It Takes Two: Why an Expert AI Leader and a Unified Asset Strategy are Crucial to Capitalizing on Gen AI

Mithu Bhargava, Executive Vice President and General Manager, Digital Solutions, Iron Mountain

The growing prominence of generative AI has provided organizations worldwide with opportunities to enhance innovation, efficiency and competitiveness. While these opportunities are exciting, an increased awareness of the risks and challenges posed by “shadow AI,” the unsanctioned and hidden use of generative AI in organizations, is causing ripples of unease for IT and data leaders.

Research commissioned by Iron Mountain found that enterprise IT and data decision-makers recognize that a new type of executive is essential to tackle the challenges posed by both sanctioned and unsanctioned use of generative AI: a focused AI leader, such as the emerging role of the chief AI officer (CAIO).

Augmenting traditional technical C-suite roles, a focused AI leader is a strategic linchpin for the C-suite as enterprises venture into generative AI use. According to our research, this executive leader should oversee the responsible adoption of generative AI within the organization while mitigating its risks so the organization can compete in an increasingly AI-driven world. The importance of this role for organizations was highlighted when the U.S. government mandated the need for a CAIO within its agencies.

Balancing Innovation with Risk

Our data underscores the scale at which enterprises use generative AI, with 93% of respondents surveyed saying their organizations use the technology in some capacity. Half of the respondents’ organizations use generative AI to create content. Interacting with customers (49%), adding value to services and products (47%) and increasing team collaboration (46%) are other ways they employ generative AI.

Amid this surge of potential, leaders also identified challenges and risks when implementing generative AI. The most prominent challenge identified was planning for IT resources to train and implement generative AI models (38%). Respondents also highlighted the challenges of sourcing, protecting, and preparing the data (38%), ensuring the accuracy and transparency of AI models (37%), protecting and managing the data and other assets created by generative AI (36%), and creating and enforcing generative AI policies (35%).

Our research points to two critical elements that can help solve these challenges: a focused AI leader and a unified asset strategy.

The Role of a Focused AI Leader

A decisive 98% of survey respondents agree that a focused AI leader can accelerate the effective adoption of generative AI. However, only 32% say their organizations have on boarded one.

AI leaders can be strategic visionaries, ethics and risk managers, and practice leaders for their enterprise.

Strategically, these leaders can shape their organizations’ AI future by aligning initiatives with long-term business goals and market trends to create data and asset strategies.

Ethically, these leaders can help cultivate trust in AI by fostering responsible use. These leaders can establish exacting standards for transparency and accountability, advancing robust ethics, privacy, and security measures to guide the use of AI. Doing so protects organizations from the adverse effects of shadow AI and prepares them for evolving risks.

Practically, these leaders can help with the application of generative AI, ensuring processes are optimized and adapted to suit the day-to-day needs of employees and customers.

The Need for a Unified Asset Strategy

While the research highlights that AI leadership is essential for capitalizing on generative AI opportunities, respondents also say that these leaders must ensure a unified asset strategy is in place to help organizations discover, manage and optimize digital and physical assets used in generative AI applications. A nearly unanimous 96% of respondents assert that a unified asset strategy is critical to the success of generative AI use cases.

The research suggests a powerful connection between the challenges that generative AI presents and the value of focused AI leadership and a unified asset strategy to support this leader. By implementing a unified asset strategy, enterprises can evolve outdated asset lifecycle management approaches, optimize physical and digital asset protection and management at scale and catalyze value creation. Taking these steps will help these leaders remove roadblocks that impede innovation.

A Call to Action

The unstoppable march of generative AI demands that enterprises leverage a combination of new skills and strategies to address challenges and harness benefits. At the forefront of these shifts, decision- makers must consider the gaps within their organizations and how effective AI leadership and a unified asset strategy can help. Organizations need all pieces of the puzzle to balance opportunities and risks and capitalize on the technology’s potential before they’re left behind.

Mithu Bhargava is Executive Vice President and General Manager, Digital Solutions at Iron Mountain.

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