What's Your Number?

What's Your Number?

NOTE: While this transcript has been reviewed, it may contain errors. Please review the episode audio before quoting from this transcript.

Jill Wiltfong:

Hi, I’m Jill Wiltfong, Chief Marketing Officer for Korn Ferry, and this is Briefings, our deep dive into the topics corporate leaders need to care about.

To automate or not to automate—that is a question company leaders have been asking in a big way ever since AI burst onto the scene.

Last year, one study found that four in 10 business leaders cut staffing because of AI, believing it had made certain roles redundant. The only problem? More than half of those leaders now admit they made a mistake.

It turns out that while AI can make parts of some jobs redundant, other parts still require human involvement. The big question is: What percentage of a job can be done by AI, and what percentage cannot?

That is exactly what our guests today have been investigating. Through fascinating and high-stakes research, they have developed a tool called the AI Impact Score—a number designed to tell leaders precisely which responsibilities AI can handle within a given role and which require humans.

If their efforts catch on, they may take us into a future where the only real question at the office is: What’s your number?

Before we begin, if you’re watching on YouTube, please be sure to like, subscribe, and leave a comment to let us know your thoughts on this topic.

I’m joined by Karin Visser and Daisy Grewal of Korn Ferry. Their work on AI’s role in companies could create an entirely new paradigm. It’s great to have you both here.

Daisy Grewal:

Thanks for having us.

Karin Visser:

Thank you.

Jill Wiltfong:

Daisy, let me start with you. Can you briefly walk us through how you determine a role’s AI Impact Score and explain what a particular score means?

Daisy Grewal:

There is a lot that goes into the methodology, but essentially, we use large language models to assess AI’s impact across our vast library of success profiles.

We have more than 10,000 success profiles, which gives us a very strong signal at scale about how AI is changing jobs across different functions, industries, and geographies.

Jill Wiltfong:

There have, of course, been other systems for determining how automatable a role is. Karin, you have said that Korn Ferry’s success profiles are the “secret sauce” that differentiates the AI Impact Score from those other systems.

What are success profiles, and why do they make the AI Impact Score more meaningful?

Karin Visser:

Success profiles are a comprehensive way of defining what great performance looks like in a role. They identify the responsibilities that must be delivered and connect those responsibilities to the competencies, skills, traits, and drivers that enable successful performance, based on scientific measures.

Many approaches to AI automation look at broad job titles or occupations and ask, “How automatable is this role?” But that can be very misleading. Two people with the same title may do very different work, while two people in very different functions may share many similar responsibilities.

AI does not really disrupt job titles. It disrupts the underlying work.

[Clip from Modern Times]

Jill Wiltfong:

That’s Charlie Chaplin in a scene from the movie Modern Times, in which automation is taken to an extreme as Chaplin is fed lunch by a machine.

Of course, we doubt things will ever become quite that absurd. But Daisy, you have said that leaders need to view AI through the lens of job redesign rather than job elimination. You have used software developers as an example of how AI might restructure a role. Tell me more.

Daisy Grewal:

That is the question dominating the media right now: How many fewer software engineers will companies need because of AI?

It is a reasonable question, given that AI is so capable at coding and has been trained on such a large volume of coding data. But when you look inside the job, you find that a software engineer has a broad range of responsibilities.

There are responsibilities such as coding, where AI has a significant impact—although impact does not necessarily mean full automation. Then there are many other responsibilities involving what to build, how to build it, and how to think through its structure. AI is not as capable in those areas.

We therefore see fairly mixed results. Early data also indicates that hiring for software engineers is not decreasing. I think that is interesting validation that AI is not creating a simple one-to-one replacement.

Jill Wiltfong:

Karin, how much pushback do you think employees will have when their companies tell them that, for example, 40% of their job can be automated? Is that going to cause friction in the workplace?

Karin Visser:

Yes. There will absolutely be pushback if that message is communicated poorly.

If an employee hears, “Forty percent of your job can be automated,” they are likely to interpret that as, “Forty percent of my value is disappearing.” That creates fear, defensiveness, and friction.

A better way to frame it is to say that a portion of the employee’s current responsibilities is exposed to automation or redesign. That is a very different message. It says the work is changing, not that the person is becoming less valuable.

Jill Wiltfong:

Daisy, let’s talk a little more about that. Many leaders have conducted AI-related layoffs only to regret them later.

I want to end with a very practical question: How can leaders implement an AI Impact Score concept within their companies so they can make better decisions going forward and approach this differently than they may have to date?

Daisy Grewal:

There is a real opportunity for companies that think about this differently rather than focusing only on automation and workforce reduction.

For many years, all the signs have pointed toward a future labor shortage. Talent is becoming increasingly scarce, and those trends are not changing—they are accelerating.

Companies should ask, “How can I use AI to do what it does best so that I can get more from my human talent, retain people, and enable them to perform higher-value work?”

When you frame the question that way, you are likely to receive a much greater return on your investment, both now and over the long term.

Jill Wiltfong:

Daisy and Karin, thank you both for joining me today and sharing your research. You have certainly made me—and probably all of us—think differently about how to deploy AI. I appreciate it.

Karin Visser:

Thank you.

Jill Wiltfong:

We have explored how an AI Impact Score could help companies make better use of AI across their organizations.

After the break, we will speak with a professor of AI and communication about how companies and employees can integrate this kind of job redesign into everyday working life. Stay with us.

Jill Wiltfong:

Welcome back.

In the first half of this episode, we discussed how an AI Impact Score might help companies avoid AI-related layoffs that they later regret.

Now, we are going to explore how companies and employees should navigate this new world of job redesign.

Joining me is Emily DeGer, faculty lead for the executive education program on AI and communication at Carnegie Mellon University’s Tepper School of Business.

Emily, thanks for joining me.

Emily DeGer:

Of course. Thanks for having me.

[Clip from YouTuber Phil Andrews on the Maxonomics channel]

Jill Wiltfong:

That last clip featured YouTuber Phil Andrews speaking on his Maxonomics channel about how people originally thought ATMs would replace bank tellers, when in fact the number of tellers increased.

AI seems to be going through a similar cycle: fear about job cuts followed, hopefully, by a period of relative calm.

You have mentioned, however, that one important difference may be that certain roles are being asked to become more technical than they were in the past. You cited product managers as an example. Tell me more about that.

Emily DeGer:

There are roles, such as product management and certain areas of marketing, that we do not traditionally think of as highly technical.

Before AI, product managers as a group were not necessarily equipped to create mock-ups or draft prototypes. AI now enables them to do that.

We are increasingly hearing about lean teams in which product managers are expected to do everything they would normally do while also leaning further into technical work.

Jill Wiltfong:

Interesting.

If something like the AI Impact Score becomes more mainstream within companies, it raises the question of what employees are supposed to do when, for example, 20% of their job is suddenly automated.

To your point, much of the job description may have changed. How should company leaders think about filling that time? Should employees take on new responsibilities, or should they do more of—or improve—the work they were already doing?

Emily DeGer:

We have to consider Jevons paradox, particularly in relation to the example of bank tellers and ATMs.

Economists would say that when something becomes more efficient or less expensive, we tend to want more of it. Consumption increases.

A commonly used example is that as steam-powered trains became more efficient, coal consumption increased.

There is therefore a case to be made that when certain tasks become partially or highly automated, organizations may simply want employees to do more of those tasks. If report writing becomes largely automated, for example, the demand for reports within an organization may increase.

[Clip of computer scientist Yoshua Bengio speaking at TED about the dangers of AI for workers]

Jill Wiltfong:

That was computer scientist Yoshua Bengio speaking at TED about the dangers AI may pose to workers.

There may be pushback when jobs are reshuffled, and it is a sensitive issue. It is so sensitive that a study earlier this year found that three in 10 employees were deliberately undermining their company’s AI rollout.

As we have discussed, AI may shift workers’ responsibilities rather than replace them, but that message does not seem to be getting through.

Emily, how should leaders communicate with their workforce and alleviate employees’ fears when they face this kind of resistance?

Emily DeGer:

Leaders in the C-suite should resist the urge to imagine and create an AI-adoption strategy independently of the people on their teams.

For instance, imagine approaching a sales team and saying, “A consultant and I discussed this, we built this tool, and this is how you are going to use it.” I can immediately imagine significant resistance and pushback.

The response would be completely different if leaders started with the sales team and interviewed its members to better understand their jobs.

They could ask, “Where do you see opportunities to improve efficiency?” or “Which tasks feel low-value to you and could be completed more quickly or removed from your workload entirely?”

Jill Wiltfong:

You are in an interesting position because you are training the next generation of workers and leaders.

You have said that, because of AI, we may not see the same payoff from doubling down on technical education that we have seen in the past. In fact, you believe human-centered skills will become increasingly valuable over time, which I find interesting.

Tell me more about that.

Emily DeGer:

For several decades, we have sent a strong message to students and their families: You need to enter a technical field.

I think we are going to see diminishing returns from those skills alone.

What we are hearing from alumni and recruiters is that it may no longer be essential for someone to write every line of raw code personally. People will still need the underlying technical knowledge, but they may increasingly supervise AI agents that write the code.

The work will become much more about judgment and discernment. Those capabilities fall within the realm of behavioral skills.

We have downplayed the value of those skills for several decades, but I think that is changing. People are beginning to recognize that employees need to enter the workplace with strong behavioral and human-centered capabilities.

Jill Wiltfong:

Emily, this has been a thought-provoking conversation. I very much enjoyed it. Thank you for being here today.

Emily DeGer:

Thank you.

Jill Wiltfong

The executive producer of Briefings is Jonathan Dahl. Today’s episode was produced by Rupak Bhattacharyya and Zachary Dore, and it was edited by Jaren Henry McRae.

It contains reporting by Russell Pearlman, Ariane Cohen, Peter Lauria, and Meghan Walsh. Our video segment contains original artwork by Fraser Milton, Haley Kennel, Jonathan Pink, and Sasha Kotzek. Our web operations are managed by Ed McLaurin.

Don’t forget to read our magazine—available at newsstands and at kornferry.com/briefings.

That’s it for Korn Ferry Briefings. I’m Jill Wiltfong. See you next time.