(Gen) AI continues to be THE trending topic across the technology world and there seems to be no stopping it. It is now “trending” since basically November 2022, so more than 18 months, and it has intensified in scale and boldness ever since. It feels different than the hype we had around blockchains (anyone talking about that still?) and the Metaverse (anyone actually using this anywhere else than Learning? – I think even Meta has lost faith…) – but it is still a good question if it actually is any different. Or is it?
I think we have seen great use cases of (Gen) AI across multiple industries and products, especially in the R&D space, in the Legal space (funnily as it seems they are one of the first ones to use it but are very concerned when others want to use as well), or of course Software Development. But what about the HR space?
I think there are interesting use cases – many have to prove that they are value adding or cost effective. But according to a recent McKinsey study, only 12% of HR departments are using Generative AI today. So what is going on? Why is there so much talk and how come only 12% of HR Departments use it?
I believe this is not a contradiction but these stats fit well. If you look closer, the McKinsey study was asking about Gen AI only – but if you look into the wider noise, it is not only Gen AI but also AI (without Gen) that is talked about and utilized. And this is where the explanation lies.
Generative AI despite the November 2022 / 18 months timeline is still fairly new and on top of that it is seen and marketed as a product. AI (without the Gen) though is with us in the HR tech space for a much longer time – it is just that we haven’t hyped about it anywhere, we have probably even downplayed it as Machine Learning or algorithms. Used wording that makes it sound boring – but it is not any lesser useful or impactful. Who remembers enterprise or knowledge base search before the introduction of AI? key-words, meta-information, tons of unwanted search results (if any that you actually wanted), etc. – and who is using now semantic search and is thrilled with the results? I bet the majority here.
What I am trying to convey is that there are different approaches on how Gen AI and AI are marketed and usage proposed. AI is a feature built into a product or solution that supports an overarching user need or functionality. Gen AI IS the product.
This is where we get it wrong and where the noise has to tone down, we have to focus on functionality and use-cases that will improve with the use of Gen AI vs. trying to identify use-cases where Gen AI can be a solution or even worse, where Gen AI can be used (without adding significant value). And I think if we do that, if we go down that route, we will quickly transition into the Plateau of Productivity – and enable further growth. No need to build the 100th chat bot or 1000th solution that writes you a job description. But where and how can Gen AI be a feature and not the product – and by that make a difference and pay for itself? This is the question we should answer and this is how we should look at it.
It is very similar to the way we were looking a “Digital mindset” first. We were trying to transport analog processes into the digital world without making use of the actual strength of digital over analog. It took us some time to be digital first and truly build digital solutions. And now we need ourselves as HR as well as help all of our colleagues to think Gen AI first vs. how the current process can utilize Gen AI.
There is still a way to go to truly embed and make value of Gen AI but it is different than other hyped tech topics I am sure and we will see the impact soon. We just need to see it as a feature, not a product.
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