Evaluate whether your organization needs a chief AI officer

What are a chief AI officer's responsibilities?

The role of the CAIO is to be the leader and point person for all AI strategies and projects inside an organization. Their main responsibilities include the following:

• Develop a forward-thinking AI vision that fits the organization's goals. This involves identifying areas where AI can have the most significant impact, such as improving customer service or optimizing supply chain logistics.
• Execute an organization's AI strategy. This includes coordinating with different departments to ensure alignment with AI initiatives, as well as managing resources and establishing a roadmap for scaling AI applications across the organization.
• Take responsibility for AI ethics and compliance. The CAIO must ensure that any AI tools and applications the organization purchases, builds, implements and sells adhere to ethical standards and comply with relevant regulations. This might encompass safeguarding user privacy, ensuring AI systems are free from biases and becoming the in-house expert on AI regulations.
• Serve as the organization's internal AI evangelist. The CAIO should dispel AI-related misconceptions at all levels and educate internal stakeholders about AI's potential to achieve business goals.
• Champion a culture of continuous learning. For example, the CAIO might launch and lead an AI center of excellence, or secure the budget and corporate support for solution architects and programmers to pursue company-sponsored AI training and vendor certifications.

Why should businesses hire a chief AI officer?

There are many reasons to appoint a full-time CAIO.

First, appointing a CAIO is necessary for organizations that require leadership over their AI transformation at the C-suite level. A CAIO provides strategic leadership and vision for AI initiatives; putting AI expertise and advocacy in the C-suite positions the CAIO to advocate for what the organization needs to integrate AI into its business and technology platforms.

An executive-level AI leader will be well positioned to coordinate cross-functional projects, especially at the peer level of the CIO in the case of internal initiatives or the CTO for product companies. Being an executive should also position a CAIO well for budget discussions.

Other benefits driving the need for a CAIO include the following:

• Operationalize underutilized data from a cost center into a strategic business asset that can save the organization money.
• Ensure the maximum ROI on AI at a time when AI hype is doing more harm than good to AI adoption inside business and government.
• Plan, develop, deliver and operate AI systems across the business.
• Manage risk for key AI initiatives the organization undertakes, especially regarding security and data privacy.

When not to appoint a chief AI officer

If an organization is still working on foundational digital transformation and data management initiatives, it's too early to appoint a full-time CAIO. Instead, invest time, budget and resources into building data management, governance and tools -- the usual domains of chief data and analytics officers.

The corporate culture also needs to be ready for a CAIO. Other executives jockeying for AI in the name of corporate politics can potentially hobble the CAIO role.

The rise of the fractional CAIO

Organizations also have the option of hiring a fractional CAIO. Bringing an AI expert into the organization on a temporary, fractional or consulting basis can provide companies with otherwise missing domain expertise at crucial points in their AI journeys.

The popularity of choosing a fractional CAIO begins with cost-effectiveness. Startups and even scaleups operate with limited budgets, making access to full-time, senior-level AI expertise difficult to afford. A larger company might also choose a fractional CAIO if the organization is taking a phased investment approach to AI or can't find a full-time executive candidate with the right blend of AI practitioner, management and business expertise. Either arrangement for a fractional CAIO enables the organization to utilize AI expertise and insights as needed in an efficient and cost-effective way.

However, hiring a fractional CAIO will increasingly become challenging, as AI expertise is in limited supply and high demand across industries. Organizations must treat this as a strategic role tied to business outcomes and be willing to pay for an external perspective and in-depth knowledge often lacking in first-time AI initiatives.

The fractional CAIO's role is to enable strategies and best practices that fit the organization's needs. Permanent members of the organization can set up the fractional CAIO for success by keeping them out of internal politics, which might flare up if executives want to latch on to AI to prove their relevance -- even to the detriment of the initiative.

Another part of the fractional CAIO role is authoring AI playbooks, roadmaps and frameworks that provide a replicable delivery process for current and future AI initiatives. Fractional CAIOs also develop and conduct AI opportunity assessments and train employees on how to continue doing so after their consulting engagement ends.

Even organizations with mature product management and development teams in place can benefit from the input of a fractional CAIO. By injecting their expertise, the fractional CAIO can help teams learn how to implement AI effectively for business value.

Will Kelly is a technology writer, content strategist and marketer. He has written extensively about the cloud, DevOps and enterprise mobility for industry publications and corporate clients and worked on teams introducing DevOps and cloud computing into commercial and public sector enterprises.