Why Some AI Efforts Succeed While Many Fail

Report shows that companies generating the most value from AI exhibit a distinct set of organizational behaviors

MIT IDE
MIT IDE
Jan 21, 2020 · 5 min read
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Photo by Alex Kotliarskyi on Unsplash

By Irving Wladawsky-Berger

Winning with AI— an October 2019 report based on a survey jointly conducted by the MIT Sloan Management Review and the Boston Consulting Group— found that 90% of respondents agree that AI represents a business opportunity for their company. The global survey attracted over 2,500 respondents from 29 industries and 97 countries, and conducted interviews with 17 executives leaders of AI initiatives in large organizations.

The report classified the total survey population into four subgroups based on their understanding of AI tools and concepts and their levels of adoption of AI applications: Pioneers (20%) are leading-edge organizations that both understand and have widely adopted AI; Investigators (30%) understand AI but have not deployed applications beyond the pilot stage; Experimenters (18%) are learning by doing, conducting pilots without a deep understanding of AI; and Passives (32%) have not adopted AI and have little understanding of the technology.

“Many AI initiatives fail,” was the report’s overriding finding. “Seven out of 10 companies surveyed report minimal or no impact from AI so far. Among the 90% of companies that have made at least some investment in AI, fewer than 2 out of 5 report obtaining any business gains from AI in the past three years. This number improves to 3 out of 5 when we include companies that have made significant investments in AI. Even so, this means 40% of organizations making significant investments do not report business gains from AI.”

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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 the 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.

  1. Integrate the AI strategy with the overall business strategy

A common mistake companies make is to assume that, since AI is so dependent on advanced research and technical innovations, 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 will insure 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 near impossible for any transformative technology to succeed.

“Companies with AI initiatives housed under the chief information officer — where IT technology typically lives — are only half as likely to obtain value from AI as companies with AI initiatives managed or led by a different C-level executive. Companies with CIOs in charge of AI have seen value in 17% of cases versus 34% for companies that house AI directly under the CEO. When other C-level executives lead a company’s AI efforts (for example, a chief digital officer), AI- related value is generated at an even higher rate (37%).”

The survey found that integrating AI and digital transformation initiatives is particularly important, since both typically require large-scale, enterprise-wide efforts to redesign work processes, systems and structures. The study found that almost 90% of the leading-edge Pioneer companies have tightly integrated (36%) or at least loosely connected (52%) their AI and digital initiatives.

2. 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. Pioneers are twice as likely (53%) to use AI to increase revenues than Experimenters (24%).

Cost-cutting and productivity benefits are a good way to get on the AI learning curve and to realize 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.

3. Apply AI throughout the business

The leading Pioneer companies have been able 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.

“Among Pioneers, 35% have invested in 20 or more AI projects, double that of Experimenters and Investigators. 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.” But, despite the greater risk, Pioneers are also able to scale more projects on average, most likely, due their superior AI maturity which enables them to choose their projects very carefully and strategically.

When first launching their AI initiatives, companies should start out by pursuing smaller, simpler projects that can yield quick wins Such quick wins can generate the necessary momentum and funding for more ambitious, longer-term AI projects, and help them join the Pioneer ranks as their AI maturity increases.

4. Invest in AI talent, data governance, and process change

Almost all survey respondents said that 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 the existing workforce, hiring new talent and looking to outside experts. 65% of organizations investing in all three talent routes are seeing a business impact from AI. In particular, the 59% of companies that are actively re-skilling their existing workforce 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 a major transformative initiative involving people, processes, culture, and business strategy. To succeed, AI should be managed as a cross-functional collaboration that bring together technologists, data scientists, business managers, process owners and support functions like 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.

“In sum, the leaders not only anchor their applications of AI in their fundamental business strategy, they approach the use of AI as an organizational initiative, in which data and technology are foundational but organizational behaviors and ways of working make the difference in generating business value,” the report concludes. “If AI initiatives are not core to a company’s business strategy, they are unlikely to create meaningful value and scale.”

Originally published at https://blog.irvingwb.com.

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