Improving engineering productivity is crucial to the work we do at Dropbox. The more quickly we can deliver high-quality features to our customers, the more value they can get from our products. This rapid iteration has been key to developing tools like Dropbox Dash, context-aware AI that connects to all your work apps, so you can search, ask questions about, and organize all your content.
In the process of building Dash, we’ve become big adopters of AI tools in our own work, from Claude Code to Cursor. The early results have been promising, but there are still a lot of open questions about how to work with these tools most effectively and where they can have the most impact. To push this conversation forward, Dropbox CTO Ali Dasdan hosted an executive roundtable on December 11, 2025, at our San Francisco studio. We brought together a small group of technology leaders from top companies for an afternoon of open discussion, idea-sharing, and a deep dive into the evolving world of engineering productivity and AI. Here’s how it went.
How Dropbox is accelerating progress with AI
Adopting AI tooling for the sake of AI is meaningless; it must be tied to tangible business results. As we navigate this shift, we’ve had to ask ourselves: Which approach is the right one? What existing processes need to be upgraded in light of AI workflows? To kick off the event—and show attendees how we’ve been thinking through these questions at Dropbox—Uma Namasivayam, Senior Director of Engineering Productivity, took a closer look at our own experimentation, adoption, and enablement cycle to accelerate engineering productivity with AI.
We started by working with Dropbox leadership to gain buy-in and establish the importance of AI tooling, and together made AI adoption a company-level priority. This turned AI from a grassroots experiment into an urgent organizational priority, and helped everyone get aligned. Teams were now empowered to experiment with tooling, and we reduced the overhead associated with getting contracts approved to pilot new tooling at Dropbox.
In our experimentation, Dropbox saw impact across the entire software development life cycle, from code review and documentation to debugging and testing. Like other large organizations, Dropbox has our unique challenges. Off-the-shelf AI tools don’t always fit our scale constraints—we have a very large, multi-language monorepo—so we’ve had to be deliberate about where to adopt, where to extend, and where to build our own capabilities. For example, Dropbox built our own AI tooling that listens for failed builds on pull requests and uses our AI platform to propose fixes to them.
As a result of our efforts, most Dropbox developers are now using at least one AI tool in their workflows. We track pull request (PR) throughput per month, per engineer as a core metric. You can see how users who are engaging more with AI coding tools have an outsized impact on the code shipped, measured by PR throughput per month.
We also closely monitor the sentiment of engineers internally regarding AI tooling. As strong positive sentiment increases, we’re seeing the share of negative sentiment go down.
Most importantly, developers feel less friction using AI to accelerate their work because we’ve made it easier to adopt tooling according to what they feel works best for their team.
The executive roundtable
The heart of the evening was a roundtable discussion designed to cross-pollinate ideas across different industries. To facilitate this, we divided attendees into three cohorts, rotating the groups for each question so that every leader could learn from three different peer groups.
The discussion centered around three core pillars:
- Measuring impact. What are the top three ways attendees are measuring AI-driven engineering productivity gains and what are the top three ways of measuring the resulting business impact?
- Leadership alignment. Describe three ways of aligning with company leadership on the progress and pace of AI deployment and use for productivity.
- The human element. What are the top three ways attendees are recruiting, evaluating, and growing their workforce for AI competency and productivity? What lessons can be applied to make non-developers more productive?
Following the structured session, the conversation continued over a cocktail hour, where leaders shared further insights into the commitment to craft required to lead in the age of AI.
What we learned, and what’s next
The overarching themes that emerged from the roundtable discussions centered around the following:
- Balance. Productivity gains must be carefully balanced against potential trade-offs in quality and long-term maintenance costs.
- The role of leadership. Management, particularly technical leadership, is pivotal in establishing and enforcing effective AI usage norms.
- Formalization. Formalizing AI competency within career frameworks signals a long-term commitment to its strategic importance.
Still, there are a number of open questions, such as: If AI is giving us more capacity, where is that capacity actually going? For Dropbox, this capacity is currently being channeled into areas like addressing tech debt, executing migrations, and improving reliability.
However, a key challenge remains in effectively connecting these productivity gains to tangible business outcomes—a challenge also voiced by many attendees during the roundtable. Therefore, the focus for 2026 will be on mapping productivity directly to specific outcomes, extending operational rigor beyond engineering teams, and ultimately driving end-to-end product velocity.
A huge thank you to everyone who made the trip to our San Francisco studio and contributed to such a memorable event. If you missed out this time, keep an eye on our events page for future opportunities to connect!
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If building innovative products, experiences, and infrastructure excites you, come build the future with us! Visit jobs.dropbox.com to see our open roles.
