Artificial intelligence and machine learning: How to invest for the enterprise Posted on : May 29 - 2017

Investing in AI requires different skills and approaches from buying process automation software like ERP or CRM. A world-leading expert explains what you need to know.

Virtually every enterprise software vendor is creating noise in the market about artificial intelligence. Unfortunately, much of that marketing buzz offers little substance and creates confusion for customers about what's real. Given this FUD, the challenge for business people is deciding where to invest.

Although market confusion is an issue, the underlying reality is that achieving results with AI needs different strategies, skills, and goals than deploying traditional process automation software.

With traditional software like ERP or CRM, for example, managers re-engineer processes like customer service or manufacturing to find repeatable improvements and efficiencies. Although implementation is often complicated, the benefits and risks are well known.

In contrast, investments in AI demand a different kind of analysis than with traditional enterprise software. Not only is AI technology new to most managers, but getting the desired results depends on having sufficiently large and relevant data sets to feed the AI machine.

Because AI can create results that go far beyond process improvement and efficiency, defining investment outcomes and goals can be far more complex than with traditional process automation software.

Making successful investments in AI, therefore, requires experts across a range of disciplines to think in terms of frameworks and models. The activities include:

• Analyzing the impact on current and future business models
• Selecting processes and operations in which to invest
• Examining machine intelligence technology
• Rigorously applying data science to proposed solutions and outcomes

The skills and activities are significantly different than those needed when buying and implementing traditional enterprise software.

Given the importance, complexity, and risk around AI investment, I invited one of the most experienced AI investors in the world to be a guest on Episode 220 of the CXOTALK series of conversations with innovators.

James Cham is a partner with Bloomberg Beta, a venture capital firm with a strong focus on companies related to machine learning. James and his colleague, Shivon Zillis, created a detailed machine learning market landscape.

I asked James to give enterprise leaders advice on how to invest in AI. During our discussion, Cham addresses points such as:

• Avoid significant waste on AI projects that offer little value or benefit
• Creating a useful economic framework for investing in AI
• Understanding the shift from being data-centric to model-centric
• Building, managing, testing model-centric AI applications

You can watch the conversation in the video embedded above and read the complete transcript on the CXOTALK site. You can also download the podcast on iTunes. Below is an edited portion of important points from the discussion. View More