The explosion of generative AI in the past several months has shaken every industry, leaving leaders with questions. Should they even touch it? Probably. So who’s to lead it, manage the way it’s implemented, its governance, and overall strategy?
There may be no right answer, with every company taking its own path to incorporating generative AI into workflows and processes. And, even though it’s early days, several different models are emerging for oversight, each with its advocates.
We spoke with four executives who spend their days focused on AI leadership in the enterprise, and each had a different view. In some cases, a single, C-level leader may be appropriate; in other cases, generative AI might be more distributed throughout the organization.
What’s already clear is that the AI revolution isn’t just changing how business gets done, it’s changing how businesses are led. Here’s the view on managing generative AI now.
Daniel Linden, founder and chief AI officer of chiefaiofficer.com and a former digital marketing entrepreneur
CEOs ask me whether they need a chief AI officer in the C-suite. Well, yes, but don’t frame the role as chief AI officer. Think of it as chief transformation officer.
This is a moment of transformation. Job roles at every level of the organization are going to change, even within the C-suite. How companies do business and how workers do their jobs—within five years, everything will have changed.
The way most companies are using generative AI today is fractured and siloed, and the larger the company, the more refracted it tends to be. The first responsibility of a chief AI officer is to standardize tools, standardize governance, and implement broad, cohesive changes within the company itself.
At every turn, a CAIO has to ask: Are these tools safe? Are they compliant? Are we using them ethically? Regulation is coming in the UK and EU much faster than it is in the U.S., but laws and regulatory oversight is coming, and the CAIO needs to help a company prepare. A key part of the job is to future-proof the business as much as possible.
Allison Sagraves, instructor, Carnegie Mellon Chief Data Officer Program, and former chief data officer, M&T Bank
Since more than 80% of companies have chief data officers today, many companies are expanding that role into “chief data and AI officer,” tacking AI leadership onto an already busy job.
I think that’s a flawed approach. It’s a simplistic notion that we’re going to find the right C-suite role and say, “That’s the leader.” But that’s missing out on a rich opportunity. We’re at a moment of true democratization. Everybody needs to take ownership of AI.
This is less about a particular skill and more about a mindset to be able to think of your business context and how your business can benefit from the new capabilities that are emerging every day in the world. You need people from multiple perspectives, and you need to pull them together into a team to determine an AI strategy and infuse it throughout the organization.
AI is a business opportunity. Rather than looking exclusively to a technologist for solutions, I would ask, “Who are high-performing people across the spectrum who understand business and understand change?” You need people who are interdisciplinary, execution-focused problem solvers with the skills and mindset to use AI to solve business challenges, not simply to apply the technology because it’s there.
A CDO or CDAIO might make sense to coach a cross-functional team, because what is the raw material for AI? Of course, it’s data. But we need to evolve the idea of leadership as a collective instead of leadership as an individual.
Vin Vashishta, founder and AI advisor of strategic management consultancy V Squared, and author of “From Data to Profit”
When a business begins its AI journey, talent is scattered. Data is scattered. Ownership of resources and infrastructure are scattered. There are multiple competing visions for what AI will do for the business. With a lack of alignment, an organization can’t move forward in a single direction.
A center of excellence (CoE) can help centralize data, talent, and strategy so the business can translate it from the aspirational into something that will deliver tangible results. A CoE brings together a C-level data leader, such as a CDO (or another leader with data science expertise), with data engineers, data scientists, data and AI product managers, and data analysts.
On day one, this team’s mission is to build out an AI strategy: How does this technology deliver value? Where does it fit into our transformational roadmap? Without a strategy in place, a CAIO will be set up to fail. A company can’t bring someone new into the business to announce, “Here’s what our AI strategy is.” It has to be driven first by core business strategy.
I have seen AI strategy disconnected from the business fail time and again. A CoE will provide the consolidated infrastructure to support not just what the business needs today, but what it will need in 12 months, 24 months, and 36 months.
Nick Tzitzon, chief strategy and corporate affairs officer, ServiceNow
Every C-level leader is having the same conversation right now: What are we doing to plan for generative AI?
Whenever there’s a significant opportunity for change, there’s a typical pattern that follows: Let’s hire a new chief AI officer. Let’s lock consultants into a conference room to design our strategy. Let’s concentrate on cost-takeout (usually accompanied by some imposing workforce reduction projections). And to be fair, these might well be legitimate options in some cases.
Candidly, there’s a better approach to AI strategy. Companies should invite everyone into the conversation. We should broaden the number of colleagues who have a stake in how this technology gets deployed. AI leadership shouldn’t be a topic for only a few, it should be a movement that engages many.
Some simple questions can ignite this process:
- How do you think this can help our business run better?
- How could it help you do your job more efficiently?
- What big ideas do you have?
By opening up the discussion, leaders are likely to get more meaningful ideas faster, lessen resistance to change, and ultimately get the best ROI from this technology.
The true potential of generative AI—of all AI, in fact—is to bring more intelligence into how work gets done. Generative AI uses natural human language that allows people to consume information in a way that will make them more productive, more efficient, and more effective.
Will there need to be governance? Of course. And that will fall to technology leaders and their teams. But that’s only one important conversation. An equally important conversation should be focused on the art of the possible: Where do we waste time on work that doesn’t actually drive our results?
If we can use generative AI to reduce even 25% of that work and free up people to focus on higher value tasks, that’s an incalculable return on investment. For all the talk about how AI will replace people, if it’s thoughtfully deployed, we’ll see people doing different and better things at work. We’ll feel more fulfilled, and we’ll ultimately make bigger contributions to our workplaces.
Empowering people is the real dividend of shared ownership of AI.
Find out more about putting AI to work for people.
