Across industries, companies are applying artificial intelligence to their businesses, with mixed results.

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SwissCognitive, AI, Artificial Intelligence, Bots, CDO, CIO, CI, Cognitive Computing, Deep Learning, IoT, Machine Learning, NLP, Robot, Virtual reality, learning“What separates the AI projects that succeed from the ones that don’t often has to do with the business strategies organizations follow when applying AI,” writes Wharton professor of operations, information and decisions Kartik Hosanagar in this opinion piece. Hosanagar is faculty director of Wharton AI for Business, a new Analytics at Wharton initiative that will support students through research, curriculum, and experiential learning to investigate AI applications. He also designed and instructs Wharton Online’s Artificial Intelligence for Business course.

While many people perceive artificial intelligence to be the technology of the future, AI is already here. Many companies across a range of industries have been applying AI to improve their businesses — from Spotify using machine learning for music recommendations to smart home devices like Google Home and Amazon Alexa. That said, there have also been some early failures, such as Microsoft’s social-learning chatbot, Tay, which turned anti-social after interacting with hostile Twitter followers, and IBM Watson’s inability to deliver results in personalized health care. What separates the AI projects that succeed from the ones that don’t often has to do with the business strategies organizations follow when applying AI. The following strategies can help business leaders not only effectively apply AI in their organizations, but succeed in adapting it to innovate, compete and excel.

1. View AI as a tool, not a goal.

One pitfall companies might encounter in the process of starting new AI initiatives is that the concentrated focus and excitement around AI might lead to AI being viewed as a goal in and of itself. But executives should be cautious about developing a strategy specifically for AI, and instead focus on the role AI can play in supporting the broader strategy of the company. A recent report from MIT Sloan Management Review and Boston Consulting Group calls this “backward from strategy, not forward from AI.”

As such, instead of exhaustively looking for all the areas AI could fit in, a better approach would be for companies to analyze existing goals and challenges with a close eye for the problems that AI is uniquely equipped to solve. For example, machine learning algorithms bring distinct strengths in terms of their predictive power given high-quality training data. Companies can start by looking for existing challenges that could benefit from these strengths, as those areas are likely to be ones where applying AI is not only possible, but could actually disproportionately benefit the business.

The application of machine learning algorithms for credit card fraud detection is one example of where AI’s particular strengths make it a very valuable tool in assisting with a longstanding problem. In the past, fraudulent transactions were generally only identified after the fact. However, AI allows banks to detect and block fraud in real time. Because banks already had large volumes of data on past fraudulent transactions and their characteristics, the raw material from which to train machine learning algorithms is readily available. Moreover, predicting whether particular transactions are fraudulent and blocking them in real time is precisely the type of repetitive task that an algorithm can do at a speed and scale that humans cannot match.

2. Take a portfolio approach.

Over the long term, viewing AI as a tool and finding AI applications that are particularly well matched with business strategy will be most valuable. However, I wouldn’t recommend that companies pool all their AI resources into a single, large, moonshot project when they are first getting started. Rather, I advocate taking a portfolio approach to AI projects that includes both quick wins and long-term projects. This approach will allow companies to gain experience with AI and build consensus internally, which can then support the success of larger, more strategic and transformative projects later down the line. […]

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