At a glance: In six months, the product organization of a Fortune 100 financial services company grew its self-described advanced AI users from 19% to 52% and weekly use of its core AI ticketing tool from 27% to 74%. Most people reported saving an hour or more per week, roughly 12 full-time employees' worth of capacity, and 78% said AI also improved the quality of their work. Prodify Senior Fractional PM Jen Wulf led the engagement by treating AI adoption as a product problem.
Most large companies have rolled out AI tools by now. Not all have product teams that actually use them effectively. When a Fortune 100 financial services company brought Prodify in to drive AI adoption across the product organization behind its consumer app, leadership had the mandate and the tools, but not the results. Six months later, weekly use of the team's core AI ticketing tool had jumped from 27% to 74%, the share of product owners who considered themselves advanced AI users had nearly tripled from 19% to 52%, and teams were reporting that their product deliverables were getting both faster and higher quality. Here's how Prodify Senior Fractional PM Jen Wulf got them there, and what other large organizations can take from it.
Executive leadership had a clear sense of urgency. Peers across the industry were moving quickly on AI, and the company wanted to operate like a modern, tech-forward business. There were already promising pockets of success: teams were using AI to extract requirements from decades-old legacy systems, and to prototype and feed user feedback into AI agents so they could iterate faster in a heavily regulated environment with many layers of approval.
Inside the product team, though, adoption was concentrated among the top ~5% of AI early adopters. Several things were getting in the way for the rest of the organization:
Complicating all of this, different departments operated very differently, from product process to tooling. A single, company-wide mandate was never going to land the same way everywhere.
"There's so much urgency and buzz around AI that people lose sight of the fact that it's still a product problem. It's understanding your users, their pains and their goals, and then making a plan based on that." - Jen Wulf, Senior Fractional PM, Prodify
The work started where any good product work starts: understanding users, their pains, and their goals.
Jen surveyed roughly two-thirds of the product organization (about 200 people) and followed up with interviews of 15 product team members and cross-functional collaborators. That research surfaced hidden blockers. The biggest: many people were spending seven or more hours a day in meetings. They simply had no time to learn AI, a frustrating chicken-and-egg problem, since AI was one of the best ways to win that time back.
Because meetings were the biggest drain on time and energy, that's where we started. The organization didn't have a transcription service, and an internal tool was still being built. Rather than wait for that internal tool, Jen pushed to get everyone upgraded access to Microsoft Teams Premium so meetings could be transcribed automatically.
Those transcripts became the context product managers fed into their AI tools, forming the foundation for product deliverables like requirements, user stories, and follow-ups. This also allowed product team members to have their AI "attend" meetings for them when they were unavailable or optional, synthesize action items and begin executing on next steps. Jen paired the rollout with case studies on using meeting-support tools well, plus examples from early adopters. That small win opened up just enough space for people to start learning: how to prompt, which tool fits which task, and what good output actually looks like. This triggered a massive shift towards AI adoption.
Generic "AI for PMs" advice online rarely survives contact with a large enterprise's specific processes, systems, and regulatory requirements. So Jen recruited a group of 10–15 AI early adopters, identified through the survey, interviews, and referrals, who were already doing impressive things. One had built an integration across monitoring, code, and ticketing tools that caught bugs before users reported them and proposed a fix.
These early adopters led bi-weekly demo sessions showing colleagues exactly how they used AI within the company's own tools and guardrails. Along the way, Jen documented a library of company-specific use cases, including some that aren't classic PM use cases but mattered enormously here. Examples included writing new stories and epics according to detailed organizational guidelines, creating agents to support self-service internal tools, and creating user persona agents.
Jen also closed the gap between tools being launched and tools being used:
Measured between the baseline and follow-up surveys (Q1 to Q3), the results were significant:
| Metric | Q1 baseline | Q3 follow-up |
|---|---|---|
| Weekly use of the AI ticketing tool | 27% | 74% |
| Product owners who rate themselves advanced AI users | 19% | 52% |
| Respondents saving an hour or more per week | Not measured | Majority (~12 FTEs of capacity weekly) |
| Respondents reporting better-quality work | Not measured | 78% |
Because self-reported metrics can be optimistic, we validated them against actual tool usage data. Usage trends moved in the same direction as the survey results over the same period.
Two other patterns stood out. People who engaged consistently with the program's resources saw significantly stronger results, which held true across seniority levels and internal teams. Ultimately, the conversation inside the organization shifted from "Should I use AI?" to "How do I get even more effective with it?"
"I was so shocked how impactful the enablement was. We were doing demos bi-weekly and building a library of information, but I would have never imagined it would have had this kind of impact." - Jen Wulf, Senior Fractional PM, Prodify
Have AI tools but not AI results yet?
If you lead a product organization at a large, complex company, here's what we'd want you to know before your next AI push:
In this engagement, measurable change took about six months, from a Q1 baseline survey to a Q3 follow-up. The first win, freeing up time with automatic meeting transcription, created room for people to start learning before deeper enablement began.
Run a baseline survey covering tool usage, self-rated AI skill, time saved and work quality, repeat it a few months later, and check the self-reported trends against actual tool usage data. Treat adoption as a leading indicator and tie your metrics to outcomes the business cares about, such as time saved, quality and development velocity.
Building in-house can protect enterprise data, but it can slow adoption if the tool lags behind commercial assistants. Here, an internal platform with confusing model selection and unreliable chat history led many people to conclude that "AI doesn't work," and adoption picked up once a leading commercial AI assistant was rolled out with proper enablement.
Give it dedicated ownership, whether that's an internal hire or an experienced partner like Prodify, rather than volunteers fitting it around full-time jobs. Volunteer-run efforts tend to produce inconsistent programming and little coordination with company-wide AI initiatives.
Show them peers doing it inside the same systems and guardrails. Bi-weekly demos led by 10 to 15 internal early adopters, plus a library of company-specific use cases, were far more persuasive than generic "AI for PMs" advice.
If your product organization has AI tools but not yet AI results, Prodify can help. Our coaches have led AI enablement and product transformations inside large, highly regulated enterprises, as well as with scale-ups. Learn more about our AI advising, or request a call to talk about where your teams are today and what it would take to move faster and build better.
Related reading: Client Case Study: Laying an AI Foundation to Drive Growth via Personalization and Incorporating AI Into Your Product Strategy.