There’s nothing new about organizations and their leaders to be fumbling around for a coherent, business-relevant strategy any time a new technology appears on the scene. We’ve been seeing this in recent years with the rise of digital, raising issues from defining what exactly digital is, to defining what it means to succeed.

SwissCognitiveNow, such is the case with the constellation of cognitive solutions — artificial intelligence, machine learning and so forth — that are now starting to be embraced. Develop an overarching AI strategy With AI and cognitive computing the flavor of the month (or year), it’s time to start exploring what, exactly, it can do for business growth, and how to go about achieving it. Some good news: AI isn’t quite as amorphous and squishy as digital. More good news: many of the bread-and-butter issues arising from previous generations of technology apply with AI as well — starting with the most fundamental of fundamental principles: don’t implement technology for technology’s sake, have a business goal in mind.

The not-so-good news is that a lot of money is starting to be poured into AI and cognitive technologies, vendors are hyper-ventilating about it, and analysts are telling us that if we don’t do it we will all be quickly put out of misery. So, with more and more money being invested in it, it’s really important to strategize things a bit more, to give it a broader purpose in the enterprise. As Thomas Davenport and Vikram Mahidhar, recently observed in MIT Sloan Management Review, “few companies are yet getting value from their investments. Many of the projects companies undertake aren’t targeted at important business problems or opportunities. Most organizations don’t have a strategy for cognitive technologies.”

So what are the essential components of an AI strategy, or something close to a strategy. Here are a few pointers from leading voices in the field.

Step back and look at AI from an industry perspective: “Many companies that develop or provide AI to others have considerable strength in the technology itself and the data scientists needed to make it work, but they can lack a deep understanding of end markets,” as pointed out by Michael Chui and a team of fellow McKinsey analysts. Companies that seek to provide AI-driven offerings should not only analyze the value of their AI initiative, but also AI adoption across their industries.

Make AI about people and empowerment. AI may lead to many autonomous processes, but people will decide how it will drive the business. “The vision of AI should always be about empowering technical professionals and the business citizens to build a better user experience,” says Carlton Sapp, analyst with Gartner. “Technology cannot do this alone, and neither can AI. ”

Leverage data. This is the fuel that powers AI outputs. “Part of the reason machine learning has been so successful is that of its ability to train models based on data—as opposed to traditional methods that explicitly defined how the application would behave,” says Sapp. “Leveraging machine learning in your organization tells the world that you are truly data-driven.” Chui suggests building a “data plan” built on use cases and capable of producing “results and predictions, which can be fed either into designed interfaces for humans to act on or into transaction systems.” This includes mapping out how data is created, acquired, managed and delivered to AI engines. […]

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