Artificial intelligence is constantly opening up new possibilities. New models, tools and ways of working are emerging that allow us to tackle tasks differently and come up with solutions that were beyond our reach until recently.
For a tech company like WATA Factory, experiencing this evolution from the inside is an opportunity to keep learning, expand our knowledge and find new ways to do our work better.
That is why research is playing an increasingly significant role in our day-to-day work. We dedicate time to exploring new AI tools, automations and workflows; we carry out tests and share what we discover. We are keen to find out first-hand what possibilities they offer and, above all, to see how they can help us with real projects and processes.
We have been conducting internal tests with various tools for some time now, analysing how AI can influence not only specific tasks but also the way in which we define, develop and validate a product. We previously discussed this evolution when we shared our approach to artificial intelligence at WATA Factory for the coming years. Now we want to focus on another aspect of that process: everything we are learning as we move forward.
Researching to discover new possibilities
Dedicating time to research allows us to step back from the context of a specific project and explore what else we can do. We investigate AI tools, models, automations and new workflows, put them to the test and look for applications that might make sense within our work.
The most interesting part comes when we start using them. That’s when we gain a better understanding of what a tool can offer, what context it requires, how we can get the most out of it, or what other uses might emerge from an initial idea. A test that begins with a specific objective can lead us to discover applications we hadn’t considered at the outset.
Nor is this research limited to tools created specifically around AI. We also keep a close eye on how the technologies we already work with are evolving. A recent example is WordPress 7.0 and its AI integration, which introduces new possibilities for connecting WordPress with AI models and agents. Keeping track of these changes helps us understand how they might affect our work and what opportunities they might open up for future projects.
From experiment to real-world application
Research is the starting point. When we come across an idea with potential, we look for a specific scenario where it might help us and start testing it. From there, we fine-tune it, compare results and assess how we can take it a step further.
It is a process that allows us to explore AI applications across different areas. In software development, it can assist us with certain tasks related to coding, testing or documentation. In QA, it can support the generation of test cases and streamline part of the validation process. We also explore possibilities related to support, analytics, automation and knowledge management.
In practice, we follow both approaches. Sometimes we start with a new tool and test it to understand what it can do and in which situations it might be useful. At other times, the starting point is a specific need, and we look for the technology or approach that can best help us resolve it. Combining both ways of experimenting allows us to discover possibilities we might not otherwise have sought out and, at the same time, to apply what we’ve learnt when a real problem arises.
This approach to experimenting, validating and applying is also the driving force behind our AI Lab. We want to have a space where we can explore new possibilities and test their usefulness before taking them further. An idea might start as a small trial and, if the results are positive, end up being applied to a project, an internal process or a team’s way of working.
What we learn grows when we share it
All this time spent on research is far more valuable when the learning doesn’t remain solely with the person who carried out the test.
When someone finds an interesting tool, achieves good results with a particular way of working, or discovers something that might be useful to others, we try to pool our knowledge. We share tools, prompts, documentation and conclusions so that others can build on what we’ve already learnt rather than starting from scratch.
Our All Hands sessions and Ibérica Labs are part of this process. They are spaces where we showcase what we’re investigating and share experiences across teams. A solution developed in the development team might prove useful to QA; a test carried out on one project could serve as a starting point for another; and a tool that someone began investigating might end up addressing a different need months later.
We also want that knowledge to be documented so it can be reused. In this way, the experience we gain on one project can be applied to the next, and what one person learns ultimately adds to what we all know.
AI is also helping our team to grow
There is another consequence of this approach that we find particularly valuable: people can learn about and gain experience with AI as part of their own work.
Nor does this learning follow a single path for the whole team. Each person approaches AI from their own experience, but through experimentation and shared knowledge, they can gradually explore areas close to their own specialism. This opens the door to collaborating in new ways, gaining a better understanding of the work of other roles, and broadening what each person can contribute to a project.
Each person’s expertise remains fundamental. A thorough understanding of a discipline enables us to ask better questions, spot when a result needs reviewing, and identify uses that genuinely add value. AI expands the tools at our disposal, but it is the knowledge of those working with them that allows us to make the most of them.
For those of us at WATA Factory, this means being able to continue developing in our specialism whilst gaining practical experience with technologies that are becoming increasingly important in our sector. And that learning doesn’t just happen through training courses or by reading documentation. It happens by solving problems, talking to colleagues, running tests and seeing what happens when we apply an idea to a real-world scenario.
All that knowledge benefits our clients
What we learn internally ultimately has a direct impact on our projects. When a client presents us with a requirement, we can draw on past experiences, tools we’ve already tested, and knowledge shared by other colleagues.
This allows us to start with a broader understanding of the context and ask better questions. Before deciding which technology to use, we understand how the process works, where there is room for improvement, and what outcome we want to achieve. From there, we can assess whether artificial intelligence, automation or another solution might help us.
For our clients, the advantage lies not simply in working with a team that is familiar with AI tools. It lies in having people who have spent time working with them and who can draw on their experience to assess when they make sense, how they can be integrated and what they can contribute to a specific project.
Furthermore, this knowledge accumulates over time. What we learn during one research project can be applied to a subsequent one, and what we discover during that project is then fed back into the team. In this way, each experience provides us with a better starting point for the next.
Learning is part of our work
Artificial intelligence is giving us an opportunity to learn, expand our knowledge and re-evaluate many of the ways in which we work. We want to make the most of this moment by making room for research, but also by ensuring that what we learn has a lasting impact within WATA Factory.
A test might start with one person, be shared with another team, and ultimately find an application in a completely different project. For our team, this means having the space to learn and experiment whilst continuing to grow within their specialism. For our clients, it means having professionals who bring all that learning to every new project.
And that is the vision we want to continue building at WATA Factory: that learning is part of the job, and that what one person learns can ultimately help us all.
