*By Andriei Gutierrez
2023 was the year that ordinary people discovered artificial intelligence (AI). Everyone was taken by surprise by the size of the technological advance and its potential impact. Even among experts in AI and the technology sector, it is possible to say that the scale of success that was the adoption and interest in technology in a very short space of time was not expected. In this context of AI success, practically every Chief Executive Officer (CEO), investor, board of directors started to demand and ask about plans to adopt AI, innovate with AI, get ahead of competitors with the use of AI.
Suddenly, AI became the new 100-meter race to win at any cost.
There is no doubt that it is very important for organizations and professionals to begin to have contact with technology, test it and begin to rethink and redesign operations, products, processes and services. However, this needs to be done as part of a larger digital transformation strategy.
Those who follow my classes and lectures must be tired of hearing me say for almost a decade that AI is the icing on the cake of the Digital Revolution: a long period (of around two decades) in which societies are in transition from a society predominantly industrial-manufacturing for a mostly digital economy, driven by data and digital services. Organizations that understand and prepare for this macrotrend will have a greater chance of success in the medium and long term.
Data strategy
In this context, it is necessary to have a data strategy such as color of the organization's strategy. In addition to the digitalization of processes, including the production process, the creation of new services and products data-driven like this AI-driven, organizations will increasingly be faced with the need to establish a data strategy. The technological definition, the design of the processes, the management of the human resources involved and the rules for managing the organization's information assets are important components of this strategy.
Data strategy is an indispensable foundation pillar for building a solid and reliable edifice that will enable adoption and structural innovation through AI. I have no doubt that maturity in digital transformation is, and will increasingly be, a differentiating element in the efficient adoption of AI by organizations (and consequently in their success).
Algorithmic accountability (date)
There is a second essential pillar of the foundation of this building. It is about understanding the relevance of ethical and responsible handling of data and algorithms.
I would venture to say that a whole new discipline has been created within the scope of ESG practices (Environmental, Social and Governance), digital governance. Perhaps as a result of maturity and pressure from society for a more responsible, transparent and fair assimilation of new technologies. The fact is that many of these good practices have already left the nice to have and increasingly occupy the place of must have, whether due to regulations or market pressure.
Privacy and protection of personal data is an excellent example that has progressed by leaps and bounds, with several countries publishing specific laws and regulations on the subject. Cybersecurity is another discipline with fantastic growth, especially in best practices, international seals and certifications, as well as in legislative and regulatory debates.
And AI is on the same path as numerous recent major initiatives; I highlight here the framework for AI risk management by National Institute of Standards and Technology (NIST), the debates of Organization for Economic Co-operation and Development (OECD) for Trustworthy AI, the publication of a G7 Code of Conduct for Responsible AI, debates around definitions and standards of best practices in AI by International Organization for Standardization (ISO), between others.
Furthermore, legislative and regulatory debates are also heated around the world, with emphasis on the President Joe Biden's Executive Order, new advances in discussions around the AI Act of the European Union and relevant initiatives by the United Kingdom, Singapore and Japan, among other countries. In Brazil, the debate on the regulation of AI is also heated, which promises to continue progressing.
Both Data Strategy and Algorithmic Accountability must be a central field for building corporate strategy. Both disciplines should be frequent agendas for boards of directors, as well as the management of organizations. Those who truly understand and take ownership of these concepts will be better able to compete in the marathon of competition and innovation driven by AI (even if they possibly lose the first 100 meters).
*Andriei Gutierrez is vice-president and leader of the Regulatory Committee of the Brazilian Association of Companies Software (ABES) and president of the Digital Economy and Innovation Council of the Federation of Commerce of Goods, Services and Tourism of São Paulo (Fecomércio/SP).
Notice: The opinion presented in this article is the responsibility of its author and not of ABES - Brazilian Association of Software Companies
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