Lessons From Millionaire AI Entrepreneur

David Ma is the technical co-founder of Dynasty, a conversational AI company for real estate. Dynasty was acquired by AppFolio for $60 million USD, 2 years after their pivot into artificial intelligence.

In an interview with Joma Tech, he shared some of the lessons that made him and his company successful.

David mentioned some of the aspects of AI adoption we have covered previously. It’s great to see other AI entrepreneurs applying the same principles successfully.

Here are David’s top 3 takeaway lessons for successful AI/ML adoption in business:

1) Business is more important than AI

According to David, the biggest reason why AI startups fail is because the (technical) founders love the AI and machine learning too much. They love the latest-and-greatest algorithms, ‘juicy’ math, academic papers, and they focus all their energy into the AI itself.

What ends up happening?

They never understand customers’ needs, pains, desires, and goals.

They never actually solve a real business problem.

They never really understand the business domain, business problems, and business operations/systems.

They build fancy algorithms that nobody wants.

They run out of VC funding and go out of business.

Instead, focus on understanding the business deeply, much more than the AI/ML. The algorithms themselves are not the top priority, as long as you have reached acceptable performance. All that matters is solving real business problems.

2) Focus on integrating AI/ML into existing business processes

This is one of the keys to successful AI adoption – especially at the beginning phases.

Analyze your operations and look for places where there are manual, repetitive, inefficient, predictable processes and/or tasks performed by humans.

Identify a problem you are already solving within these parameters and build a machine learning solution for it. This allows you to have a performance benchmark and metrics so you can gauge the effectiveness of your solution.

Having these existing metrics in place is crucial when assessing value and ROI for your organization. Otherwise, it’s easy to fall into the common trap of building an isolated POC/pilot without much certainty of success or business integration.

One of our main goals when implementing a machine learning solution is matching (and, ideally, exceeding) human performance for the task(s). Once we build the appropriate systems to support the ML process, our solution can scale.

Check out my blog post titled, “How To Identify AI Opportunities In Your Company” for an example of how we used AI to enhance existing operations for our enterprise clients.

3) Avoid solving too much at once with AI

David says that another big mistake made by AI practitioners is trying to solve too much too soon, and all at once. They want to ‘solve the world’s problems’ with artificial intelligence without proving the value on a smaller scale.

He suggests the proper way to adopt AI is to choose a smaller problem at first. Build only what is needed, as simple as possible, deploy, get user feedback, and iterate. This lean methodology is used by startups and Fortune 100 companies alike who are adopting AI successfully.

Check out my blog post titled, “3 Steps In Your AI Journey” where we cover the AI adoption journey and methodology in more detail.

These points are counterintuitive to technical people, especially with a long academic background (which has nothing to do with the real world), but it’s crucial to keep them in mind if they wish to bring valuable AI products and services into the marketplace.

Don’t get me wrong – I absolutely love the technical aspects of artificial intelligence. As a technical founder myself, I had to grow beyond my comfort zone and immerse myself in the business operations/processes of our clients. This was the only way I could add immense value to them.

It’s easy to get stuck honing machine learning skills and lose sight of WHY we are doing it. For me, it’s to help our clients improve operational efficiency and profitability by enhancing existing processes with AI/ML. It’s highly rewarding when you help others achieve transformation while doing what you love.

Here is a link to the full interview with David Ma on YouTube: https://www.youtube.com/watch?v=fB7nyxXaczY

If you need help to accelerate your company’s machine learning efforts, or if you need help getting started with enterprise AI adoption, send me a LinkedIn message or email me at info@carloslaraai.com and I will be happy to help you.

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