Businesses need expert AI leaders to get ahead

The unstoppable march of generative AI demands that enterprises leverage a combination of new skills and strategies to address challenges and harness benefits, writes Mithu Bhargava.

The growing prominence of generative AI has provided organisations 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 organisations, is causing ripples of unease for IT and data leaders.

Our research recently found that enterprise IT and data decision-makers recognise 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 organisation while mitigating its risks so the organisation can compete in an increasingly AI-driven world. The importance of this role for organisations was highlighted by the Australian government’s mandatory regulation for high-risk AI. Businesses will be handed a major say in the future of AI, as the government plans to set the direction of regulation on the technology that shapes how Australian organisations approach how it is deployed.

Balancing innovation with risk

Our data underscores the scale at which enterprises use generative AI, with 93 per cent of respondents surveyed saying their organisations use the technology in some capacity. Half of the respondents’ organisations use generative AI to create content. Interacting with customers (49 per cent), adding value to services and products (47 per cent), and increasing team collaboration (46 per cent) 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 per cent). Respondents also highlighted the challenges of sourcing, protecting, and preparing the data (38 per cent), ensuring the accuracy and transparency of AI models (37 per cent), protecting and managing the data and other assets created by generative AI (36 per cent), and creating and enforcing generative AI policies (35 per cent).

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 per cent of survey respondents agreed that a focused AI leader can accelerate the effective adoption of generative AI. However, only 32 per cent said their organisations had onboarded one.

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

Strategically, these leaders can shape their organisations’ 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 organisations 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 optimised 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 capitalising on generative AI opportunities, respondents also said these leaders must ensure a unified asset strategy is in place to help organisations discover, manage, and optimise digital and physical assets used in generative AI applications. A nearly unanimous 96 per cent 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 life cycle management approaches, optimise physical and digital asset protection and management at scale and catalyse 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 organisations and how effective AI leadership and a unified asset strategy can help. Organisations need all pieces of the puzzle to balance opportunities and risks and capitalise on the technology’s potential before they’re left behind.

Mithu Bhargava is the executive vice president and general manager of digital solutions at Iron Mountain.