As we have all no doubt been hearing about recently, there has been a massive jump in the capability of generative AIs.
These include everything from VALL-E (a new AI from Microsoft that can generate a full model of someone’s voice from just a three-second clip); to OpenAI’s DALL-E 2 (which can create original images from a text prompt), and many more besides.
And this, in my opinion, is an opportunity people analytics professionals should seize.
But how?
I believe there are three areas they should be focusing on:
The first area of focus (automating the analytics department’s own work), is both an easy place to start and a good foundation for moving to the second area of focus (supporting AI implementation across the organization).
It’s easy because you don’t need anyone’s permission to automate your own work.
It’s also easier to use AI to automate processes you are highly familiar with than trying to automate someone else’s processes.
And, let’s face it, it’s also a good foundation for helping the whole organization adopt AI. This is because analytics professionals will themselves know what is involved in implementing AI having – because it is that that will have successfully done it.
It’s likely your analytics team has already begun to play with generative AI, but my point is that this should go beyond playing.
These new AI tools will be very important for the organization, so the people analytics team should be aggressive in developing their expertise by deploying them wherever it is possible.
It’s not quite that the analytics team needs to call a “Code Red” with respect to AI as Google has done, but they should recognize this is a historic moment.
While making the analytics function more efficient is a great thing, it pales in comparison to the opportunities generative AI has for the organization as a whole.
There will be various groups in the organization who can contribute to helping to deploy AI, but one of those groups that should be leading front and centre is the people analytics team. It would be a real pity if people analytics misses the opportunity to have an impact across the organization.
The one area where the analytics team needs to exercise caution, however, is protecting confidential data.
This is an issue the whole organization needs to deal with so the work of any analytics teams that figures out how to handle this will pay off handsomely.
Evidence-based decision-making
The last area people analytics should be focusing on, chatbots to support evidence-based management, is a bit different in that it’s a specific application and it’s an experiment that may or may not pan out.
Most people are in favor of evidence-based decision-making in theory, but we don’t see much of it in practice.
One reason we don’t see much in practice is that looking for evidence to guide a decision is slow and tedious.
It’s possible that a chatbot trained on high-quality data sources could make seeking evidence easy enough that managers would be doing it as a matter of course.
But since the whole purpose of people analytics is to help managers make better decisions about people, the opportunity presented by these chatbots to bring decision-making support to a whole new level is exciting.
The bottom line is that generative AI has made dramatic breakthroughs and it opens up important opportunities for the people analytics team.
The people analytics team should not carry on with business as usual.
It should re-think its priorities and put AI work near the top of its to-do list.
