by: Kay Francoeur
At ECA, our mandate is helping companies hire star performers who will create value across their organizations. We have a rigorous, evidence-based vetting process that allows us to say with a high degree of accuracy who is most likely to succeed in a role – and we have a 94% retention rate to back that up.
With the advent of generative AI like ChatGPT, however, some of the markers of what makes for a great hire are poised to change. The advance of genAI will impact workers’ productivity as well as employers’ understandings of what success, problem solving, and high achievement mean in their fields.
As HBR’s IdeaCast podcast recently discussed in their ongoing series on the implications of genAI on the business world, there are at least 4 different outcomes that companies should consider:
- Everybody becomes more productive. Some research indicates this might already be happening, and in a big way. In early experiments testing the impact of ChatGPT on productivity, researchers found improvements of 30-50%, with people not even trained on using the system. To put this in perspective, the jump in productivity when American plants added steam power back in the 1800s was only about 25%.
- The best employees become more productive. Those who are already great at solving business problems will also become experts in ChatGPT and other genAI, learning to deploy these new resources more effectively and creatively than their peers. Relevant to the work ECA does, Wharton Associate Professor Ethan Mollick asserts that in this scenario, companies will want to hire far more star performers, and might end up needing fewer lower-level hires.
- It could end up leveling the playing field. The worst performers could catch up to the best performers because ChatGPT is helping them solve problems they wouldn’t otherwise know how to tackle, and at a much faster pace.
- AI creates a new category of jobs focused on prompt engineering. In this new environment, some people might demonstrate exceptionally high aptitude for getting the most out of genAI, even if they wouldn’t have been considered top performers by other metrics in the past. Finding and hiring people with this skillset would be imperative for companies wanting to stay competitive in an AI-enabled market.
It’s reasonable to speculate that as genAI technology is mainstreamed, all four of these outcomes will manifest to varying degrees across different fields. The first two scenarios – that productivity will go up across the board, and that top performers will use AI to enhance their work and remain at the forefront of their fields – are already becoming relevant to the hiring market. Speaking from the executive search side, how might these changes impact how we evaluate top candidates for clients?
- Certain components of the interview process are going to become more important. Specifically, we’ll have to vet communication skills even more carefully, through interviews and other methods that test how candidates communicate without AI enhancement.
Increasingly, folks across industries are using AI to improve clarity in their writing. A polished CV or LinkedIn profile might no longer point to someone being a skilled communicator offline – though it might mean that they’re skilled in serving up inputs to ChatGPT that yield competent results, which will be a value-add for some companies.
- We’ll need to listen for slightly different things in these interviews. Employers might want to know about candidates’ exposure to and facility using AI tools. It’s also likely that questions that test candidates’ ability to sift through large amounts of information and pull out the ideas that are factually correct, novel/creative, and also actionable will become crucial to finding star performers with high potential to harness AI effectively.
- On that note: it’s likely that casing becomes more important. It’s also possible that a new class of casing that tests how potential employees design and respond to AI prompts will be warranted.
Already, many companies use some form of casing exercise in their interview process to assess how candidates think on their feet, often in the form of mini case studies where candidates quickly think through business problems to which they had no prior exposure and show their work by walking the interviewer through their analytical process.
There is a potential risk to including case studies in an interview process, as the addition of cases might contribute to candidates feeling overburdened or offended by the request, if they feel like the ask stems from a question mark around their analytical or cognitive abilities. But casing can provide a useful data point for companies, especially in this new environment where keen sensitivity to verifying, editing, and effectively implementing genAI’s copious output will be key to companies retaining (or gaining) a competitive advantage.
At ECA, we remain curious about how genAI will change our clients’ priorities and definitions of star performers going forward. We’re evaluating best practices for tweaking our existing process to match evolving understandings of high achievement and will continue to connect star performers with great roles in PE firms and portfolio companies.
Kay Francoeur is a Project Manager at ECA Partners. She can be reached at [email protected]
