# AI winners move from adoption to measurable growth

Markdown copy of https://realalpha.app/s/ai-winners-move-from-adoption-to-measurable-growth/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy.

- Speaker: [Asha Sharma](https://realalpha.app/people/asha-sharma/) · Asha Sharma is chief vice president of product for Microsoft's AI platform. · Ran Microsoft's CoreAI product org and Instacart's operations before leading Xbox, after scaling Messenger at Meta.
- Host: [Lenny Rachitsky](https://realalpha.app/people/lenny-rachitsky/) · Host, Lenny's Podcast · Spent seven years at Airbnb; his product newsletter has over 1M subscribers.
- Show: Lenny's Podcast
- Episode: [How 80,000 companies build with AI: Products as organisms and the death of org charts | Asha Sharma](https://realalpha.app/v/J9UWaltU-7Q/), Aug 28, 2025
- Moment: 9:05 to 16:15 of the episode (7 min), playing on https://realalpha.app/s/ai-winners-move-from-adoption-to-measurable-growth/; the episode from there: https://realalpha.app/v/J9UWaltU-7Q/?t=545

## Quote

> “And we saw a massive difference from when we used, you know, synthetic fine tuning to when we annotated **600,000 patient physician interactions** by experts”
> — [Asha Sharma](https://realalpha.app/people/asha-sharma/) at [15:25](https://realalpha.app/v/J9UWaltU-7Q/?t=925) · Lenny's Podcast

## Takeaways

1. Make teams AI-fluent before redesigning processes.
2. Measure impact and connect AI work to business outcomes.
3. Expert-labeled data helped raise Dragon’s acceptance rate to 83%.

## About this segment

Asha Sharma lays out a three-phase path for companies adopting AI, then explains why many efforts fail. One example shows how expert-labeled data raised a medical product’s acceptance rate.

From “How 80,000 companies build with AI: Products as organisms and the death of org charts | Asha Sharma”, Aug 28, 2025. [Watch the full episode](https://realalpha.app/v/J9UWaltU-7Q/)

## Transcript

Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there.

[9:11](https://realalpha.app/v/J9UWaltU-7Q/?t=551) of those things too. Let me kind of zoom out a little bit and ask you this question. You work with a bunch of companies that are building AI products on your platform, other platforms. I imagine some just do an awesome job and are killing it. Some are struggling. What do you find are kind of common patterns across the companies that do really well and have a lot of success building really successful AI products and ones that don't? Yeah. So I think there's things that are kind of more broadly applying to the organization themselves. And then there's things that are applying to the people who are building the AI products too. So more broadly, I think there's, there's a pattern that's starting to emerge for successful companies. Like one is they are embracing AI and everybody becomes AI fluent. So I think everybody's using some sort

[10:00](https://realalpha.app/v/J9UWaltU-7Q/?t=600) of copilot or some sort of AI in their day-to-day workflows, like job one. So everyone's not afraid of it, understands how it can raise the ceiling and kind of lower, lower the floor for like all sorts of skills and tasks. Number two, from there, they start to say, okay, how can I take a process that already exists and apply AI to making it better. That might be something like customer support or taking fraud down from 15 days to kind of cure to 10 days.

[10:26](https://realalpha.app/v/J9UWaltU-7Q/?t=626) And like going through that entire loop of mapping out the process, applying AI to it, seeing some sort of impact and then feeling the P&L or the kind of intrinsic benefits that that looks like. The third thing then is like, okay, great. Now that you've seen impact, everybody is using it. How do you actually use it to inflect growth? And that can be something like improving the customer experience.

[10:49](https://realalpha.app/v/J9UWaltU-7Q/?t=649) So your LTV or retention improves. It could be co-creating a new kind of set of concepts or categories. It could be, you know, going from agents that are embedded to agents that are embodied and then being able to take on, you know, exponential number of tasks. I think that where companies fail is that they're doing AI for AI's sake. They have a ton of projects that they're kicking off at the same time without a blueprint to understand how it actually worked from what their stack looks like. And they aren't treating it like a real investment. And so they don't have the measurement and the observability and the evals all kind of set up. It's going to do that end to end. I think the tricky thing is for enterprises is the technology is changing. There's something like 70,000 enterprise tools like in the AI space

[11:34](https://realalpha.app/v/J9UWaltU-7Q/?t=694) launched last year. It's really hard to know which one you should use for what outcome. And so you really need to bet on a platform or some sort of app server type layer that allows you to swap things in and out and not really be beholden to anything any any one technology or any one tool because the reality is is the whole thing is going to change feel like you have to actually build for the slope instead of the snapshot of where you are so that's That's kind of what I see at the enterprise level. I think the builders themselves are actually changing pretty fundamentally too, right? Every single advent, like change of technology has invented like a changing set of roles, like mainframes to PCs, like the whole garage engineers.

[12:17](https://realalpha.app/v/J9UWaltU-7Q/?t=737) And then when we went from, you know, server to cloud and mobile, there was like SEO specialists and cdns and you know growth pms and uxr and and and you know front end back end and yada yada and now i think we're seeing this advent of the polymath and where um i think that full stack builders are kind of having their renaissance where if you take like an average organization it takes probably 10 steps to launch a product um it could be security review it could be spec it could be you know user research and there's what five plus functions maybe maybe six or seven i'm being generous for for a normal organization and then you have like six or seven layers so all of a sudden you have 500 different touch points that have to happen to get a product out and when there are 500 models available a week or 500 new technologies that is insufficient and so i really

[13:16](https://realalpha.app/v/J9UWaltU-7Q/?t=796) believe in the concept of a full stack builder. You're seeing it with a bunch of the AI native companies that are coming up. I'm even seeing it in enterprises that have been around for 50 years starting to operate in that way. And I think that gives you velocity and throughput and then gives you the whole loop to start to actually metabolize and go through that much faster. That's definitely a recurring theme in these conversations is just kind of the Venn diagrams of PM engineering design are starting to converge and more and more of other disciplines within your role. So PM needs to level up on design and or engineering. Yeah, I completely agree. I think it's all about the loop, not the lane here. And so I think that whatever function you are, you have to be obsessed with trying to understand the efficiency or the cost of the product,

[14:05](https://realalpha.app/v/J9UWaltU-7Q/?t=845) the actual rewards, or system design that you're going after, the actual UI, UX, how that actually manifests for agents or people. You have to start to get really good at that really quickly. I like this phrase. You just use the loop and not the lane. Can you say more about that? Oh, it's just going back to our previous discussion on, you know, the signals loop and products evolving and becoming these living organisms and not these artifacts. And if you think about getting really good at that loop, I think that is the product. That is the IP. That is the future of every organization. And I think feedback becomes continuous and observability becomes the culture. And I think that functions start to blur in future workforces.

[14:48](https://realalpha.app/v/J9UWaltU-7Q/?t=888) To make this even more real, is there an example of a product or a company that is a really good example of doing this well, living this kind of loop life? I think most companies that we're seeing in the space from an AI perspective are doing this. I can tell you about a couple that we're working on. Obviously, in the coding space, you mentioned cursor. GitHub has very similar features that we're using kind of as an ensemble of models that have been fine-tuned across, you know, 30 different countries, all of the languages to actually then go iterate in a loop for next edit suggestions or code completions and things like that. We've got an AI product called Dragon that's for physicians. And we saw a massive difference from when we used, you know, synthetic fine tuning to when we annotated 600,000 patient

[15:40](https://realalpha.app/v/J9UWaltU-7Q/?t=940) physician interactions by experts and actually fed that into the model and continuously optimized it to then produce like, you know, I think we're sitting between 30 and 60 character acceptance rate depending on the run to something like 83%. And so that required a small group of individuals, not a large. organization that we're able to actually iterate in this loop across functions and kind of all of those lines dissolving. That's super interesting. So kind of what I'm hearing here is if you can