Shifting toward Enterprise-grade AI

Confronting data issues and bridging the artificial intelligence (AI) skills gap are critical for realizing AI’s value to the enterprise.
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AI capabilities are rapidly maturing. And so, too, is enterprise adoption. More executives than ever before are actively conceiving where and how to leverage AI. But executives are also more discriminating about their organizational priorities for AI and how these leading-edge technologies are rolled out.

While CEOs were experimenting broadly with AI across their organizations in 2016, they are now highly focused on five priority areas. In 2016, executives deemed customer satisfaction and retention as value drivers for their AI investments – now that focus on customer and other growth metrics is even deeper. And while technology availability was the leading concern for most executives in 2016, now it’s all about how they can best cultivate AI skills and use data most effectively.

So what do these changes mean? Moving from experimentation to implementation is not straightforward, and many companies are struggling with the transition. However, some businesses are achieving AI at scale successfully – and they are disproportionately financial outperformers. Confronting data issues and bridging the AI skills gap are critical to scaling AI and realizing value in the enterprise.

Enterprise-wide AI has the potential to bring exponential competitive advantage to companies that adopt the technology. But many businesses struggle as they move from AI experimentation to implementation. Some, however, are successfully achieving AI at scale. They’re disproportionately financial outperformers, and their experiences offer valuable lessons for organizations looking to adopt AI.

To gain their insight, we surveyed C-level executives and top functional leaders about AI and cognitive computing. Based on feedback from more than 5,000 global executives, we explored how organizational views on AI have evolved in four key areas:

1. Companies have a sharper focus on AI. Ninety-three percent of outperformers* are at least considering AI adoption.

2. They’re also placing more emphasis on topline growth. Seventy-seven percent of outperformers now cite customer satisfaction as a key value driver for AI.

3. Data is becoming increasingly important. Eighty-six percent of all outperformers now have enterprise-wide data governance.

4. Skills are becoming an even bigger concern. Sixty-three percent of all respondents now see skills as a top barrier to achieving success in AI.

To help companies can get started on their own successful AI implementations, our report offers real-word examples of enterprise-grade AI initiatives, as well as a set of high-level tactics for leveraging data to enable AI.

*Outperformers are those organizations that self-identify as having outperformed their peers on revenue growth and profitability for private sector organizations or revenue growth and effectiveness at achieving objectives for public sector organizations.

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Additional content

Meet the authors

Francesco Brenna, Executive Partner and European Leader, AI Practice,

Manish Goyal, Global Leader, Artificial Intelligence (AI) practice, IBM Consulting

Giorgio Danesi

Connect with author:

Glenn Finch, General Manager and Global Leader, Cognitive Business Decision Support, IBM Consulting

Brian Goehring

Connect with author:

, Global Research Lead, AI, IBM Institute for Business Value

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Originally published 18 September 2018