I have been thinking a lot about how long it takes for an idea to become something real. Not a spec or another backlog item waiting for the right sprint. Something alive, slightly rough around the edges, but real enough that people can actually use it.
The distance between idea and execution — a.k.a agile — feels heavier than it should be.
Inside larger engineering environments, nobody is trying to slow progress down. The friction usually comes from good intentions. Alignment meetings, architecture reviews, risk controls, sprint plannings, reviews. Each one makes sense in isolation, but together they stretch momentum thin. By the time something ships, the original curiosity that sparked it has often faded into routine delivery. It is like planting a tree you are excited about and then waiting years to see it grow; you know it will be worth it, but the initial spark fades while you wait.
Then you look at smaller teams. They move fast, not because they have perfect structure, but because they do not have the luxury of waiting. Someone sees a gap, builds a first version, releases it, and learns in real time. Their limitation eventually becomes budget or scale. They might prove something meaningful, yet never have the runway to push it further.
That contrast is what led me to thinking about a Faster to Value approach.
What is Faster to Value?
This is not an amazing new framework. More like a way of creating space where execution happens closer to the moment an idea appears. Anyone should be able to raise a concept. Interest from the relevant community becomes the signal that it is worth exploring. The focus shifts from planning everything upfront to building something tangible quickly.
It shouldn’t be about the issue either. We are not solving bugs here, it’s about adding value. Think of it like refactoring versus building new capabilities: patching a bug restores expected behaviour, but redesigning a system or introducing a new feature changes what the software can actually do and how effectively it can scale.
The objective is straightforward: move from concept to a production-ready early version within days. It doesn’t need to be polished or perfect, just real enough for early adopters to engage with. Once something exists in the real world, conversations shift, assumptions are tested more quickly, and decisions become grounded in actual behaviour rather than theory. This approach also reinforces a fail-fast mindset, one of the most effective ways to learn: build quickly, learn from what doesn’t work, and adjust toward what delivers real value.
I keep thinking about this while trying to understand what the role of an engineer looks like in an AI shaped environment. The pace of execution is changing. Writing code is (currently) still important, but guiding systems, shaping context, and thinking in flows feels just as critical, if not more. Titles and seniority start to matter less than clarity and momentum.
Maybe Faster to Value teams are not only about speed. Maybe they are part of figuring out what engineering becomes next.
The type of person I imagine thriving in this environment is not defined by hierarchy. It is the system thinker. The engineer who looks at a small change and instinctively considers how it affects the entire system. The one who sees connections between components, teams, and outcomes without needing everything documented upfront.
They are often the ones asking quieter questions. What happens downstream if we change this? Does this unlock value somewhere else? Can a smaller adjustment move the whole system forward? Instead of focusing only on their own piece of work, they think holistically about flow, impact, and leverage.
I recently tuned into an OfferZen webinar where Stephen Van der Heijden described a team made up of senior engineers alongside so-called “vibe coders” — people who may not have deep traditional programming backgrounds. At first, the idea feels counterintuitive. But when you frame it through the lens of speed to value, it starts to click: the differentiator isn’t raw coding ability, it’s the capacity for systems thinking and understanding how pieces fit together to deliver outcomes faster.
In a Faster to Value setting, that mindset keeps speed grounded. The team is not moving fast just for the sake of movement. They are looking for the smallest meaningful shift that creates real impact across the system.
The way I imagine it, this kind of team operates more like a real world lab. Small group. High autonomy. Strong ownership. There is a sense of urgency, but not chaos. The expectation is simple: build something real quickly and learn from what happens next.
The output is not a big launch announcement. It is a controlled release. Something intentionally early that reaches a small group of users. Their interaction becomes the feedback loop. Instead of predicting value months ahead, the team observes it almost immediately.
There is a risk though. If the team cannot reach production quickly, it stops being faster to value and turns into another internal experiment that never leaves the building. Speed without clarity creates noise, but clarity without movement creates stagnation.
I have not seen this model fully implemented yet. Right now it exists more as a concept forming through observation. Watching how small teams move, how larger organisations struggle with momentum, and how AI is reshaping the way engineers think about execution.
Perhaps the most significant change is not technical but cultural. It involves creating environments where engineers feel empowered to build early, release responsibly, and learn openly. Large enterprises are often guarded, and for good reason, but this can unintentionally promote an engineering mindset that prioritises caution over delivering value. This is the challenge that Faster to Value aims to address.
The role of an engineer six months from now might look different from what we expect today. Less focused on guarding process. More focused on navigating complexity and turning ideas into working systems quickly. Faster to Value is just one way I am trying to make sense of that shift. Not as a final answer, but as a direction worth exploring.
Emile — 90% Human Written | 99% AI Approved