Spin the big wheel!
Most of the time, when people recruit me for exec roles, they have a clear idea of the work they're asking me to consider. Over the last few months, I've had delightful conversations with companies looking for execs to lead AI transformation both in their products and in their internal operations. Many organizations are doing amazing work.
But recently, I decided to explore one role in particular that was more nebulously defined.
Let's play Job Roulette!
Do not try this at home
It all started when I got a call from a recruiter about a role I would not have previously considered: a role leading talent acquisition at a major enterprise.
I was surprised by the outreach. My PhD is in natural language processing; before I was a CEO, I worked in product, engineering, and product marketing, not HR. Then again, I did serve talent leaders for nine years as Textio's CEO, building AI solutions for HR teams. I also have a strong POV on how to build people systems from having built great ones and bad ones at my own company. I'm feeling some operator FOMO this year, and the recruiter caught me at the right time. I took the call.
On the call, the recruiter explained that they weren't looking for someone with a conventional talent background. Rather, they were looking for someone to rebuild their talent systems, ideally with AI at the center. In other words, they were looking for an AI-native leader with combo skills. Interesting!
However, they didn't know whether the team would include recruiters or engineers; which parts of the talent system were up for reimagination; whether the team would include 10 people, 200 people, or 2000 people; whether the exec was supposed to hire more people or downsize the team; or what a good performance would look like 12 months out.
Shortly after I got that call, I got some others pitched along the same lines as that first one, with varying degrees of ambiguity. A few have been clear about what they're looking for. Ultimately, though, most companies are deeply uncertain about how to organize AI transformation work. To embrace a job in AI transformation, especially at a leadership level, you gotta be a bit of a gambler.
Here are some questions I've use to help me evaluate these roles. This stuff is especially applicable for AI transformation roles, but much of it applies any time you're considering taking a job.
Question #1: What role are you interviewing me for?
This sounds so obvious, right? Like of course people know what role they're interviewing you for. Otherwise why would they be recruiting you?
In fact, people are rarely clear about the role they're trying to fill. Remember that AI transformation talent acquisition role I mentioned above? After their exec team spent about a zillion hours with me, they ultimately thanked me for helping them realize they weren't ready to transform talent with AI after all. (Uh, glad to help, you wanna comp me for my consulting time?)
This is obviously an extreme case, but role ambiguity is much more common than you might think, especially in leadership hiring. Sometimes people don't start off with clear job specs up front. But even when they do start with c, the spec often drifts during the hiring process; the hiring manager's thinking evolves as they talk to real candidates and consider the possibilities.
With AI transformation jobs in particular, few people are clear on exactly what the work entails. If someone isn't clear what they're recruiting you for, help them get clear.
Question #2: Who makes the decisions?
Broadly speaking, companies are organizing AI transformation work in two ways. In the first approach, individual leaders are responsible for the transformation of their own departments. In other words, the Chief Legal Officer makes decisions about legal systems and tools, including the use of AI. The CMO makes decisions about marketing. You get the idea.
This approach has the benefit of single-threaded ownership: the person responsible for delivery also builds the systems that drive delivery. But there's a downside: few ops leaders know how to build technical systems effectively.
That's why several companies have taken a more horizontal approach to AI transformation, introducing Chief Transformation Officers who partner with functional leaders to drive changes. Depending on the company, the Chief Transformation Officer reports to the CEO, COO, CIO, or CHRO.
The upside: This person can look at how the entire system works together and design useful principles and playbooks for leaders to follow. The downside: They usually don't have decision-making authority or resources to make anything happen.
In either setup, doing AI transformation work requires serious listening and influence skills, because the person building the system is often missing either functional or technical context.
Question #3: In 12 months, how will you know if I did a good job?
A few months ago, I got a call from a different large enterprise about a future of work role. They wanted an exec to report to the CHRO to define the future of work and jobs in their organization.
The initial role description had all the signs of "be an AI cheerleader on LinkedIn without actually owning anything," so my first question was: In 12 months, how will you know if I did a good job?
The answer was an honest, "I actually don't know." And then they did something surprising, awesome, and exceedingly rare: They decided they needed to do more work to think through the role before talking to candidates.
Now that's a good hiring manager.
Kieran
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