From 14% to 98% AI accuracy: the 9-month context engineering playbook | Meenal Iyer, SurveyMonkey

Meenal Iyer (0:00)

I told my people, there is two options. You can either fear AI, or you can get ahead of it, and dictate as to what direction AI would take in your destiny and in your career. The challenge I see with organizations is, they did a good job with saying that, hey, you know, let's bring in Cloud, or let's bring in ChatJPG, or let's bring in Gemini across the organization. But then the focus was directly on the technology. Sorry, that's not how it works. Where exactly is it going to be used? What is the outcome going to be? Are we really looking to change?

Ameya (0:30)

So if I have to sort of summarize, essentially, don't focus on the technology, find the actual problem, and then see if technology can solve it, instead of work backwards on the problem.

Meenal Iyer (0:40)

You have to know that why you're bringing it in, and there has to be a reason for it. Like, are there processes you believe that actually need to be optimized? The tool can enable, but the tool cannot dictate strategy.

Ameya (0:54)

Welcome to AI Impact podcast.

Laridin is an AI measurement company. If you are spending a lot of money on AI, we tell you what works, what doesn't work. But we are essentially a measurement company.

But honestly, many years ago, they started one company that measured everything in the world. They measured sentiment, everything happening from sports to finance to business. And that company is SurveyMonkey. They are the true OG of measurement. And today, I have a pleasure to talk to Meenal Iyer. She is a Senior Vice President of Data and AI at SurveyMonkey. Meenal, welcome to the show.

Meenal Iyer (1:30)

Hi, thank you so much, Ameya. Thanks for inviting me.

Ameya (1:33)

Absolutely. It's really true, isn't it, that the SurveyMonkey is the true OG of measurement?

Meenal Iyer (1:38)

Absolutely. Yeah, data is our mode. And we truly believe that measurement is the way to go if you really want to manage something very well. So yes, we kind of lead in that space.

Ameya (1:48)

Absolutely. All right. Awesome. You worked for almost 25 years across many industries, from travel to FinTech to now SaaS.

Let's begin. What sort of transferred between these roles and maybe what you had to unlearn?

Meenal Iyer (2:04)

So I very intentionally moved across industries through my career. It's been 27 years, but then who's counting?

And I very intentionally transferred between industries for the fact that one, I wanted to learn new businesses with everywhere I went. And second, is I always believed people had new and different data problems to solve as we moved across industries. And so it just was, you know, you just get out of something and then you go in, you learn something completely new and different and see that if you can drive that similar impact. And it got to a point to where I now have a playbook. So when I go into an organization, I know exactly what to do to have data start to drive value. So that was the most important thing for me. I'm hoping I can do the same for AI as well, where I can get to a playbook of where how can we make AI successful within enterprises and how to truly build the blueprint of an operating model.

Ameya (2:57)

That's very interesting. We are going to go into AI, but since you touched upon this data piece, do you want to talk a little bit about this playbook you mentioned? What happens when you go into the organization and you have all this data?

What's that playbook looks like?

Meenal Iyer (3:14)

Yeah, so as you know, as I said, I jump into different industries. So the first thing I essentially focus on as I get in is to know that what this business looks like in terms of data. And from data, I mean, like in terms of metrics, what are their key KPIs? What do they really want to measure and what are they looking to impact across functions? So if you notice that across these industries that you said that I have worked at, the functions operate very, very differently. The things that they want to impact, the funnels, they all look very, very different. And so through each of these, that's one thing that's very important for me. So the first two to three months, I spend in fully understanding the business.

So almost I do like a quick crash course in that industry. The second thing I do is I talk to all the leaders to start with, and then I go down. So by the time I'm done, I'm speaking to about 100, 150 people across the organization, and fully understand one is where and how they use data. Second is how is that data impacting them daily? And then third is what would they do differently given the fact that now I have come in, what would they tell me should be my first focus and priority? I take all of that, and then I kind of consolidate the messaging into seeing like what my priorities should be.