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title: The Two Investments That Helped One Fintech Company More Than Double AI Adoption
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# The Two Investments That Helped One Fintech Company More Than Double AI Adoption


by 

[Brian Elliott](/author/brian-elliott/)

Executive-in-residence, Charter

Brian Elliott is Charter's executive-in-residence and CEO of Work Forward.

Jun 25, 2026 9:30 AM CUT

![](https://static.time.com/v3/assets/bltea6093859af6183b/bltfa16815d0d5e0300/6a3c5463820aa393369e5459/charter-ai-adoption_(1%29.jpg?branch=production&width=3840&quality=75&auto=webp&crop=3:2)

Art: Charter

by 

[Brian Elliott](/author/brian-elliott/)

Executive-in-residence, Charter

Brian Elliott is Charter's executive-in-residence and CEO of Work Forward.

Jun 25, 2026 9:30 AM CUT

When many companies push to accelerate AI adoption, they tout higher productivity, set up leader boards that compare AI use, and even threaten consequences for those who avoid the tools. 

But, financial services platform [Circle](https://www.circle.com/ "undefined") took a different approach, focusing instead on two things: setting up simple AI building block tools that help non-engineers experiment, and giving all employees the permission to fail in a low-stakes company-wide challenge. In one quarter, weekly active users of AI coding tools across Circle jumped from roughly 35% to 85% of employees. "The goal was not to build a product. It was all about supporting the learning process," said [Brian Christman](https://www.linkedin.com/in/brianxman/ "undefined"), Circle's chief people officer, at a recent [Charter Forum](https://www.charterworks.com/forum/ "undefined") session. 


Christman and [Liz Liu](https://www.linkedin.com/in/elizabeth-liu-104745115/ "undefined"), the company's director of AI strategy and transformation, shared more about what they learned with Forum members under [Chatham House Rule](https://www.chathamhouse.org/about-us/chatham-house-rule "undefined"), walking us through what worked, what didn't, and what comes next. Excerpts here are shared with permission.

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## **A transformation product manager**

Circle's approach to expanding AI adoption started with hiring Liu, who joined from Scale AI, where she worked as a product manager. She joined Circle wanting to apply what she had been building inside an organization.

As I've [written before](https://www.charterworks.com/make-every-manager-a-product-manager/ "undefined"), leaders who successfully drive AI transformation tend to share a product manager's mindset. The instinct to ask what problems AI can actually solve, identify the business value of solving those problems, and rally cross-functional resources to redesign workflows are at the core of product management. 

Adding Liu’s role, designed by chief technology and AI officer Li Fan and backed by the executive team, was just one part of a series of investments that, as a regulated fintech company, included training and security monitoring to ensure AI initiatives live within appropriate guardrails.


"Every single connector, every tool, had to go through a whole procurement and review process," Liu said. "It doesn't get easier. It shouldn't, frankly." 

## **Two investments that made all the difference**

Circle's plan had two pillars, and neither involved simply telling employees to use AI more.

**The first was to build engineering building blocks into Circle’s core systems.** Before non-technical employees could meaningfully build with AI, Circle's engineering team made it possible for AI tools to safely read from and write to more than 30 core systems. They also developed a shared in-house workspace called VibeBox where employee-built tools could live and be used by others. VibeBox lets employees build internal prototypes and applications that are safe and secure to use internally without going through security and testing processes for each tool independently. As of late April, more than 500 apps had been launched on the platform, and nearly half of app owners came from non-technical functions.


"If you build something internally, you need some way for that code to live and be shareable," Liu said. "That was the prerequisite."

**The other move was creating a low-stakes challenge that encouraged AI use.** Circle’s leaders asked every employee to pick one daily problem and try to solve it with AI over the course of a month, pairing that request with an investment in training. There were no grades assigned, no incentives, and no comparison across submissions. The only assessment, Christman said, was whether people made a good-faith effort.

More than 1,000 submissions came in, including an HR promotion portal that fed into Workday, a finance dashboard that tracks treasury reserves, and a tool that tracks whether knowledge management documents (like policies and procedures) are out of date.

That combination of guardrails built into their systems plus permission to fail gave people the confidence to truly experiment, Christman said. One team member told him she'd felt "like the stupidest person on earth when I started this process," but then launched something through VibeBox she described as one of the proudest things she'd done in her career.


## **Emphasize fluency, not specific tools**

To support those two pillars, Circle ran weekly mandatory learning sessions throughout March. The framing taught concepts such as how to set up AI skills, connect AI assistants to local files, and build agents. "We emphasized fluency rather than tooling, because tooling changes all the time," Liu said. "The war between OpenAI and Anthropic is ongoing. We really don't know who's going to win."

By the end of the first quarter, internal surveys found that employees rated their confidence using Circle-approved tools at 9.0 out of 10, their understanding of the company's AI strategy at 8.6, and Circle's support for AI skill-building at 8.4\. 

While all those parallel AI experiments produced plenty of learning, there was also a lot of duplication. The next step is to see coordinated results from all that activity. "Q1 had a lot of energy, but some might define it as chaotic energy," Liu said. "Q2 is about translating that energy into more structured business impact."


Now, roughly 120 senior leaders called "AI drivers" are setting team-level strategy, and "power users" embedded in each function are building tools for their teams. Together they form mini-pods inside each function, organized around three roadmap themes: faster execution cycles, proactive intelligence gathering, and templates for common situations like project updates or sharing news.

Still, big challenges remain. As one Charter Forum participant asked Christman, what happens to spending on AI and people’s jobs once everyone is fluent?

Circle has set hard spending limits per tool and given employees visibility into their own usage, he said, while leaders are openly contemplating a future where token allocation may need to be budgeted.

On the harder question about the future and jobs, Christman has deliberately not sold AI to employees as risk-free. Some repetitive skills and tasks will be automated, and employees will have both a say in which ones and time to learn new skills. As Christman put it, "The main message we're giving employees is control what you can control, and get as much out of the investments in training as you possibly can."


## **Four takeaways for people leaders**

Circle's approach won't work for every organization, but the concepts are portable. Four things to consider:

**Put product managers in charge of transformation.** The skill set required for transformation project leadership can look a lot like a [product manager’s](https://www.charterworks.com/make-every-manager-a-product-manager/ "undefined"): customer empathy, analytical thinking, and the ability to align across functions are all essential skills.

**Build the guardrails before you measure adoption.** If non-engineers can't connect AI tools to your systems or share what they build, adoption will plateau no matter how many training sessions you run.

**Lower the stakes when you want learning.** A low-stakes challenge with a thousand submissions taught Circle's workforce more than any mandated training. Save accountability for what comes after the learning phase, not during it.

**Be honest about what's changing.** Christman's framing (invest in skills, control what you can control, don't pretend the future is risk-free) is harder than a feel-good message, but it builds the trust needed for the work that comes after widespread adoption.


---

## Read more from Charter

[READ MORE](https://time.com/partner-content/charter/)

[Open article: The 'Intern Test' for Working with AI](https://time.com/partner-content/charter/the-intern-test-for-working-with-ai/)

![](https://static.time.com/v3/assets/bltea6093859af6183b/bltd69842f9be73aa46/6a57f3bc6a33e5387326480a/charter-kupp.jpg?branch=production&width=3840&quality=75&auto=webp)

### The 'Intern Test' for Working with AI

[READ MORE](https://time.com/partner-content/charter/the-intern-test-for-working-with-ai/)

[Open article: What AI-Native Companies Do Differently](https://time.com/partner-content/charter/what-ai-native-companies-do-differently/)

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### What AI-Native Companies Do Differently

[READ MORE](https://time.com/partner-content/charter/what-ai-native-companies-do-differently/)

[Open article: AI Is Pushing Some Workers Back to the Office—and Others Out of It](https://time.com/partner-content/charter/ai-is-pushing-some-workers-back-to-the-office-and-others-out-of-it/)

![](https://static.time.com/v3/assets/bltea6093859af6183b/bltf62716af92183584/6a1e0cff084a70752dcc4541/charter-ai-office-design-thomas-barwick-getty.jpg?branch=production&width=3840&quality=75&auto=webp)

### AI Is Pushing Some Workers Back to the Office—and Others Out of It

[READ MORE](https://time.com/partner-content/charter/ai-is-pushing-some-workers-back-to-the-office-and-others-out-of-it/)

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