The State of B2B Go-to-Market in 2026: The ICONIQ Findings

Live from Schenectady, New York, it's the Metrics Brothers, Growth and CAC.

And I'm Growth, better known as Ray Reich, founder and CEO of Benchmarkit.

And I'm CAC, better known as Dave Kellogg, EIR at Balderton Capital, independent consultant, and the author of Kelblog.

And together we are the Metrics Brothers.

And we go together like Waze and road trips.

I really like this one, Dave, because I can still get from point A to point B without Waze, but why would I?

Why would you indeed? If getting, you know, getting there as quickly as possible is the primary goal, why not use Waze or some level of intelligent GPS to make that trip a little more efficient?

Ah, I see what you did there. It's a little bit like using AI in the go-to-market. It might work doing the old playbook, but not as efficiently or effectively as using AI to drive the customer acquisition, retention, and expansion process.

Yes, easy for you to say. Before we drive into the ditch here, Growth Brother, let's share today's topic. Enough dancing around. ICONIQ's 2026 State of GTM report, which does in fact highlight some of the key benefits of using AI in your GTM processes.

Okay, this will be interesting, especially since we covered a similar go-to-market report from New West 2 episodes ago.

New West?

Old West?

Norwest? I don't know, Ray. Norwest, yeah.

Did I say north instead of norwest?

You said new, New West, like, yeah, I don't know.

Oh, wow. Boy, I can't remember or read anymore. But before we put this car into gear, let's hear a word from our sponsor. The Metrics Brothers is proudly sponsored by Benchmarkit, the B2B AI and software industry's largest, broadest, and most contextualized benchmarking index. Dave Kellogg and Ray Groth Wright have put the Metrics Brothers podcast on hiatus for the remainder of 2026. All episodes we have ever recorded are still available on your favorite podcasting app. You can reach Dave at Kellogg and Ray on LinkedIn at Ray Reich. Thank you so much for listening to the Metrics Brothers podcast. Okay, Dave, real quick here, a report overview of this iconic 2026 Go-to-Market Benchmark Report. It's a 32-page report. It was based upon a survey that they conducted in January of 2026. So very recent. Around 155 executives responded from chief revenue officers, heads of sales, CEOs, and RevOps. And what surprised me most about the participant profile was the large value of the ACV, the annual contract value. 42% of these participants had $100,000 to $500,000 ACV, and another 10% were $500,000 to $5 million. So over 52% were greater than $100K. And revenue size, once again, a little bit bigger than most VC reports that you see. 35% were in the $25 million to $100 million range, but 27% were greater than $100 million, Dave.

Yeah. Yeah.

What did you say?

What percent was greater than $100 million?

I have 27% were in the $100 million to $250 million range.

Got it. Okay. Yeah. So, so look, this all is fairly consistent for Iconiq, who does good work in this space. Let me just talk about the way I think about size, Ray. I look at it more bottom-up, cumulative. So I say, oh, 50% of the people are less than $100 million in this survey. Um, and that's great. And you got to remember that a lot of these surveys, 100% of the people are less than $100 million, to your point. Um, so here, 75% are less than $250 million, and then the other 25% is above $250 million. And this is consistent with Iconiq. They're kind of a growth stage, high-quality investor. And look, I'm always going to raise this with Iconiq, but to the extent they're including their portfolio companies, you're getting a pretty high bar on who's in the survey. And they don't talk about how many of those 150+ companies— not people, by the way, so interesting, they're saying companies, not respondents— they don't talk about how many of those companies are in the portfolio or not. But look, Ray, their portfolio is 140 companies. And like having run companies, when your investor asks you to fill in a survey, you usually do. So I gotta believe this thing is pretty heavily weighted towards their portfolio. And I think you can see that coming out in the numbers.

Yeah, that's an interesting insight because when we did this on the Norwest, it was like 40% were portfolio companies and 60% were non-portfolios. So that's a really interesting insight.

And I think N were humans, not companies, right? Another big difference. But, but, but keep going. I'm not sure what N is here. They're kind of alleging it's companies.

I think it was respondents.

I, I, I think it was respondents who are humans as well. So do I. Yeah.

So, hey, let's go to topic 1. And once again, I love it when different reports look at different things than the others I see out there in the industry. So on slide 8, they talk about strategic GTM shifts and they show this revenue mix. And here, they split it based upon who they sell to. A top-down motion, which is selling to executives, versus a bottoms-up motion, and then a hybrid. Now, at the end of the day, about 60% are using a hybrid motion. So, close to 2/3. But what was interesting at the margin here for me, Dave, was that for these high-growth companies, and unfortunately, they don't define what a high growth company is. But 2022—

Yes, you do, Ray. I hate to interrupt you, growth brother. Look at the bottom of slide 4. That's how they define it. Um, it's a— I would call it a variable growth bar that's a function of size. That's a lot of syllables. Um, but what it means is if you're growing 200% between $10 and $25 million, 100% or more between $25 and $50 million, 50% between $100 and $250 million, right? So it's this variable growth bar, and if you're over it, then you count as high growth. So it's interesting analytically, kind of makes sense, right? Law of large numbers. It's hard to grow fast at, you know, 250. And a quarter of the sample fits in that bar. So back to you, Ray. They do define it though.

I'm really glad you pointed that out because I totally missed that on the bottom of slide 4. So what was interesting to me though was that these high-growth companies defined at the bottom of slide 4, 22% of them are focused on a top down motion, but for the other companies, so I'll use those as non-high growth, i.e., lower growth, 33% are using a top-down motion. And when I flip it on the other side, 14% of the high-growth companies are using more of a bottom-up and only 8% of the lower growth. So to me, it sounds like maybe it's things like PLG motions are actually resulting in higher growth. That's an assumption I took away from this slide, Dave.

Yeah, so a couple things, Ray. One, VCs, you know, they have skin in the game, right? They invest in lots of companies, and one of the things they try to do with these benchmarks is say, hey, keep up, you're not keeping up, right? And the message here, it's not super clearly articulated, is, hey, the fast-growing kids are more into hybrid and bottom-up than the regular kids. That's all they're trying to say here. It's not dramatic from the numbers, But I see how they're doing that. And just in that vein, Ray, I don't know if you noticed this one because we caught them doing it before, but at some point the benchmark switches to top quartile, like the growth slide on slide 16, where it's like— because by default you think you're looking at a median, right? And it's like, hey, wait a minute, like they just did the old bait and switch. And now I'm looking at top quartile. I'm looking at it again on NDR. So, and again, this is all part of the VC sometimes not so subtle pressure on companies. First, let's take the kind of Ivy League companies that we invest in. Second, let's take the Ivy League of that, right? So the honor roll, the people who are growing above that growth target. And then thirdly is, well, you don't do both at the same time, I don't think. But third, then let's take the upper quartile of that. So it's a subtle way of kind of putting pressure on people to say, you're not good enough or you're not keeping up. So, you just, whenever you read these things, you need to keep an eye out for that.

Yeah, it's actually really good insight. Now, one of— I had 2 favorite slides. This one on slide 9 was one of them. And the reason I say that was it shows the breakdown of the revenue by these. It's really 4 GTM motions: direct sales versus channel partnerships and self-serve. And self— I'm sorry, in channel partnerships was about 27% to 29% of the revenue from high-growth companies. And Dave, I've always seen this in the 11%, 12%, maybe 15% to 16%. So, what was interesting to me, this chart suggests that companies are investing a lot more in channel and partnership distribution. And I haven't seen that before.

Yeah.

in the B2B SaaS industry.

Yeah, so this is revenue split by GTM motion, right? That's what it is. So I assume companies are taking 100% of their revenue and busting it into 4 buckets that add to 100, and then we're looking at the average of those things per the little footnote, uh, subtitle of the chart. So, uh, long story short, yeah, I was both happy and surprised to see channels being quite so big. Um, And frankly, I was even more surprised to see self-serve being so small, right? Because the new AI high flyers, to me, it's— I always call it the revenge of PLG, right? There was a lot of hype 5 years ago about PLG that by and large didn't deliver. And now, you know, Cursor, Lovable, we've talked about them in separate episodes. These companies are the revenge of PLG and you don't see it in these numbers. Maybe they don't invest in them. Maybe they're too early because these guys are a later stage investor. Maybe they don't have as much of them in the portfolio. But I was happy to see channels be big. I'm a big believer in channels. 31% is big because certainly in SaaS, you know, in on-prem, channels were a big deal. SaaS made them smaller, right? Because there was less implementation work to do. So yeah, this is kind of surprising data.

And I think this is an important trend to keep looking at. Now, another one that I really liked, which was different, was I believe it's page 12, is quota setting and commission structures. And basically, I've never seen this before in a report, but it basically says on the left-hand side, what do you use for quota setting? And the majority— and by the way, even though it didn't say this was a multi-select, when I added up all the percentages, it adds up to like 123%. So I believe this was a multi-select. But 56% of companies are using ACV and 34% are using TCV, total contract value. to set quota, but 30% are actually using forecasted consumption-based usage, which tells me companies are setting next year's quota based upon the expected usage of customers signed in 2025. So I thought that was really interesting.

So 2 things here, Ray. So one, you've driven me to the footnotes as you discuss this. And note the N size is 64. So why is it that 80 people something who did this survey didn't answer this question? That's, to use the kids' words, sus for suspect. The other thing I find funny in the footer is they say private and strictly confidential, which is just a straight up mistake. It's a public benchmark report. But as long as you have me looking down at the footer, I'm going to notice that too. So I like the question. I've not seen it covered anywhere else. I was surprised a third on TCV. I mean, I thought that went out of style a long time ago. ACV was lower than I would have guessed. It's definitely, to your point, adding up to more than 100. So it was a multi-select. This for the bazillionth time on this series or this podcast, please show us the questions, right? So we can know what we're looking at. But I like what it's showing there in terms of people setting quotas based on forecasted usage. And I think you're going to talk about commissions in a minute, but I love these questions.

Yeah, they, they ask about what your commission structure was and 21% said, hey, we're paying upfront on expected usage. So even if it's consumption or outcome-based pricing, almost a quarter paying upfront, another quarter, but 27% are saying, They're paying mostly upfront, but we do save a little bit of portion of your commission and it's tied to future usage. 29%, so a little bit more, have phased payouts. So I'm assuming here they pay out over the year based upon actual consumption. And then 21% is only paying upon fully its real consumption and we've seen the revenue. So It's very distributed between 4 different payment timeframes. And to me, it shows the immaturity of how companies are structuring and paying VC in a primarily usage-based world.

Yeah. And if you look at winners and losers, what's going down is fully consumption-based, which wasn't a surprise, by 10 percentage points year over year. And what's kind of going up are the 2 in the middle, like you said, mostly upfront with a portion tied to usage and phased payouts. I would point out on the multi-select thing, yeah, this is the old famous 60-48 distribution, i.e., not 60-40 between the last 2 and the first 2. So yeah, you got to help people understand what they're looking at here. It has to be either there's a math error or it's multi-select. But if it's a multi-select, I'm kind of surprised how low it is, right? It means most people are not doing both or sorry, more than one. Yeah.

Well, here's what this type of variable commission structure leads to, and it's on page 13. And as a former revenue leader, I hate this word, and it's clawbacks, specifically account executive clawbacks. And effectively what it says— and let me make sure I get to the slide here— is that a large percent across revenue ranges— when I say revenue ranges, I mean ACV are using clawbacks. Only about 50% to 55% have no clawbacks, which says 50% to 45% are using clawbacks. And one of the things that always did for me was it really created a morale issue of sales organizations where we say, well, we're going to pay some upfront. But if you don't see the usage that was forecasted, We're going to ask to take that money back. And that's a very difficult environment to run a sales team in, Dave.

Yeah. 2 things here, Ray. Got to look down again at the end sizes there, but these are teeny numbers, right? 2026 number for when are clawbacks applied is 17, right? So, so you're taking 12% of 17 and you're getting like 2-point-something people. So it's actually 2 people. So There's not enough data here really to do meaningful analysis for some of these, uh, first. Second, yeah, I thought clawbacks were in the sales history museum. Like, like we used to have them, and I'm shocked if you believe this data, they're coming back in style as a result of prepaid outcome or usage-based pricing. But salespeople hate Hate, hate clawbacks. You know who hates clawbacks even more than sales?

Finance.

Finance, because they have to do them, right? Um, and the guys here are sales ops right now, either finance or— so I'm, I'm shocked about this. I would certainly recommend you build a plan where clawbacks are greatly the exception, not the rule, and you do that by not paying everything up front, right? The easiest way to do it is I always say You get cash when I get cash. Now, that can be a little too far the other way, but it's not you get cash way before I get cash because that's when you end up having to do clawbacks.

Yeah.

The other 2 things or the other thing that these last 2 slides really shows me, we're going back to a full lifecycle account executive. If you're getting commissions based upon how much product usage they're doing over the next 12 months, There's no way you're not involved in trying to drive that.

Yeah, and this is changing the role of customer success, right? For better or worse. The good news is the salesperson now cares about usage. The bad news is unless customer success is careful, they're going to end up just really being a support resource. Obviously, there are ways around that where you can stay tied to revenue, get paid on revenue. But as we talked about in the Norwest, or should I call it the New West benchmark that we did last week or 2 weeks ago, You know, customer success is decreasingly responsible for renewals, which makes me sad personally. And I think you're right, Ray. These compensation plans mean sales is like the full lifecycle AE, as you call them.

Yeah. Okay. Moving on to page 20. I liked it because they showed that they were showing some conversion rates in the left-hand chart. And it showed that 50% of free trials or proof of concepts were converting into production. And then it tried to show you what type of resource was being allocated to those proof of concepts. And dedicated solution architect, so that person is dedicated to making that proof of concept successful, that was, you know, 25 to 49% of the cases. And it was higher when the ACV increased. Makes sense. The shared solution architect, well, about 50% of the time. And what they tried to show also was, hey, regarding chat support or automated AI support, it was being used very, very little. But what surprised me about this slide, and the only thing I found interesting, was, is a solution architect different than a forward-deployed engineer that we've talked about a couple of times? And if it is, why didn't they include forward-deployed engineers as part of the resource gold to transition from pilots or proof of concepts to production?

Yeah, so I think the answer there, a couple of things on this one, Ray. If I had 4 deployed engineers and I had to answer this question, I would consider them, depending on whether they're shared or not, either dedicated or shared solutions architects. Personally, I think that would be the natural response. It would be nice if they called them out because I'd love to see how people think of them and what they are. That would have been interesting. But the other thing I'd say, Ray, I'm kind of surprised you picked this slide out because this one didn't do it for me. I'm kind of surprised you skipped that one, but back up one page. I like the funnel conversion data. Not many benchmarks have this. They do it with a year over year, 2025 to 2026, but you know, some benchmarks have them, but I'm still always happy to see conversion rates. Um, and the biggest thing they showed, I think, year over year was a big upstep, uh, in free trial to paid version conversion.

Yeah.

Sorry to say version twice, but that's the way it comes out. Conversion, conversion, POC to paid version conversion went from 36 to— easy for me to say— it went from 36 to 50%. But I like that slide. The slide you picked out, I just had trouble understanding it. So I kind of glossed over, to be honest.

Yeah, no, I just, I do think that they increased that 14 points. It's pretty important. Okay. I want to cover 2 more before we say goodbye to the audience today. And topic 5 is AI's impact on go-to-market performance, where it's working and where it isn't. So page 27 is interesting because it shows the top AI use cases by top of funnel and then also middle of funnel. And then at the bottom, it shows post-sales. And not surprisingly, lead gen and lead scoring is the number one use case at top of funnel, around 74% for lead gen, 63% for lead scoring. 3% for lead scoring and chatbots around 50%. And then they went down to middle funnel. And because this is more of a sales manager versus marketing and sales together, number one was call transcriptions. And that didn't really change. Almost 3 quarters of companies are using those, but the largest increase in usage was for forecasting, but it only increased from 26% of companies last year to 38% this year. So, I believe that companies are missing a huge management credibility enhancement opportunity by not using more AI for forecasting. Things like looking at the number of emails, the meetings, who they're meeting with to determine whether that's a forecastable deal or not. So, that was my main point. And the second point is I would like to see how traditional SaaS companies versus AI-native software companies are using AI in their go-to-market process? Because from what I'm seeing, Dave, with my projects, I'm seeing a dramatic and material difference in how AI-native companies are using AI in go-to-market versus traditional SaaS companies.

Yeah. So the thing I would say, Ray, a lot of the use cases you highlighted are shown in green on Iconiq's slide. And the green means use cases with the largest year-over-year increase. So you may not be happy with their absolute level of adoption, Like AI-driven forecasting, surprisingly low at 38%. But, but it is a high momentum use case because, uh, they have it growing from 26 to 38. So it's one of their fastest growing use cases. And I think in general, when you pointed one out, at least if they're not high, they're, they're fast growing. I personally was surprised at the same spirit, churn detection. I mean, gosh, AI is great for churn detection. Some of the AI use cases they talk about here are kind of AI lowercase, you know, Call transcription analysis. Okay. I mean, I guess it's AI, but it's, you know, I'm surprised it's only 75% of people. If you're not doing transcription of sales calls, I don't know why. That should be 100%, right? But in general, I think it's important to, when you study this, to look at the use cases. And I agree with you, Ray. I don't think I've seen any other ones. That were shocking to me.

Yeah.

Lead scoring. Yeah, people hate lead scoring. It's talked about, I think it was in the Norwest report, right? That lead scoring went under the bus. Was it that one or some other one? I can't remember. I think, oh, it was. They talked about how they defined MQL.

All right. Yep. Okay. And then I skipped, even though pages 28 are pretty good, because it talks about some of the impact of AI, whether it was impacting productivity or cost savings, revenue uplift.

I thought that would have been your favorite slide, Ray, Mr. AI to ROI. This one doesn't get Did we get covered?

My favorite slide was slide 31. It's the one I'm going to wrap up on, Dave. If you want to add one, please do. But it showed the total amount of go-to-market FTEs by AI adoption, that high adoption versus lower medium adoption. And in every revenue segment, $10 to $25 million, low adoption, 35 GTM employees, 20. with high adoption. And at $25 million to $100 million, it was 65 for the low and 45 for high adoption. Same exact trend at $100 million to $250 million. And at $250 to $500 million, 350 GTM employees with low adoption, 275. So, these average about 30% reductions. And Dave, my hypothesis has always been over time, Revenue per employee will be the number one measurement of the impact of AI. And then it'll be operating expense as a percent of revenue. So I love this slide.

Yeah. Okay. It's a good slide. I mean, I wish they would add a bucket for all just so you could see how the segments are performing relative to the average, right? Because it's hard to eyeball it. But, you know, because maybe hopefully everyone's getting better. Actually, not hopefully. We know for a fact year over year, people, everyone's getting better at basically revenue per FTE, right? So, uh, and this isn't quite measuring revenue by FTE, but it's measuring FTE within revenue buckets, right? So it's kind of a proxy for it. Um, so it would be nice to show this slightly differently, but I think it was a good slide. I want to go back to the 2 slides before it, Ray, real quick, because I, I can't resist dry humor. And slide 29 basically shows very nice uplifts in kind of You know, hey, AI conversion rates from new lead to MQL and MQL to SQL. And, you know, heavy AI users see like 10 percentage point increases in those conversion rates. So it looks pretty good. You don't see as good results later in the funnel, unfortunately. Um, but then you look at the kind of economics on slide 30. And, uh, what is it? AI shows up everywhere but in the productivity statistics, right? Like you, you just don't see it. The cost per lead is higher. So I presume it means while AI may get the leads more efficiently, quote unquote, from other expenses, not so much more efficiently that it pays for itself, right? That's kind of my conclusion. Cost per opti is down ever so slightly from $8.9 to $8.5, depending whether you're a high adopter or low adopter. Sales cycles are the same. Pipeline coverage is a little bit higher, but I think I saw somewhere else that the number of deals closing wasn't. So you're just— if that's true, then you're just getting a little bit more pipeline, but you're actually closing the same amount out of it. So this was not a super encouraging slide for AI adoption. I don't know if you had any thoughts on those 2.

I totally agree. The pipeline efficiency and economics did not bear out. There is a lot of value right now to the AI investments. So Dave, I think we need to wrap up. I was a fan of this report. I think Iconiq always does a good job. They provided some of those different Insights I've never seen before, especially the quota setting and commission structure. I think for any listener who's got quota setting or quota payment responsibility, I should say VC payment responsibility, very, very important things to be aware of.

Yeah, overall, it's a great report. It's only 30 slides, which I like, and there's basically, in my opinion, good information on every slide. So this one is worth breaking out and reading. And just bear in mind, keep some of the caveats we mentioned in mind as you do so.

Thank you, Dave. See you next time.

Thanks, Ray.

The Metrics Brothers AI, SaaS, and More is a production of the Metrics Brothers Growth and CAC and a member of the Benchmarkit Podcast Network. By accessing this podcast, you acknowledge that the Metrics Brothers make no warranty, guarantee, or representation as to the accuracy or sufficiency of the information provided or the humor content of the jokes told. Ray, information, opinions, and recommendations presented are, according to our spouses, probably wrong and provided for information purposes only. This podcast should not be considered professional or for that matter, unprofessional advice. We disclaim any and all liability for any direct, indirect, indirect, misdirect, incidental, special, ordinary, consequential, inconsequential, or other damages arising out of any use of, or God help you, reliance upon the information presented here. Ray Grothreich is based in New York City and available on X @RayReich.

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