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.
Applying changes across microservices is difficult because business logic is distributed across multiple services, each with its own data, contracts, and responsibilities.
In our experiment at Kaizen Softworks, we tested whether an AI system could safely apply coordinated changes across a microservices architecture using only minimal input.
Short answer: Yes, but only when the AI has enough architectural context.
Why are coordinated changes in microservices so hard?
In distributed systems, a single business change rarely affects just one service.
It often requires:
Updating multiple microservices
Modifying message contracts
Keeping DTOs (Data Transfer Objects) consistent
Respecting domain boundaries defined by Domain-Driven Design (DDD)
Key entities in this system:
Microservice: An independently deployable service responsible for a specific domain
Aggregate (DDD): A cluster of domain objects treated as a single unit
DTO (Data Transfer Object): A structured format used to transfer data between services
Message/Event: A communication mechanism between services
The complexity is not in the code, it’s in the relationships between components.
The experiment: Can AI reason across services with minimal input?
We designed a controlled experiment to test whether an AI model could apply system-wide changes with limited information.
Input given to the AI:
Message definitions (events between services)
DTOs (data contracts)
Tasks the AI had to perform:
Identify affected aggregates
Determine service ownership
Apply coordinated changes across services
Maintain consistency in messages and DTOs
In other words, the AI had to behave like a software architect, not just a code generator.
What was the biggest obstacle?
The biggest challenge was not technical, it was contextual.
Problem: unclear service naming
Instead of descriptive names like:
order-service
billing-service
Our services were named:
john
sally
roger
This removed any semantic clues about responsibility.
Result: The AI could not infer which service owned which domain logic.
The missing piece: aggregate ownership mapping
To solve this, we introduced a simple but powerful structure:
Aggregate → Service mapping
Order → john
Shipment → sally
Invoice → roger
This created a clear relationship between domain concepts and system components.
Once ownership was explicit, the architecture became understandable.
How we used AI to generate architectural context
Instead of building this mapping manually, we used AI to analyze the codebase and extract:
Where each aggregate was defined
Which microservice implemented it
The relationship between domain and infrastructure
The result was a machine-readable architecture map.
In practice, we used AI to generate the context that AI itself needed.
Results: Can AI safely apply distributed changes?
With the architecture map in place, the AI was able to:
Trace message flows across services
Identify affected aggregates
Locate the correct microservices
Apply coordinated updates
Maintain consistency between DTOs and messages
While not perfect, the system worked reliably as a proof of concept.
What is the real limitation of AI in microservices?
The main limitation of AI is not code generation, it’s architectural understanding.
Without knowing:
Which components exist
How they relate
Who owns what
AI cannot safely modify a distributed system.
AI performance depends more on context quality than model capability.
When can AI safely modify microservices?
AI works well when:
Aggregate ownership is clearly defined
Message contracts are explicit
Architecture is structured and consistent
AI struggles when:
Naming is ambiguous
Relationships are implicit
Context is incomplete
Simple rule: If the architecture is clear, AI can reason. If not, it guesses.
Final thoughts
This experiment revealed something important:
AI doesn’t fail because it can’t write code. It fails because it can’t see the system.
As teams move toward AI-assisted development, the focus will likely shift from:
Writing better code to Designing better systems for machines to understand
At Kaizen Softworks, we see this as a foundational shift.
Because when AI can understand architecture, it doesn’t just generate code, it helps evolve systems.
There's a myth that in flat organizations, everyone decides on everything.
That's not how it works. At least not at Kaizen.
When people hear "no managers," they often picture one of two extremes: either total chaos where nobody is accountable, or endless meetings where 80 people vote on which coffee to buy. The reality is neither.
Not everyone decides on everything. Not everyone votes. What we do have is a clear set of decision-making methods that we choose based on context.
It depends on who's affected and how deep the impact goes
Before choosing how to decide, we ask ourselves a few questions:
Who is affected? A decision that only impacts one team doesn't need the whole company involved. A decision that affects everyone's daily work does.
How deep is the impact? Changing the office furniture is wide but shallow. Changing the salary model is deep and lasting.
Is it reversible? If we can easily undo it, we can move fast and just inform. If it's hard to reverse, we slow down and include more people.
How urgent is it? And here we're careful to distinguish real urgency from anxiety, the pressure to decide quickly because someone already has "the answer" in mind.
These dimensions help us pick the right method. Not every decision deserves the same process.
Our decision-making toolkit
Over the years, we've landed on a few methods that we use depending on the situation:
1. Role-based decisions
Some decisions belong to a specific role. If someone owns a responsibility, say, office logistics or hiring for a team, they decide within that domain. No committee needed. The key is that roles are transparent: everyone knows who owns what, and the scope of each role's authority is clear.
2. Advice Process
When a decision doesn't clearly belong to one role, or when it crosses boundaries, we use the advice process. Here's how it works:
Someone takes the initiative. They identify the problem and own the process.
They gather input from people who are affected and people with expertise.
They seek advice, real conversations, not rubber-stamping.
They make the decision and communicate it, including what advice they incorporated and what they didn't (and why).
The decision-maker is not a committee. It's one person (or a small group) who takes responsibility. But they don't decide in isolation, they bring in the perspectives that matter.
We sometimes call this "Team Advice" when a working group forms around an issue that doesn't naturally fall into anyone's area, and "Area Advice" when a team opens up a topic that exceeds their own scope.
3. Consent (not consensus)
Consent is not "everyone agrees." Consent means "no one has a strong enough objection to block this." We do use a poll, but not to count votes — we use a 1-to-5 scale to measure the level of agreement and surface objections, not to let the majority rule.
We use it in two flavors:
High-participation consent: For decisions with deep, company-wide impact. This is our most expensive and slowest method, which is exactly why we reserve it for high-impact decisions that affect many people. The Board sets the boundaries, for example, when we moved offices, they defined the monthly budget. Then a working group produced proposals, collected feedback, evolved them, and the whole company expressed their position for the final decision. Silence is not approval; we explicitly ask people to weigh in, even if it's just "I have no objection."
Lightweight consent: For decisions that are broad but not deep. Participation is optional, anyone who's interested can jump in. We share the proposal, open a window for objections, and if nobody opposes, we move forward. This gives us speed without sacrificing transparency. If nobody engages, that's a signal too, maybe the proposal doesn't add enough value, or we're using the wrong channel.
4. Inform, don't fake-consult
Not everything needs participation. When a decision has already been made through a legitimate process, the right move is to inform, not to fake-consult. One of the fastest ways to kill self-management is to ask for feedback and then ignore it. If you're not going to change course based on input, don't ask for it, just be transparent about the decision and the reasons behind it.
What we explicitly avoid
Decision by Voting. In a company context, majority rule creates losers. And losers become detractors, often generating more resistance than an autocratic decision would have. Instead of voting, we prefer to evolve a proposal through feedback until it's "good enough for now," and then introduce a review point to adjust later. If voting happens at all, it's the cherry on top, not the main course.
The "surprise" approach. Working behind closed doors and then unveiling a finished decision is a recipe for frustration. Adults don't need surprises. Adults need to feel like they're part of the process. The complaints that follow a surprise aren't about the decision itself, they're about not being included.
Why we work this way
We didn't adopt these methods because they're trendy. We adopted them because they solve real problems:
Better decisions. When you include affected people, you get information you wouldn't have had otherwise. Ideas emerge that no single person would have come up with alone.
Less resistance. A person who feels heard is far less likely to resist a decision, even one they wouldn't have made themselves.
Faster execution. It sounds counterintuitive, but participative decisions often execute faster because people already understand and support them. The time you "save" by deciding alone, you spend later managing pushback.
Distributed authority. When people can make decisions within their domain without escalating everything to a founder, the organization scales. The bottleneck disappears.
Resilience. If a shared decision fails, the group adjusts together. If a top-down decision fails, the blame falls on one person and the chances of proactive correction drop.
The real principle behind all of this
Transparency is the foundation. Every method we use, from role-based decisions to high-participation consent, works because information flows openly. People know what's being decided, who's deciding it, and how they can participate.
Horizontal doesn't mean structureless. It means fewer hierarchical levels, clearer roles, and intentional decision-making processes that match the weight of each decision.
Not everyone decides on everything. But everyone knows how things get decided.
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