AI priority: process automation
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It is undeniable that AI has impacted all kinds of industries at levels as fundamental as their business model. There are many sectors that already trust the capabilities offered by this technology and its future is linked to its progress.
Since bdeo They have organized a round table with the motto, “The AI that writes the future”, where there has been a debate on how AI can transform the way in which companies manage their different processes and on the challenges that they will have to face.
“SThe technology has matured because companies, in order to streamline processes and optimize costs, have decided to rely on it. In addition, the profile of the user has also changed, who increasingly wants to have greater control of the processes”, explains Ana Asuero, CPO of Bdeo.
The AI that writes the future
The participants agreed that companies should incorporate AI into their processes with the aim of adding value to the end customer, and this is achieved, among other ways, by streamlining processes and saving time when issuing diagnoses, verdicts or conclusions, one of the priority objectives of these AI developer startups.
Technical challenges and regulation
The capabilities of Artificial Intelligence evolve every day. However, with them do the technical challenges it faces. In the case of Idoven, its CTO Chema Lillo indicated that “The most important challenge in the short term is the regulation of AI and how it is implemented in the healthcare system. There are far fewer algorithms in medicine compared to other sectors, so there are fewer use cases. There is still a lot to decide and this slows adoption down.”
Process automation, a priority objective of AI
Although not all challenges are technical. Ana Asuero from Bdeo highlighted the importance of building trust, to achieve an ever greater and more agile fit of AI in the different sectors and therefore favor their growth.
In this line of trust, Chema Lillo pointed out that “we use a double yardstick, we demand from Artificial Intelligence and its algorithms commitments and ethical barriers that we do not demand of human beings afterwards. It is very evident in the case of the autonomous vehicle: we raise our hands to our heads when there is an accident, but we do not flinch with the hundreds of accidents that occur every week due to human error”.
Lack of tech talent
One of the main challenges in which these technological startups coincided is the difficulty in finding talent. According to the participants, in order to work with AI, it is necessary to know how to express what it is based on and have solid basic knowledge, a change that must begin with educational institutions.
Among the requirements that a candidate must meet to work in this area, all the participants agreed on the need to have prior knowledge of machine learning and ability to adapt to each system.