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Scaling Without Slowing Delivery
Maximize the value you ship this week and you’ll minimize the value you ship this year.
This is a dynamic that can happen in growth-stage companies when they continue using startup-stage product engineering approaches long after their effectiveness declines.
Focusing all development efforts on a feature roadmap ordered by urgency makes sense for startups – which need to quickly find PMF, have small user bases and teams, and are developing their codebases from scratch.
But continuing this approach in a scaleup slows delivery rates over time and raises delivery unpredictability.
This in turn slows company growth and makes it vulnerable to competition, even as it appears on the surface to be moving as fast as possible.
Scaleups instead need to adopt new product engineering practices suited to their stage if they wish to sustain delivery speed as they grow.
It isn’t enough to professionalize the business with additional layers of management, team reorganizations, and new departments.
Engineering strategy also needs to be realigned to the realities of a post-PMF company with a large existing codebase forged on the fly during a chaotic PMF hunt.
AI amplifies this. Using AI the way startups do will produce lackluster results and squander opportunities that AI creates elsewhere (which I discuss further here).
In short, you need to engineer your engineering for maximum delivery speed and reliability, using approaches that match the current stage of your product and company.
How Scale-ups Can Get the Most Out of AI
Engineering leaders are eager for their engineers to fully leverage AI to accelerate roadmap delivery.
They’re also keenly aware of the competitive threat posed by AI – whether from customers choosing to self-build or from competing startups enjoying compressed development timelines.
But often the way they apply AI doesn’t match the needs of a scale-up and leaves most of the potential development throughput gains on the table.
Rather than relying on simpler AI development approaches that may suit startups, scale-ups need a broader application of AI that matches their stage and maximizes throughput without increasing delivery unpredictability.
In this article I cover what that broader application entails and why it substantially increases the value a scale-up can get from AI.
Estimating ROI of AI Developer Tooling
As engineering leaders see their AI tooling bills rise, many are asking how to estimate the ROI of that spend.
Their engineers offer qualitative feedback that AI agents are significantly speeding up certain tasks, but the finance department sees quantitative dollar figures. This contrast creates a pressure to quantify the AI payoff.
Measuring engineering output effectively is infamously difficult to achieve. Fortunately, there is still a way to estimate AI effectiveness – by comparing outcomes between when AI assistance is present and absent. The following is an experimental technique for doing so.