Consulting Industry Research

Should You Build Or Buy Your AI?

Should You Build Or Buy Your AI?

The days of asking if your company needs () are over. The answer, across nearly every industry and spanning the globe, is a resounding yes.

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SwissCognitiveThe research firm Gartner last year estimated that global 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 , business leaders are tapping the power of to enhance customer experience, create new revenue and reduce costs. So the issue is no longer whether to adopt , 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 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 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 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 for a big, transformative “moon shot” that will define your company? Or do you want for the low-hanging fruit—fairly easy-to-accomplish enterprise applications that will deliver immediate value?

If the goal is an project that handles routine activities and delivers immediate value, it’s almost never a good idea to build your own , says Thomas Davenport, professor in management and information technology at Babson College. Even using open source tools, build-it-yourself can cost millions of dollars, and it can take months to train a algorithm to do what most vendors have already accomplished.

If the goal is an project that handles routine activities and delivers immediate value, it’s almost never a good idea to build your own , 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 projects that these were also the most successful.[…]

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