It is no surprise that businesses and governments around the world are racing to keep pace on AI as it quickly becomes table-stakes technology. Organizations are strategizing how they can accelerate and coordinate their adoption of the latest and greatest technology.
They’re considering how AI can be used to drive growth, increase productivity, enhance experiences and accelerate innovation. Some are considering new operation models and data center investments as a result.
Hiring for the role of Chief AI Officer (CAIO) has risen to prominence as a way for organizations to balance these considerations, evaluate and lead their AI strategies.
The United States Office of Management and Budget (OMB) recently issued a government-wide policy asking all federal agencies to name a Chief AI Officer, and according to FedScoop, as many as two-thirds of U.S. federal agencies have done so. Dell research found almost 20% of organizations surveyed globally identified a central team or individual to set AI strategy, and a separate report found that the number of “head” of AI jobs has tripled over the last five years.
Chief AI Officers play a vital role as organizations look to optimize their operations and gain a competitive advantage with AI. For the public sector, the Chief AI Officer can offer structure and guidance to provide better citizen services as well as promote innovation and competition. For private industries, the Chief AI Officer can develop efficiencies within the organization, bringing greater productivity for team members and a better experience for their end customers.
In the new world of Chief AI Officers, I’m a veteran. I took the role last September after more than 25 years at Dell Technologies when only 30 companies had appointed Chief AI Officers. There are now more than 120 Chief AI Officers across all industries, with more being named by the day.
In my time as CAIO, many of our customers have sought my guidance on the role and what considerations are necessary within their own organizations. While every organization is different, I will offer a few words of wisdom as other AI leaders look to embrace the technology:
- Understand your organization’s overall AI strategy. What is it that you want to achieve or create with AI? What does quality data look like, and what is the process for identifying quality data? Without a clear understanding of your objectives or outcomes, it’s hard to quickly move forward on decisions around the right governance model, data strategy and overall technology investments that lead to AI success for the future.
- There is no one-size-fits-all approach when it comes to adopting AI. What works for one organization may not work for others. For instance, not every GenAI use case will require the same infrastructure investment, and not every workload will run in the cloud. There is a wide spectrum of models emerging from industry-specific large language models (LLMs) to purpose-built smaller models that can run efficiently on-prem and at the edge—right where the data is. Clearly define your goals and what you’d like to accomplish, and then determine how (or if) you should approach it with AI.
- Approach AI with a holistic focus, beyond just the technology itself. We approach AI with three distinct perspectives in mind:
- The business side requires leaders to recognize the transformative potential of AI, how to leverage that with the right use cases, gain leadership buy-in and identify opportunities to apply it to your work by testing and learning. This also includes leadership beyond those with technical focuses, to include HR, legal, communication and other critical disciplines to ensure alignment early and often.
- The technical side necessitates a deep understanding of the fundamentals of AI technology and how it can be used to solve real-world problems.
- The people side communicates the why behind your AI strategy to employees, customers and partners with aligned leadership—building support for its adoption with internal stakeholders—and how it enables us to work in better, different ways.
- Approach AI openly: AI innovation requires a broad and open ecosystem. Openness provides equal opportunity across the tech ecosystem and supports the creation of new AI breakthroughs through greater access to innovation and flexibility. In fact, open models and technologies accelerated the journey of many AI first adopters, Dell included. Access to open models and technologies can accelerate progress, fueling a global “innovation engine” across all corners of industry and government.
These principles have helped me define and refine Dell’s AI strategy, keeping in mind that my role as Chief AI Officer is to balance the promise of AI while mitigating the risks. As organizations around the globe begin to embrace the newest face in their C-suite, I’m looking forward to continuing working with my peers to navigate this new era of technology.
