AI Team- Few executives have stood closer to the leading edge of artificial intelligence (AI) than Andrew Ng.
Ng is the founder and CEO of Landing AI, an enterprise artificial-intelligence company. He has directed Stanford University’s AI lab and led the Google Brain AI research team. As chief scientist at Chinese search giant Baidu, he developed innovative image- and speech-recognition applications. When it comes to grasping both the technology and AI’s business potential, few can match Ng’s resume.
The rapid rise of AI means executives have lots of questions, such as how to manage computing systems that are beyond human comprehension, and how to ensure they’re used ethically. Ng considers another set of questions even more important: What type of organization is needed to succeed with AI? What skills do you need to recruit for? What organizational structures work best? Do companies need an AI boss in the C-suite?
“A lot of companies are realizing the importance of how AI will impact their business,” Ng says. “And they’re starting to build in-house AI teams.”
Ng spoke with Forbes AI about how companies can build well-rounded teams to get the most out of their AI.
Executives don’t have much experience building AI teams, primarily because AI wasn’t as big of a necessity as it is today. How should companies go about selecting AI team members?
You need talent that has a deep knowledge of modeling. They need to understand the capabilities of computer learning. Then this centralized team can work cross-functionally with all business leaders to develop specific AI applications. Ultimately it will be the teamwork that drives the application.
In most companies, AI will be used for multiple projects. For example, AI has been reducing work in agriculture. There are dozens of applications—everything from weed control to interpreting satellite imagery to optimizing the harvesters. It’s very difficult to hire AI talent that knows all of these vertical applications.
That’s why it’s critical to test a few applications first to really understand what AI can do in one particular area of business.
What team structure enables this?
A small team empowered to act quickly and even fail should be able to test an application within six to 12 months. Giving the team its own budget, rather than forcing it to be funded by the business units, can give it a faster start.
What roles and skills do you need to recruit for when building an AI team?
The leader of the AI team—whether a CAIO (chief AI officer), VP of AI or other role—needs to understand enough about the technology to have a sense of what can and cannot be done. Further, they need to work cross-functionally with business leaders to identify and drive projects forward in AI+X, where X is whatever sector AI is meant to add value to.
The AI team also needs the engineering talent to execute on the AI project. Depending on the specific project, this would include machine-learning engineers, data scientists, applied scientists, data engineers or other roles. Some teams will also have product managers.