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The promise of artificial intelligence (AI) to reveal valuable insights, reduce time-to-market, re-engineer costs and expand offerings is well understood by executives and technical leaders. What we have not yet defined is how to address the gap between these conceptual ideas and a real, tangible pathway to measurable success.

Learning from successful pioneers is vital, which is why we commissioned a study with over 550 executives and departmental leaders in companies with over 500 employees from Inc.Digital.

What we found[1] is that the experimental dynamic still dominates. Executives surveyed believe there is perceived real power in AI, but despite the AI hype, many organizations are reporting they are still in testing mode. In fact, over half of the executives are either experimenting or see it on a limited basis around the organization. 1 in 7 are only at the planning stage. They are also fully committed to AI within their organizations.

Given the experimental nature of enterprise AI, it’s no surprise that no one form of AI is the “golden ticket.” Companies are experimenting with a wide array of AI technologies. In only 2 percent of occasions did an organization use machine learning-based, deep learning-based, natural language-based, or visual learning-based applications in isolation. In 15 percent of occasions, all four forms of AI are being used in a company. The most likely combinations are around the pairings of ML, DL and visual AI (a combination of/all in use is 69 percent).

With all this experimentation, we have to ask whether there are companies that are thriving with AI. Our answer is a resounding “Yes!”, and we found a common set of understandings about how these most successful organizational leaders think about AI.

Companies see results from AI when they keep the strategy, data, technology, people and processes close to the core and controlled. Businesses that are committed to AI refuse to outsource that work. These organizations dominate the highest ROI across financial metrics, as well as better see better performance in product and service design. Additionally, the study found that while 1 in 4 AI projects fail, companies that build on highly dedicated on-premise AI infrastructure are 500 percent less likely to fail.

Big business issues trump technology issues by 71 percent and collaboration is a challenge everywhere. This is not surprising when you think about how hard it is to design and scale AI — AI is inherently difficult because it is an iterative process, not a step-by-step process. Building an

AI center of gravity requires new rules for technology, data and infrastructure, but it also demands new organizational approaches, new collaboration models and new processes.

If we’re not outsourcing AI projects, the biggest challenge is managing the human capital that is necessary to build those AI programs well.

“Organizing for success is often the defining variable for thriving. This research illustrates that while we mostly share the same ideals, challenges and desires for AI, those who really thrive do some simple things very differently. Keeping AI close to that core overcomes issues of collaboration and even measurement that are so vital for scaling.” – Michael Gale, author of the Wall Street Journal bestselling book on digital transformation, The Digital Helix, and a global top 5 AI influencer.

We’ve learned that building an AI center of gravity means keeping your technology platforms, data and teams closely knitted together, at the core of your business. Companies that do this show measurably higher success.

Read the research report.

Learn more about AI solutions with IBM.

[1] AI strategy for measured success. The DNA of companies thriving with AI. Published January 2, 2020. Research survey commissioned by IBM.

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