The days of asking if your company needs artificial intelligence (AI) are over. The answer, across nearly every industry and spanning the globe, is a resounding yes.
copyright by www.forbes.com
The research firm Gartner last year estimated that global AI business value would reach $1.2 trillion by the end of 2018, up 70% from the previous year, and will more than triple by 2022. Taking advantage of greater computing power and developments in machine learning, business leaders are tapping the power of AI to enhance customer experience, create new revenue and reduce costs. So the issue is no longer whether to adopt AI, but how to do it. And that’s a question of whether to build or to buy.
There’s a simple answer, and a more complicated one. For companies that need AI to power their core business or to ensure strategic success, building is the way to go. Think Uber and autonomous vehicles, or Netflix’s sophisticated recommendation engine. For the majority of business needs, such as improving non-core activities like human resources, finance and accounting or customer service, buying one of the many well-tested, off-the-shelf AI products is sufficient.
“That’s the high-level and basic answer,” says Thomas Malone, founding director of MIT’s Center for Collective Intelligence. “It’s based on the same factors that apply to any build-or-buy decision. It comes down to how strategic and unique to your company are your applications of AI likely to be?”
From there, it gets complicated.
The Decision Tree
Think of the choice as a standard decision tree involving a few essential questions. The first one concerns your chief objective: Do you want AI for a big, transformative “moon shot” that will define your company? Or do you want AI for the low-hanging fruit—fairly easy-to-accomplish enterprise applications that will deliver immediate value?
If the goal is an AI project that handles routine activities and delivers immediate value, it’s almost never a good idea to build your own AI, says Thomas Davenport, professor in management and information technology at Babson College. Even using open source tools, build-it-yourself AI can cost millions of dollars, and it can take months to train a machine learning algorithm to do what most vendors have already accomplished.
If the goal is an AI project that handles routine activities and delivers immediate value, it’s almost never a good idea to build your own AI, says Thomas Davenport, professor in management and information technology at Babson College.
Machine learning excels at repetitive back-office administrative tasks such as sniffing out redundant customer records or checking supplier invoices to verify shipments, and tools that do these things can be purchased ready-made. Davenport, who is also a senior adviser at Deloitte Analytics, found in a study of 152 AI projects that these were also the most successful.[…]