StartArticlesAI Race: How to Avoid the Trap of Rushed Adoption

AI Race: How to Avoid the Trap of Rushed Adoption

When we think of the most disruptive and popular technologies gaining ground in the business world, it is impossible not to consider artificial intelligence as one of the main tools. And this is no coincidence, as the research 'The State of AI in Early 2024: Gen AI Adoption Spikes and Starts to Generate Value', conducted by McKinsey, reveals that 72% of companies are already using AI. Enthusiasm is mainly fueled by the possibility of eliminating repetitive tasks through automation, optimizing professionals' time, which can be used for higher-value and more relevant activities, reducing costs and increasing efficiency.

This frenzy may cause managers who have not yet adopted this technology to feel at a disadvantage. In highly competitive markets, it is common to seek innovative solutions for organizations to stand out and achieve success. However, it is crucial for managers to think strategically before adopting new technologies, avoiding hasty decisions that only seek the appearance of innovation. There is a need to ensure that the acceptance of these solutions is aligned with the company's real needs and that it is understood how they can, in fact, drive growth.

Adoption must be carefully studied, as any change in daily work involves changes in processes, organizational structures, and culture, which require both time and resources.

To support decision-making, specialists like Alexandre Nascimento, a researcher at MIT, present studies that can be fundamental in developing an AI plan for the business. An example is the AI2M (Artificial Intelligence Adoption Intention Model), created by him, which considers five main factors that influence the intention to adopt AI: facilitating conditions, which assess whether the user believes they have the necessary resources to use AI; performance expectancy, which measures whether the user believes AI will improve their work performance; effort expectancy, which reflects the user's perception of the difficulty of learning and using AI; self-efficacy, which is the user's confidence in their ability to use AI; and social influence, which assesses the perceived pressure from others to adopt AI.

In a more generalist way, these decision-makers should consider the following scenario: what is the problem I face and how can AI help solve it, rather than adopting the reverse approach, which would be to decide to implement AI without considering where and how it will be applied. These questions are not intended to present a negative view of AI integration, as it is evident how much it can benefit work processes. Instead, the goal is to emphasize that AI should be seen as a tool, not as the miracle solution, as the enthusiasm and buzz generated by the media's frequent attention often make it seem. Thus, organizations can maximize the benefits of AI and ensure an effective transformation.

Paulo Watanave
Paulo Watanave
Paulo Watanave is head of Data & Analytics at Nava Technology for Business.
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