We’ve all seen the hyperbole about the artificial intelligence (AI) revolution and the predictions that it will add trillions of dollars to the global economy in the coming decades. But that all depends on enterprises actually understanding AI and adopting it wisely. A host of companies jumping in unprepared—eager to capitalize on trumped-up promises of superhuman capabilities and market domination—will result in lots of fast money for “AI vendors” followed by disappointment for everyone involved.
So what’s a CIO, or even CEO, tasked with implementing an AI strategy to do? The best place to start is to get up to speed on what AI actually means and doesn’t mean, and what it actually can and cannot do (hint: almost everything you see in the movies or read in clickbait headlines is false). This will help you conceive of useful AI applications within your own company (i.e., what’s easily automatable) and also evaluate the claims of software vendors and SaaS providers that promise AI-powered goodness.
That last point is critical when it comes to saving time and budget. AI vendors is in quotes above because, according to recent research, 40 percent of European companies classified as AI companies actually are not such in any material way. That number seems fair, and those findings very possibly apply to U.S. companies, as well. The right reason to buy a product is because it works, not because it checks the AI box on the strategy checklist.
One useful resource to get started learning from the ground up is the AI for Everyone course on Coursera, which is taught by deep learning pioneer and Stanford professor Andrew Ng (who has also published an “AI Transformation Playbook” for companies). It’s not perfect nor is it applicable to all situations (e.g., it largely overlooks cloud-based AI services), but Ng explains things clearly and provides a fundamental knowledge of how to get from idea to Alexa.
The course is a series of short lectures that first lay out in plain English what terms like AI, machine learning, deep learning and data science actually mean, and what you can realistically expect from investments in those areas today. After that, it delves into how to start building initial AI projects to establish its viability for particular tasks, and what companies need to do in order to successfully put AI to use. Some of those things involve culture and organizational structure, while others are technical—namely, getting your data infrastructure in proper order.
Another option, just announced this week, is Microsoft’s AI Business School. It’s less focused on laying the groundwork for AI than on case studies from Microsoft customers, but that’s still useful because, in the end, AI is really about ideas. And when it comes to finding relevant ideas, it can be hard to beat real-world examples from companies operating in similar spaces. (As a bonus, here’s a good synopsis on how to think about applying AI in the automotive industry for everything from logistics to connected cars.)
To drive the point home, here are some stories—just from the past week—that highlight the risks of betting on AI as a concept without really understanding the technology or how your customers (or employees or whomever) want to use it:
- Why AI underperforms, and what companies can do about it (Harvard Business Review)
- Amazon’s Alexa has 80,000 apps—and no runaway hit (Bloomberg)
- Faster robots demoralize coworkers (Cornell University)
- Stanford professor: Don’t let artificial intelligence pick your employees (Fast Company)