We've all heard the promise: AI will revolutionize software development, making us faster and more efficient than ever. And yeah, AI can supercharge velocity. But in a recent project, we learned a lesson: unchecked speed often comes with some serious, hidden costs. This wasn't because the use of AI itself was flawed; it was a side effect of a project where speed was the only directive.
Our journey started like a lot of ambitious projects do: with a huge goal and a killer deadline. To hit that aggressive timeline, we pitched our client on something big: throwing AI-assisted development tools into our workflow.
The client was on board, but with one crucial condition: go fast. So, we dove into an intense two-week sprint. We slashed processes, cut back on team meetings, and pushed hard to see just how much we could get done, and how quickly.
We knew from the start that accelerating delivery by cutting some processes would introduce risk. But we also understood that this was the only viable path to meet the expectations and timeline. So we leaned into the challenge, intentionally adjusting our approach, and staying mindful of the trade-offs.
We jumped into a two-week sprint, reduced meeting time, trimmed ceremonies, and focused purely on output. The result? We moved fast, really fast. But not blindly. As we progressed, we actively tracked risks, noted what we were skipping, and prepared ourselves to course-correct after delivery.
Yes, speed delivered results, but it also surfaced important lessons about what’s worth protecting, even under pressure. And those lessons are shaping how we approach future fast-paced projects, especially when AI is in the mix.
1. Inconsistent Standards
Our codebase exploded, but it was a bit of a hot mess. Without solid process guardrails, our AI-fueled momentum became chaotic. We started asking ourselves some hard questions:
What does "good enough" even look like for AI-generated code?
What's our absolute minimum quality bar?
When is this insane speed actually helping, and when is it quietly breaking everything?
We quickly realized that how you use AI is just as important as whether you use it at all.
AI as an Assistant: Here, the developer stays in control. AI offers support, tossing out solutions or suggestions when you hit a wall. It's a more manual process, and slower, but it consistently delivers higher-quality results.
AI as the Driver: The developer gives a prompt, and AI goes wild generating code. It's lightning-fast. But the code quality? Often compromised.
The real challenge became finding that sweet spot. Too much control, and you're not really getting the most out of AI. Too little, and you're stuck with spaghetti code that's a nightmare to maintain.
2. Unrealistic Expectations
At first, expectations were through the roof. Could we really double or triple our output overnight? The reality turned out to be a lot more nuanced.
AI shines when you're starting from scratch, making independent decisions on brand new projects, even early prototypes. But as soon as we hit real-world complexity (legacy systems, tricky architectural decisions) AI started to struggle. It needed a lot more hands-on guidance and oversight.
Suddenly, we weren't just coding. We were coaching. When we ran into complex problems, our role shifted from writing code to guiding the AI and making critical architectural calls. AI needed guardrails, decisions, and context.
That dream of 20x speed, born from unrestrained experimentation, gave way to a more practical understanding. While fast, the "anything goes" approach wasn't sustainable; it quickly led to a messy, unmanageable codebase.
Our focus shifted from how each individual used AI to the quality of the final product. We started evaluating solutions not just by how fast they were delivered, but also by how well they stuck to our defined quality standards. Does the code work? Is it well-architected? Does it meet our minimum quality thresholds?
3. AI Magnifies What’s Already There, for Better or Worse
AI didn’t just speed things up. It amplified everything.
Think about a developer's core strengths: speed, code quality, problem-solving. Every developer naturally prioritizes some over others. With AI, these personal weightings become amplified.
A developer who leans towards speed will become exponentially fast with AI, while someone who prioritizes quality might seem comparatively "slower" (though still faster than pre-AI). The gap between these approaches widened, really highlighting the differences within the team.
This brought up a crucial team-level discussion:
What do we collectively prioritize?
What standards should we uphold as a team?
Are we truly rewarding speed… or are we rewarding actual outcomes?
We're actively working to nail that balance. The tricky part? The team isn't making all the decisions; there's a client calling the shots.
4. The Client Sees the Speed, Not the Tradeoffs
Once you show a client you can move three times faster, how do you then turn around and say, "Actually, we could go super fast, but we're going to slow down a bit to prioritize architectural decisions"? The client will almost always push for continued speed, without fully grasping the long-term implications or the technical debt it can create. These trade-offs, which are always a part of software development, are now even more obvious and a lot harder to navigate.
This creates internal friction. A client might see one developer moving at warp speed, because that developer prioritizes speed, and then demand that everyone else match that pace. They don't see the unseen work or the critical long-term considerations other team members are prioritizing. This can lead to a perception of uneven performance within the team. AI simply highlights all of these dynamics.
The use of AI isn't up for debate anymore. It's a tempting tool for development managers, offering the illusion of doing three times more with the same resources and budget. It feels like a massive jump in computational power just by adopting AI. This has, in turn, inflated expectations, especially in the short term.
It's no longer about being 10x faster than without AI; it's now, "Hey, why are we a little slower than last week?"
The benchmark just keeps rising. Every new speed metric shared with the client becomes the new expectation. It's a continuous upward adjustment.
Closing the Loop: From Chaos to Control
It's important to remember that our experience was rooted in a high-pressure, and relentlessly fast-paced project.
We made deliberate choices to adapt to what the project needed most: speed. We knew that came with trade-offs: technical debt, uneven team pacing, and the constant pressure of rising expectations.
But instead of ignoring those challenges, we tackled them head-on. We automated where it made sense, adjusted roles, and made tough calls to preserve quality without bringing momentum to a halt.
What once felt like an impossible timeline is now within reach. The codebase is solid, the delivery date is realistic, and we’ve found a way of working that’s stable, maintainable, and far more sustainable than it was just a few months ago.
We've all heard the promise: AI will revolutionize software development, making us faster and more efficient than ever. And yeah, AI can supercharge velocity. But in a recent project, we learned a lesson: unchecked speed often comes with some serious, hidden costs. This wasn't because the use of AI itself was flawed; it was a side effect of a project where speed was the only directive.
Our journey started like a lot of ambitious projects do: with a huge goal and a killer deadline. To hit that aggressive timeline, we pitched our client on something big: throwing AI-assisted development tools into our workflow.
The client was on board, but with one crucial condition: go fast. So, we dove into an intense two-week sprint. We slashed processes, cut back on team meetings, and pushed hard to see just how much we could get done, and how quickly.
We knew from the start that accelerating delivery by cutting some processes would introduce risk. But we also understood that this was the only viable path to meet the expectations and timeline. So we leaned into the challenge, intentionally adjusting our approach, and staying mindful of the trade-offs.
We jumped into a two-week sprint, reduced meeting time, trimmed ceremonies, and focused purely on output. The result? We moved fast, really fast. But not blindly. As we progressed, we actively tracked risks, noted what we were skipping, and prepared ourselves to course-correct after delivery.
Yes, speed delivered results, but it also surfaced important lessons about what’s worth protecting, even under pressure. And those lessons are shaping how we approach future fast-paced projects, especially when AI is in the mix.
1. Inconsistent Standards
Our codebase exploded, but it was a bit of a hot mess. Without solid process guardrails, our AI-fueled momentum became chaotic. We started asking ourselves some hard questions:
What does "good enough" even look like for AI-generated code?
What's our absolute minimum quality bar?
When is this insane speed actually helping, and when is it quietly breaking everything?
We quickly realized that how you use AI is just as important as whether you use it at all.
AI as an Assistant: Here, the developer stays in control. AI offers support, tossing out solutions or suggestions when you hit a wall. It's a more manual process, and slower, but it consistently delivers higher-quality results.
AI as the Driver: The developer gives a prompt, and AI goes wild generating code. It's lightning-fast. But the code quality? Often compromised.
The real challenge became finding that sweet spot. Too much control, and you're not really getting the most out of AI. Too little, and you're stuck with spaghetti code that's a nightmare to maintain.
2. Unrealistic Expectations
At first, expectations were through the roof. Could we really double or triple our output overnight? The reality turned out to be a lot more nuanced.
AI shines when you're starting from scratch, making independent decisions on brand new projects, even early prototypes. But as soon as we hit real-world complexity (legacy systems, tricky architectural decisions) AI started to struggle. It needed a lot more hands-on guidance and oversight.
Suddenly, we weren't just coding. We were coaching. When we ran into complex problems, our role shifted from writing code to guiding the AI and making critical architectural calls. AI needed guardrails, decisions, and context.
That dream of 20x speed, born from unrestrained experimentation, gave way to a more practical understanding. While fast, the "anything goes" approach wasn't sustainable; it quickly led to a messy, unmanageable codebase.
Our focus shifted from how each individual used AI to the quality of the final product. We started evaluating solutions not just by how fast they were delivered, but also by how well they stuck to our defined quality standards. Does the code work? Is it well-architected? Does it meet our minimum quality thresholds?
3. AI Magnifies What’s Already There, for Better or Worse
AI didn’t just speed things up. It amplified everything.
Think about a developer's core strengths: speed, code quality, problem-solving. Every developer naturally prioritizes some over others. With AI, these personal weightings become amplified.
A developer who leans towards speed will become exponentially fast with AI, while someone who prioritizes quality might seem comparatively "slower" (though still faster than pre-AI). The gap between these approaches widened, really highlighting the differences within the team.
This brought up a crucial team-level discussion:
What do we collectively prioritize?
What standards should we uphold as a team?
Are we truly rewarding speed… or are we rewarding actual outcomes?
We're actively working to nail that balance. The tricky part? The team isn't making all the decisions; there's a client calling the shots.
4. The Client Sees the Speed, Not the Tradeoffs
Once you show a client you can move three times faster, how do you then turn around and say, "Actually, we could go super fast, but we're going to slow down a bit to prioritize architectural decisions"? The client will almost always push for continued speed, without fully grasping the long-term implications or the technical debt it can create. These trade-offs, which are always a part of software development, are now even more obvious and a lot harder to navigate.
This creates internal friction. A client might see one developer moving at warp speed, because that developer prioritizes speed, and then demand that everyone else match that pace. They don't see the unseen work or the critical long-term considerations other team members are prioritizing. This can lead to a perception of uneven performance within the team. AI simply highlights all of these dynamics.
The use of AI isn't up for debate anymore. It's a tempting tool for development managers, offering the illusion of doing three times more with the same resources and budget. It feels like a massive jump in computational power just by adopting AI. This has, in turn, inflated expectations, especially in the short term.
It's no longer about being 10x faster than without AI; it's now, "Hey, why are we a little slower than last week?"
The benchmark just keeps rising. Every new speed metric shared with the client becomes the new expectation. It's a continuous upward adjustment.
Closing the Loop: From Chaos to Control
It's important to remember that our experience was rooted in a high-pressure, and relentlessly fast-paced project.
We made deliberate choices to adapt to what the project needed most: speed. We knew that came with trade-offs: technical debt, uneven team pacing, and the constant pressure of rising expectations.
But instead of ignoring those challenges, we tackled them head-on. We automated where it made sense, adjusted roles, and made tough calls to preserve quality without bringing momentum to a halt.
What once felt like an impossible timeline is now within reach. The codebase is solid, the delivery date is realistic, and we’ve found a way of working that’s stable, maintainable, and far more sustainable than it was just a few months ago.
A while ago we noticed something pretty common: everyone wanted to share more knowledge internally, but nobody wanted another heavy corporate ritual.
Internal talks usually start with good intentions and slowly disappear. They take time, preparation, and energy. And at some point people start feeling like they need to be experts before presenting anything.
So we tried the opposite.
15 minute talks.
Small topics.
Low pressure.
And one important rule: every session had to leave something useful behind. A tool, a workflow, an idea, a shortcut, a new way to approach a problem. Something people could actually use after the talk ended.
We didn’t want theory that went nowhere.
Somehow, that ended up working much better than we expected.
The idea was to reduce friction
Screenshot of the shared topic pool
Tiny Knowledge Bytes is intentionally simple:
anyone can suggest topics
anyone can end up presenting
you don’t need to master the topic
talks can come from experiments, client problems, tools or random discoveries
sessions should leave something practical behind
if nobody volunteers, the system picks someone for us
The goal was making knowledge sharing feel lightweight instead of exhausting.
Some of the best talks start with:
“I tried this yesterday and it was weird.”
The topic pool started growing on its own
Over time, topics started coming from everywhere.
Sometimes someone took a course and used a Tiny Knowledge Byte as a way to give something back to the team. Other times, a client problem triggered research into new tools, workflows or AI approaches.
A lot of sessions start from curiosity or necessity more than planning.
And honestly, the mix is part of what makes it interesting.
Sometimes a UX session drifts into Computer Vision. Sometimes someone technical shares a visual workflow that half the design team ends up adopting later.
There’s not much curation. It behaves more like a constant exploration system.
Then another problem appeared: choosing who presents
And this is where things became unnecessarily dramatic.
Nobody wanted to be “the person who chooses”. So we started adding absurd layers of randomness until we somehow ended up building a full internal app called 2FS.
Two Factor Sorteo.
Yes, it’s real.
The wheel proposes. The oracle decides.
The logic is simple.
First, a wheel picks someone.
Then a Magic 8 Ball decides whether destiny approves the selection.
If the oracle rejects the person, the process starts again.
That’s it.
The app accidentally became part of the learning loop too
Apps developed for the Tiny Knowledge Bytes.
2FS originally started as an excuse to experiment with:
Claude Code
Claude Design
design systems
editorial interfaces
motion and microinteractions
Eventually those same explorations turned into future Tiny Knowledge Bytes.
The tool we used to select speakers started generating new topics itself.
The system started feeding itself
One of the most interesting side effects is that people started building things outside their usual role because of previous Tiny Knowledge Bytes.
2FS itself is a good example. A designer saw sessions about Claude tooling and AI workflows and thought:
“Maybe I can actually build this.”
What started as a ridiculous speaker selection tool became a real product experiment involving Claude Code, interface systems and interaction design.
Then it came back into the Tiny Knowledge Bytes circuit as a new talk.
That loop became surprisingly valuable:
someone learns something,
tries it,
builds something with it,
and eventually inspires someone else to do the same.
What ended up mattering most
Final Oracle Certificate.
Over time we realized knowledge sharing works much better when:
it doesn’t require huge preparation
it’s allowed to be imperfect
it mixes different disciplines
it leaves something practical behind
and somehow involves a mystical wheel connected to a Magic 8 Ball
At that point, it stops feeling like another internal obligation and starts feeling like something people genuinely want to keep alive.
Just this month, I built a full design system in about 20 hours.
What used to take weeks, sometimes months, is now dramatically faster. So… what actually changed? And more importantly: what didn’t?
Design systems take time. On complex platforms, they can take hundreds of hours.
We were working with a large and complex product where inconsistencies had started to pile up. Different modules had evolved in isolation, teams were making independent decisions, and there were no shared guidelines. The answer was clear: we needed a design system.
AI tools were just starting to emerge back then. They were mostly useful for simple tasks as they tended to hallucinate when things got complex. Developers had started using them earlier than designers, MCP didn't exist yet, and Figma plugins were the best automation we had.
But the context has changed. Fast.
The Manual Era
We did what most teams did. We stopped, and we built it. Manually.
Picture two designers, a mountain of inconsistencies, and no map. We had to cross-reference information manually, digging through the code, detecting what could be merged, agreeing on naming conventions, deciding how to name components. Hours and hours of discussion until we finally landed on a solution.
In the end, we got there. A cleaner system, faster workflows, and for the first time, both teams speaking the same visual language. Hard-won, but it worked.
But now every month a new AI model seems to be released. Design is finally catching up with what developers faced about two years ago. New tools arose, and with that, the scope of our work as designers completely changed.
The Human Factor
For an internal project, I used our Kaizen site as a reference, combined with documentation from industry leaders as a guideline.
I started in v0, which is essentially a chat interface where you can generate UI components through prompts. I fed it the colors, typographies, and a reference image, and from there it was a back-and-forth: the AI generated, I reacted, adjusted, and pushed until the output matched what I had in my head. And just like that, I started prompting my way through a Design System.
Once a component was ready, I used the html.to.design plugin to bring it into Figma (yes, plugins are still alive!). Think of it as a bridge: the plugin exports designs directly from the browser into a Figma file.
Inside Figma, the intervention was more hands-on. First, I checked that everything was visually consistent with what was defined in v0: colors, typography, styles. Then I used Figma's built-in AI to rename all the component layers using BEM convention (something that would have taken a significant amount of time to do so manually).
BEM, which stands for Block Element Modifier, is a widely adopted naming convention in CSS. It structures layer names hierarchically and predictably, for example: button__label--disabled.
Using it keeps the code clean, readable, and consistent, especially when you're working alongside a developer who needs to understand what came out the other side.
Beyond naming, I also made sure the layer structure would generate the right properties when building component sets in Figma, so that all the variants would be correctly exposed and usable. My team also pointed out that adding descriptions to components and variants was key as context for any agent using them through an MCP.
The last step was connecting everything to Windsurf via MCP. With a frame selected in Dev Mode, Windsurf could read the Figma file and use the components to build more complex screens.
We worked closely with a developer throughout this phase. Not just for the technical knowledge, but because having someone who reads code fluently meant catching things we wouldn't have spotted otherwise. The design role here was direction and supervision: making sure the AI used the components correctly and didn't invent solutions where context was missing.
Every step of the process had a human decision behind it.
An Unexpected Discovery
At one point, before we had any of the naming conventions figured out, I selected a frame and asked Windsurf to build a form using the components inside it, styled to match a specific card. The developer next to me was skeptical until he saw the result, and then he was just as surprised as I was.
What we realized is that the MCP wasn't reading layer names to understand context. It was reading everything inside the frame, even the loose text sitting alongside the components. Good naming is still worth doing. But the MCP doesn't need it to understand what it's looking at.
Learning to Talk to an AI
The more specific and contained your prompt, the better the outcome. We started with the most atomic component: the button, and worked outward from there. Each approved component became context for the next one, so the system gradually picked up the visual language we were building.
At some point I got ambitious and asked for five cards in a single prompt: blog card, service card, testimonial card, stats card, feature card… structures, states and all. The AI delivered.
Visually, everything looked fine. Then the developer looked at the code and pointed out that all five cards were independent components instead of variants of one. For a design system, that breaks everything.
One correction prompt fixed it. But it was a good reminder: the AI does exactly what you ask, not what you mean. And fixing it after the fact can cost more than getting it right from the start.
Some Things Learned Along the Way
Precision is key. Natural language is fine when you're asking for a cooking recipe, but when referring to a component, if you say things like "create" instead of "add", you'll probably end up with a whole new set of components instead of additional variants of an existing one.
The "Frame" is the context: MCPs can read everything inside the frame you select. This is a game-changer. It means the "naming conventions" debate might be shifting. If the AI understands the context visually and structurally, will we still spend hours discussing nomenclature in 2027?
No matter what happens, you can always roll back in less than 5 minutes and start over.
Work closely with a developer: they can help you understand MCPs and clear up any code-related doubts. Once you start to grasp their logic, you'll learn very quickly how to prompt in ways that AI actually understands.
There's nothing to lose by asking the AI to follow a specific naming convention for the code. It keeps everything clean and readable, and it takes no extra effort.
The AI covers roughly 80% of the work (generation, variations, exploration...), but the remaining 20% is where quality lives, and that part is not delegable. The AI executes. The judgment is still yours. And if you skip the review, you're not saving time: you'll spend it later.
Context matters more than tooling. What you don't define, the AI will invent. Small components may be resolved well, but large interfaces require more definition from the start. A well-defined system scales. An undefined one generates inconsistencies faster than you can fix them.
Figma is no longer the mandatory starting point. It's useful as a visual reference, a QA space, or a consolidation layer. But the AI doesn't need it. We still do.
There's no single right workflow yet. What you do depends on the project. We're in a transition moment where the tools change faster than the standards. The best thing you can do right now is experiment.
What AI Still Can’t Replace
Through all of this, a few things became very clear. These are the parts that didn’t change:
Knowing when something looks off. The AI generates, but it doesn't notice when the result doesn't feel right. That eye is yours.
Direction and supervision. The AI used the components we gave it, but without someone supervising it, it invents solutions where there is no context to work from.
The definition of done is still a human call, whether it's a conversation with a PO, a stakeholder, or just the designer's criteria. There's no prompt for that.
The context: knowing why certain decisions matter, what a component should communicate, what the user will actually feel. Business knowledge, stakeholder dynamics, unwritten rules, empathy for the end user. These take years to build and live in the people doing the work, not in the tools they use.
My Two Cents
The tools changed, and that gave me the chills, but throughout this experience I found that the designer's role is more alive than ever.
What once took a team weeks can now be prototyped in hours. That’s not a threat; it’s an invitation to get curious.
I'm still figuring a lot of this out, and I suspect most of us are. There's no right workflow yet, and honestly, that's fine. We are in a transition where tools change faster than standards. The best thing you can do is experiment. Don't wait for a "definitive" workflow, it might be obsolete by next month.
Go ahead, try prompting your way through a component. You might be surprised how fast the system starts to take shape.
