As companies increasingly turn to AI and machine learning, a clearer picture of what it takes to succeed with real-world AI is beginning to take shape. Beyond the small circle of tech giants and early adopters, a different set of skills and approaches is emerging as must-haves for enterprise AI teams.
Not every organization can compete with the likes of Google and Facebook for top AI talent. And it’s not just data science PhDs that companies are looking for. To meet their business needs, CIOs assembling AI teams are looking for subject matter expertise, software engineering skills, and the ability to translate learning algorithms into actual business value. Here, advances in machine learning technologies are helping pave the way.
“We’re seeing a shift right now,” says Scott Likens, emerging technology leader at PricewaterhouseCoopers. “There’s a lot of maturity and commoditization in some of the well-used ML, and a lot of the big providers have algorithms and AI models available. You’re able to piece together what you need, so you’re looking for high-end software engineers to glue together these different algorithms.”
Instead of hiring high-level PhDs to create new models, companies now seek blended teams to get the right data, and choose the right models to get the right decisions, he says.
