First Design Thinking Workshop at Kaizen Softworks
Published on
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April 10, 2026
Last updated on
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April 10, 2026
Time to read
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12
Valentina Ibinete
Marketing Lead
Given the high competition in the market, companies need to develop products or provide services with an agile innovation component, delivering differential value to users. Thus, numerous methodologies and processes have proliferated in order to achieve this goal. Among them, in this article, we would like to talk about how Design Thinking contributes to adding value.
At Kaizen, we believe that innovation is achieved through collaboration. For this reason, we decided to implement our inaugural Design Thinking Workshop at Kaizen.
The workshop objectives were to create a space for our collaborators for reflection and dialogue as a team, and to learn a creative problem solving approach applicable to their own areas of work.
Carried out by Pablo Manzoni, leader of the UI/UX Design team, the 7-hour workshop was attended by 20 Kaizen’s members from different departments such as design, software development, sales and marketing.
Useful concepts of Design Thinking were covered and participants were able to understand how this process can be applied to their own challenges. We are glad to share with you some useful insights. Enjoy!
What is Design Thinking?
Design Thinking is a creative thinking process to design innovative solutions to complex problems, through a user centric mindset. The focus is on understanding the user and discovering their unmet needs to create solutions that matter.
Although it was born in the world of design, today its application goes beyond the creation of products, and can also be applied to all business areas.
Design Thinking Process Map
Design Thinking Process explained
Design Thinking is a nonlinear iterative process, whose stages overlap and are not necessarily strictly sequential. It is somewhat of an “experimental” methodology, in the sense that you shouldn't be afraid to try out new ideas, tools, or processes, especially early in the flow.
Design Thinking Process Stages
Empathize
Key question: are users' needs and wants understood and considered?
Through deep user research and data analysis, Design Thinking focuses on deeply understanding user needs and wants. Its goal is to put assumptions aside and get insights from real user experiences to discover their problems, motivations, and understand current user behavior.
At the end of this stage, a lot of useful information is obtained, which will help guide the next steps.
Define
Key question: is the problem properly defined?
Based on the analysis of the observations and the information collected in the previous stage, irrelevant aspects should be eliminated and connections should be made, to define problems and core needs.
The main objective is to define a key problem statement to address.
It is important to maintain a human-centered approach, and try to develop a “point of view” that is as aligned as possible with the needs and motivations of the target users.
Ideate
Key question: what are the possible solutions to the defined problem?
Now that problems and challenges have been identified, it is time to start thinking about potential solutions that will solve the defined problem statement.
This phase represents the transition from problem identification to solution creation. The goal is to let creativity flow and discuss as a team the risks and benefits of each potential solution.
Prototype
Key question: does the solution meet the real needs of users in an innovative way?
In this experimental stage the objective is to select the best idea, which will work as a solution for the defined problem.
After the ideas have been selected, prototypes are made. Each prototype is then tested to see if they are valid solutions to the defined problem. This stage is faithfully experimental and iterative, since the ideas are put to the test, accepting, rejecting or modifying them to be put to the test again.
Test
Key question: which solution better addresses the defined problem?
To mitigate the risks of relying on assumptions, prototypes are continuously tested and evaluated to identify the best solution for the defined problem statement.
Through a user centered approach, usability tests are carried out to collect feedback from real users. These discoveries are used to identify improvements and refine the problems to be solved.
Because the process is iterative, results are constantly analyzed by doing more iterations to finally define the final solution that best meets the needs and motivations of the users.
This stage is essential as it saves production time and prevents loss of commercial value.
Implement
Key question: is it feasible?
In this stage the solution finally becomes real and is launched and tested in the real market. Many designs will never reach this stage. While the design may be wonderful, it may not meet the user's needs as expected. Going back to the ideation stage to rework ideas and improve them is also a valid stage of the process.
Since the process is not linear, it is essential to take in what is learned throughout the process and continue iterating to add improvements.
Conclusion
The challenge was successfully met: we managed to understand the essence of Design Thinking, which is a mindset change towards innovative solutions of complex problems, in different contexts beyond design.
Through success stories and different practical workshop activities, participants worked as a team and discovered the potential of this process for our daily challenges in our respective areas of the company.
Thanks to Pablo Manzoni for creating a space to teach other members of the Kaizen team, offering the opportunity to learn new knowledge and skills in pursuit of continuous improvement.
Until the next Design Thinking Workshop!
Given the high competition in the market, companies need to develop products or provide services with an agile innovation component, delivering differential value to users. Thus, numerous methodologies and processes have proliferated in order to achieve this goal. Among them, in this article, we would like to talk about how Design Thinking contributes to adding value.
At Kaizen, we believe that innovation is achieved through collaboration. For this reason, we decided to implement our inaugural Design Thinking Workshop at Kaizen.
The workshop objectives were to create a space for our collaborators for reflection and dialogue as a team, and to learn a creative problem solving approach applicable to their own areas of work.
Carried out by Pablo Manzoni, leader of the UI/UX Design team, the 7-hour workshop was attended by 20 Kaizen’s members from different departments such as design, software development, sales and marketing.
Useful concepts of Design Thinking were covered and participants were able to understand how this process can be applied to their own challenges. We are glad to share with you some useful insights. Enjoy!
What is Design Thinking?
Design Thinking is a creative thinking process to design innovative solutions to complex problems, through a user centric mindset. The focus is on understanding the user and discovering their unmet needs to create solutions that matter.
Although it was born in the world of design, today its application goes beyond the creation of products, and can also be applied to all business areas.
Design Thinking Process Map
Design Thinking Process explained
Design Thinking is a nonlinear iterative process, whose stages overlap and are not necessarily strictly sequential. It is somewhat of an “experimental” methodology, in the sense that you shouldn't be afraid to try out new ideas, tools, or processes, especially early in the flow.
Design Thinking Process Stages
Empathize
Key question: are users' needs and wants understood and considered?
Through deep user research and data analysis, Design Thinking focuses on deeply understanding user needs and wants. Its goal is to put assumptions aside and get insights from real user experiences to discover their problems, motivations, and understand current user behavior.
At the end of this stage, a lot of useful information is obtained, which will help guide the next steps.
Define
Key question: is the problem properly defined?
Based on the analysis of the observations and the information collected in the previous stage, irrelevant aspects should be eliminated and connections should be made, to define problems and core needs.
The main objective is to define a key problem statement to address.
It is important to maintain a human-centered approach, and try to develop a “point of view” that is as aligned as possible with the needs and motivations of the target users.
Ideate
Key question: what are the possible solutions to the defined problem?
Now that problems and challenges have been identified, it is time to start thinking about potential solutions that will solve the defined problem statement.
This phase represents the transition from problem identification to solution creation. The goal is to let creativity flow and discuss as a team the risks and benefits of each potential solution.
Prototype
Key question: does the solution meet the real needs of users in an innovative way?
In this experimental stage the objective is to select the best idea, which will work as a solution for the defined problem.
After the ideas have been selected, prototypes are made. Each prototype is then tested to see if they are valid solutions to the defined problem. This stage is faithfully experimental and iterative, since the ideas are put to the test, accepting, rejecting or modifying them to be put to the test again.
Test
Key question: which solution better addresses the defined problem?
To mitigate the risks of relying on assumptions, prototypes are continuously tested and evaluated to identify the best solution for the defined problem statement.
Through a user centered approach, usability tests are carried out to collect feedback from real users. These discoveries are used to identify improvements and refine the problems to be solved.
Because the process is iterative, results are constantly analyzed by doing more iterations to finally define the final solution that best meets the needs and motivations of the users.
This stage is essential as it saves production time and prevents loss of commercial value.
Implement
Key question: is it feasible?
In this stage the solution finally becomes real and is launched and tested in the real market. Many designs will never reach this stage. While the design may be wonderful, it may not meet the user's needs as expected. Going back to the ideation stage to rework ideas and improve them is also a valid stage of the process.
Since the process is not linear, it is essential to take in what is learned throughout the process and continue iterating to add improvements.
Conclusion
The challenge was successfully met: we managed to understand the essence of Design Thinking, which is a mindset change towards innovative solutions of complex problems, in different contexts beyond design.
Through success stories and different practical workshop activities, participants worked as a team and discovered the potential of this process for our daily challenges in our respective areas of the company.
Thanks to Pablo Manzoni for creating a space to teach other members of the Kaizen team, offering the opportunity to learn new knowledge and skills in pursuit of continuous improvement.
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.
