#192 - Slowing Things Down to Speed Up Research with Jared Forney of Okta

But what are some of the most important areas for research and research ops to think about when it comes to upskilling and evolving right now?

Yeah, there's a lot there, so it's a great question to start on.

I think, you know, the things that I know I'm thinking about a lot and my team's thinking a lot about are two kind of key functions.

You know, you mentioned scale.

So scale is really like one of the first ones is how do we grow not only research's impact, but the scope of our work and the scope of the people that we impact.

And I think the other one that we really focus on is rigor.

You know, especially those two things sometimes feel diametrically opposed because there's an increasing, there's always an increasing demand for research and research insight, which is a good problem to have.

But at the same time, They're, especially with all of the new tooling and, of course, AI enhancements that are coming with access to research tooling, there's an increasing demand for people outside of traditional research roles who have a desire to do research and research-like activities.

Kate Tauzy famously referred to these people as powders, people who do research, PWDRs.

The thing that's really most prevalent with that is the upskilling is coming from more than just the research team.

We're having to educate and help guide a new legion of these folks who are doing research activities, who may have had limited exposure to research methodologies before, who are not used to conducting research of their own at scale in a certain degree.

That increasingly is the challenge of not only researchers performing the day-to-day work of research itself, you know, whether it's generational, exploratory, the messy stickiness of people in general, but also like helping to enable and I will use the D word early here, democratize research outside of our traditional roles.

Because as we've seen with the AI revolution in general, everyone's roles are blurring together, right? Product roles are blurring with design.

Design is blurring with engineering.

Research is blurring with both those fields.

And so like increasingly, we're all being asked to upskill in different ways.

OK, yeah, great.

So that's a great place to start.

You mentioned scale and rigor, and I can imagine a sort of two by two.

You can imagine there's like an inverse relationship between these.

How do you think about them? Like, is it possible to have our cake and eat it, too? Can we get more scale and more speed and more rigor? Are they always, you know, opposing each other? How do you think about the tradeoffs there?

Yeah, I mean, it is a balance, right? I mean, I think my answer to that would be like, it depends on how big your cake is and how many layers.

I think it really, the balance comes from understanding and having a really fundamental grasp on what your goals and objectives are.

I think that's what something that like, as ironic as it is, sometimes I feel like that gets lost amongst higher levels of stakeholders of like, there's this frenetic energy that comes with all of these AI adoption models where it's like, well, we're already behind if we're not adapting all these tools and doing this and doing that.

But sometimes we lose track of the why and to what end.