Quality assurance · Production

How Plaid grew AI coding adoption to over 75% of engineers

The problem

Plaid needed to shift hundreds of highly effective engineers to AI coding tools without stalling productivity, while navigating a fast-moving vendor landscape and the compliance constraints of operating in regulated consumer finance.

First attempt

Simply announcing tool general availability did not sustain engineer adoption; after internal announcements, adoption quickly plateaued without dedicated ownership or follow-through.

Workflow diagram · grounded in source
1
Tool pilot evaluation
validation
“we first do real development on large open-source or public projects to assess baseline quality before beginning any procurement”
2
Legal/security classification
validation
“We developed a framework for classifying each tool based on the inputs (what kind of data is being sent, where is it being sent to, etc) and outputs (what are we doing with the result, what could be the legal or compliance implications t…”
3
Adoption dashboard tracking
feedback_loop
“This team spun up a basic internal dashboard to think of adoption as our own product. We were able to track not just usage over time but retention by cohorts and teams. This allowed us to spot trends that led to further investigation, li…”
4
Churned user outreach
human_review
“message every churned user and dive deep into what they were happy or unhappy with when using certain tools”
5
In-house content creation
output
“making a small amount of in-house content showing common workflows in action was very helpful in getting more engineers to organically try the tools”
6
Engineering manager targeting
routing
“we began targeting EMs more directly with training content and Plaid-specific examples to show (not tell) why AI tools are impactful. We were also able to use our adoption analytics to surface insights into the reports our managers alrea…”
7
Company-wide AI Day
trigger
“Plaid held a company-wide AI Day to shake everyone out of their normal day-to-day and focus on using AI tools in dedicated workshops”
Reported outcome

Plaid grew regular AI coding tool use to over 75% of engineers, cut new tool pilot timelines from weeks to days, and ran a company-wide AI Day with 80%+ engineering participation and 90%+ CSAT.

Reported metrics
engineers using AI coding tools regularly> 75%
AI tool pilot timelinedays instead of weeks
AI Day engineering participation80%+
AI Day CSAT90%+
Reported stack
CursorVS CodeJetBrainsSlackOktaLLMs
Source
https://plaid.com/blog/ai-coding-adoption-plaid/
Read source ↗

Frequently asked questions

What did this team achieve with this AI workflow?

Plaid grew regular AI coding tool use to over 75% of engineers, cut new tool pilot timelines from weeks to days, and ran a company-wide AI Day with 80%+ engineering participation and 90%+ CSAT.

What tools did this team use?

Cursor, VS Code, JetBrains, Slack, Okta, LLMs.

What results were reported?

engineers using AI coding tools regularly: > 75%; AI tool pilot timeline: days instead of weeks; AI Day engineering participation: 80%+; AI Day CSAT: 90%+ (source-reported, not independently verified).

What failed first in this deployment?

Simply announcing tool general availability did not sustain engineer adoption; after internal announcements, adoption quickly plateaued without dedicated ownership or follow-through.

How is this quality assurance AI workflow structured?

Tool pilot evaluation → Legal/security classification → Adoption dashboard tracking → Churned user outreach → In-house content creation → Engineering manager targeting → Company-wide AI Day.

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