👋 Hi, it’s Kyle and welcome to Growth Unhinged, my weekly newsletter exploring the hidden playbooks behind the fastest-growing startups.

Readers know I love sharing practical AI GTM workflows. But most of these have been top-of-funnel use cases like smarter outbound plays or inbound lead qualifying. Today I’m excited to bring you the perspective of Dee Kapila, the senior director of customer success at Fin.

Dee’s entire team has become AI-first in 2026. She walks through how she uses Claude Code as a GTM operator and her reflections on how GTM leaders can enable AI adoption. Keep reading to the end for an AI workflow that finally gave Dee her Sundays back and how to copy it yourself.

At the start of this calendar year, Fin's data engineering team provided unfettered access to a data layer they had built on top of Anthropic's Claude. That's right. No limits, no token rations, no express mandate from on high.

Claude4Data, or C4D as we all refer to it internally, was a simplified, pre-packaged installation the engineering team felt would give the power of Claude to non-technical teams who, in the words of my data engineering colleague Andrii Yakovenko, "need to build things just as urgently as engineers do." The engineering team packaged C4D for quick installation and rolled it out without much fanfare. Six months later, I can't imagine going back to doing things the way I was before.

In the time it took to write this piece, our customer-focused teams – customer success (high touch, scaled, and digital), CX Programs (customer education, community etc), Services, Solutions and Ops – started pursuing more sophisticated use cases and churning out even more creative builds. By the time you read this piece, we will have leapt forward even more.

Here is a rundown of how we enabled the team, a few examples of what we’ve built, and a deep dive into how I automated a task that previously took me two hours every Sunday.

I first invited Andrii to do a two hour master class and suggested the agenda shared below. We added some projects from across Fin’s R&D organization and some from GTM that the team could look at for inspiration ahead of the class. Then we nailed down pre-reqs the team would need, set up a Slack channel for Q&A, and scheduled the master class, getting it on the team’s calendars in advance, ensuring no all hands etc would interfere with the 2 hour block. Easy!

From there I asked my team to complete the following pre-reqs, which shouldn’t take you long to set up on the backend, especially if your org is already supportive of AI upskilling. These pre-reqs took the team one hour, tops.

This led to our two hour master class on becoming AI-first. Here’s the actual Google Doc with the agenda Andrii and I aligned on for the initial master class.

I kicked off the class with a brief overview of the power of these tools, demos of my first builds, inspiration from across the org and then handed it over to professor Andrii. We spent 30-40 minutes on the session content before getting into the hands-on/building part.

My biggest piece of advice as you set up something similar for your team is spend the majority of the time hands-on! It's crucial. By the end of the session, everyone on the team had built something. We had a few things to troubleshoot async after (yay aforementioned Slack channel), but for the most part setup was quick, time-to-first-product was fast, and the team was able to branch out to multiple use cases on their own after this. Two weeks later Andrii hosted office hours (I recommend keeping an office hours session at 1 hour and offering 2 or more to hit all your time zones) where people could drop in and ask him questions or troubleshoot any blockers they were running into (ie. an MCP connector not working). By this point, we had someone on the team who had built and published 100 things on the company Claude for Data (C4D) repository. 100!

Of course a lot of this was exploratory, testing, trying, and not all builds were adopted, but success at this stage is absolutely quantity. Get familiar with the tools, what they can do, and how far you can go. Seeing my team be a mini-Claude code factory was enthralling.

Andrii and I plan refresher sessions every so often, just for my team. This is on top of the high level enablement Anthropic and Fin already provide. It speeds up innovation when you get to building in your own domain so definitely supplement scaled education provided by Anthropic and your org with your own team-specific builds so you can zero-in on your own problems and needs.

I'd describe the team’s enablement journey in the following three phases though they did not feel this clean or linear in the moment: Initial adoption, Maturity, Optimization.

The success org has published thousands of builds across our C4D repository. The team has built apps, skills, agents, workflows, automations. I’d say the main batches of builds fall across three categories,(1) data, reporting and insights, (2) customer-facing solutions, and (3) operations.

A few examples:

One of our CSMs built an attribution model for our scaled workshops, showcasing impact over time on each accounts’ instance after participating. We were overjoyed because it is so hard to build attribution models in GTM outside of marketing. You don’t typically have the army of analysts marketing teams have, modeling is more complex in terms of inputs and the diversity of work a CSM executes, and so on.

Isolating workshops as a use case, then focusing on a clear time horizon and a shortlist of metrics was the key here. Then, we validated the approach with our data team, who were eventually able to run a cohort analysis further showcasing how impactful the workshops were against control groups that didn’t attend them. This helped us understand that we were underestimating the impact of the workshops and, along with customer feedback to validate the demand, we were able to increase the number of offerings.

One CSM built a simple interactive heat map app breaking down where the customers in their book of business were located so that they could select the best locations for our in-person workshops. What I love the most about both of these examples is how quickly the CSMs went from asking a question to generating an app that helped them find an answer, and immediately executed a decision or next step. Powerful insights and immediate utility are a winning combo.

Our customer education team built a certification program with hands-on labs that help learners test their Fin and AI skills in playground style environments, guiding them along the way. One of our multimedia designers also built custom teleprompting software in Claude and saved the team the cost of several licenses for software that was best in class but still didn’t meet our edu team’s specific needs. It was much more user friendly, particularly when we invited customers on set to share their learnings on Fin Academy.

One of our CSMs has built skills that autopopulate requests for him to approve before official submission in Salesforce. He created a feature request skill that extracts structured feature requests from Gong transcripts, Slack threads, support cases, emails and meeting notes. Raw conversations that go straight into submission-ready formats have saved him a ton of time and shortened timelines. He’s also created engagement request skills that help speed up the process of requesting other GTM resources like solutions architects, trainers or services leads.

My biggest Claude Code use cases are bucketed across three main categories: inspecting my business, running my business, and prototyping. A few examples from my world:

I start my week in my personal cockpit. This is a simple dashboard that gives me a pacing update across all our customer segments so I can see how my team is tracking against growth targets and where we are with accounts up for renewal this quarter. I have a section that flags risks and aggregates the latest sales/CSM comments on mitigation, and a section that pulls status updates from my CX programs teams who manage their workstreams in Coda. By the time I finish my morning coffee, I know what needs my support or attention, and can jump in to action top priorities.

I’m currently building a customer pulse section that summarizes product feedback themes, program sentiment, interesting insights from Gong calls, Slack account channels, etc. Building this as an early adopter got me requesting MCPs and features from our data engineering team that are in heavy use today across the org and I gotta say that is some real street cred, thankyouverymuch.

In addition to this report, I run my entire weekly ops review through an app I built in Claude. The app helps me run offense on the biggest growth vectors in our segment, helps us uncover low performing cohorts (aka Lowhorts) and also helps us assess which areas to focus more resources in. You can learn more about this review here. All these result in clear next steps, more targeted conversations, and faster execution.

My team manages platforms like our community forum, Fin Academy, and more. Whenever we are building a customer-facing asset, webpage, or app, we generate prototypes in Claude to review and discuss. It's so much easier to communicate and co-build when you’ve got a high fidelity visual starting point. I often share my vision with my team or cross-functional partners this way, and I can communicate feedback more accurately this way as well. With Claude Design, generating assets in accordance with Fin's brand is fast and high quality so we have reduced the number of small, irritating requests we may have for our brand team.