Mahesh Chayel
This as-told-to essay is based on a conversation with Mahesh Chayel, a product management lead at Meta. This interview has been edited for length and clarity.
Since 2018, I have mentored 12 startups, especially those operating in the enterprise and AI spaces. The biggest gap I’ve seen is essentially, why use Generative AI?
I work closely with early-stage founders to shape product strategy, refine their go-to-market approach, and explore how AI can be meaningfully integrated into their solutions.
I bring a unique blend of deep product experience at scale, having worked on Meta Ads, along with a strategic understanding of enterprise pain points from my time in Silicon Valley.
I also help teams clarify what real customer problems are worth solving, how to validate their solution early, and how to position their offering to resonate with decision-makers, especially in business-to-business environments.
This is what I tell founders who are building in AI.
1. Hyper-focus on the customer
I work with a couple of founders who are like, “Let’s use AI and then let’s build a product.”
If this problem could have been solved in other ways, what would it look like? And if they are using Gen AI, is AI the best use of technology in that case? In some cases, it can be.
As a customer facing a problem, you really don’t care how to solve it. It’s more about, are you solving these problems for the customer in a better way?
Founders usually start thinking: If somebody else is going to use the same idea, use AI or Gen AI to solve the problem faster. Founders iterate a lot and keep trying to solve the problem through different mechanisms.
Can you be super laser-focused on the customer? Can you really identify how this technology can specifically solve the problem? If it doesn’t, find other ways to solve it.
2. Get product-market fit right
People sometimes measure product-market fit in an incorrect way.
For some, even before building the product, their product market fit is not very clear.
It’s not about 100 or 10,000 people looking at this product and just being aware of it. Many times, the first-time sale is not a good mechanism because all of those metrics can be gamified.
You can spend a lot of money on ads and make the product available to a lot of people. You can provide discounts and sell the product first time to a set of customers.
The most important thing for product market fit is: Do your set of customers really love and trust the product to keep using it and coming back?
That’s the real crux of it. Product market fit comes down to retention metrics or the repeat purchase of a product.
3. Understand the business model
A startup I gave advice to was essentially building an AI tool for young adults — AI companions. The founder was not generating money from this.
There are a couple of parts. Young adults are not the real users who are going to pay for the product. You would need to identify who can really pay. It can be the parents, it can be the schools where these kids are studying.
It was a breakthrough to help this founder really understand the business model that can work in such a space. We uncovered that the market wasn’t quite there yet, at least not in the way the founder had imagined.
While disappointing at first, it helped them redirect their focus toward a more validated pain point, saving months of effort and repositioning the company for a better product-market fit.
Take a step back: Who is the product used by? Who is the product paid by? How can it scale?
As a founder, sometimes you get so attached to the problem that you don’t see the larger space.
This restart has helped the startup build a more sustained business now.
Whether it’s unlocking growth or steering away from misaligned markets, I measure success by how much clarity and conviction founders gain in their next move.
The post I mentor startups outside my Meta product job. I tell founders to take 3 steps to sell their AI vision. appeared first on Business Insider.
