Journalist Patricia Stark interviews three ADP AI experts about the need for good data and data privacy. Chief product and data officer Amin Venjara shares how using good data is like cooking with farm fresh ingredients. Global chief privacy officer Jason Albert discusses how guardrails around data can make sure AI only uses the data it needs for a job. And chief product owner Naomi Lariviere says understanding what problems you’re trying to solve is the first step to using AI the right way. For more on AI tools, visit https://bit.ly/4dMrODY

Video Transcript:

Amin Venjara
When you have an AI system, you want to ensure that it has that right capability to have access to good data, market data, and textual data to make good decisions.

Patricia Stark
Amin, everyone says that AI is really only as good as its data. So in workforce technology, what does that really mean in practice?

Amin Venjara
I think about the relationship between data and AI. It's like the ingredients and a meal. My sister has a farm and when I go up there, we pick ingredients from the farm and then we cook it that evening. And you can taste the difference. That's like the relationship between data and AI. Like if you have really good ingredients, you can make an awesome meal, but no amount of AI is going to make up for poor data.

And that is that relationship of being able to think about when I have AI, what is it relying on? What data is feeding into it? What context does it have? Does it have enough understanding of the person that I'm dealing with, of the organization that I'm looking at to be able to think about the work process and the outcome it's trying to achieve?

And oftentimes we put a lot of emphasis on the AI or the models. But so much of that now is we're seeing it depends upon the data that it depends on. the what's happening to somebody today. What what pages were they clicking on. Is it been a busy time of year for them? What's happening in their personal life?

All those kinds of things can come into making the right decision. We know that as as humans, when we engage with somebody, we have to we try to engage and understand, is this person having an off day? Is it the right time to talk to them? How do I talk to them? That's the same kind of data that we all collect when we interact with somebody.

Then AI needs. And so when you have an AI system, you want to ensure that it has that right capability to have access to good data, market data and contextual data to make good decisions.

Patricia Stark
I love that analogy. Difference between a cook and a great chef. Jason, your role is focused on global data privacy. And of course governance is essential. What guardrails should employers really have in place?

Jason Albert
Well, when you think about the guardrails that employers should have in place with respect to data, you have to start with thinking about access controls. You have to make sure that the right people have access only to the data that they need for their jobs and for their role. We call this in privacy data minimization, and it's a critical element, and it's equally true of AI.

AI can develop all these great insights. All these great recommendations can act now with agents on data, but it's important to make sure that the AI also only has access to the data that it needs for its task. So I'd start with data minimization and thinking about the purpose you're trying to accomplish, and how the data you have relate to the purpose.

Then, of course, you need the appropriate guardrails around security to make sure that the data isn't accessed by bad actors. And you also have to think about what transparency you're going to offer your employees, letting them know how their data is going to be used. Because there's nothing so important in the workplace as trust. And employees will trust employers if they understand how their data is being used.

And there's transparency there about that use.

Patricia Stark
Yeah, absolutely. I think trust is more important than ever.

Jason Albert
Absolutely. It's the foundation, right? Data is the lifeblood of AI, and people won't let AI act on their data if they don't trust how it's going to act. So you're right. It's absolutely essential in this new age.

Patricia Stark
Yeah, absolutely. And Naomi, once that foundation is set, how do you turn reliable data into a product that actually improves a specific business outcome?

Naomi Lariviere
So for me, I think when we're talking about data, data alone isn't the sole answer. You know, I mean, we talked about it. You have to have context. You have to understand the employee, the organization, the workflow, what they have access to, what they what they don't have access to, and really what the moment is meeting. So if you understand all of that and you've got data paired with context, then you also have to think about, okay, well what problem am I actually trying to solve?

Because if you look at how AI can improve stuff, you can throw AI into anything, right? But I think what's important is what is the problem. Do you understand that problem? And do you understand where there's opportunity to augment with AI to help you get through to the right answer, and then that creates the value that you're looking for that outcome.