Ep. 123 -Measuring Agentic AI, ROI, and the Future of GTM Benchmarks with Ray Rike - Part 2

General Episode Description:

In this continuation of Selling Intelligence, Mark Petruzzi and KK Anderson sit down with Ray Rike, founder and CEO of BenchMarket, to go deeper into how companies should measure, operationalize, and compete with agentic AI in go-to-market functions.

Ray breaks down why most companies still lack basic GTM measurement discipline, what new AI-specific benchmarks leaders should track, and how legacy SaaS companies can realistically compete with AI-native organizations that are operating at dramatically higher efficiency.

The conversation also tackles the hard truth about workforce reduction, the rise of AI operators, and why companies must rethink their entire operating model, not just layer AI on top of existing processes.  

What You’ll Learn:

• Measurement Before AI: Why most companies must fix GTM analytics before introducing AI.
• AI-Specific Benchmarks: The emerging metrics for measuring agentic GTM performance.
• Competing with AI-Native Companies: Why legacy SaaS teams must rethink their entire playbook.
• The Role of AI Operators: Why AI expertise is becoming more critical than traditional RevOps.
• From Pilot to Scale: What success should look like at 90 days and 180 days.

Key Topics:

• Cost per pipeline and cost per ARR before vs after AI
• Agent cost per opportunity, pipeline, and revenue
• Designing modular AI workflows instead of “monster agents”
• The four-layer framework: productivity, effectiveness, efficiency, and ROI
• Revenue per FTE gap between SaaS and AI-native companies
• Why legacy SaaS companies struggle to match AI-native efficiency
• Radical restructuring: reducing headcount and rebuilding with AI-first processes
• AI enabling deeper personalization at scale for outbound teams
• The rise of “AI operators” as a new critical role
• The “SaaSpocalypse” and pressure on net revenue retention (NRR)
• Using AI to improve retention, expansion, and customer insights
• Benchmark expectations for agentic SDR performance at 90 and 180 days

Guest Spotlight: Ray Rike

Ray Rike is the founder and CEO of BenchMarket, a leading provider of B2B SaaS performance benchmarks. With decades of experience as a go-to-market leader, he helps organizations move from intuition to data-driven execution. Ray is also the creator of the AI to ROI newsletter, where he analyzes hundreds of AI developments weekly to help leaders understand what actually drives business outcomes.  

Resources & Mentions:

• BenchMarket
• AI to ROI Newsletter
• Concept: Agentic AI in go-to-market
• Concept: AI-first operating models
• Concept: Revenue per employee as a key efficiency metric
• Concept: AI operators vs traditional RevOps
• Concept: SaaSpocalypse and NRR pressure
• Framework: Productivity, effectiveness, efficiency, ROI

🎧 Listen now and follow Selling Intelligence for more insights on AI benchmarks, GTM transformation, and building high-performance revenue organizations.

Mark (00:26)

Excellent. All right, well, Ray, let me bring you back into the days of you and I starting all our research and all the pre-work for data and diagnosis driven selling. So I hope that doesn't cause you to develop a Twitch or anything like that, because as you and I both know, that was hard work. So, but let's, you know.

What we really saw right up front is, and we really pushed hard on this, is the idea is you can't manage what you don't measure. And you need external benchmarks, not just internal comparisons to know if your metrics are actually good. You know, it's great. It's great to be able to say, improved this process by 20%. But if you were 45 % behind most of your competitors before that,

That 20 % still has you on the back of the pack. So how do you bring that same philosophy to measuring an agentic BDR or an AI-powered deal coaching agent? And we've touched on this, but what's the equivalent of a CAC payback for agents and the entire investment?

Ray Rike (01:33)

Well, I would start with let's make sure you have your go to market measurements in place, because honestly, we've been talking about these for years. Less than 50 percent of companies have great GTM analytics. ⁓ So things like cost per dollar a pipeline, less than 40 percent of people are measuring cost per dollar a pipeline. So make sure you do that and look at your current state before AI and then measure it post AI introduction. Right.

So cost and I'm talking right now, I'm looking very specifically at the customer acquisition process. So cost per dollar pipeline before and after cost per dollar of new AR before and after when rate before and after your average and your contract for you before and after. Cause those are all going to be hopefully much better with AI to your point, Mark. I mean, let's use outreach. Everybody had to have a sales engagement platform, right?

How many companies actually said, well, after I invested $1,500 per SCR, I had a better conversion rate or a lower cost per dollar of acquisition? Nobody. You're going to need to do that with AI. So that's my first thing. The second thing, which haven't been defined yet, but I'm working with some VCs on this right now, is AI specific customer acquisition efficacy. So I'm looking at agent costs per opportunity.

Agent cost per dollar pipeline, agent cost per dollar of new ARR, agent dollar per cost of retained ARR. So you can think about your gross revenue retention and agent cost per dollar of expansion ARR. Now I'm projecting that we're going to be using agentic AI a lot in those processes or sub processes. And by the way, that's the other best practices.

When you design a process, it's better to design a lot of subprocesses underneath so you don't have one large unwieldy AI agent. You have a lot of subprocesses that you have different people auditing and evaluating.

KK Anderson (03:28)

All right. Let's dig into that design a little bit. So walk us through, and I know this is new, as we've said multiple times for so many of us, what a well-instrumented, agentic, you know, GTM for the purposes of our audience, pilot could look like. You just gave us one great clue, which is, you know, don't make monster agents and, and to break them up so that you can, you know, be more agile and

and predictable with those. You've walked us through some baseline metrics that you want to set before you launch, things that haven't necessarily been done in the past with programs like our outreach launches over the years. But what does the pilot to scale gate look like? And how do you separate the agent did something from the agent created revenue attributed value?

Ray Rike (04:14)

Well, let me go to the baseline metrics first, KK. So I think I have four levels of metrics I like to see in any initiative, including the Gentic AI. So one is a productivity metric, and that is outputs per time, you know, whether it's outputs per human hour, outputs per day or time spent per activity.

That's what we've been measuring for the last year and a half and AI in marketing and sales. But that's what then you have effectiveness. How effective is my AI enabled process going to be? ⁓ How many desired outputs am I getting versus the inputs? Hey, for every hundred emails my agent sending, how many meetings do I get set up? Right. Then there's efficiency. That's the cost per outcome. And then there's

actual ROI, which is outcome value divided by the AI investment. So productivity, effectiveness, efficiency, and ROI. I'm not going to go into detail what that's like for just an SDR program, but at least it gives you a framework and a layer approach to designing those four layers of metrics you need to measure.

Mark (05:19)

Excellent. All right, let's move into topic three, the benchmarked view, and tell us a little bit about what the data has been telling you and your team at Benchmarkit. And let's go with, guess, a baseline of that. Some of your initial benchmarks that you have been working on are showing AI native companies hitting two to three times higher ARR per FTE.

than a legacy SaaS, as one example. From our listeners who are operators inside non-AI native companies that are trying to deploy these agentic go-to-market programs we're describing, how do they close that gap? What are the structural differences they need to address? Not just the tooling, but the operating model as well.

Ray Rike (06:02)

In the let's be real. It's going to be real hard. And the reason being is a lot of the AI native companies are getting new budget. They're getting experimentation budgets. They're getting budget from labor versus budget from I.T. investments. Right. So I hate to say it, but it's going to be hard. But hey, there's a lot of people out there in legacy says companies that they need to get there. Right.

So number one is you've got to be laser focused on number one, the effectiveness. Can AI help you get more conversions? Can they get higher ACV? Can they increase your win rate? And you got to go right to cost per. If AI is not getting you a reduced cost per dollar of new ARR, it's going to be really hard. And honestly, Mark,

The hardest part is for me, if it was me and he brought me into a $50 million SaaS company, I would say we got to throw out the old playbook and start from scratch. But I don't have the new playbook based upon years and years of experience. So it's going to be really hard to throw out the old playbook and build the new one. So that's why I bring in someone who's got maybe six, 12, 18 months of experience