Building an AI-powered Intelligent Enterprise: How a Data Leader Steers Her Team Through the AI Journey - Meenal Iyer
Yoni, our CEO & Co-Founder, recently sat down with Meenal Iyer, VP of Data at SurveyMonkey, to discuss her team's AI journey. The conversation was enlightening - covering the journey through '23-'26.
I recently started recording a podcast with leaders and influencers in our space, specifically ones who have worked to build something of substance with AI. The last episode I recorded, and will be available soon, is the one with Meenal Iyer. I consider Meenal to be a friend, and excited to have been able to hear the story in her own words.
Meenal is the Vice President of Data at SurveyMonkey. She leads the company’s efforts across data, analytics, data science, and AI/ML—driving the transformation of SurveyMonkey into an intelligent enterprise. Her work has delivered measurable impact on revenue and operations, shaping everything from pipeline quality and customer retention to upsell opportunities and product roadmap decisions.
With more than 25 years of experience spanning SaaS, financial services, travel, and retail, Meenal has built a proven track record of creating data-first organizations. Her expertise ranges from embedding AI and advanced analytics into enterprise workflows, to establishing governance and ethics frameworks for responsible AI, to building innovation labs and data-driven playbooks that unlock new growth. She partners closely with C-suites and boards to turn insights into strategy.
Recognized as one of the Top 100 AI Influencers of 2025, Meenal also advises venture capital firms and AI startups, helping shape the next generation of data and AI innovation.
Step One: Hands-on experimentation, laying foundations
Many of us remember that fateful day, on November 30th, 2022, when ChatGPT was released to the world. It was a moment that changed the course of many people, and businesses, forever. Probably even humankind.
At the time, Meenal, like many of us, started experimenting with ChatGPT. Learning what it can do, what works, what doesn’t and how you can benefit from it. Throughout 2023, she built her own muscle of using GenAI for various needs and purposes, personal and professional.
Since Meenal has been in the data space for over 25 years, she obviously started thinking of how this can be applied to data and analytics. However, the times were early, and GenAI wasn’t yet reliable enough to use it in this space.
As 2024 rolled around, Meenal’s team had a top-priority project centered around “Data Literacy” - helping business users at SurveyMonkey learn how to leverage the organization’s data better when making business decisions. While this project did not have any AI associated with it, it did provide a major advantage in this journey: to succeed with data literacy, the data itself needed to be better organized. Meenal’s team spent 2024 laying the foundations for a clean, easily usable, data mart.
This data mart would prove useful for both human users, and AI. It is this solid foundation that makes the future initiatives possible.
In parallel to the work on the data literacy project, Meenal asked her own team to begin experimenting with AI themselves. It was important for her to build AI-literacy within the team as an investment for the work she envisioned will come in 2025 and beyond.
Step Two: Massive experimentation
I often say that these days, in 2025, it feels like we’re all in this global experiment: teams from around the world (both in D&A and outside of it), are experimenting with AI - learning what it can do well and what it can’t, identifying its potential but also the challenges on the way to becoming production-grade.
Meenal’s team is no different, and actually is in more of a leadership position than most I speak with. Meenal has actively been encouraging her team to experiment with AI - build applications that use AI to make data more accessible and more useful for the business.
As part of this effort, her team does AI hackathons on a monthly basis - four hours on a Friday, are dedicated to allowing people to build whatever is on their mind. This not only allows her team to get their hands dirty, but also results in some very interesting applications. She also observed that many members of her team will continue to work on those applications even beyond the hackathons, as they are quite excited by the work.
Some of these applications are truly promising, and will likely serve as the foundation for production-grade applications in 2026. Many of the projects her team will be working on in 2026, are conceived in these AI hackathons.
Step Three: From experimentation to production
As 2026 rolls around, Meenal predicts that a good portion of her team’s work in the new year will be building production-grade, AI based applications of two main types:
“Chat with your data” - make data more accessible to business users who do not have SQL skills or do not fully understand the data. This is an extension of the Data Literacy project her team started in 2024. Back then, it was about helping business users understand the potential for data to help them in their day to day, and with these chat capabilities they will be able to leverage that data with ease.
Automated, AI-driven, workflows - here Meenal’s team will select a few repetitive, valuable, business workflows that can be partially automated through AI. For example, consider a marketing campaign executed by the marketing department - an AI workflow can evaluate the results of that campaign, live, and offer courses of action to grow (or improve) those results. Such an AI, under direction of a human, can even make some modifications to the marketing campaign.
By automating part of the work of analyzing a marketing campaign, you are not only saving time for the marketing team (efficiency), but also driving up their results (effectiveness). Meenal stressed the importance of the latter, as much more of a driver than the former.
You’ll notice that in both types of applications, AI is augmenting humans, not replacing them. And in both types of applications, data, which mostly resides in their data warehouse, is the key. This is core to Meenal’s approach. She marries it with effective communications, and building trust in AI, as a critical ingredient for success.
She predicts that this coming year is when her internal customers will begin experiencing true, tangible, and considerable, value from GenAI. We, at Solid, are excited to partner with Meenal and her team on this journey.
Many D&A teams are in a similar boat as Meenal’s team - a transitionary period from the “old” way of doing D&A, to the GenAI-way. It’s exciting to consider what the future might bring!
I thank Meenal for spending the time with me on the podcast, and will share the episode with you all soon enough. In the meantime, if you would like to learn more about Solid, reach out to us.