AI Monetization Strategies - Balancing Revenue and Adoption with Gary Survis and Ethan DeSilva, Insight Partners

**Ray Rike** (0:00)
This episode of the Metrics that Measure Up podcast is from a session at SAS Metrics Palooza 24 If you'd like to listen to other speakers and gain access to your presentations from SAS Metrics Palooza, please visit benchmarkit.ai, that's benchmarkit, that's with an it.ai, and go to events and select SAS Metrics Palooza 24 Hello, I'm Ray Reich, Founder and CEO of Benchmarkit, and your host of the Metrics That Measure Up podcast. We talked to a wide variety of the top B2B SAS and Cloud thought leaders, CEOs, executives, investors, and people just like you to discuss the metrics and benchmarks they use to make metrics-informed and benchmark-validated decisions. Now, on to today's show. So with that, I want to introduce Gary Survis, Operating Partner at Insight Partners, and Ethan DeSilva, the Head of the Monetization Strategy.

**Gary Survis** (1:09)
Thanks for having us, Ray. We're excited to share a little bit of our thinking here. And I'm going to turn it over to Ethan to kick us off here. Cool.

**Ethan DeSilva** (1:23)
So excited to speak with you all today. And I think to set the stage, since GENAI has taken center stage over the last two years. We've seen dozens of companies really struggle to figure out how to monetize their generative functionality. And very recently, Salesforce made a big splash at Dreamshorse sharing their plan to monetize agents through usage-based pricing, which is a meaningful shift from a company that has heavily associated with seat-based pricing for the past couple of years. Now, while there's many reasons to believe that monetizing gen AI through usage-based, hybrid, or even outcome-based pricing is the future for SaaS businesses, we believe above all else, SaaS businesses must align and phase our monetization strategy with the velocity of customer adoption and value realization regardless of the price model, which is what we're excited to discuss with you today.

**Ray Rike** (2:18)
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**Ethan DeSilva** (3:19)
Now, the problem statement is clear in that most SaaS businesses are struggling to drive revenue broke with their GENAI functionality today. Today's session, we want to focus on three key hypotheses that we have that are critical to solving this problem. The first is that there are several external factors that make it very difficult for SaaS businesses to monetize GENAI functionality right now.
Second, we believe the most critical barrier to monetization is actually that customers aren't quite ready to adopt this technology at scale yet. Gary will talk through what we've identified as the AI scalability gap. And third, we believe SaaS businesses need to develop a phase monetization strategy that aligns to the velocity of customer adoption to be successful in the long term. With that, I'll hand it over to Gary to talk through number one.

**Gary Survis** (4:12)
Yes. So, hypothesis number one is, it really is a tough time to be trying to monetize these AI capabilities. And we've been working with multiple portfolio companies. We've been going to conferences. We've been listening to the market. And we believe there are several things that are poor to an unfavorable environment for SaaS companies to drive gen AI monetization. So, the first one is just about tech spending in general. I think that what we've seen is, as we emerge from this zero interest rate environment, a lot has changed. And one of those things is that we're not seeing huge budgets against the spend for buying new gen AI solutions. There's a lot of try before you buy, and that is very much the CIO and IT areas' view on how to bring tech into their new tech, into their stack. I think there's a second area here that I would love to talk about, which is the organizations themselves for so many reasons aren't ready for GENAI at scale. There are all kinds of gaps in end user trust of this technology. There's less skills. I like to say there are no GENAI experts today, so folks are skilling up in general. And then if you look at the way that customers really view this, there's this promise of productivity gains, but there are just not real long focused strategies to deliver on those productivity gains. And I always look at the comparable of how we were looking through cloud transformation and the plans that were there for that over extended periods of time. We don't see the same thing yet around how AI is going to be driven into these organizations. I think the next point here is about ROI. And I believe that so far it's been elusive. I think organizations as we were talking about the technology are trying lots of things. And to be clear, this environment of trying ends up in some case being higher churn because organizations are jumping to the next shiny thing. But because they haven't figured out exactly how to scale this very powerful technology, they haven't been able to see the ROI yet that they were hoping to see. And the last point is around regulation. I think the environment that we are operating today, rules are starting to come in place. The EU, California, the US is talking about this. But the fact that we aren't clear on what the guardrails are yet has made organizations a little more tentative on where they're going to apply this technology and therefore being a little more risk averse as they look at that. So there's a huge continuum on an organization's risk tolerance and therefore the amount of applications that they're leveraging generative.