LUSA interview - DareData’s UK expansion and vision on Gen AI

In which countries is DareData present?

‍

DareData is present in Portugal, Spain, Norway, Australia, the United States, France, Greece, and Brazil. We have specialists from our freelance network based in these countries, collaborating on international projects, including in the United States and the United Kingdom.

‍

Are there expansion projects planned for this year? If so, where?

‍

We have a very clear and exciting vision. We want to expand our presence in the United Kingdom this year and are already working towards that goal. The UK market is highly developed in terms of AI systems, and DareData is eager to pursue this path, as the challenges faced by companies there serve as a benchmark for our technical capabilities and market differentiation, which is not based on lower salaries/costs.

‍

What is the planned investment for this year?

‍

We are looking at an investment of around one million euros. This year, our focus is indeed on our internationalization project, which will first involve a strong investment in the UK market and secondly in the articulated development of customized GenAI solutions, increasingly capable of addressing the new paradigms faced by companies (and society).

‍

How much did you invest last year?

‍

Last year, we invested half a million euros. This investment was divided between marketing, sales, and the development of customized in-house solutions.

‍

Do you plan to recruit more people this year? If so, how many?

‍

Yes. We plan to add between 15 and 20 people to our network. It’s worth noting that in January alone, we already welcomed six new technical specialists to our network, so we are very excited about our growth ambitions. We want to remain a highly differentiated company, both in terms of the people we hire and the quality of solutions we deliver, so growing at this pace is a challenge.

‍

How do you see the development of Generative AI over the past year?

‍

We must approach this topic with caution. There have been several cases where the implementation of GenAI has gone wrong. I can cite a recent example with Chevrolet, where some customers tricked the chatbot—one even managed to get the bot to say they could sell a car for one dollar. Another example is a case that went to court between Air Canada and a passenger who was “tricked” by the airline’s chatbot and ended up receiving compensation. These issues are caused by flawed GenAI implementations. However, it’s important to understand that this brings opportunities, especially for companies that have the technical know-how to create cutting-edge solutions. Companies with highly specialized knowledge in machine learning, particularly with a modern MLOps vision, will avoid the problems I mentioned. Additionally, I foresee a growing market for observability and quality measurement, in which we are particularly interested: defining the quality of responses/conversations in a generative conversational system is far from simple, and it seems to us that, together with the regulation of these agents (including what they can or cannot do), this will be an area of high market demand and discussion. The ethics of using machine learning in general, and GenAI in particular, is a topic that will only gain relevance.

‍

At DareData, we are confident that GenAI will transform organizations worldwide. It will automate manual and time-consuming processes across the board and bring about new capabilities, some of which we can’t even imagine yet. However, it’s important not to overlook or ignore the human factor. We are strong advocates of human-centric AI because that is our mission. The trend in developing GenAI tools and solutions should precisely follow this human-centric path, enhancing each individual’s capabilities. Entities that fail to do this and focus solely on process automation without considering the consequences will make serious mistakes. For example, in the recruitment field, the use of AI in Human Resources is heavily debated. But this use must be balanced with human involvement from professionals in the area, or there will be misunderstandings regarding employment. People cannot be evaluated by machines alone. I emphasize the importance of developing human-centric AI solutions.

‍

No one can predict what’s coming. It’s difficult to foresee whether new jobs will be created, how quickly more “traditional” jobs will disappear, and how society will prepare for this new market. But one thing is certain: there is room to improve processes, expand professionals’ capabilities, significantly increase productivity, and, above all, make life more rewarding for people and professionals.

‍