Digital Strategies Are So 2016: Time For An Artificial Intelligence Strategy

There’s nothing new about organizations and their leaders 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. Now, such is the case with the constellation of cognitive solutions — artificial intelligence, machine learning and so forth — that are now starting to be embraced.

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.

The type of data involved may determine the best cognitive solution, Davenport and Mahidhar suggest. “Organizations with voluminous and rapidly changing structured data about customers may find that machine learning provides insight into customer preferences. However, if the need is to identify and sort unstructured information — such as sounds and images — deep-learning neural networks will work better.”

Leverage content. If you own proprietary content, bring that into your AI world. This is a valuable asset that helps put the intelligence into artificial intelligence. To do this, Davenport and Mahidhar advise creating a ” knowledge graph ” that manages relationships between content assets and the business.

Make change management a top priority. Bring employees into the AI planning process as early as possible. Get them involved.AI projects “that go beyond the pilot or proof of concept stage are also intended to help transform organizational culture, behavior, and attitudes,” Davenport and Mahidhar state. “These are not small challenges, especially given the apparent threat to people’s jobs.” To generate support, describe how AI and related efforts “can provide improvements over the status quo, such as substantially increasing capacity or accomplishing tasks that weren’t possible before,” they advise.

Rethink processes, then rethink them some more. AI means developing new ways to manage data, applications and workflows, according to Chui and his McKinsey co-authors. “On the technical side, organizations will have to develop robust data maintenance and governance processes, and implement modern software disciplines such as Agile and DevOps. Even more challenging, in terms of scale, is overcoming the last-mile problem of making sure the superior insights provided by AI are instantiated in the behavior of the people and processes of an enterprise.”

Establish good governance. It’s important that everyone is on the same page, standards are set, and outcomes are monitored. As with all technology initiatives, it’s important in AI to “focus on doing things right and doing the right thing,” Sapp states. “The more AI is fused into the fabric of your organization, the more governance is needed to ensure you are doing the right thing by avoiding algorithmic bias, misuse of data, improper data wrangling techniques, and exposing private data.” He adds this broad hint as well: “Wow-governance-related AI jobs might just be the next major global job trend.”

Nurture AI-savvy skills. Surveys show skills deficiencies will hold back AI progress more than anything else. Training and continuous learning are needed to help fill these gaps. “Much of the construction and optimization of deep neural networks remains something of an art requiring real experts to deliver step-change performance increases,” Chui points out. “Demand for these skills far outstrips supply at present; according to some estimates, fewer than 10,000 people have the skills necessary to tackle serious AI problems. and competition for them is fierce among the tech giants.”


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