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From Guesswork To Answers: How People.ai Transforms Sales

• Evan Kirstel

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Forecast calls shouldn’t feel like theater. We sat down with People got ai’s new CEO Jason Ambrose to unpack how turning raw activity—emails, meetings, call transcripts—into crisp answers can transform forecasting, end CRM fatigue, and give leaders the clarity they actually need. Instead of forcing reps to feed fields, the platform reads what is already happening and returns guidance you can use right now: which deals are at risk, which stakeholders are missing, and where to act to hit the number.

We trace People ai’s path from early activity capture to training models on billions of interactions across years of market shocks. That history pays off when patterns shift; the system sees signals static dashboards miss. Jason explains why most forecasting rituals only assign accountability, not risk, and how a risk-first approach reframes the conversation: timing, probability, engagement, and next best actions. The result is a plan you can execute, not a spreadsheet you defend.

We also dig into the CRM reality. Systems of record still matter, but the monolithic UI is giving way to flexible experiences, agents, and chat surfaces that pull answers into the tools your teams already use. Executives want to ask open questions—what’s happening in Japan, where a product stands—and get a directionally correct answer in minutes, not days. Agents are set to automate internal churn like account plans, win wires, and pricing checks, so sellers spend more time with customers and less time in tabs. Jason’s vision is simple and ambitious: make accurate, actionable answers available anywhere, integrate with the stack you have, and scale from pilot wins to trusted operations.

If you’re ready to replace hunches with clarity, tune in, share with a teammate, and leave a review so we can keep bringing you conversations that cut through the noise.

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SPEAKER_00:

Hey everybody, fascinating chat. Today we're talking about how AI is finally bringing clarity to revenue and sales with people.ai. Jason, how are you?

SPEAKER_01:

I am great, Evan. Great to be here.

SPEAKER_00:

Great having you. Really excited for this conversation as someone who spent 30 plus years in sales. Everything has changed. Before that, how do you describe uh the mission at peopleday.ai these days?

SPEAKER_01:

Yeah, so at People AI, we're focused on finding answers from the activity of what's happening in your sales organization. So to your point of everything is changing, what's what's different now and what's been hard in sales organization is to really understand what's going on and get action answers that are actionable. And so the way that we do that is our AI looks at all the activity, the emails, the calendar appointments, the transcripts of what's happening on the ground with customers talking with everybody in your field. And we use that to generate answers that come from our AI either out to users or other agents wherever you want those answers. So that's us in a nutshell.

SPEAKER_00:

Fantastic. And for what I'm reading, you've trained your AI on billions of real sales interactions over nearly a decade. That sounds amazing. Tell us about that journey.

SPEAKER_01:

It's been quite a journey, yes. So uh we've been at it a long time. And if you think about those 10 years, how much the world has changed. You know, we've had COVID, we've had geopolitical issues, you know, uh many different things, which helps give us uh a data set that's much richer and is working from periods of time that you just can't go back and recreate. Um, so the journey of the company has been to focus first on this activity capture of how do how do we actually capture what's happening with those calls, with those emails, with those meetings. And historically, we started with let's put that in CRM and attach it to records, which you know has a certain set of value, but uh reps don't like doing it. It's a lot of work to keep this activity up to date. And that just keeping the records of this activity is really just a means to an end. So the second chapter for us was taking AI and building our own AI to look at that information because we had that data set. We had unique proprietary information that we could really train our own models to generate some of these answers. Uh, and now with LLMs and people thinking differently about the experiences that they want to provide to their sellers or even other roles in the function, now that's a big opportunity for us to open up and be a platform for providing these answers. So it just comes back to your theme of the game is changing, the world is changing. There's a lot of things that we could do now that we we couldn't do before.

SPEAKER_00:

Fantastic. When the media talks about AI and sales, um, they think about these voice bots that are proliferating and you know, the uncanny valley of uh bots that sound human. But yeah what's the real impact of AI changing a sales rep job on a day-to-day basis?

SPEAKER_01:

It's a great question, and it's an interesting question. And I I uh because I think there's two things to you to what you said, I think the media is speaking very differently from what's happening on the ground. So I can talk about what we see in our customers, and you know, I we talk about this notion of everybody's on a maturity curve and a and a journey, right? And I think we know about the idea of experimentation, but how does that move into actually operationalizing and how does that move into transforming the sales organization? So for us, one of our biggest customers and our inspiration is Red Hat. So Matt, the CEO, has started from his chair all the way down through the whole organization to say we all need to be thinking about what is the agentic complement to what we do on a daily basis. And what he has found in their organization is there's sort of this pyramid where this sort of at the at the ground level for the sellers, there is a lot of focus on automation, right? You know, what is the day-to-day activities that I can take off your plate? Mostly in the realm of taking you out of the systems in the tabs, right? So we have this expression that we believe in uh people need to work with people, and AI does the rest. It goes back to the brand. But uh what we've all struggled with, especially as sellers, is we don't like to spend time entering information into fields, bouncing between tabs to deal with data, to provide answers of what my forecast is going to be. You got into selling because you want to sell to people. You want to work with people, right? And I think that that's what's really starting to change at the ground level for sellers. But coming back to what Matt has found in the rest of the organization, that's really where a chain of thought is landing, and there's higher reasoning use cases to help, let's take somebody like Matt, plan out the, you know, how am I going to get to my growth targets for the year? And for that, you need this kinds of answers that we provide in our solution. But I think we're seeing this from the ground level. There is still a lot of focus on automation so that we can lift you into higher level questions. And then that's rolling up into the organization to solve uh bigger, more abstract issues with AI.

SPEAKER_00:

Fantastic. Another big issue need solving is revenue forecasting. Um, always been a somewhat broken process. Best guesses and hunches. Um, there has to be a better way. What's the big idea that you guys have come up with?

SPEAKER_01:

Yeah, so we think differently, like so. From my experience of having spent in a ton of forecasting calls, I saw a lot of this at Annaplan with our customers and ourselves. You know, we we've absent anything else, we've accepted this idea that, you know, we we need to run a process. And to your point, like people make commitments, but there's a lot of judgment on that. And what the forecasting process does and the way that we do it today is really just assign accountability. It doesn't really identify or assess risks. It just says, hey, you are committing to deliver$4 million. We talk about some numbers of how you're going to get there, but the reality is we don't really care how you do it. You just need to get the$4 million, right? The issue with that is then there's bias of how you make these commitments. And we all know it and experience it of reps or sales managers sandbagging numbers or feeling like they have to because as soon as they put something in, uh, everybody uh, you know, crawls down their throat about what the what the number means. And so it's just inherently a very biased process when it comes to managing a call like that. What you really want from forecasting is something that surfaces to you. What are the risks that I have in getting to that number that I can go act on? And what are the ones that are big enough for me to pay attention to? So back to this question of the answers that we generate, we put those in the context of the timing and the probability of your deals. We want forecasting to be a start to the set of questions of how you're going to go deliver that number as opposed to just a number roll-up exercise.

SPEAKER_00:

Interesting. Um let's talk CRMs, the cornerstone of any sales rep's day. I feel like CRM is was the bane of my existence for for decades. Still is. I know I should be logging all my activity and leads and but it just doesn't work. And um even companies in the space, I think, recognize there are big challenges with CRM. Even the leader, I think Salesforce is talking about rebranding to something else. Yeah, that was wild, wasn't it? I mean, yeah. So why hasn't it worked? And you have some ideas to fix it.

SPEAKER_01:

So I spent many years uh working on CRM implementations, and I I think my uh the the crystallizing experience was when I was at McKinsey. So I was quote unquote the Salesforce guy. So anything that came up in the firm, you know, from clients, it would come to me as if there was a Salesforce topic, it would I I would be the one who would be the expert who'd get on the call. And I spent most of my time there dealing with customers who said, I I got to get more value out of this thing. I hate this thing, my reps hate this thing, you know, what can I do about it? Which was surprising to me because some of those were, you know, up on the posters that were sitting in Dreamforce, right? And I always felt it instinctively of it's just a very manual and burdensome experience, and it didn't feel like I was getting anything out of it. Um so it was it was wild to spend my time and hear that level of feedback from at uh senior levels in a lot of organizations. So, you know, why is that and what needs to change? I think CRM fundamentally came from this place where we didn't have information to work with, we didn't have data sources, so we asked the reps to sit down and put this in, this information in. This goes all the way back to SEBL, right? So this, you know, this forms-based interaction uh and extraction of information out of reps where they they may or may not be helpful to them. It's not their priority to put in the information. Again, you've got these bias, it just fundamentally created a difficulty and a flaw in the quality of the data, but a burden on the sellers because they feel like, what am I getting out of this? I just need the system to tell me what to do. So I do think there's for a period of time a place for CRM to still be this system of record of how we think about accounts and opportunities and contacts and structured data because we've built so many workflows against those, but that needs to be complemented with a better experience for reps and the ability to get these answers wherever you want them. So what's interesting to me is I see a lot of our customers start to break open the wall garden and they're saying things like, look, I don't want to go back to one big monolithic UX to rule them all because it was so difficult to work in Salesforce or Dynamics or whatever else. And maybe there's a place for that for certain workflows, but the agents that I'm building reps may need to get direct access to that. Or I'm thinking about how to remix this in different UXs that I own. How much am I going to put through a chat GPT or a cloud and for which roles? So they're really resetting the question of what that digital experience looks like. And I think if you say that and you say, what are the data sources that matter, then you start to ask questions about what CRM is supposed to do and is it really worth it?

SPEAKER_00:

Yeah, so well said. Um 2025 has been a big year of experimentation. Uh, lots of AI pilots and you know projects happening at once. One insurance company, uh, at Gartner Expo a few weeks back, talked about 78 pilots they were running in the company at once. How are you helping your customers move from this testing and trialing to trusting AI?

SPEAKER_01:

It's a it's a great question and an important one as you start that experimentation of where do you go next with it? And uh, you know, with a platform like ours, there's a lot of different ways that you can use the answers that we generate. And we try to be very purposeful in the journey that we assist our customers with in our customer success organization. You know, my friend Natalie has done some great work in setting up a program to take people through these so that we understand, okay, when you experiment with us and you see what we can do in the platform, we try to take the friction down as much as possible so that you can get real answers out of our platform with your information to for the light bulb to go on. But then when you're experimenting, what are you experimenting for? Right? What is it that you are trying to learn that takes you on that journey? And then as you're designing that experimentation, always having an eye for what is it that we're looking to change, right? So whether it's a specific process like forecasting or uh maybe some other cases about the types of information you're bringing to your sellers or your leaders, we want to understand, have the end in mind as we think about that experimentation and make the decisions to move into operationalization and then effectively transformation. That that back half piece of like how do you transform a business? It's not really new muscles, right? It's just we've got a new thing that's driving it with AI, but you know how to change an organization. That part really isn't that different. It's it's got to be recalibrated for the speed at which you can bring these innovations forward, and you got to get comfortable with that. But you know, change management is change management on some level.

SPEAKER_00:

Absolutely. Let's talk a little bit about people.ai and where you're delivering the most value. How is it enabling you know, customers to do things differently day to day?

SPEAKER_01:

I think one of the most impactful ways is, you know, if you think about being a CRO or even a CEO or a sales leader, ask them a lot. Like, what is your frustration when it comes to working with the way the process operates? You have all this information, right? Why is it still so difficult? We've been doing this for 20 years, we've surfaced all kinds of data, we've got all this smart AI doing all these things, and invariably I get the same reaction as I just want the answer, right? They'll have a question like, what's going on in the Japan market? You know, we built this product. Where do we stand? How does that work? What's happening in this account? That feels like a simple exercise to ask a question and get an answer. But what happens is, you know, somebody has to run off, pull information from a lot of different systems. You know, you're working off of structured data that doesn't tell the whole story, and which should be a you know, five, 10-minute response becomes a weak response, you know, where it takes days to go process the information and it's got to be massaged through all the layers so people feel comfortable with that sales leader is feeling or hearing in the answer. Uh, and that's frustrating for executives, right? So when they see things like I can pull up Claude or Chat GPT and I can ask the question of that prompt, and it's gonna go talk to our AI to go find those answers and lift those out and tell you exactly what you said. And maybe it's not fully right, but it gives you enough of an answer to go explore, you know, with your team, is this right? And for the most part, directionally, it's it's pretty darn close. That's a very powerful experience for them because it's not tied to any processes, like it doesn't just have to be forecasting. We we can keep it totally open-ended for anything that's strategic at their level. We do have impact lower in the organization, but you know, to your question, that's the one that I think is the oh wow moment that we see from executives.

SPEAKER_00:

Fantastic. And as we're almost at 2026, we're excited about AI agents becoming real. Um, do you do you see them changing how sales operates uh into this new year?

SPEAKER_01:

I think they do. Um and I think it gets back to what's the work that you're doing, right? So let's let's start simplistically at at the I shouldn't say simplistically, but let's start at the base level of the rep, right? Think about how much time goes to you know, filling out your account plan or filling out your forecast or uh communicating back into the organization or trying to get insights of what you can or can't do in pricing. I think AI is gonna do a lot of that work for you, you know, even things like writing the win wire after you've won a deal or a win-loss report, right? There's there's a lot of time that gets pulled back into the organization. And you know, there's a number of studies that quote ranges of half the time to 70% of your time, you know, back internally instead of being out with customers, right? I think that's gonna flip with AI, right? So these agents are gonna take care of a lot of that internal work, a lot of those working with system types, activities, communicating information to where now sellers are gonna have a lot more time to ask uh thoughtful questions about how am I gonna get to my number? How am I gonna move this account? Who do I need to get to? And what do I know about that executive and how I can influence them? Right. Uh we've been kicking around the idea of you know, these shadow agents that sort of model your top sellers and give you a, you know, what would your top seller do type capability to the rest of the sales team. So, you know, if you have questions about, hey, how how would the top seller approach this situation or handle this objection? You know, if you're tracking the information of everything that top seller has done, that AI should get to the point where, you know, you've effectively got your best seller in the organization asking as a coach for you. So all of that leads to, I think for the seller, a lot more time thinking about the customer, interacting with the customer, getting out with the customer, and doing the right activity that actually makes you more productive.

SPEAKER_00:

Fantastic. You were recently appointed CEO at people.ai. Congratulations on that. Thank you. What are your uh what's your vision for next year? Where as you begin to get settled in and you know, get into fighting shape. What uh are you excited about uh during the year, people-wise, process, roadmap, and and otherwise?

SPEAKER_01:

Yeah, so I'm really excited about this idea of of having us focus and follow through on the question of uh how we deliver these answers. There's just we find so many different ways that we can do this. And historically, we've we've focused on very specific processes like account planning or forecasting. Forecasting is still very important in what we do, but particularly because there's so many different ways that RAI can help with these uh productivity questions and things that all parts of the organization want to do, we don't have time to get at everything. And we see great prospects for collaboration with in-house tools, with other agents, with other platforms. The how you take actions and what you automate, there's just so much activity going on there, but it all benefits from having our answers. So taking this focus to say, look, we want to make it as open as possible. We want to get in and find all these use cases for customers. We want to lean in hard on this question of how our AI generates these answers and how do we make them actionable, how do we make them clear? Uh, you know, there's there's lots of detail in providing good answers that needs a lot of innovation from the AI itself and that specialization. So I'm even as I say that, I'm so excited about the results that we see when we plug this into. To our customers and their reactions and how often we hear this. I can't believe you guys can do that already. I want to hear that a hundred more times than the times that I hear it today. So that's what I have excited. That's what excites me about the next year.

SPEAKER_00:

Yeah, and with customers like Red Hat and Datacoo 5.9 Notion, these are all companies I know and trust. That's that's quite a uh uh quite an opportunity with those kind of customers. Well, thanks so much for joining, sharing a bit of the vision. Really intriguing work you're doing.

SPEAKER_01:

Absolutely. Thanks for having me, Evan. Glad to be here.

SPEAKER_00:

Thanks so much. And thanks everyone for listening, watching, sharing the episode. Be sure to check out our TV show, techimpact.tv on Bloomberg Television and Fox Business. Thanks, Jason. Thanks, everyone.

SPEAKER_01:

Thanks, Evan.