Capitalizing on generative artificial intelligence: the role of a dedicated AI leader and a unified asset strategy


As organizations embrace opportunities associated with generative AI, they also grapple with the challenges and risks it raises.

Debra Slapak
Debra Slapak
Senior Director, Innovation Strategic Initiatives at Iron Mountain
April 29, 20247 mins
Woman holding a laptop


Artificial intelligence (AI) has dramatically altered how we interact with the world and how it interacts with us. Over the past decade, conveniences slipped unobtrusively into our lives as things grew more “intelligent” — our cars, appliances, retail and streaming apps, phones, scales, even our suitcases. The progress mainly enabled by data scientists activating discriminative AI use cases and machine learning capabilities typically arrived without much fanfare from the general public.

By contrast, generative AI is flourishing on a groundswell of support from citizens ready to discover inspiration while losing the tedium from their jobs and personal pursuits. Developers, engineers, marketers, creators, educators, legal and finance professionals, healthcare providers and researchers, and many others have joined data scientists in their quest to uplift the human condition one use case (or sometimes, one question) at a time.

According to our research with IT and data decision-makers, 93% of respondents’ organizations already use generative AI in some capacity. But it’s not all smooth sailing. Similar to long-standing shadow IT concerns, the ready availability of generative AI tools has created a form of “shadow AI.” End users wield AI without the education, guidance, discipline, and control data scientists and other experts have long brought to the AI value vs. risk equation. The media has documented generative AI threats at length, but briefly, the highlights are concerns about privacy, security, bias and fairness, misinformation and fake content, intellectual property, and job displacement.

We wanted to understand what IT and data leaders know about their organizations’ top generative AI uses and the barriers to success. We hypothesized that two critical elements could help these organizations navigate generative AI opportunities and risks, so we turned to the independent research firm Vanson Bourne to test the hypotheses. The results of this study with 700 IT and data decision-makers unfold on the following pages. We hope this information helps our readers along their journey to rapid and responsible generative AI adoption.

Executive summary

As organizations embrace opportunities associated with generative AI, they also grapple with the challenges and risks it raises. In a global study sponsored by Iron Mountain, Vanson Bourne explored with IT and data decision-makers several key questions. These include how their organizations use generative AI, the barriers they face, and whether having a dedicated AI leader, such as a chief AI officer (CAIO), and a unified asset strategy could help accelerate adoption, while lowering enterprise risk.

This research, conducted across six countries with 700 IT and data decision-makers, indicates that most (93%) of respondents’ organizations already use generative AI in some way. While most (98%) agree that a CAIO can accelerate generative AI adoption even further, only 32% currently have a CAIO on board. That number is expected to grow to 94%. Meanwhile, an overwhelming majority of those surveyed (96%) agree that a unified asset strategy for managing both digital and physical assets is critical to the success of generative AI initiatives.

Infographic AI

The research reveals a powerful connection between the challenges that generative AI presents, the value of a CAIO and a unified asset strategy to address them, and the ability of a CAIO to implement a unified asset strategy.

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