More Companies Are Rushing to Hire A Chief AI Officer — But Do You Need One? Here’s What You Need to Know.

This spring, the U.S. government took an unprecedented step: requiring every U.S. agency to appoint a chief AI officer. This follows on the heels of companies across diverse industries adding similar roles to their leadership ranks.

This is a move in the right direction for companies seeking to integrate AI, but it’s not enough on its own. Yes, every company must become an AI company. But expecting a chief AI officer to get the job done alone is shortsighted.

When businesses are confronted with a major technological shift, often their knee-jerk reaction is to stick with what they know: Putting a new executive in charge and hoping they can solve everything. But for AI to truly take root in a company, people at all levels of the business need to get their hands on it and start innovating, not follow orders from a gatekeeper in the C-suite.

In fact, the fastest way to integrate AI into a company, in some instances, maybe to skip the chief AI officer role altogether.

Related: The Future Founder’s Guide to Artificial Intelligence

Why having a chief AI officer might not make sense

Companies appointing a chief AI officer have good intentions as they seek to avoid getting disrupted by the technology. But they may not need this role, and any business adding it should assume that it’s temporary.

A useful comparison is the stampede in the middle of the last decade to appoint chief digital officers to oversee the digital transformation to internet and mobile technologies. In hindsight, that looks quaint.

Experts pronounced CDO the next big executive title, but it often turned out to be little more than window dressing — especially when digital skills became table stakes for most employees. In recent years, companies have been ditching the role or folding it into other jobs. In digitally native businesses, it doesn’t exist at all.

Google, for instance, never had a CDO directing how employees use web technology. Instead, they empowered employees to explore tools on their own through initiatives like 20% time, setting the stage for innovations such as Gmail.

Likewise, AI-native companies don’t have an executive overseeing AI. That would be redundant. At companies like mine, the technology is embedded from day one across the organization rather than siloed in a single role.

By default, we all leverage AI. Our marketing team uses it to better understand our customer base, our engineers deploy it to help write code, and our customer support leans heavily on AI agents. AI is written into every role, much like digital literacy now is at nearly all companies. Of course, there are areas of our business where we could use AI more and better, but making that happen doesn’t call for a specific job title. It’s everyone’s responsibility.

A better way to usher in an AI transformation

But I realize that not every company is built from the ground up on AI. So, how can legacy companies make real strides in integrating the technology?

In place of the top-down response to organizational change, consider a bottom-up approach. For a company that wants to usher in an AI transformation, the first step is to look across the roles you’re already hiring for and pick a few where AI agents can do the job today.

Customer service is an obvious place to start — today’s AI agents can now address most issues at least as well as humans. AI sales development representatives (SDRs) are also making an immediate impact, automating much of the toil involved in pursuing prospects. Another promising area — junior data analyst roles, which often consist of pulling information from reports. Then there’s coding. Autonomous software engineering agent Devin and OpenDevin, its open-source rival, can step in here.

Choosing the right technology partner to provide AI tools is equally important. When it comes to customer service, for example, companies should look for a vendor whose AI agents have a track record of resolving most issues without human intervention. Rather than following a script, they should have some ability to reason, drawing on past interactions and the conversation at hand to determine the best solution for each customer’s unique problem.

Then, it’s important to treat your agents more like employees than like a piece of software that will work straight out of the box. Onboarding, measuring and coaching — the same steps you’d take to develop any new hire — are essential to get the most out of AI tools.

The upside here is having team members experiment with AI begins to build AI expertise inside the company. For example, my company works with a financial services firm where AI employee manager has become a key position. Former customer support specialists there now teach AI agents new skills that add value throughout the business — thus making themselves an indispensable member of the team.

Companies can even make driving productivity gains via AI a criterion for career advancement. To get promoted, an employee must show their manager how they’re applying AI to deliver results for the business.

Related: How Generative AI is Revamping Digital Transformation to Change How Businesses Scale

The next stage: Those departments grow into mini centers of excellence that spread AI knowledge and best practices throughout the organization. Team members educate the rest of the business on how to hire and coordinate AI labor. AI becomes integrated into day-to-day business operations in a way that’s hard to achieve with an exclusively top-down approach.

Of course, there’s no one best way to take a company through an AI transformation. For legacy industries and large enterprises, a tandem approach — combining top-down and bottom-up — may prove a better fit.

At the very least, organizations that want to get the transformation right should think about how they can help AI bubble up through the ranks, rather than just rush to hire a chief AI officer simply because others have taken that step. As AI permanently changes companies from top to bottom, it’s just a temporary solution.