The Business Case for AI: My Takey

  • Thu, May 29, 2025
  • 5-minute read

When I started reading this book on AI, it felt very different from the usual ones. Most books i have read focus on how AI works, the code, models, and tools. But this one talked about something much more practical: how AI can help businesses and solve real-world problems.

What is AI, Really?

The book begins with the basics. It explains that AI isn’t just a fancy tech buzzword. It’s a tool that can help companies make smarter decisions and work more efficiently.

Clearing Up Common Myths About AI

One of the most interesting parts of the book was where it talked about common myths that people believe about AI.

  • Myth 1: “AI will take all our jobs.”

    • AI can do some tasks faster, but it won’t replace humans completely. For example, in customer service, AI can answer simple questions, but people are still needed for more complex conversations.

  • Myth 2: “AI is 99% accurate.”

    • AI is only as good as the data it learns from. If the data is biased or wrong, the AI will also make mistakes.

  • Myth 3: “AI gives instant results.”

    • That’s not true. A good example is self-driving cars. In 2016, experts said we’d have 10 million driverless cars by 2020. Companies like GM, Toyota, Waymo, and even Elon Musk made big promises. But even now, in 2025, fully driverless cars aren’t common.

    • Another great example is Google Search. Google started in 1996, but it took many years to improve:

      • In 2012, it introduced the Knowledge Graph to better understand what people are searching for.
      • In 2015, it added RankBrain, an AI-based system to improve search results.
      • By 2017, search became even richer with news, videos, and more.

    These examples show that real progress takes time.

  • Myth 4: “AI is less biased than humans.”

    • Not always. AI can learn biases from data. For instance, the COMPAS algorithm used in U.S. courts wrongly labeled many Black defendants as high-risk. This showed that AI can make unfair decisions if trained on biased data.

Where AI Can Be Used in Business

The book then talks about how AI can help in many areas of a company:

  • Customer Service
  • Human Resources
  • Sales
  • Marketing
  • IT Support
  • Manufacturing

A great example is Amazon’s recommendation system. It suggests products based on your behavior, and that brings in about one-third of their revenue!

It also shared the story of Google’s People Analytics Team.
They used data to learn what makes a good manager. Surprisingly, it wasn’t technical skills. It was about being a good coach, empowring and avoiding micromanagement.

Process to Build an HI-AI System

The book shares a clear process that companies can follow to build High Impact AI tools.

  1. Understand the problem
  2. Collect and clean data
  3. Build the AI model
  4. Test it
  5. Launch it
  6. Monitor and improve it over time

This helps make sure the AI is useful and keeps getting better.

The B-CIDS Framework: Is Your Company Ready for AI?

In Chapter 8, the book introduces five key things every business needs to be ready for AI. It’s called B-CIDS:

  • Budget – Is there money to support the project?
  • Culture – Do people in the company believe in data and AI?
  • Infrastructure – Do you have the tools and systems needed?
  • Data – Are you collecting and organizing your data properly?
  • Skills – Do your teams have the right knowledge to work with AI?

Each pillar comes with questions like:

  • Are you storing and logging your data?
  • Are old paper records being digitized?
  • Are leaders comfortable with using data to make decisions?

One of the best examples from the book was the story of Blockbuster and Netflix. Blockbuster had money and a big brand, but it failed to change with the times. Netflix, on the other hand, used data and technology to offer a better experience. The lesson? You don’t need the most advanced AI — you just need to be open to change and use data to make better decisions.

How to Find the Right AI Use Cases

The book ends with a very useful framework to help businesses find where AI can make the most impact. There are two ways to spot AI opportunities:

  • Proactive Discovery – Look for slow, manual, or repeated tasks in the business.
  • Organic Discovery – Take a big problem and break it down to see if AI can help with any part of it.

You can then group the problems as:

  • Old problems still done manually
  • Problems already handled by software, but not well
  • New problems with no current solution

To decide if AI is the right tool, ask:

  • Is the problem hard to solve with rules?
  • Is it something people spend a lot of time on?
  • Is good data available?
  • Are current tools not working well?

Final Thoughts

I really enjoyed this book. It helped me understand that AI isn’t the answer to everything — and that’s okay. What matters more is having the right setup, the right mindset, and a clear plan to use AI in a meaningful way.

If you’re looking to understand how AI can solve real-world problems, not just from a technical point of view, but from a business lens. I highly recommend reading this book. It’s practical, easy to follow, and full of examples that will change the way you think about AI in business.