3 reasons why AI strategy is HR strategy

By Bryan Kirschner, Vice President, Strategy at DataStax

When Karim Lakhani, co-founder of the Digital, Data, and Design Institute at Harvard University, talks about AI, I pay attention.

I’ve previously written about how national survey data collected last year by DataStax (my employer) proved out predictions Lakhani made about AI and open source back in 2020.

Now, thinking about what might be in store for 2025, I’m inclined to believe he’ll be right again: “Machines aren’t going to replace humans. Humans with machines will replace humans without machines.”

I’ve felt this was indeed inevitable – but “eventually.” One of my favorite signals of this potential is Uniphore, an AI platform that can power up salespeople in the moment by reading facial expressions and tonal emotion in real time – in the process creating better predictive data for their managers.

But ChatGPT points toward a ferociously quick timeline for two reasons. First: its unprecedentedly rapid adoption, with 100 million people bringing the power of AI to their fingertips in two months. But what’s most persuasive: empirical studies and real-world tests show double-digit productivity gains. (As a ChatGPT user, I’m confident it’s already saving me hours each week as a virtual research assistant.)

If you aren’t convinced that this makes AI as urgent a matter of HR strategy as it is of IT strategy, I’d like to offer a few hypotheticals to persuade you otherwise.

The magic AI pill

Imagine a pill hits the market that, when properly used, makes most employees, say, 30% more productive.

You’d probably hope that your competitors dilly dally about putting it to use.

You’d probably hope they took a slipshod, hit-or-miss approach to doing so.

And you’d probably be relieved if, instead of having a plan to gain share or expand into new markets using greater productive capacity, they instead leaned on the efficiencies gained from this magic pill and aimed for business as usual — but with a third fewer headcount.

Meanwhile, odds are your competitors would be sweating bullets under the assumption that you’ll move with great thoughtfulness and all deliberate speed to power up as many people as possible in order to plow added capacity into a smart plan for more growth.

The constellation of new AI capabilities made possible by and proliferating around ChatGPT and similar apps are like that pill.

Whether the point number for increased productivity is 13% or 30% (or even more), the stakes are significant. And, unlike previous AI use cases for which you could pick and choose which team or division was most ready, ubiquitous access to AI is being thrust upon more-or-less everyone via the productivity tools your employees probably already use.

3 reasons to weave AI into your organization

There are three key reasons to seize the moment, aim high, and make AI every bit as much a part of HR strategy as it is part of IT strategy.

First: empowered, cross-functional teams with ownership of a customer interaction or business process are a proven pattern for success with AI. And providing license to take risks (“psychological safety”) is a critical contributor to team effectiveness.

Many workers in your company and your industry may not be so sure that machines won’t replace humans–or worried that they won’t be the humans who are successful with the machines. Creating an environment in which employees can test, learn, and innovate confidently and collaboratively is an opportunity to steal a march on competitors.

Does this feel like stepping into a journey with an as-yet-unclear destination? It should, because it is–under conditions of great uncertainty but also tremendous possibility, it is a choice to bet on the power of a learning organization.

Second: job functions, descriptions, and team structures are going to change (maybe at significant scope and scale, according to one analysis). Managing it intentionally, in a hypothesis-driven, strengths-based approach tailored to your organization, is another way to minimize disruption and reach new levels of productivity faster.

And finally: from democratizing the ability to write code to unintentionally driving existing social biases deeper into the fabric of work, it’s easy to imagine AI affecting diversity, equity, and inclusion (DEI) for good, bad, or both at the same time.

Investments you’ve made in DEI so far have given you the makings of a learning laboratory powered up by different perspectives and lived experiences–something that may help identify an inclusive path forward not just with employees, but toward serving new customers or existing customers in new or improved ways. Figuring out how to do prompt engineering to build more inclusive AI systems may once again be an opportunity to get ahead of competitors.

Thanks to Lakhani and his collaborator Marco Iansiti, we’ve known for some time why the operating model of every organization would be rewired for ubiquitous AI. This has been proceeding apace at the level of architecture and infrastructure to enable new use cases for customers. Now it’s high time to tackle the employee experience as well.

About Bryan Kirschner:

Bryan is Vice President, Strategy at DataStax. For more than 20 years he has helped large organizations build and execute strategy when they are seeking new ways forward and a future materially different from their past. He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.

Artificial Intelligence, Machine Learning