How Caylent Is Building an Anthropic AI Practice for the Enterprise

[MUSIC] Hi channel insiders. Welcome back to another episode of channel insider partner POV. I'm joined today by two individuals from Kaelin to dive into all things and through topic AI and what it means for the growing channel ecosystem supporting anthropics push into enterprise and mid market customers. Jason Cutler is the SVP of Anthropic Consulting and Engineering, also known as ACE, the new business unit that Kaelin has spun up to further help its customers as they adopt AI workloads and push AI further into their business operations. Ryan Groves now head of Anthropic Engineering in that new business unit at Kaelin. You might have heard Ryan's name before. If you've listened to the podcast, he joined us last year to talk about AWS migration and modernization efforts that Kaelin's also known for. We dive into all things Anthropic AI with me, the two of them, and my co-host for this episode Grant Harvey co-host over at the Neuron. It's a great episode, not just for those looking for more information on Anthropic, but also how partners are addressing crucial needs across governance, security, adoption, and scaling programs past pilots. Hope you have a good listen and thanks as always for trusting us. All right, hello. I am joined today by Jason Cutler, SVP of Anthropic Consulting and Engineering at Kaelin and Grant Harvey familiar face on the podcast from Over at our Sister Show, the Neuron. Thank you both for joining me. Thanks for being here. Good to be here. Cool. So I guess maybe Jason will kind of dive right into the work that Kaelin's doing with Anthropic, but before I ask that, maybe we'll back up and ask you to introduce yourself and introduce Kaelin to the audience. Yeah, happy to be here. Jason Cutler, SVP of Anthropic here at Kaelin. I joined Kaelin back in 2021 when we were about 50 employees and made a real partnership at that point to go all in with AWS. And so from 2021 to today, we've grown from 50 to over 1,000 employees, 90% of which are consultants and engineers tied to the cloud and AI. But our journey is pretty unique in that we started with AWS and in 2022 after the creation of ChatGPT4, which led to itself to everyone getting very hyped about Genai. Amazon released a program called Prop 100 and Kaelin had to go deliver 40 rag chatbots in 90 days. And I wouldn't wish that upon my worst enemy. But we learned a ton over those 90 days that it actually let it ourselves to go be the AWS Genai partner of the year for the last two years. Wow. So as part of that, we started building great AI products on top of Betrock with cloud models. And so we became an anthropic partner two and a half years ago as we were building out agents and products, leveraging these great frontier models. Now when anthropic and cloud then developed out cloud co-work, it really enabled us as a partner to start diving in deep with anthropic as well. And so we attended the partner summit earlier this year. We met with anthropic leadership. And we described to them how important it was for us to develop out a practice and manage that going forward. And so we announced our practice about a month ago after the partner summit. And we are now investing very heavily into our anthropic relationship and our AWS relationship at the same time. So it's great to have those two companies coming together and surfacing the great AI models to all of our customers. But we are also really, really ingrained at the anthropic level as they are creating new products and services to the market. And now we have people dedicated to that piece of the business too. A lot there to dive in on. I know Grant probably has about a million anthropic questions. But before we go on the model saw in the tech side, since we are on channel insider partner POV, I'm curious, Jason, I know you've said you've worked with anthropic for about two and a half years now all in. I think for many partners anthropic kind of started being a channel player earlier this year when they started building out the partner network and what that looked like from a finalized approach. Could you walk us through maybe what it's been like to get to know anthropic as a company, how you've developed that relationship with them and what that partnership has kind of evolved into today's practice? I mean, traditionally anthropic has been a research lab. And so creating frontier models and bringing those to customers was what they were great at. And again, our partnership with them really extended to that level. It was going and building great AI products and making sure that we had the best model to fit what the customer outcome was. And so we were partnering great with their field account teams, with their applied AI specialists, all the different folks that captures that type of work. I think what we've seen in like the last six months though is there's been this growth of go to market and partnerships on the anthropic side as their products have grown. And we've seen across LinkedIn and other social media just them explain how they were kind of cut off guard by how fast they were growing, where we then actually got a dedicated partner person earlier this year. And so we've seen the increase in head count has actually associated to actually increase and go to market activity with the anthropic because we can now lean in heavier on what are the things that they care about, what are things that our customers care about, and how we can create great solutions to kind of capture those two things. - Grant, I know you cover anthropic pretty heavily over on the neuron from what you just heard from Jason, do you want to dive in here maybe on that growth that anthropic has experienced or whether that surprises you to hear at all or not? - No, it doesn't surprise me at all, just because basically, the meme in the industry is something happened over a Christmas break, December 2025, all of a sudden people realized how powerful the models really were, and they actually had time off to play with them, and everyone had a project in 2025, they all came back, Cloud Code Pilt, and they were extremely bullish on anthropic, and then anthropic realized very quickly that a lot of people are using Cloud Code for non-coding related work. And so they needed to launch something to meet the users where they were, and so they've launched co-work shortly after that, I think in January, and ever since co-work came out, and they actually had a product to meet the demand for people who realized how good Cloud Code really was, for just doing general purpose office work on your computer, the demand just skyrocketed, and it's been the story of the first half of the year without question in the media. So no surprises there, but I guess I'm wondering, as they were writing this wave, Jason, they've launched a lot of new products. They just mentioned co-work, but they also offered managed agents, and a lot of this other stuff. How has it been managing the growth side, including the growth of the products that they're offering now, and how are you managing that in terms of how you're working with your customers in order to guide people in the right direction of what product they should use? >> Yeah, we're used to this type of working together with the partner in terms of new things coming up to market. I mean, AWS has over 240 services, but obviously what anthropic is doing is at a pace that no one's ever seen. The fact is that each week we're seeing three major product releases happening that we then have to capture and somehow be able to explain back to customers. So it was actually one of the reasons why we've created a dedicated practice, was because we know customers right now are facing those same concerns that you just mentioned, Grant, that how do I keep up with something like this? And that's why you're going to need partners that are keeping up with it are dedicated to it. And as part of our enablement and training and the things that we're doing for operating models and governance, we can keep iterating on your environment over time because we are keeping up to speed with what's happening within anthropic and the models directly. And so it's really been one of the main drivers for us in terms of developing up this practice in just customer conversations in the last three months. It's one of the main drivers for customers in terms of why they're looking for a partner because to keep up to speed and do your day job is almost impossible. It's kind of funny. My day job is trying to keep up to speed and it's impossible. So I can imagine someone also trying to work while doing what I'm trying to do. Yeah, and I mean Jason, to your point on the why customers and why enterprises are going to keep looking for partners in this space, right? To your kind of expertise, AI isn't necessarily anything new for Kaelin, but maybe going to market specifically with some of the anthropic tech is something you've built out in this practice over time. So could you walk us through kind of what ACE looks like as a suite of offerings and a consulting practice and how you built that to maybe address what you were seeing in terms of pain points from customers? I mean, we've been the J.I. partner of the year with AWS the last two years globally. So we know AI and we know Jenny, I very well. We've been using anthropic and cloud models for two and a half years. So we know the model situation pretty well. I think what we're seeing now is work changing with AI and how that is it being adapted both across code, co-work and API. So what we've done is we've kind of internalized that into three different practices. So the first practice that we created was around a GENTIQ SDL C and PDLC. So the software development and product development life cycle and making sure that we can speak very clearly and articulate the value of Clawed Code to that subset of user. The next is knowledge worker transformation. And this is where co-work and agents are coming in in terms of how you can leverage those AI solutions to really see and fulfill against how knowledge workers, SGNA functions, business functions can use AI and leverage AI to get work done more efficiently. And then the last piece is around AI products and agents. And the idea there is how are you creating either external or internal products and agents that can drive innovation faster across those three areas of SDLC, PDLC, knowledge worker transformation, AI products and agents. We try and then understand what is your training and enablement look like? How are you helping people inside the organization leverage these tools? How do you know if people who are leveraging these tools are actually doing so thoughtfully, efficiently or in a way that's actually driving further value to the business? Beyond that, we then look at the operating model and governance that is required. So how do you put cost allocations in practice? How do you choose the different models that are available to you? How do you put things inside of a regulated industry in terms of having the right security guard rails in place? And so we're helping organizations understand this. And as you can imagine, doing that for a company that's 80% software development would be completely different than a customer that's 80% knowledge workers. And so we're diving in deep trying to understand, again, through our forward deployed motion, what the business requires and what they need and how to do it safely. And then our mindset is by really honing in on enablement and training and the operating model and governance is actually how you get transformation. That's how you can see AI transformation come out of an organization is once you've kind of tackled those two areas concretely, then we feel like we're on a good pace forward. And for us, it really stems to working with customers. So we love the idea of forward deployed engineering and product and AI leads and product managers because for us, that's how we've been doing work for a very long time with AWS. We've always talked about doing with customers not do for and forward deployed is the epitome of doing with the customer that you're ingrained with the customer showcasing prototyping to them quickly of how this technology can be working in a very safe manner. And so we've seen a lot of interest in a short amount of time. And for us, it's also just a massive, massive, massive help to be partnered with the Anthropic because they are trying to bring all this technology to those different type of users across SDLC, PDLC and knowledge workers. Grant, I see the wheels turning. Yeah, well, I have a couple of questions. And one of the things that you said was interesting. So this was one of the questions that we had for companies with mixed users, for example, right? So you've got business teams and Claw, you've got engineers in Clawed Code. We have leaders asking for agents, how are you recommending that they all come together to avoid like three separate AI strategies? Is that sort of where you're anchoring the forward deployed engineering that you do where you kind of come in? And actually, if you could just explain how that actually works, you know, are you building it for them and then teaching them how to do it? Are you giving them frameworks that they can use? Love to hear about that. Yeah, so, you know, I think it's important, you know, you start with training and enablement and what that looks like. And the idea there is, are you teaching someone like where the buttons are inside the application? Or are you training them on how they're going to change their workouts with AI? And so while we talk about training and enablement, really what we're trying to do is focus on enabling users to make sure they can use AI in a safe manner that can help them accelerate their work. And so, one of the things that we always kind of strive for these conversations is great, let's talk about your personas today inside your organization. What are the goals of that persona? What are the challenges of that persona? And what are the different tools that they have today to really get their work done? And the idea there is you want to be able to then integrate those tools into AI to really solve for the challenges of the role to accomplish the goals. And so, we're looking at that through MCP, we're looking at through that through custom connectors, right? But we're driving that innovation through a champions network of personas inside the organization to rethink the way that work gets done. And that has been a massive driver, I think, within our organizations and also organizations as a whole, who might have just started adopting AI without the why or the how. And they're kind of stuck or they've seen, and this happens a lot, right? You have some users take off, but that's 10% of your total user base and 90% are still stuck here because they're unsure how to prompt. And so, our idea is to understand great, how can we codify some of the work within the persona to where it's not the person that has to prompt the AI, but the AI keeps the human in the loop so that they are also being included inside of the work itself and they get more familiar with it. And so, that's kind of how we've been approaching it. And again, how you see that across an organization is making sure that you're, you really have this centralized governance and operating model in place to where there is good use. Like, you understand the security guardrails around it, you understand the observability, all the right things that you need from a centralized governance model are kind of included in that area. Where are you seeing the strongest results right now? Are you seeing, you know, following that same thread? Are you seeing the strongest results bringing Claude into existing SaaS apps or bringing existing SaaS apps like company tools and data into Claude as plugins or MCPs? Like, is there one pattern that beats the rest? Or, yeah, what do you see? So, I'll give you the bad answer. It depends. Right? Everyone hates that answer. But, you know, we see it within our customer base who are creating products. They are looking to find ways to leverage AI within their own products to their own customers to enhance that experience. And so, that would follow more as that AI products and agents, kind of category that we create in terms of like how we create Claude or we manage Claude inside of their existing environment itself. On the other side, you know, there's been this proliferation of SaaS tools across ecosystems internally over the last 10, 15 years. And at this point, one of the major drivers that we see of, I would say, a lot of enhancement has been taking those solutions, integrating that into AI and then leveraging the human in the loop to really drive towards the outcome that you need. And so, you know, there's been a massive growth in what we've seen with Databricks and Snowflake in terms of congregating data. But now it's like, okay, well, what do you do once you have the data? And so, where does that go? And so, that's where Claude is coming in. We've had a lot of interesting conversations and deployments already in terms of what people are looking to do with co-work and in finance departments and accounting in sales and marketing. And so, while they have these tools, they haven't yet put it around an AI wrapper to actually get the work done. I'm curious, Jason. I know Kaelin kind of offers services depending on the type of industry that your customers in and you kind of verticalize around ultimately what your customer needs from a use case perspective. So, when you think about the ways you've seen customers be successful with AI, how specific do you think business leaders need to get about what exactly they want out of their AI systems and what that ROI, so to speak that everyone's always talking about looks like. And have you seen business leaders kind of evolve their ways of thinking for maybe two years ago when you really started diving into AI? It's a great question. I think one of the things that we always try and talk about are the outcomes. So, what is the ideal outcome coming out of this? And for a long time, that's been cost reduction or new revenue coming in. And for us, what we try and really understand, again, goes back to the individuals inside the organization and what are the goals of those roles? I think a lot of times everyone's put together a JD. But does the JD actually describe what that role does day in and day out or just the themes of the role itself? And the reason why Cloud Code has been the fastest growing products ever was because developers leverage code effectively across an organization and the AI can quickly learn how to supplement that over time and continue to keep the person in the loop. And knowledge worker information today, nothing is codified. And so, one of the things that we've talked about, how do you codify the roles so that AI understands what has to get done in a meaningful capacity? And so, that is actually one of the main drivers in conversation tracks that we have right now, which is similar to what Cloud Code did with codifying the developer and engineering experience. We have to codify the knowledge worker experience. And really understand where human in the loop becomes a critical function for folks over time to do more work. and really the highly thought of work that you really need people to be a part of. - Yeah, because it's really hard. It's easy for AI to improve things that are verifiable, right? Code is very much verifiable. The code runs or it doesn't. It works with the code base or it doesn't. But knowledge work can be a lot more squishy, like I guess. And so having documented processes for what does each person at this company actually do from day to day and then where can AI assist that? I'm sure that's a very tricky problem. I do wonder. So in your public facing marketing, you mentioned a 66% productivity increase and a 2x faster prototype to production rate. I guess I was wondering anecdotally, do you have any specific customer success stories you can share that show what use cases specifically are driving that ROI? - In a lot of cases, we've been developing out these AI products for a very long time. On the code side, where we've seen an enhancement, right, has been around how engineering teams are moving from leveraging Cloud Code as an assistant to a co-pilot to a multi-agent orchestrator. So we were working with a healthcare SaaS company where they were able to get Cloud Code across the organization entirely and they were seeing more code getting generated than ever before. And now when they were going under the hood of like understanding the code itself, it was code. It was good code, but it wasn't the kind of code that they would have accepted through QA and other areas. And so the idea thought process there was, well, then how do we get humans involved to make sure that they're in that process of the loop and that they're spending their time wisely of making sure that the code can actually go into production. And so we then have taken them on a journey across those different areas to now, their engineers are set up as these agent orchestrators that are coming back to them with cues of things to look at where they might have existing problems or where there's actually dependencies on the code in other areas of the application itself that they have to look at. And so that is a big piece and something that we've really spent a lot of time on with our customers is kind of going through that journey, talk to another company today, even around their QA process and how they should be thinking, rethinking the way that Cloud Code can help them accelerate their QA across the organization. And they're one of the, you know, 10 most visited websites in the world. So for us, it's been, you know, a journey of understanding kind of, again, the adoption curve inside these organizations and then where their engineers are spending their time, but really where could they be spending it better? That makes me wonder in trying to take that same process of improving the engineers workflows from assistant to co-pilot to manage like orchestrator essentially. If you're trying to apply that same growth pattern to knowledge workers, do you kind of have to teach knowledge workers to think more like engineers? Well, maybe I would never recommend that 1 million years. You're right. Like, yes, you probably would, but it would never work. One of the common pieces of information that we get of why people are even looking for a partner is because they're finding that people even have a hard time going to Cloud to set up the prompt inside an organization. Right? And so there's this fear of like, hey, how do I do this? What can we do? And so for us, enablement is that key function to help that subset of users. And it really goes back to understanding kind of the goal, kind of the role itself. What are some of the challenges they face? And then how could you make their work easier over time based on the different tools that they use? And so we aren't necessarily asking a knowledge worker to go in and code up, vibe code, nap. It connects everything together. But for us, it's about creating that process where we've automated it through AI, 90% of what the function is. But in the end, it actually still needs that 10% review where that person can actually change the outcome of what AI is looking at it with their own perspective tied to it. And so it becomes more of like this pull push, which is we want to push our folks, our customers, to use AI to help accelerate their adoption and making sure that we're driving those very high value use cases to where people are not being intimidated, that they're using it freely, and that they're seeing the benefit. And I think once you do that, you start uncovering even more use cases inside the organization that can continue to drive further enhancement inside the organization itself. Hi, everyone. Ryan Gross, I lead the engineering side for our anthropoconsulting and engineering practice. So that means helping to shape both how we deliver software, delivering lifecycle enablement for our clients, as well as how we drive the applied AI side. I heard Jason talking about quite a bit here of how do we actually drive roles to use an agentic approach while not having them need to build software to do that. And then in terms of background, I've been doing consulting for about 15 years, working in a data and AI for about a decade now. And over the last 18 months, I've been doing a lot more work on how do you use generative AI and then agentic approaches in order to actually shape the software delivery lifecycle? I know this conversation is very heavily anthropic focused, obviously, because of the consulting and engineering arm you've launched. When you think about the kind of technical capabilities within anthropic and its models, whether that's specific firms of cloud or cloud coworker, what have you, what stands out to you on the engineering side of the house so to speak in terms of what you've been able to build with anthropic and then unlock on the customer environment? In terms of what we're building with anthropoconsulting, obviously, there's multiple levels to that. So what we're building from a technical perspective started, like I said, maybe about 18 months ago, where we started down the path of using cloud for modernization work. So we actually built our own harness and wrapper around cloud LLM models in order to help do things like database transformation, which Victoria, I know we've talked about in the past session I did on this podcast. And through that process, we kind of learned this agentic loop in what it looks like to really verify the results and how you can delegate more and more work to an LLM. Over that same time period, anthropoc was really iterating on cloud code and the kind of new foundations of agentic engineering. And over time, we found that they pretty quickly overtook our capabilities in terms of what our pre-custom built harnesses did. And so we've now been working for the better part of the year, building on top of cloud code and how do we configure it to solve those types of problems. And as we've gone through that and done an enablement with our teams, we have started to realize that those tools are actually great for enabling other teams as well. And so that's some of the genesis of the partnership between Kaelin and anthropic expanding from the more engineering-focused partnership that we've had for a couple of years to the new ACE practice and the ability to do enablement as the primary service where we're working on engagements together with Anthropic. In terms of what we've done, like IP-wise internal to the practice, we have harvested back some of the things that we've learned using this for Kaelin to be some of that IP that we can go in and work with clients to ensure that their best practices are getting out to all of their team as well. Ryan, what do you find is the biggest unlock when you're first working with a new team that's just adopting cloud code. We'll focus on the engineers for the first part of this. When they're first adopting cloud code and they're just getting up and running, what's the biggest unlock that you've found where they couldn't figure it out. And then all of a sudden, it clicks into place for them. There's almost always two waves to it. It starts out with the biggest unlock being getting the right hierarchy so that you have cloud MD and the various other information about your underlying code. And then that gets you to a certain point where people don't have to prompt everything into every message and feel like they're just using it as smart auto-complete and they're burning a lot of tokens. And over time, that gets to a few people who realize that the skill and plug-in ecosystem is the real unlock. And so those power users end up running out ahead of the rest of the team. Typically, they're doing it, they're working their tinkering on their own. And they'll either start to work to distribute that back to the broader team on their own. Or you'll get to a point where the team kind of plateaus where some people are doing great, some people aren't. That's one of our most common entry points with customers is like, road out cloud code, it's been two months. These four people are just all of a sudden so much more productive. Everybody else is a little bit more productive. How do we actually go from those power users back to a fully enabled team? And that's actually some of where we focus. So we have some tooling that'll let us come in essentially harvest back what the power users are doing. Use that literally run through their session histories to truly understand not just have them describe it to us. us and then build a plug-in and automated fashion. Obviously then we queue in and loop and inject our inexpertise into it and use that to drive some of the training that we do for the broader team so that it's not just the concepts in abstract terms but it's like this is someone on your team that can help you explain this long term. So you have a champion already and also it's in your repo using the MCP connections to your tools and dealing with the limitations that you are going to have to work with and that really leads to the broad unlock of many more people kind of getting up to that power user level. Yeah that's awesome that makes a ton of sense and then how would you you know if you're doing this already how do you then apply that same kind of system thinking to the code the you know knowledge work side of the equation. Yeah and this is it's still emerging there like the tooling is much more mature on the cloud code side especially the ecosystem around it like I think that ultimately underneath they're the same underlying SDK the same underlying models so like the capabilities that are being built on are about the same it's all of the other things that have been built up around it already that are more mature on the cloud code side. So what we're seeing there is that same process of harvesting back a plug-in based on what someone who is more advanced works in the context of co-work if you're just talking the chat tool then there's a lot more limitations in there and you need to be a little bit more careful you're often times just pulling in some of the anthropic developed or other custom developed skills that will enable it to understand the the legal domain is one that's actually come up several times recently in that space. And then the other big thing I will say here is that document authoring capabilities that tend to be the core of a lot of your co-work knowledge worker based workflows are not as clear in terms of what does it mean to quality control that document what does it mean to even you know validate that the formatting is right because you know code the formatting you have blinters and so forth that'll automatically do all that stuff for you there's no such equivalent for Excel spreadsheets. I guess yeah for for a word document maybe be Grammarly or yeah yeah for a word doctorate but you know a lot of times the more important stuff runs through Excel versus Word. You're right yeah I guess you'd have to do some sort of like formula check like check they should make a linter for spreadsheets. Yeah I mean that's kind of where it's like it trends towards that over time and if you think about like harvesting out a plug-in from what somebody in you know F P and A has done that's essentially what you end up with is you end up with this like set of you know scripts that get derived in a bunch of prompts that are being a linter for Excel spreadsheets. I have one more question on this so like following the AI space on X you know you see a ton of cool demos being built with Claude but what is in you know Jason Ryan feel either be feel free to answer what is your strongest recommendation for workflow and or a use case that an enterprise can actually trust in production and apply like right now like what's like the first thing you would recommend they do yeah. I think one of the things that we as an organization having done you know a ton of agente use cases dating back to the 40 rag chat bots one of the things that we understand is AI is a moving target right and you have to create something that is beyond just a point of time I think Ryan mentions like we're building for the future not necessarily the past and so one of the things that we drive towards is we've created a framework to think like that we call it 321 and the idea is that we spend three days to do discovery and use case analysis for our customer with the idea that we could prototype something back to them in two weeks and bring that into production within a month and the idea by that point is you are moving fast enough to where the AI cannot kind of skip ahead of you well anthropic maybe can because they're creating 325 but besides them you at least are creating this box of time to create what the use cases how it gets delivered people are getting familiar with it and then it's in production and the idea is that we are framing 321 where you could have multiple 321s happening inside an organization at the same time thinking of it as like a module but we want clear intended outcomes from each of them that real people can feel in a meaningful capacity and that's that's the main driver that we are trying to align to and honestly it's one of the things I think that customers have really accepted to adopt because they've seen that within their own AI use cases that have been either developed or they've been working on and we saw that internally here at Kaelin for a long time where if you followed the same agile framework of delivery you actually might never get to a point of done because new new feature enhancements continue to come in new people want to be involved and so the backlog just continues to grow over time to where there is no done 321 meant to give you that that point of time to say hey we're going to spend three days to tell you when done is done and you can iterate from that point forward but after a month this thing is in production and people are using it I think the key thing there and like if you're thinking about what to select into that type of framework is something where you're looking to drive a process transformation of some kind the reason you don't want to continue to change the thing a hundred times after that is like a lot of the work comes in we've prototyped and we've started to work through what the new version of doing this task might look like so pick an example here of in finance and you've got a long laborious process for doing month and close a lot of that's manual steps along the way instead we're going to think about okay well here's the set of data sources this is really just a data processing challenge we have these old Excel spreadsheets we could bind back to get a good idea of what good looks like or what done looks like doing that that means that the process is going to change and we're ideally going to try to do it in a day or two will still start early at the beginning we can prototype out a if you had all this information would that allow you to sign off and then we iterate several times do that prototype phase to get to like the full set of details that an actual financial analyst is going to need to see in order to feel confident to sign off on something like this because it's like there's a pretty critical asset that's being produced yeah you do not want any hallucinations exactly and so but that allows us to get to a pretty clear definition of what would drive that change what would actually get like people to use it as opposed to it exists in wow isn't that cool but now I go back through my whole spreadsheet anyway just to double check what Claude said and then in that month we can make it real it's like a trust problem as much as it's a technical problem right like you have to help people build trust it yeah especially on the knowledge worker side I think it was a trust problem on the developer side for a while enough people have seen enough news and you know oftentimes your first week and a half of trying out Claude code for most developers they get over the hub quickly these days I'm like yep these things check out and do most of what I would expect them to do we're going through that same cycle it does feel like a lot of it is like six months behind of like knowledge worker side is six months behind although the tooling is accelerating so fast that you know it may catch up and not actually be six months between as we go forward. Jason I want to bring you in here on this point that Grant highlighted with the trust capacity because I think when I talk to service providers and partners who are also kind of building these AI-focused practices for customers what they hear a lot is you know the executives all in the leadership teams all in great we're going to do all this AI work and then you get to you know Brenda who's been in the office for 50 years and and has everything down walk and doesn't want to learn something new right or you have the people who are like I didn't ask for this I have to re-wear my job now how do I what's going on is part of the kind of enablement focus you've built into ace helping the entire company kind of get over the hump so to speak of going okay this is why my leadership is investing in this process and how often do you see that disconnect from customers we never see that yeah no one of yeah so no I mean I think look I'm sure when people started using personal computers the same exact talk track was happening I can't redo the way I work today because I'm it's gonna slow me down I don't think all any of us could imagine a life without a computer at this point it would be impossible right and so we're gonna be going through that same exact piece here with AI over time and it's it's really upon us to make sure that that person Brenda you know she feels comfortable enough to leverage AI but that really that AI is there to help accelerate her with her job and make her job easier and that it's driving more efficiency to let her do more important things like that in the end becomes the driver and gets actually people more open to using more AI because they start seeing all the great advancements it's made in their day to day life and now they want to learn what else they can do so for a lot of organizations that we talk to you know we don't necessarily want to go after the most important thing inside the organization because that most important thing might also be probably the most complex thing and that doesn't necessarily always equate to the best outcome where we might want to find time is hey what's happening inside of your law firm today where you have lawyers spending 20 or 30% of the population. of their time on Monday in tasks where they might be able to spend more time billing and working with clients if we're able to take that 20 or 30% of time away from them and automate that through an AI function. While that work is not necessarily high value, it's actually allowing the lawyer to go spend more of their time on high value pieces. So that is our job as a partner to help understand inside of an organization, to make sure that we are setting that organization up for success, but also we're setting Brenda up for success to do the things that are going to be highly valuable to her. To our earlier point about how fast all of this is moving, do you find that you have to spend a lot of time with the brandas of the world and updating their priors on this topic? Because I feel like from my observation, so many people still have AI's capabilities in the back of their head from where it was six months ago, and they haven't updated to where we are now. The key thing here is we spend time with champions oftentimes that are in the organization alongside these people. And so you're not necessarily starting with those people who are coming from a place of not wanting to change. You're starting with those that maybe you've already seen it or maybe they got tapped on their shoulder by the boss and said you're going to go figure this out. But the ability to then work through with those people, what it really looks like to do this within this organization, and then go alongside them to those that maybe are not necessarily going to be the first ones to adapt are generally going to go better. It's still not a immediate win, but that capability to build plugins and skills that encapsulate some of what you're going to do does change the equation from past versions of software where like ERP roll out and you had to go train everyone on like you're going to need to go click all these 17 buttons in this order and these obscure screens. In this case, it's so much more self-describing and you can get it to a place where they just go in and ask, like, hey, I need to go do the thing that I always do. Please go take the next couple of steps for me. And they're going to get an output then ideally that you've tailored to give them the information they need to feel comfortable with it. And over time, the amount of time they spend on reviewing everything there and then going back to their old way, like as long as that continues to check out the first, I don't know, six or seven times, maybe 15 for something more important than somebody that's more risk of personal. Yeah. Then the time declines that they spend reviewing outputs, they're still there for the past. The risk you start to run is the opposite. Some people were too eagerly adopt, never read any of the checks, assume that it's perfect, and then get slapped on the wrist or cause some negative outcome at which point then the question becomes like, is that a personal responsibility of that person and you're going to say like, you were bad for doing this or do you want to build the process so that there's less of a way for someone to just blindly trust. And so I think we spend as much time fighting the over trust issue versus helping get people across the risk of verse kind of chasm. So to both of you, as we wrap this conversation up, what excites you the most about kind of the next six months to a year of working with Anthropic and building out, continuing to build out the ACE program for customers? I mean, how excited are you to keep growing alongside probably one of the fastest growing companies in the market right now? Jason, maybe we'll start with you. I've been working with Google, Microsoft, AWS, the last 13 years in a partner capacity. And what we've done in the last four months, I have it really felt in 13 years. Because of the pace at which the product is moving, the pace of which customers are adopting, and really the pace of which we have to grow ourselves. And so you combine those three things together and it really does feel like you're on a rocket ship because we are literally going to uncharted territories in terms of where this is actually going ahead. And that part to me is incredibly exciting. I think we're seeing it from the people that we're interviewing to come to be part of an ACE practice. They are incredibly interested in partnering on this journey because they know that this is the beginning of the next 10 years of an Anthropic Services Partners life. And the fact that we are a preferred Anthropic partner right now, which there are less than 30 in the world. And the fact that we are ingrained with the Anthropic on the field level that we are ingrained with the Anthropic on the product level that we have our partnership with AWS and AWS and then the Anthropics partnership is massive. You know, we're at the beginning of I think is going to be a very exciting time. And you know, I tell this to Ryan and the rest of the theme is like, this is going to be the busiest and the most fun and probably the most part of your life because there is no playbook that you can say we've done cloud implementations for the last 10 years. They just don't exist. And so we are building these out. We are finding these use cases that are being common inside of organizations. And it's just it's a really exciting time to be part of kind of this practice and be part of this partnership with an Anthropic. Yeah. And maybe just to play off of that ability to use this technology ourselves has been one of the key really exciting things for me so far. Like being able to use cloud in a whole bunch of that had context to make it so that as we're running a consulting business, we're doing in the way as if you started it really feels like an internal startup that we've spun up here that allows us to keep up with the pace that Anthropic is growing because I think that's going to really be one of the differentiators between those that really are the true trusted partners with Anthropic and those that are really good, but maybe can't scale as fast as Anthropic needs them to. And then I think the second thing here, I'm going to give the super nerdy answer as well, you may have seen in the news that Carpethy joined Anthropic recently and he joined as an individual contribute on the pre-training team. So I've been talking about this for maybe the last six months or so like part of the next step function change here is the models right now are really good at the things that have been able to be verified easily. What he's been working on recently with the last couple of releases that he had that made the news before joining Anthropic is this ability to kind of build those closed loops of learning a new domain. And so all of this knowledge work and part of the reason I was saying before that like that timeline might compress between code and software engineering and other functions, all of those domains that are out there, just thousands and thousands of different ones, they're getting better and better now at making it so that the model can reason its way through a new domain that it doesn't actually have too much data in the broad internet scale that they've been using to train these models. So I could really see in, I don't know what the horizon is on this and this is not any insight knowledge is just be speculating. The next six months a step change in terms of the model capability and you're already hearing about mythos and the step change in terms of code and so forth there that's soon to be released. And so that applied to all of these other domains means that you're going to be just driving this massive amount of change based on something that's not like playing within the limitations of what we're dealing with today where it's like I've got to spend a lot of time custom tailoring all the knowledge and context. It's that exploratory discovery baked into the model that is I think going to be really that transformative next step there. So what I'm hearing is we have this conversation two months from now and it'll probably feel very different even though we had it two months ago it would have felt very different than today and it meant like that two month timeline versus the year that we're used to in this world is a lot I think what Jason is talking about like the pace is just so unprecedented. Well I appreciate you both spending some time today with us to dig into it so again Ryan Jason thank you for the time. Grant thanks for piggybacking and co-hosting with me. Yeah it's fun thanks for having me. Yeah absolutely thank you all. (upbeat music)