Beyond Adoption: Jellyfish’s New Module Measures Real AI Impact

Hey everyone. Welcome back here to Textron tv. We've got another good interview here for you to learn a little bit about.

Um, I want to introduce you to Ryan Ccho. Ryan is the SVP and field CTO for jellyfish. And let's welcome Ryan to Tech Drunk tv.

Hey Ryan, how you doing, man? Hey, Alan, I'm doing great. Thanks so much for having us today.

We really appreciate it. It's a pleasure to have you on here with us. Um, Ryan, you know, let's start with you before we even jump into jellyfish right.

Give us kind of an idea of your journey, where you've been. Yeah. Um, my background is a 50 50 split between operating and consulting roles, but all things around r and d.

Um, I love tech, I love innovation. I love the process of building something software oriented. So I cut my teeth technically doing things like bike code instrumentation for performance diagnostics.

And then prior to joining Jellyfish about four and a half years ago, um, I was working in the private equity ecosystem serving as the advisor, private equity, both on value creation as well as software due diligence type initiatives. Excellent. And then, so you've been at jellyfish about four, four and a half years.

Mm-hmm. It's a long time already. Yeah.

I'm loving it here. Good for you. For people maybe out here who are not familiar with jellyfish, how would you describe it to them?

Yeah, uh, the jellyfish, uh, we've been around since 2017. Uh, we consider ourselves what's considered a software engineering intelligence platform. Uh, so essentially we're sitting alongside the tools that engineers use day in, day out, and the idea is to surface the key insights or actions that help your engineers, whether you're a platform engineer or you're A CTO up and down the stack, understand how you can do your job a bit more effectively, servicing key insights that help you do your day-to-day job.

Love it. Mm-hmm. Love it.

And people who maybe want to get more information about jellyfish, where do they go? co. You can learn a bit more about our use case is what we do, um, and where we're driving value across our customer base.

Very good. All right, Ryan, if it's okay with you, I want to pivot now and talk about it, uh, recent announcement outta jellyfish about, uh, new new product offering. What do we got?

Yeah, we, uh, just recently announced, um, our AI impact module, um, and extension of that, uh, to cover a few more capabilities. Um, I can go into detail, but maybe first down, does it make sense to maybe just talk a little bit about what our AI impact module is before talking about the most recent announcement? Sure, Of course.

Um, so our AI impact module as a whole is essentially looking at your r and d ecosystem, your full SDLC, and looking at where is AI actually injecting across the SDLC, um, and what changes is that making, right? And so I think we're all seeing this high pace of innovation today where, you know, every three weeks it feels like something new is coming out, a new product, a new offering, and it's, it's driving a lot of change. Um, throughout your SDLC engineers are being asked to use more tools, there's pressure to drive more efficiency, but it's really tough to actually understand what's happening across your TLC and how are your engineers actually behaving and what new bottlenecks are being created.

So the AI impact module is designed to help r and d leaders and engineering managers themselves fly less blind in this new ecosystem. Fair. The new release here extends that a bit, right?

So before we were measuring a little bit more around what is the gen, uh, gen AI impact, right? So how much do tools like copilot or cursor cloud code impact your pure productivity, right? As we think about this broader ecosystem, we're actually now extending that to have multi-tool comparison.

So you compare tool A versus tool B, and in what scenarios is a certain tool actually driving more productivity or more effectiveness or, you know, maintaining quality across your organization. Um, the second thing we're doing is we're extending from the code generation phase of your SDLC into the broader code review area, right? So now we're looking at code review agents, right?

These are tools like code rabbit, uh, graphite reptile, things of that nature that are actually assessing your code in real time and helping with your code review process, which if you're moving that next bottleneck that people are finding, if you're writing code faster, how do you keep up with the reviews and ensure that you're able to maintain what's happening as more code is being generated? And then lastly, we're actually starting to track AI spend. So you can start understanding cost modules are changing right before it was by user seat.

Now you're starting to have some variable pricing based on a call, a little bit of amorous token pricing. And so how do you understand where is actual token spend coming? Let's not get surprised by a bill.

Are we using it most effectively in questions like that? Good. Help me.

How you doing that? Yeah. Um, what's really cool is, you know, what Jelly officials our broader SEI platforms doing is we're already sitting above all the tools engineers are working in day to day, right?

So that's your, your GI platforms, your issue tracking, your production monitoring solutions, even your CI/CD solutions. And in there we're understanding how engineers are operating day to day for the AI tools. We're actually plugging in directly with the tools themselves to surface the things that do come outta their APIs, right?

You get things like usage, sometimes things like spend, um, but you're not getting enough to actually understand the impact your r and d. So we're actually fusing all of that together with an intelligence layer on the backend that's helping you understand, hey, cursor was used for this particular PR and here's what it actually looked like, right? That piece of code was actually, uh, a new feature request versus support, right?

And what did that do to your broader SDLC, right? Did it cause a bottleneck? Did it go faster, right?

What was the quality? Did it cause an incident? So to be able to give you that full 360 view of what's happening by blending the two systems together and then surfacing that in a way that helped you action them as a leader.

Very cool. Very cool. Um, is this, this is available now?

This is available now, yes. Um, getting great feedback thus far from the market, which has been super exciting for us. But, uh, really we want to, are we plugging in and understand what's happening in our organization?

Because this is a fast moving space, right? Mm-hmm. And we're quickly expanding what we're covering.

Um, some really cool things, hopefully back on with you soon now, and to share some of those. Um, but this is a space that's moving fast and leaning into driving that adoption today and understanding the impact and unlocking and becoming an AI first software development company, um, is what we wanna help people with that helps 'em succeed into the future. Lemme make sure I got this right.

Jellyfish AI impact is sort of a module that goes onto the intelligence platform That is correct. The jellyfish software intelligence platform? That is correct.

It is a standalone module as well. Um, but it, Oh, so you don't need the, the whole platform. You could just grab the module if you want it.

That Is 100% correct. You can buy AI impact module on its own. Um, and we're looking forward to having more people sign up for that too.

What about as we see more agentic AI being used, how will that kinda change the equation here? Yeah, it's a, it's a great point. So where this is all heading, right?

Whether the AI is actually gonna be injected across the SDLC, you're already seeing that today with tools like lovable on the prototyping side, right? And today you're seeing code generation, code review and some production monitoring stuff. Um, what you're starting to see now is the next wave is you're starting to see now more agent come across the SDLC, right?

So we're actually plugging in with tools and have new term versions out, um, or early versions out of a agentic, um, development tools. You know, for example, tools like Devon that are out in the marketplace today and today, even Claude code that has an agentic mode. But we're seeing that agent workflow or agents of agents even, um, accelerate both, um, the design, the development and the release of, of code into production.

Um, and we're seeing that becoming just an additional challenge to this existing thing where you still have to have a human in the loop. You still have to have somebody to call when things go wrong. So how do you understand what's happening and where the cost goes?

The number one question that might come up with that Alan, is both, I guess two questions there. One is likely the quality, how do you maintain that quality and control? Um, but then second is how do you maintain that spend?

It's very quick to, you know, have a, have a bill run up on you if you were to have multiple agents running, um, and accidentally spend too much on an individual feature support ticket if you just deployed an agent without the controls or visibility in place. Sure. Um, let me ask you rubber meets the road question.

What percentage of Jelly fish's clients are using AI in their SDLC? Great question. Uh, we are seeing over 90% of our customers using AI today in their SDLC.

The vast majority of them are using more than one tool today. Um, that can be more, more than one co-generation tool and or tool type. So starting to see more of that PR review agents, more agent to code development, as well as more prototyping tools like the lovable of the world.

90% craz, huh? It's wild. We actually saw a leap I think early in 2025.

We were coming off of some 2024 reports that were putting us closer at 60% and we saw a leap heading into Q2. Um, that jumped it to about 90%. And what we're seeing as the big change, there was one, the stepwise change in the models, but also the release of additional tooling, um, and the capabilities of those that were already in the marketplace, they coincided together.

But we've seen a demonstrative change into Q2. Um, and then Q3 of this year just saw an acceleration of that. It wasn't just getting it deployed, it was moving from the state of adoption into actually driving productivity.

And then we expect having more into actually driving greater business outcomes in Q4 here. Yeah, I mean, these things follow sort of a typical thing. You know, it's, it gets adopted by individuals, teams, divisions, enterprise-wise, right?

And, and, um, and not necessarily that linear, right? You'll get a team over here doing it and they're a little bit ahead of a couple of guys over here doing it. And you know, all of a sudden the CIO says we're gonna standardize on doing it.

And so it, it's a little helter skelter, but it does kind of follow that pattern. And I, and I think ultimately, you know, because doing anything in enterprise scale is, you know, it's not something you snap your fingers to really do enterprise scale. It, it takes some time.

Um, but we're, we're, I agree with you. We're on our way. I think these numbers prove it out.

This sounds like a great useful, uh, module here for jellyfish or standalone. It's, you know, even a standalone. So good.

Good for you guys, Ryan. Um, you'll come back on and tell us as this continues to develop, I assume, Oh, we definitely will. We'd love the opportunity to hop back on and yeah, maybe I'll leave with a parting thought a little bit here.

Uh, and one of those is, you know, what we're learning in the market today and we published an AI impact framework around this, um, in terms of how to drive the greatest impact with AI, is essentially, first you have to focus on that adoption to your point, right? Some people are more skeptical, you know, others are leaning in. But it really is a change management exercise and you have to build the trust with folks.

We're seeing a take over a quarter between, from the time someone gets a tool to when they're actually starting to see productivity gains. So lean in, expect some time and focus on change management. The second thing there is to understand where the bottlenecks are happening.

If you're accelerating code, right? Where's that next bottleneck unlocking? Is it more enablement and when to use what, right?

Is it getting more PR review agents or focusing more on that PR review process of quality, but make sure you're seeing the right results, um, that continue to build that success. And then at that point, focus on the business outcomes, right? Are you actually releasing more features into production?

Are you getting a better return on that ROI of the AI spend? And it helps you both communicate to your teams, but also communicate to the pre external pressures outside of r and d. Um, Very cool.

Hey Ryan, thanks for coming on. Good luck to you and all the folks at Jellyfish. co That is correct.

co. Yep. 'cause I've made that mistake.

Um, hey, we're gonna take a break here on Textron Gang, on, excuse me, our text drug tv. We'll be back in a moment. Alright, thanks for having us out.