Lead by example: 5 critical actions to fill the AI leadership gap

1. Increase collaboration

38% of respondents say a dedicated AI leader would increase collaboration between senior IT/technology and employees responsible for data management

Regular meetings and workshops will bring technology teams together with data management staff. IT and data leaders must facilitate these relationships. Working together, the teams can coordinate projects, share insights on data quality, and ease data accessibility. They can discuss technological needs and any current challenges. Collaborative tools and platforms are vital, giving all stakeholders access to communicate and share important information.

2. Act on AI-driven insights to accelerate innovation

31% of respondents cite the benefit of value creation through using AI-driven insights, content, or processes

Everyone expects AI to drive innovation, so IT and data leaders must proactively identify opportunities and provide practical support and resources to drive AI adoption. A smart approach involves staying informed about the latest AI technologies and trends. Then, leaders must understand new technology and trends within their industry and organization and the best practices for applying them. Pilot projects are a great way to integrate AI into existing processes, highlighting the benefits and inspiring wider adoption.

3. Align on an AI strategy

31% of respondents say a dedicated AI leader would provide strategic alignment on using AI across the organization

By looking at their organization’s business goals, IT and data leaders can map out a complementary AI strategy. This strategy must be clearly communicated throughout the enterprise. Specific use cases can show how AI will support each department. Regular strategy sessions and updates will help maintain alignment as AI capabilities evolve and organizational needs shift.

4. Use AI consistently across departments

27% of respondents say a dedicated AI leader would benefit the organization by driving consistent and more prevalent use of AI across departments

AI tools must be accessible and tailored for specific needs to ensure consistent use. IT and data leaders can fast-track AI adoption by supervising careful roll-outs and providing helpful training and resources. An additional tip is to celebrate successful AI implementations within the organization. This can demonstrate the effectiveness of AI tools and encourage broader use.

5. Roll out new AI initiatives faster

27% of respondents say a dedicated AI leader would ensure faster roll-outs of new AI initiatives

In the absence of a dedicated AI leader, a dedicated task force or committee can accelerate AI initiatives. Potential members include staff from IT, data management, and other relevant departments. Those on the task force or committee would take responsibility for keeping AI projects moving without unnecessary delays. The work would include directing the phases of development, testing, and implementation. To expedite progress, teams should maintain standard procedures for how AI solutions are evaluated, approved, and deployed.

Setting an effective foundation for critical action

There’s something foundational underlying the five actions organizations must take to fill the AI leadership gap. It’s called a unified asset strategy, and the research report, Capitalizing on generative artificial intelligence, revealed its importance.

Nearly all respondents (96%) agree that a unified asset strategy is critical to the success of generative AI use cases. When asked what an AI leader could help organizations achieve, IT and data decision-makers’ top response is ensuring that a unified asset strategy is in place (50% of all respondents and 70% of public sector respondents).

What does a unified asset strategy do? A unified asset strategy makes data easier to manage and access by enabling the holistic intake, capture, and digital conversion of data.

A unified asset strategy calls for scalable, secure digital and physical storage so resources are protected and accessible across departments. It lets assets be cataloged and measured against policies for compliance and governance. A unified asset strategy also ensures data and metadata can be extracted and enriched for better insights, smarter decision-making, and workflow automation that boosts efficiency.

Finally, a unified asset strategy helps further an organization’s sustainability goals via activities such as data center colocation and infrastructure asset lifecycle management.

The road ahead for leadership

Amid the rapid evolution of generative AI technologies, very little is certain. But the research report, Capitalizing on generative artificial intelligence, reveals two certainties: a perceived need by IT and data decision-makers for an AI leader, and that a unified asset strategy is critical to the success of generative AI use cases.

To learn more

Dive deeper into the research report and infographic to learn more about what IT and data decision-makers say about generative AI in their organizations and to discover how enterprises can realize more value from their digital and physical assets as new AI technologies emerge.

For a broader look at how a unified asset strategy can help optimize your digital future, visit our resource page.