Clari’s AI Survey Results – Techstrong AI Podcast EP26

Hello, and welcome to the Techstrong AI Podcast. I'm Amanda Razani, and I'm excited to be here today with Julien Sauvage. He is the General Vice President of Clary.

How are you doing? I'm doing great, Amanda. Hi.

And uh, good morning everybody. Can you first share a little bit about CL and the services provided by CL and then a little bit about your background? Yeah, of course.

Uh, so cl we're, uh, an industry leading, uh, AI powered revenue platform provider. Um, so B2B SaaS. Uh, we help companies really optimize their end-to-end revenue process.

Um, customers would include Okta, zoom, Workday, Adobe, and, uh, and others. Um, and my background has always been kind of like, uh, that intersection of AI and, and revenue. I am French, as I'm sure a lot of you can, uh, can, uh, can imagine.

Uh, and I actually majored in math and I was doing AI in France before AI was, uh, was a big deal. And then I came to the States about 10 years ago, uh, and worked with, uh, companies like Salesforce, gong and Clary, which are very much in that revenue space. So I've, I've always been really fascinated by the intersection of, of AI and, and revenue.

Wonderful. And as we've seen, AI has just exploded over the past couple of years, Correct? Yeah.

Past, I don't know, 18 months or something, right? Mm-Hmm. It sounds crazy, but it's, it's still pretty new in a way.

Yes, it is. Well, that brings us to our topic today. So y'all published a recent survey and, um, it shows that companies are leaving a shocking amount of revenue on the table because they're not making the most of AI technology.

Yeah. And in fact, uh, one of the pieces of the report says, revenue leaders report losing out on 26% in global revenue. So my question for you is, how and why is that?

Yeah, that's a good question. Uh, so that, that phenomenal, uh, phenomenon, uh, we call revenue leak, uh, and the definition for that thing, revenue leak is, um, breaks in your revenue process. As I said, clarity has always very much been in, uh, of the, uh, of the companies who believe that revenue is not just an outcome at the end of the year, at the end of the quarter, at the end of the month.

It's really a process that has to be governed, end to end from data to results. And so with that in mind, what we have seen is that in that process, there is breakdowns, right? Certain times, uh, the rev ops leaders are not able to capture the right data to give the visibility and the governance to the leaders.

Uh, sometimes your front client manager, your account executives are not able to act on that data. So there's a lot of like, potential sources of, of revenue leak. And so the report pretty much discusses, uh, the examples of said revenue leak and tries to, uh, quantify the impact and give solutions for leaders to implement in their organizations.

Okay. So maybe you can go into that in more detail. So data seems to be at the root of a lot of it, and I know 67% of these companies losing out on the global revenue currently are planning to use AI to improve their revenue this year.

So how and where do they start this endeavor? And maybe you can start with these use case examples in the solutions. Absolutely.

Yeah, yeah, yeah. Let's start with the use cases maybe, and then we can talk about the actual kind of like solutions. Of course, it's, it's gonna be a multi-year journey, right?

Like 26% of revenue loss, uh, loss to revenue league. Like that's such a massive stat. So it's not like with a blink of an eye, you can just go and capture 20 of the 26%, like it's, you, you embark on a big strategic, uh, uh, AI journey and revenue platform journey.

So examples of revenue league to start there really span across the funnel and across the personas. So the way we think about funnel at, at Clari for the revenue teams is three phases. Create pipeline, convert pipeline.

So move deals forward faster, and then close the pipeline. We talk about create, convert, and close all the time. Create convert close.

And so then alongside those three dimensions, which we did is we surveyed our, uh, respondents, uh, I think it's over 450, um, companies, right? And we asked them, we, we had like a list of 10 revenue leak examples for create, 10 for convert, 10 for pipeline close. And we asked them to just like staggering them.

Uh, and if you take a look at the report, I don't, um, I'm going to take a look at my screen here 'cause I think I have all of that data. Yeah, right here. Um, top examples for pipeline create would be of revenue league would be poor and slow upsell, um, across, uh, and cross sell pipeline tracking or missing or broken marketing to sales, uh, uh, uh, lead handoff in effective pipe generation programs.

Those are examples of revenue leaks that concern the pipe create. And then pipe conversion would be very much around unsure where the pipeline or is slip is dropping off, uh, uh, inability to diagnose a deal progression, which is very much linked to the lack of data. Uh, and when it comes to closing pipeline, some of the top examples of reported revenue leak would be slipped deals.

So it's typically one of those where you think your deal is gonna close next week and then out of the blue out of the sudden that just slip to the next quarter and then just keeps going. So those are example of, um, of revenue leak across pipe, create, convert, and close. Um, pause here.

That was a long answer. I know I still gotta answer the, okay, what do, what do you do with it? But does that, that make sense, Amanda, do the, these examples make sense to you?

Yes. Thanks for sharing the, those examples and, and feel free to move on to now what are the solutions? Yeah.

So the solutions, uh, are very much, uh, um, kind of like, um, you know, like not every company has the same sources of leaks, right? Like certain companies are more mature, uh, on the process sides of things. Others are better at ai, others are bit better at data.

Uh, this may be just a stronger salesman shaping others. So we recognize that if you were to ask 10 companies, uh, what are the top sources of revenue leak, uh, you probably would get 10 very different answers. But also you probably, the first answer you would get would probably be, I don't know, like people don't really know, right?

'cause it's part of the, like, it's in the process. So sometimes people know where the process breaks down, but sometimes they just don't know. So we always say our POV here at PLE is always diagnose, diagnose your areas of revenue leak.

You can do that with, um, you know, our products. And, uh, there's also other products out there. Um, but like the first thing is be like, run a really tight assessment of your revenue process so that you know, in your funnel and for all the, the people in the roles and your revenue team, where are the sources of revenue leak?

So that's step one, identify and di and diagnose and assess. Then step two is really the solution is implement what we call a revenue cadence. So a revenue cadence is, uh, in a way, uh, what we like to call the operating system of the revenue team, right?

So it's a very prescriptive, highly detailed step-by-step playbook in which you are able to nail the big revenue workflows that matter, uh, across all these revenue movement. So I'll give you, again, I'll give you examples. One of them would be, um, uh, a team performance, uh, cadence.

So we defined all the workflows and all the touch points and all the meetings that need to happen so that the team performance is always enhanced based on, you know, all of that data and based on the identification of leaks. That's one example of a cadence. There's seven cadences in our playbook, but we believe that the solution to, uh, stopping the leaks is to implement a fully governed revenue cadence strategy that really will drive execution up and down the org.

Wonderful. So what are some of the roadblocks that these companies encounter as they begin to implement these solutions and Trying to harness the full potential of ai? Yeah, good question.

I mean, there's so many of them as we know it. As I said at the beginning, it, it was easy. Everybody would be doing it, right?

So it's not, it's not that easy. I would say the first roadblock is, um, the legacy systems or getting in the way. I think, and I've seen that firsthand at pretty much every, in any company I've been with, uh, to like, there's still no process in place, right?

People, um, to kind of try to know what deals are slipping and, uh, what's happening and gain visibility into, uh, their current, uh, quarter pipeline or the next quarter pipeline. They start like cobbling together data sources from Excel and CRM, and then they realize that they also need another source of data. So they can, they would like put that together and then they're like, oh, I can't visualize that in Excel 'cause it's too big.

And then they go to their BI tool and they, so it just becomes like a very and like non governed process to kinda like try to, you know, sift through all of those data sources across all those disparate systems. Um, so the first obstacle is the legacy way of doing things. That combination of CRM, uh, Excel, and, um, bi it's just not enough.

So that's one of the things that we believe, we believe that people need a unified purpose built platform to run revenue. Uh, that's the first obstacle. So move past the, the previous generation tools.

Uh, the second I would say obstacles is very much around data. No surprise there, it's all garbage in, garbage out, right? So, um, not good data means not good AI and not good outcomes of ai.

Um, so I think we always, um, advise our customers to fully think through their revenue data strategy, uh, go beyond CRM data, think about, um, the snapshotting of CRM, you know, keeping track of everything that happened up to this day. It's not just about your accounts and contacts and opportunities today. It's also about the evolution of those over time.

Uh, think about what's happening on your sales calls. You need conversation intelligence to do that. Think about all the activities that are happening outside, um, all the emails and the meeting invites sent and received.

So start with the strong, uh, revenue data strategy, um, would be, or the lack, the, the lack of would be the, the second obstacle. Um, and the third obstacle would be I think ai, which comes really to the, one of the things I'd love for us to discuss. Um, people still don't understand.

People don't know, they don't know what type of ai AI has been around for forever. And also it's very new, so it's very confusing and people are like, wait, but like I can summarize things with generative ai. I thought generative was gonna generate content, not summarize it.

So I think there's just a lot of like education that has to happen around the purpose and the, and the goal and the potential of, of ai. So those would be my three obstacles. Legacy systems, revenue, data and purpose-built ai.

Oh, and absolutely with the ai, there are so many AI tools out there for so many different things. So just wrapping our brains around all the different AI tools and how we can use them and which tools are actually needed and which aren't Absolutely honestly side, uh, node, even as a marketer, I still struggle. It's a different space than revenue, right?

Like it's not in sales or in in the revenue team, but like, I still struggle finding the right AI tool for the right marketing application myself. So if I, another, I'm, I just explained I'm a math guy and I did AI in France before when AI wasn't as hot. So if I still struggle, uh, a lot of people still struggle for sure.

Yes. And I know there's some fear there. So part of it is just getting over the fear of, of just jumping in and trying out some AI tools.

It's not gonna hurt you to just try some out A hundred percent. Yeah, I, I agree with that. I think you just have to, it's a bit of a leap of faith, uh, but you just have to go at, go out and, and try them.

What I always say is, the more you use AI in your consumer life, um, the, the more at ease you will become with AI for the enterprise, with your enterprise use case. So I always even push my own team to use AI as much as they can, uh, in their day-to-day life and also at work. Um, I would say the other thing that I think brings trust to all of us using AI is being able to see and measure the outcome.

And that's something that, like, that's the one thing that people don't do well, right? They like, oh yeah, I have this leak issue. I'm gonna go ahead and like, try some ai, but there's no success criteria.

And so they try a bunch of stuff they not, like, there's no expected outcomes really mapped out and, and, and planned ahead. And then fast forward, I don't know, a week or six months, they have some outcome of ai. They just don't know if they're good or not.

And that's when the trust, I think starts to fall out. So always, I know it sounds very basic, but like, like any program, any project, if you don't have the end in mind, the end first, then there's no use even kicking off an AI project. Yeah.

Not just implementing AI for AI's sake, but having a clear end goal and, and then, um, tracking that return on investment. Exactly. Yeah, that's also a good point.

Like any good program and project either there, you need a cadence, you need tracking mechanism, you need leading indicators to your ultimate success metric that you, you're targeting at the end. Uh, also, there's never really an end, right? Like we all know it's kind of a flywheel thing.

Like if your AI project first project is successful, well guess what? That's gonna generate value. And then of that value, people will probably have more activities and they will lose more, uh, revenue in our case, and that will generate more data.

And then guess what? That data can be analyzed by AI again. And so you're in that, in that virtuous circle of, from AI to, uh, I mean from data to AI to outcomes to more data, then you're, you're good.

So, Exactly. Yeah. Yeah.

And of course, AI is, is rapidly evolving and advancing. So yeah. What are, what are your tips for business leaders as far as staying ahead of how rapidly it's advancing?

Yeah, that's a great question. I would say, uh, I, I, I love it depends how people consume content, right? But like, the type of report this, I know it sounds a little like self-promoting, but the type of content that we put together is a good start if you're in a, in a revenue team.

But just in general, uh, my tip would be be curious, read, um, as many kinda like, not theoretical, but more like applicable reports as possible. Uh, talk to your peers. Uh, I know we love running a, an advisory board at Clary.

We also have an AI council that we invite our prospects and customers to. So try to just like, you know, network and see what others are doing. And then as we said, uh, give it a shot.

Pick that one use case, don't ball the ocean. Pick that one use case, have a clear success criteria in mind, a clear plan, and then you just go take that leap of faith and if it doesn't work, then at least you will have tried. And if it worked, then it's gonna be great.

So read and educate network and give it a shot. Those would be my three tips. Wonderful.

Well, we're almost to the end of our segment. So if you could share one key takeaway for our audience today, what would that be? One key takeaway.

Oh man, that's a hard question because I don't know, it feels like it was, this was a rich discussion, uh, and we peppered in some, some stats and whatnot. I would say the one takeaway is think of revenue as a process. Think about assessing where your revenue process is breaking down and then start educating yourself around how AI can help you with that, with those breakdowns in your process.

Alright, well thank you for coming on our show and sharing your insights with us today. And I wanna thank our audience and stay tuned. There's more.