𝗯𝗲 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲.

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.

𝗔𝗜 𝗠𝗮𝗱𝗲 𝘁𝗵𝗲 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻. 𝗪𝗵𝗼 𝗜𝘀 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝗲?

As businesses increasingly use artificial intelligence to make decisions and perform important tasks, a critical question is becoming harder to ignore: who is responsible when AI gets it wrong?

What happens if an AI system is manipulated into giving away your services for free? What if it makes a decision that creates a financial or legal problem for your business? How do you know that the system is safe, reliable, and doing what you expect it to do?

AI governance means asking these questions before problems occur.

𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗔𝗜 𝗹𝗶𝗮𝗯𝗶𝗹𝗶𝘁𝘆 is becoming an important part of responsible AI adoption, risk management, cybersecurity, and business operations.

Ken Fanger examines why organizations need to understand how their AI systems work, what decisions they are making, and how to maintain accountability when AI becomes part of the business.

𝗬𝗼𝘂 𝗱𝗼 𝗻𝗼𝘁 𝗮𝗹𝘄𝗮𝘆𝘀 𝗻𝗲𝗲𝗱 𝗮 𝗿𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗮𝗿𝘆 𝗰𝗵𝗮𝗻𝗴𝗲 𝘁𝗼 𝗰𝗿𝗲𝗮𝘁𝗲 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗿𝗲𝘀𝘂𝗹𝘁.

Sometimes you just need to take the first step.

Ken Fanger shares how he went from never running to running two miles and eventually building a routine of walking four miles every day.

He did not start by deciding to walk four miles.

He started by walking to the first driveway.

Then the second.

Small changes became a consistent habit, and that consistency eventually created a much bigger change.

The same principle can apply to business, leadership, personal goals, and growth.

You do not have to change everything at once. Start with something you can actually do, build from there, and keep moving forward.

Watch the full video on YouTube to hear Ken’s story and his perspective on evolutionary versus revolutionary change.

𝗪𝗵𝗮𝘁 𝗶𝗳 𝘁𝗵𝗲 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝘀𝘁𝗼𝗿𝘆 𝘆𝗼𝘂 𝗮𝗿𝗲 𝗳𝗼𝗹𝗹𝗼𝘄𝗶𝗻𝗴 𝗶𝘀 𝗼𝗻𝗹𝘆 𝘀𝗵𝗼𝘄𝗶𝗻𝗴 𝘆𝗼𝘂 𝗵𝗮𝗹𝗳 𝘁𝗵𝗲 𝘀𝘁𝗼𝗿𝘆?

Many business and leadership books use successful people and their results to explain what works.

But Ken Fanger points out something small business owners should be careful about: Winner’s Bias.

Seeing what worked for someone who succeeded does not necessarily mean that applying the same action will create the same result for you.

Some stories show the path to success. Others also show the mistakes, setbacks, and decisions that did not work.

That difference matters.

Success is not simply about finding someone who made it and copying what they did. It is about understanding the lessons, evaluating what applies to your situation, and finding the approach that works for you.

Watch the full video on YouTube: https://www.youtube.com/watch?v=Re-YeBEBYfU

𝗔𝗜 𝗶𝘀 𝗰𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗻𝗲𝘄 𝗼𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 𝗳𝗼𝗿 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀𝗲𝘀. 𝗕𝘂𝘁 𝗮𝗿𝗲 𝘄𝗲 𝗽𝗮𝘆𝗶𝗻𝗴 𝗲𝗻𝗼𝘂𝗴𝗵 𝗮𝘁𝘁𝗲𝗻𝘁𝗶𝗼𝗻 𝘁𝗼 𝘁𝗵𝗲 𝗿𝗶𝘀𝗸𝘀?

Before your organization moves forward with AI, there are four critical areas you need to understand.

Liability: What happens if your AI makes a decision that creates a problem for your business?

Data ownership: Who owns the information you give to an AI system, and what happens to that data?

Shadow AI: Do you know who in your company is using AI and what information they are sharing?

AI hallucinations: What happens when AI gives you incorrect information and your team uses it to make important decisions?

These risks can directly affect your data, your business, and the people relying on AI to do their jobs.

AI governance starts with asking the right questions before problems happen.

In this episode, Ken Fanger breaks down these risks and explains why businesses need a thoughtful approach to AI governance.

Watch the full video on YouTube to hear the complete conversation about the risks businesses need to consider before moving forward with AI. https://www.youtube.com/watch?v=pxyiuj3GDRA

𝗔𝗜 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: 𝟱 𝗥𝗶𝘀𝗸𝘀 𝗘𝘃𝗲𝗿𝘆 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗡𝗲𝗲𝗱𝘀 𝘁𝗼 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱

AI is changing the way businesses work, but with that opportunity comes serious questions about risk, responsibility, data, and the role of people in the workplace.

In this episode of the AI Governance Conversation, Ken Fanger takes a practical look at five areas businesses need to consider when using artificial intelligence.

Liability: What happens if your AI makes a decision or takes an action that creates a problem and your company is held responsible?

Data ownership: What happens to your information when you use AI systems that learn from the data you provide?

Shadow AI: Who in your organization is using AI without your knowledge, and what information are they sharing?

AI hallucinations: What happens when AI provides incorrect information and your team makes important decisions based on it?

Human engagement: How do we use AI without losing the experience, knowledge, and expertise that people bring to an organization?

AI governance is not only about controlling technology. It is about understanding how AI affects your business, your data, your employees, and the decisions you make.

Watch the full video to understand the 5 risks: https://www.youtube.com/watch?v=pxyiuj3GDRA

One assumption almost turned a $75,000 opportunity into a $15,000 mistake.

When a customer's industrial plotter failed, the immediate reaction was to look for the cheapest replacement. The assumption? "They won't spend that much."

But no one had asked the most important question:

What does the customer actually need?

After taking the time to understand their workflow, it became clear they needed another high-end plotter. The right solution cost more than $75,000 and it became one of the best investments they made.

Too often, we let our own assumptions shape the recommendations we give.

The best consultants, sales professionals, and technology partners don't start with price. They start with understanding the customer's business.

Ask better questions.
Understand the problem.
Recommend what truly solves it.

That's how trust is built.