Just how is the employer branding industry adapting to life with its new BFF, Artificial Intelligence? I’d say that we appear to be at stage of grudging acceptance.
I liken it to a sports team who have just signed a star player. There’s the initial outlay to consider. There’s a confusion as to how best to get the best out of the new player. There’s huge expectation. And there’s also the realisation that someone is going to lose their place as a result of the newcomer.
There’s an appreciation that what AI brings to the table should reduce some admin-heavy tasks, that it has the capacity to align and co-ordinate disparate data sources and that it can eradicate some of the drudgery involved in research and competitor analyses. But there’s probably both suspicion and stubbornness as to what its creative role and input might be. I’d say increasingly misplaced suspicion and stubbornness.
Perhaps though we’re missing what AI’s most significant contribution to employer branding might end up being.
Much greater transparency.
Whether we like it or not, the industry has experienced varying levels of success in terms of making a transparent case for what it delivers and what it enables. The sector has always been about a strong sense of intuition, of assumption, of gut feel, of precedent, of big personalities. Success has often been measured more in terms of client wins, shiny gongs and business growth, than absolute clarity, objectivity, measurement and understanding.
Or transparency.
There’s more than an echo of John Wanamaker (or possibly Lord Leverhulme) and his remark about marketing investment. “Half the money I spend on advertising is wasted; the trouble is, I don’t know which half”.
Perhaps understandably, given Mr Wanamaker departed in 1922, there’s increasingly less foundation to the quote. And much of this recent progress is down to the clarity and transparency that AI provides. Such enhanced transparency is just as evident in employer branding as it is in any other market sector.
Up until very recently, how much confidence did we really have in terms of specific channel performance and hiring metrics? The opportunities presented by embracing AI within employer branding offer significant potential – the potential to shine much greater transparency under the bonnet of employer branding performance, delivering productive, actionable numbers against vanity metrics.
How able were we to understand which creative execution was delivering better numbers? And to which persona audiences? And what do better numbers mean, anyway? How about quality of hire as it relates to attraction source? With what sort of accuracy were we able to understand how relevant and compelling an EVP remained and over what period of time? How confident could we be that internal sentiment continued to align with external attraction messaging claims?
Let’s look at some tangible examples of employers making use of AI to create greater transparency and enhanced employer branding metrics.
Not a new story, but IBM’s use of its own Watson AI in order to identify high-performing current employees at risk of leaving achieved levels of 95% accuracy, creating the evidence and actionable context around why the organisation might be in danger of losing internal talent. Attrition reduced by up to 33% and quality of hire rose by 10%. According to IBM, the organisation has saved $300m through this initiative.
Using an AI platform, Unilever used gamified cognitive testing to assess its annual 1.8m global applicants and replace initial screening interviews. This had the effect of reducing hiring time by up to 75% and enhancing diversity hiring numbers. More than 50,000 hiring hours have been saved, according to HireView, Unilever’s partner on the project.
Another example is Coinbase, whose Global Head of Talent Brand, Angela Della Peruta, used AI-informed data from Built In to track brand perception and its evolution. This analysis was repeated every six months to better understand perceptional direction of travel. This analysis, according to Built In, enabled Coinbase to make employer brand content decisions based on fact and transparency over assumption and intuition.
Has AI optimisation reached anything approaching maturity? Absolutely not. The fact that the case studies above date back a number of years suggest both a sector juggling with how best to make use of the technology, as well as its inherent potential. (In addition to a probable reluctance to share competitive advantage).
And it is potential that is very likely to grow exponentially in the next decade – Universum’s Employer Branding NOW data from last year suggests that 70% of global employers are already making use of AI for recruitment and employer branding purposes. In 2024, 19% of US employers were actively integrating AI tools into hiring, a figure which exploded to more than 50% last year.
I’ve spent no more than half an hour today asking an AI to produce a talent brand analysis of a major digital employer, outlining its many strengths as an employer, as well as where a competitor organisation might want to target its future employer branding messaging in order to exploit certain relative cultural weaknesses. I then asked it to come up with some creative messaging based on these findings – the results weren’t going to win any RADs, nor were something to look down one’s nose at.
We talk about game changers far too often, however, AI is changing the game every day. The insights and transparency it provides are startling, actionable and of massive potential.
It also means that there’s less and less opportunity for an employer to hide behind employer branding messages which bear little internal sentiment scrutiny.
An employer brand cannot hide any more – and neither can our industry – from the real time transparency that AI enables.
Getting to the heart of your employee mindset 