🇬🇧 #36 - More Performance Through AI? The Future of Incentives & Sales with Caroline Rocha

Gregor Kohler: The Commission Corner learnings from CFOs, sales leaders and rev ups. This podcast is presented by your hosts, Gregor Kohler and Alexander Dosser. Have fun. Welcome everyone to a new episode of the Commission Corner. This time we have a guest from the incentive compensation space. And we also talk a lot about commissions and incentives today. So warm welcome to you, Caroline.

Caroline Rocha: Thank you very much. Uh, thank you for the invite. Super excited to be here having this conversation with you guys.

Alexander Dosser: Already looking forward to it. We do have um, around 25 minutes to speak about uh, sales compensation, maybe the effects of artificial intelligence in that regard and also, um, some compensation solutions. So Caroline, before we jump right into it, would you be giving the favor of introducing yourself, uh, to us and the listeners?

Caroline Rocha: Yes, absolutely. So I'm Caroline. I've been in this um, incentives and compensation world for over 15 years now. I've done a lot since the um, analytical operations parts to more strategic. So and now just trying to figure out as I guess everyone else how shape will, how AI will shape our new world and incentives. Right. Um, so yeah, in a nutshell, let's talk a lot about this today.

Gregor Kohler: Awesome.

Alexander Dosser: Yeah.

Gregor Kohler: And this brings us right to the topic of AI. I mean AI is now basically everywhere. I guess right now it's clawed. When we started initially was ChatGPT. Maybe it will change in the future. But right now everyone that's not using AI is probably left behind and also especially people working in sales. So question is Caroline, how do you see AI changing the way sales currently works or maybe also compared to uh, back then.

Caroline Rocha: Yes. So and I would split just for clarity for our audience, the sales compensation people and the sales people. Right. So AI for regardless which it's here to take over a lot of the manual things that are done. Right. So talking about a sales seller specific and then I'll link it to the sales compensation world. Um, so the seller himself herself also have a lot of data cleanup to get ready to a meeting to do research on um, their priorities. So I guess that's a big opportunity that I guess a lot of opportunities are a lot of companies are already taking advantage of. Uh, so that instead of the seller not wasting. Right. But taking a bit of his time to prepare for a meeting, then AI already does the work so that he can focus on the value adding that it's studying there and making sure that he closes the deal or making sure that he is really prepared for that meeting or for that tough conversation. Um, and then how is that going to reflect in sales compensation? I guess this is the challenge all of us sales compensation protectioners out there need to think because we've all have been talking a lot how we sales compensation may leverage AI to also have some gains in our day to day um, lives. Right. We do have a lot of operation stuff but. And then we can talk about this separately in a second. But how do we make sure we reflect on the KPIs or the targets this new work for the sales team, right? Because right now let's say they have more time to be focused on talking to the clients because some of the AI, some of the operational piece has been taken over by AI. But is this an assumption, right? Or is this a reality? Can we go ahead and make changes to targets based on an assumption? And I'm already going to tell please don't. You know, let's make sure that this becomes a reality before we are touching people's target. So I don't have the answer because I'm also learning a lot as I guess everyone out there. But just to name a few of the conversations we should be having if we're not, if you guys are not having already out there.

Alexander Dosser: So maybe a question, um, in that direction. You uh, mentioned that lots of manual work is put away from the sales rep and is shifting towards artificial intelligence. Uh, could be manual touch points, manual research. So sometimes maybe a manual cold call or an inbound call. Um, would you agree that looking at sales compensation we should make the shift even faster from the top of funnel incentivization to bottom of the funnel. So really output driven incentivization or does it still make sense especially for juniors to um, have touchpoint targets for example as a cliff.

Caroline Rocha: Well, it really depends on the role, right? So if you have more junior roles with perhaps a smaller pay mix. Yeah, I'm not saying that AI, at least for now, right? Because it's really hard to make long term predictions. But at least for now we should still have human in the loop people out there checking what AI is doing. So yes, those people are still necessary. And I mean what you call the junior people perhaps checking what AI is doing right or um, perhaps with a smaller pay mix, I don't know. It really depends on each company's strategy. But perhaps for the most senior sellers then yes, perhaps it makes sense that their KPIs is mostly changed or their focus has changed to that top of funnel. The really the quality partners. Right, the quality component, the big strategic um, um, um, how do I say the big strategic um, companies or customers or deals, you know, contracts um, so I guess this is really the way we are, um, the, the path we are following right now. And I say we, I, I mean the world, right? With AI and everyone else. But it's really each company must understand because it's also hard for me to say that everyone should be outcome based because everyone is such a broad statement. Right. Each company know what's important to them. I would say as a, as a standard in uh, this area. Yeah, it should be outcome based and we should be rewarding for the value they are bringing. But I guess there's still a lot that needs to be done on the input. Right? The people are out there checking if the calls are being made, if the meetings are being scheduled and all of that. So I guess it's a process that we should look at this point in time. AI is making us more effective, but there should still be a human uh, in the loop, at least in this first stage.

Gregor Kohler: Right. And um, maybe also talking about humans in the company or in a sales organization. There's not only sales, but there's also csm. There's usually also marketing involved and also customer support. Now the hypothesis is that with AI we get uh, more efficient, we get more effective and it also influences, let's say the outcome of our commissions. For sales reps, usually this means, uh, because they usually have a better commission part or let's say better higher commission variable involved in their compensation. How do we make sure or how do we ensure that it's still fair also compared to other functions in the revenue organization?

Caroline Rocha: Yeah. So the, I would say adjusting a seller target whenever it gets to that point, it should never be based on assumption. It should based already in a proven mathematical calculation that your performance is increasing, uh, driven by the AI efficiency and therefore the adjustment on the targets, it's not an increase but a, um, normalization um, of your new um, performance. Right. However, the other day I was talking to some other sales compensation colleagues and other companies because we are such a small community and we're all figuring this out together. And I reached out to some people that are already further along the AI journey in their company. Um, and then they shared something extremely interesting with me that the expectation was exactly what we're talking here, right? The expectation was that the performance would increase exponentially because the AI was um, doing like the operational work, right? The research before a meeting, the um, um, topics that they could cover or what's important to make sure that they would be able to close that deal. So if their AI is taking away that piece of their job. They would expect that the seller would be able to make more visits, make more meetings, make more whatever to close, close the deal. And they didn't see that happening and they are trying to understand why. And then one hypothesis, which is a big one, is that people were probably doing that work, um, after hours or in between or during lunch. So what AI and I've never thought about that before, that they told me that, right? So perhaps people are now just having a little bit more quality time in their life. Right? And so therefore they told me exactly that. Don't make the mistake to go, um, um, adjust targets based on an expectation. Make sure that this is happening. And if it's not happening, understand why the performance, the expected performance, uh, increase. It's not happening. Why, what's happening in there, right? And then you make the call to normalize targets. So that would be the advice that I give to people that are starting this path just like I am. Like all of us in this discovery. Just make sure you're doing stuff based on facts of what you're seeing happening, not assumptions.

Alexander Dosser: And that's such an interesting take to think about. That AI, um, fosters sales performance through happiness and maybe more quality time. Uh, as we all know that, uh, especially the top talents in sales usually uh, go the extra mile in the after business hours to uh, prepare all the things for the next day to still perform. And if AI supports that, we do have more quality time and that can uh, yield better performance or efficiency.

Gregor Kohler: Um,

Alexander Dosser: it's really cool, uh, because I haven't thought about that too. Um, when thinking of uh, target settings, uh, you mentioned that it shouldn't be based on assumptions but on mathematical um, data. Data. Do you have the historical view on um, your business to create targets for the new quarter, for example, or how do you in fact uh, structure your approach when it comes to target setting at your company?

Caroline Rocha: Well, um, not even my company. I would say, like, uh, hopefully everyone setting targets out there is based on historical data. Right. So we need to make sure targets, uh, they're founded in a strong, solid, uh, base. Right? Because it should be trustworthy, um, but based on the historical data. And then I would say if the early stages of uh, AI, we already see an improvement. And then you can exponentially take that into consideration in the math. So I guess every company depending on the product, so do they sell its products, its services, it's uh, con consultancy or whatever it is. Right. It might work differently, have longer or shorter sales cycles. All of that will influence. But what I can say is that it should be based on historical. We should be able to properly um, um, identify and isolate the impact of AI. And I'm not saying it's easy because so many things happen at once. Perhaps it was the launch of a new product, perhaps it was the, you know like the selling of something and a change. So it's really important that whenever companies get to that stage, right. Um, which I believe perhaps is not where um, everyone is at right now, but whenever companies get to that stage it's really important that they isolate the AI efficiency factor and then you clearly communicate that to sales team. Because I would say this is the, pretty much the most important piece because we need to work together with AI sales nor us nor anyone. Whenever we look at AI and I don't trust it, we're going to break, right everything. So um, sellers really, they should understand and um, see AI is helping their work and because of that this is the impact on targets. They should be taken along the journey, right. To really prove mathematically once everyone is there, right, um, what is happening to their targets. Because then if they mistrust or if they just understand, wow, am I being uh, punished by AI Then this is really the worst scenario we can get out there for compensation. So AI is helping us do our job better and this should really be the core message of it.

Alexander Dosser: All agree.

Gregor Kohler: Shouldn't be punishment at all.

Caroline Rocha: No.

Gregor Kohler: Um, all right Karen, now we talked about efficiency, um, brought by AI for sales folks, but maybe we can also touch upon another topic, the topic of incorporating AI in the product. Because what we see out there right now, let's take for example HubSpot as an example, they have launched Breeze AI which is also a consumption based part of their uh, of their model. I mean previously it was only seed based but now there's also a consumption based component added to it. And I guess that's something that we see a lot of throughout all products. Probably also have talked about this with other colleagues in the uh, sales compensation space already. How do you folks, I'd say factor this in, in the compensation plan for,

Caroline Rocha: for the sales reps. Um, so to clarify your question, you're asking about the tools out there.

Gregor Kohler: So um, how so? For, for example a lot of tools were selling seed based but now with AI there's another component added to it. It's seed based plus consumption based because a lot of uh, AI functionalities run on credits because there are a lot of tokens that need to be used from Claude OpenAI whatsoever. And this is basically a New type of um, how you close, how you um, set up contracts and how also targets probably are configured and how the variable pay needs to be um, adjusted.

Caroline Rocha: This one I think we need to. We're all surfing this AI hype, right? Everyone wants to go ahead, build their own solutions on top AI, you know, like, and there's this discussion, um, you know, like the AI internal tools or smaller tools that are um, being developed out there, the big ones. Right, the consolidated ones. And um, like even if we compare, compare startup in one of the big companies and we make a parallel to those new um, AI on top or new um, um, um, incentive compensation management tools. And the big ones, right, obviously the AI driven ones and with the more focused AI components they will move faster, obviously they will adapt faster, um, it's easier to code. But at the same time, is this um, is that all right when we're looking at uh, um, the tooling out there for compensation and everything, um, isn't the data accuracy, isn't the ah, audit trail, the internal compliance equally, or I would say even more important because um, a hybrid model right now would honestly be the ideal solution in my point of view. Because um, we still need the robustness of what we already know, right? So the trustworthiness of the data being able to have that governance to understand where data is coming from, from how everything is being calculated. Though sometimes those move too slowly, right? We need more dynamic settings and oh my God, there AI out there could help us out so much on identifying outliers, on identifying uh, weird behaviors or trends or stuff that we put so much effort in identifying and AI could so easily identify and that would be easily handled by those new AI features on top or AI, um, toolings out there. But that's not also, I, I'm sorry that I really don't have an answer for that. And I would say I still see that we need, we need what we already have out there, which is the, the trust, the governance, the data. But either those tools evolve faster or we start having hybrid models where those AI solutions are being able to embed it and read the data and integrate with bigger data sources out there, um, to give us the speed that we need.

Alexander Dosser: Yeah, I think you mentioned an important part of the entire AI era making decisions based on AI, for example, because in the past we made decisions based on uh, um, raw data that we took, took and we analyzed and now uh, we put raw data into a black box and ask questions to AI tools and um, they are using um, some sort of a black Box to give us an answer where we want to make a um, database decision on. So uh, we're also um, now mentioning uh, AI uh within sales compensation solutions. So whenever we um, see outliers, for example when it comes to sales compensation or maybe a target that was set wrong and AI can help us to identify those, especially in larger organizations. Uh, if it's a smaller team it's quite simple probably to detect them. But for larger teams um, AI can um, be really helpful in sales compensation solutions. Um, so question is uh, leading into uh, the area of what is one key factor of AI which helps you to create compensation plans. Do you have any, any thing uh, that you already utilize?

Caroline Rocha: Wow. I have a thousand things that are already utilized and it's really hard for me to say the, the top one because AI is from. And I would say we've all probably started with using AI to hey, help me write an email. Right? And that evolved. Hopefully it is evolving out there to help me think along. You know, like be my colleague, help me think, help me build solutions and then, and take it from there. Um, and I would say perhaps this differentiates into the maturity of each company. So if you have some bigger companies out there with already very structured data for sales compensation, perhaps you're going to use this for more tackling day to day job. Right? But we know that the reality for most companies is that the sales compensation team is quite reduced or sometimes people are using Excel sheets or Google sheets to calculate incentives. Right? So just imagine for that group of people, which I would say it's even the majority out there, the possibilities of using AI, right. Like I said in the beginning of our conversation, the human and the loop should still be there, right? They should still be checking because there are hallucinations. And I had some conversations with AI that I was like whoa, come back to earth, let's talk about that properly. But if for those people, not only the outliers, but help me figure out what is the best sales compensation, uh, design and this is what my company does and this is the strategy and let's benchmark and uh, what do you see out there? So help you think along and then when you're actually further down the process, um, help me create analysis for executive um, audience. Right? How are, how is compensation trending against the company's results? Ah, right. Is it aligned or do I need to review my KPIs? Um, and I would say you asked, um, the example here, I would say what I'm using AI the most. It's actually As a colleague, to help me think whenever I stumble across very complex, um, decisions or even AI is actually helping me a lot to figure out the new AI world. Hey, what do I do with this new scenario? What is the fair KPI? What is the fair way of looking, uh, to that and has been extremely helpful. So I would say operation wise, strategically wise, even to showcase your work. Right. There's so much that us, uh, sales compensation folks do that we fail to properly translate that into strategic language. And to putting sales compensation with a strategic lever. It is. Right. So sales compensation should not be. It should be reflective of the company's strategy. And one of the main levers that it's used is to drive the goals that the company want.

Gregor Kohler: And I guess you also have a lot of conversations, especially about how you want to align the compensation plan with the, uh, company's strategic goals with the C level, right?

Caroline Rocha: Absolutely. Yeah, absolutely.

Gregor Kohler: Is there something specifically that the C level asks you insights or anything to look at? Since AI has conquered our world, Part of our world.

Caroline Rocha: I guess the. The conversation right now is more in, um, um, exploration phase. Right. So once we reach the point where we want to be with AI, what does that mean to sales compensation? Because this really should be a conversation that we are having ahead of time. Right? Not whenever our AI is already out there and doing a lot of, um, that we should be, uh, that we should start having the conversation. Then what does that mean to comp to sales comp? Right. So the conversation right now, at least in my situation, evolves a lot into how do we make this fair to sellers? Uh, how do we translate the company's strategy whenever we reach goals A, B, C, and how do we translate that into compensation? And perhaps a lot will change. Perhaps not that much. So like I told you, like this conversation I had the other day that I was expecting a, uh, wow, everything there was a whole new status quo and it wasn't the situation. Right. So right now I would say it's really an exploration phase to m. Make sure that whenever it is here for good, for real, it is already here for good for real. But I guess that's, um, it even one step further that we properly and fairly and actionably and, and. And with trustworthiness and governance, we are prepared for that.

Alexander Dosser: Wonderful. Caroline. This podcast is coming to an end already, which is unfortunate because I think we could fill in another 30 minutes, uh, ease. But we've learned much about the, uh, influences of artificial intelligence into the sales organization when it comes to effectiveness, happiness, and, uh, yielding performance in the end, in a different way that we expected AI to perform, uh, within the sales organizations and also learned that, uh, the human should always be in the loop, um, never build targets up on assumptions and trust the data, uh, but don't put it just into a black box. So thank you so much, Caroline, for joining us today in, uh, the podcast sessions, and have a great day.

Caroline Rocha: Thank you.

Alexander Dosser: Thank you.