In the modern era of product development, Agile methodology has become essential for the success of teams that aim to innovate.  

This article explores how the Agile approach focuses on rapidly testing hypotheses through discovery workshops or design sprints, facilitating continuous iterations and adaptations, and how AI tools play a crucial role, not as standalone solutions but as assistants that accelerate the idea validation process.  

The key to success in modern Agile methodology lies in continuous learning and rapid feedback, avoiding repeated mistakes from the past. This process allows teams to move from idea to validation in the shortest possible time, innovating efficiently and delivering products that meet real user needs. 

An Expensive Lesson 

In the past, I had an interesting experience that taught me a valuable lesson about the importance of clarity in communication and proper preparation. A client requested the addition of a button to their website. It seemed like a simple and straightforward request, so I assumed I knew exactly what they wanted.  

I relayed the requirement to the development team, and after two days of work, the button was ready. Then came the shock: the client was dissatisfied. It turned out that what they had envisioned was very different from what we delivered. This misunderstanding cost us valuable resources. 

Reflecting on this situation, I realised that all of this could have been avoided if we had organised a discovery workshop before starting the actual work. A discovery workshop is a brainstorming and planning session where all involved parties—developers, designers, and the client—discuss the project's requirements and expectations in detail. This would not only have clarified what the client wanted but also prevented many other possible misunderstandings.  

Next, I will highlight what a discovery workshop can offer us, mentioning AI tools for each aspect that can simplify or streamline our work, depending on the needs of the projects we have. 

 1. Clarifying Expectations: The client is given the opportunity to describe in detail what they want and why they need that button. 

• Example AI tool: FlowMapp, a user experience planning tool that uses AI to help teams visualise and clarify client requirements. 

 2. Identifying Constraints: The development team can address essential questions about functionality, design, and the impact on the user experience. 

• Example AI tool: Lookback, which uses AI to facilitate user interviews and usability tests, helping teams identify problems and constraints. 

 3. Saving Resources: We can avoid additional costs and wasted time with a clear direction from the beginning.  

• Example AI tool: Forecast, a project management tool that uses AI to optimise planning and resource allocation, ensuring clear and efficient direction. 

Using the discovery workshop method, we can prevent such situations in the future. It's essential to invest time at the beginning of the project to ensure everyone is on the same page. This way, we not only save time and money but also build a stronger and more transparent relationship with our clients.  

Avoiding Problems Through Design Sprint  

In collaboration with a client managing an online publication where users could post articles in their field of expertise, we received a request to develop a versioning system for the articles, allowing authors to make small changes after publication.   

Unfortunately, the lack of validation of the request with the end-users led to the implementation of an inefficient solution. After 2-3 months of use, we found that users were generating new versions of articles simply by adding spaces to bring their articles to the top of the list, increasing their visibility.   

Thus, we not only failed to solve the problem but also created a system where the client was losing significant amounts of money due to contractual clauses related to the order of appearance of articles in the publication. How could we have solved this problem more simply? By using a Design Sprint when the client approached us.  

A design sprint is a five-day process that helps teams answer critical questions through design, prototyping, and testing with real users. By adopting the design sprint method and using AI tools, we could have avoided implementing an inefficient solution, saving time and resources, and ensuring that the developed solution truly met user needs. This method is essential for moments when you want to validate an idea in the shortest possible time and with reduced costs. How does it work? Here are briefly the steps to follow and some AI tools that can be helpful:  

1. Understanding and Clarifying Objectives: On the first day, the team and the client meet to define the goal, objectives, and fully understand the problem. 

• Example AI tool: Whimsical, an AI tool that facilitates mind mapping and diagram creation, useful for visualising and clarifying client requirements. 

 2. Generating Ideas: On the second day, the team generates solutions and ideas. 

• Example AI tool: Stormboard, a collaboration tool that uses AI to organise and manage brainstorming processes. 

 3. Making Decisions and Creating a Storyboard: On the third day, the team selects the best ideas and creates a detailed storyboard. 

• Example AI tool: Coda, with AI capabilities for managing and structuring information and creating interactive documents. 

 4. Prototyping: On the fourth day, the team develops a low-fidelity prototype. 

• Example AI tool: Figma, using AI to accelerate the design process and create interactive prototypes. 

 5. Testing and Learning: On the last day, the prototype is tested with real users, and feedback is collected for future iterations. 

• Example AI tool: PlaybookUX, which uses AI to facilitate interviews and usability tests, allowing rapid identification of problems and obtaining valuable feedback. 

Decisions That Don't Make It "On Paper"  

In another collaboration with a client, where the initial approach was to make decisions exclusively in video calls, various challenges arose due to the return to already agreed decisions and disagreements related to the discussed aspects, behavior that created confusion and delays in delivering the solution, negatively affecting our team's workflow and trust.   

How can we ensure that the decisions made remain clear and documented in such a context? How can we prevent situations where communication becomes a game of memory and interpretation? AI tools like Otter.ai and Fireflies.ai could have prevented these problems. 

1. Otter.ai: Automatically follows-up with call discussions, providing a detailed transcript that can be reviewed later for clarifications. Thus, every word and decision are documented in real-time, eliminating uncertainties. 

2. Fireflies.ai: 

Not only transcribes conversations but also highlights key decisions and discussed actions, offering a quick way to review and confirm important points of discussions. 

Using these tools leads to efficient communication and better monitoring of decisions, making the relationship with the client more transparent and building the necessary trust for a successful collaboration. 

Transparency and Trust Will Lead to Modern Collaboration 

My conclusion is that modern Agile relies on eliminating costly assumptions, transparent communication, and shifting the mindset to being consultants for clients, not just solution deliverers. This approach not only reduces risks and errors but also helps increase clients' trust in us as professionals, demonstrating that we are dedicated to the success of their projects.  

Eliminating assumptions and adopting an open and collaborative dialogue shows that we care about the products and are truly invested in creating them from start to finish.