As AI becomes a starting point for more kinds of work, its usefulness depends on access to an organization’s trusted knowledge and context, including the files, research, feedback, decisions, and project history that give the work meaning. Dropbox helps people bring that context into the supported AI tools they choose while preserving the permissions and controls they expect, so they can spend less time finding, uploading, and re-explaining what already exists. When AI helps them create something new, Dropbox gives that work a trusted, durable home where it can be saved, shared, reviewed, approved, and built on over time.
That focus on connected workflows also shapes how we use AI within Dropbox. Access to increasingly capable models can help teams produce more. But turning that output into sustained gains in productivity, quality, and customer impact requires examining workflows from end to end. That includes addressing new bottlenecks as output grows, adapting the platform that supports development, aligning organization-wide incentives, and measuring whether greater speed and volume lead to better outcomes.
AI also increases the importance of human judgment. People still need to choose the right problems, give agents the context they need, evaluate the validity of their output, and remain in control of important decisions and actions. Problem solving, communication, and leadership become critical to turning AI-enabled output into real value.
Dropbox Chief Technology Officer Ali Dasdan and Senior Director of Engineering Productivity Uma Namasivayam discuss lessons from deploying AI at company scale, how we assess productivity and ROI, how our progress compares with industry peers, and what organizations need to consider as they move from AI adoption to broader transformation.
What have you learned in the last year about driving productivity with AI, not just for engineers but across the wider Dropbox organization?
Ali: You have to start with the outcome you’re trying to achieve. Giving people AI tools is only the first step. As people use them, your assumptions get tested and new bottlenecks emerge. Maybe you’re coding faster, but now you have too many code reviews. Or you’re generating significantly more code, which puts additional stress on the tools and infrastructure that support development. If you want AI to have a meaningful impact, you have to look at the workflow end to end and keep adapting it until you’re producing the outcome you actually want. There’s no silver bullet.
Uma: You have to approach it with a product mindset. Leadership saying, “You need to start using AI tools,” is good, but that alone won’t drive adoption. You also need ground-up awareness through initiatives like boot camps and show-and-tells that involve people more deeply. And you have to align incentives so managers understand what ROI they’re getting from it.
How should companies think about measuring AI productivity and ROI?
Ali: If I write 10 lines of code and push them to production, it’s almost impossible to prove those 10 lines were responsible for a certain amount of customer satisfaction or revenue improvement. I might produce a million lines a day, but if it’s the wrong product and customers have no interest in it, then it’s not going to work.
The real ROI metrics are outcomes like revenue, cost, customer satisfaction, and retention. Then we look at proxy metrics that connect our output to those outcomes. Are we moving faster, producing more code, running more experiments, and pushing more features? Is the site faster and more reliable? There’s no single metric. The framework we use focuses on speed, effectiveness, quality, and impact, with multiple metrics under each. The industry hasn’t found the full solution yet.
How does Dropbox’s AI-driven engineering productivity compare with companies operating at a similar level of technical scale and complexity?
Ali: If we define peers as top technology companies we hire from and compete with for talent, we’re seeing very similar problems and metrics. We’re around 70% AI-generated code, and Uber recently shared a number around 70%. They see a need for internal agentic coding solutions, and so do we. They have their own tools, and we have Nova, our internal service for running coding agents. Larger companies have allocated more resources to these problems, while we’re super lean. Despite that, I feel like we’re comparable with that peer group across multiple metrics, including how quickly we adopted AI and reached our targets. Compared with the broader industry, it appears we’re ahead.
Uma: The industry looks at pull request throughput, or the rate at which teams complete code changes, as one proxy metric for productivity. It’s not perfect, but our throughput ranks in the top 5% in a custom benchmark of companies with similarly large, complex codebases and engineering systems. We also look at whether moving faster affects quality. Our change failure percentage, which measures how often a deployment leads to degraded performance or failure, is in line with industry peers at the 75th percentile. Our token usage is also among the lowest in that peer group, which could indicate that our teams or systems are operating efficiently. Together, these measures give us a fuller picture of how we’re doing against similar companies.
As AI usage grows, how do you decide where additional investment is worthwhile?
Ali: AI requires money, and when we talk about AI funding, we’re talking about a large portfolio. There are products used by individual functions, capabilities in tools like Zoom and Slack, company-wide deployments like ChatGPT Enterprise, automation tools, and coding models. We’re seeing benefits. People are fixing tech debt, long-running migration projects are getting finished, and we’re delivering most of the roadmap with fewer resources. But organizations still have to decide which ideas to pursue and when to invest more, often before they can draw a direct line to ROI. That requires domain expertise, the right incentives, and a lot of judgment. We’re still working through it along with the rest of the industry.
How should companies determine whether their AI spend is creating real value?
Ali: At Dropbox, we’re starting to think less about raw token consumption and more about the engineering value those tokens create. With Nova, for example, we can connect agent usage to actual engineering workflows, validation, and outcomes. That gives us a way to evaluate AI spend based on the engineering work those tokens help produce, rather than simply how many tokens we’re consuming.
As AI lowers the cost and effort required to produce software, which human skills become more valuable?
Ali: If you look fundamentally at what engineers or computer scientists do, it’s that we know how to solve a problem. That’ll never disappear. AI may help me solve bigger problems or solve them faster, but I still need to provide better context, ask the right questions, judge the validity of the answers, know how to iterate, and give a good spec and problem definition. I also need to understand how a solution fits into the broader system and adapt as the tools change. Problem solving, communication, systems thinking, and judgment become even more important because agents depend on people to guide and evaluate their work.
Uma: I would give you three things. One is problem solving, which Ali explained really well. The second is judgment. The next phase of AI could make the cost of engineering almost zero, which makes choosing the right problems and evaluating what AI produces extremely important. The third is leadership. Leaders need to understand people, navigate ambiguity as work changes, bring teams together, communicate clearly, build trust, and make sure incentives are aligned. Those skills won’t go away at all. Their value will only grow.
As AI models and agents become more widely available, what’ll distinguish the organizations that use them effectively, and what role can Dropbox play?
Ali: Models and agents can be incredibly capable, but they don’t automatically know which information an organization trusts, what decisions have already been made, who has access, or what needs to happen next. Without that context, even a strong model can produce generic or incomplete results. Dropbox can connect supported AI experiences to customer-owned content and context, then give the work AI produces a place to be saved, shared, reviewed, approved, and continued. People still make consequential decisions, while the work keeps moving without losing the sources, history, and collaborators behind it.
A year from now, what do you hope AI-enabled work looks like at Dropbox?
Uma: This year, we’ve focused heavily on engineering, and we’re now starting to understand the workflows across product, design, and other non-engineering teams. A year from now, the goal is for people across those teams to be able to come up with ideas, test them with customers, and potentially ship some of those features. If our customer experience team sees an issue, for example, they could use agents to develop a solution and see whether it works for the customer. That’s the vision we’re pursuing internally. If we can prove it works, we could potentially enable external customers to build on Dropbox platforms and use AI with the building blocks we have.
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