Why Some AI Efforts Succeed While Many Fail

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CIO Journal.
Why Some AI Efforts Succeed While Many Fail
A signboard of AI, 5G and IoT technologies on display during the SoftBank World 2019 exhibition hall in Tokyo, July 19, 2019. Photo: Rodrigo Reyes Marin/Zuma Press
By
Irving Wladawsky-Berger
Jan 24, 2020 4:37 pm ET
“Many AI initiatives fail” was the overriding finding of “ Winning with AI ,” a 2019 report based on a survey of some 2,500 respondents jointly conducted by the MIT Sloan Management Review and Boston Consulting Group. “Among the 90% of companies that have made some investment in AI, fewer than 2 out of 5 report business gains from AI in the past three years,” the report found.
Why is it so hard to realize value from AI? Why do some efforts succeed while many more fail? To help answer these questions, the study looked for patterns in the survey data and in executive interviews to uncover what the companies that are succeeding with AI are doing. It found that the companies generating the most value from AI exhibit a distinct set of organizational behaviors. Let me summarize these findings .
Don’t limit AI strategy to IT strategy
A common mistake companies make is to assume that their AI strategy should be considered primarily from a technology perspective. As a result, their AI efforts have an IT- or data-analyst-centric focus. This is the wrong approach. The companies that derive the most value are those that view AI as a core pillar of their overall business strategy. Integrating AI into the business strategy ensures that AI initiatives get the proper focus across the organization, in particular with the CEO and other senior company executives, without whose sponsorship and support it’s nearly impossible for any transformative technology to succeed.
The survey found that companies with AI initiatives led by the chief information officer saw value in 17% of cases. But when other C-level executives led AI efforts, that rose to 37%.
Prioritize revenue growth over cost reduction
Companies often look to AI to help them cut costs and increase productivity. However, the survey found that more advanced users focus their AI initiatives on revenue generation and growth opportunities.  
Cost-cutting and productivity benefits are a good way to get on the AI learning curve and to score early wins, which can spark enthusiasm for further AI initiatives. But revenue generation and growth are particularly powerful catalysts for taking AI deeper across the whole business. In addition, if a company doesn’t pursue new AI business opportunities, it’s quite likely that its competitors will.
Apply AI throughout the business
Companies leading in AI adoption have been able to extract more value from their AI investments because they’ve been applying AI pervasively across their functions, units and geographies. The survey also revealed that companies have more success with AI if they place carefully calculated bets. 
The report divided the respondents into four groups: 
Pioneers (20%) are leading-edge organizations that have widely adopted AI
Investigators (30%) understand AI but haven't deployed applications beyond the pilot stage
Experimenters (18%) are learning by doing, conducting pilots without a deep understanding of AI
Passives (32%) haven't adopted AI and have little understanding of the technology
“Among Pioneers, 35% have invested in 20 or more AI projects, double that of Experimenters and Investigators," the report said. "But the quantity of applications is not the point. Rather, Pioneers focus on projects with the potential for transformative impact, and they accept that doing so entails greater uncertainty than less transformative projects. Among Pioneers, 29% characterize their projects as high risk, at a rate roughly twice that of Experimenters and Investigators.”  
Despite the greater risk, Pioneers are also able to scale more projects on average, most likely due to their AI maturity, which enables them to choose their projects carefully and strategically.
When launching their AI initiatives, companies should start out by pursuing smaller, simpler projects that can yield quick wins. These can generate the necessary momentum and funding for more ambitious, longer-term AI projects.
Invest in AI talent
Almost all survey respondents said they’re facing a shortage of AI talent. There’s no simple answer to this problem. The survey suggested that the best approach is a combination of re-skilling workers, hiring new talent and looking to outside experts. Of organizations investing in all three talent routes, 65% are seeing a business impact from AI. In particular, the 59% of companies that are re-skilling workers have seen much bigger impact from their AI efforts than the 19% of companies not focused on re-skilling.
And, as is generally the case with transformative technologies, AI should be treated as not just a technical capability but as a major transformative initiative involving people, processes, culture and business strategy.  
To succeed, AI should be managed as a cross-functional collaboration that brings together technologists, data scientists, business managers, process owners and support functions such as finance and legal. It also requires investments in data governance and data platforms to ensure the quality and availability of the data that fuels AI.
Irving Wladawsky-Berger worked at IBM from 1970 to 2007, and has been a strategic adviser to Citigroup, HBO and Mastercard and a visiting professor at Imperial College. He's been affiliated with MIT since 2005, and is a regular contributor to CIO Journal.
 
Why Some AI Efforts Succeed While Many Fail
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