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EXPLORING CUSTOMER EXPERIENCE PERSONALIZATION WITH AI: A PRACTICAL PERSPECTIVE

Personalizing customer experience through AI is a fascinating and increasingly relevant topic in the business world. I remember an experience I had a few years ago leading a team when we were evaluating the use of Bluetooth 4.0 LE (low energy) technology through beacons to set up a proximity marketing solution. This was at a time when predictive models were not as popular and sophisticated. The core of the strategy then focused on collecting large volumes of data and the possibility of directing a consumer’s journey of purchasing products or services based on their personal preferences, geographic location, and other variables. Today, revisiting that same approach from years ago with the available AI capabilities opens up a huge horizon of possibilities. In e-commerce, for example, AI allows for the creation of product recommendations that seem tailor-made for each user. This is not only convenient for customers, but also significantly boosts

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10 MISTAKES (MORE COMMON THAN YOU MIGHT THINK) THAT CAN COMPROMISE THE AI ADOPTION JOURNEY IN LARGE ORGANIZATIONS

The adoption of Artificial Intelligence (AI) represents a revolution in business, but the path to success is riddled with challenges. Based on my experience with our team of consultants in planning and conducting various AI Journeys across organizations from different market segments, I have compiled the ten most common mistakes that can derail a large company’s successful AI implementation, ultimately generating distrust regarding the expected benefits of using this technology. 1. No Clear Strategy Companies embarking on AI initiatives without a clearly defined strategy often end up adopting technologies without a discernible purpose, resulting in a waste of resources. Picture a company deploying chatbots without considering how they improve customer service or streamline operations. A well-designed strategy aligns AI tools with specific business goals, ensuring relevance and direction. 2. Underestimating the Importance of Data AI is fueled by data. Poor-quality or mismanaged data can yield inaccurate insights and flawed decisions.

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EXPLORING THE POTENTIAL OF PARTIAL SOLUTIONS WITH GENERATIVE ARTIFICIAL INTELLIGENCE

Contrary to what is expected from typical applications of Generative Artificial Intelligence (GAI), the value generated by solutions that are still unfinished or imperfect should be especially cherished for their great intrinsic merit. In today’s fast-paced and ever-evolving business environment, agility and the ability to quickly adapt to new information are crucial. GAI, with its ability to iterate quickly and generate a variety of solutions, can be a valuable resource, even if those solutions are not perfect. It allows companies to explore multiple possibilities and identify innovative opportunities, even in their early stages. Acceptance of partial or imperfect solutions as proposed by GAI encourages rapid iteration and continuous learning within organizations. By working with these solutions, teams can continuously refine their strategies and approaches, adjusting to market needs and customer expectations. This flexibility to experiment and adjust in real-time is invaluable, allowing companies to remain competitive and innovative. Furthermore, this

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AI PROMPT ENGINEERING: COMMUNICATING (WELL) IS ESSENTIAL!

Asking the right questions has never been more crucial. In this context, organizations today rely on an important ally: AI Prompt Engineering. This is a vital technique in advancing the interaction between humans and artificial intelligence (AI) systems. It consists of formulating questions or prompts in a strategic and intelligent way to extract the most accurate and useful answers from AI language models. This technique not only improves the quality of responses, but also optimizes the time needed to obtain valuable information, representing a significant advance in efficiency and effectiveness for any type of business. The benefits of Prompt Engineering are extensive and applicable across a variety of industries. First, it facilitates more effective communication with AI systems, resulting in more precise and relevant interactions. This is paramount, as clarity in communication can mean the difference between success and failure in AI applications. Additionally, well-developed prompts can be tailored to

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ARTIFICIAL INTELLIGENCE AND THE FUTURE OF WORK (OR IS IT THE PRESENT?)

It is well known, and each one of us perceives the impacts of the revolution caused by the advancement of Artificial Intelligence (AI) in its different forms on the work landscape, changing employment structures and creating a paradigm for the workforce. As technology moves forward, machines and algorithms are taking over tasks that were once exclusive to humans, from routine activities to complex decision-making. This scenario is redefining the market, requiring workers and companies to adapt quickly to the new realities. The automation and efficiency brought about by AI are eliminating some jobs, but also creating opportunities in emerging fields. Professions involving creativity, critical thinking, and empathy are becoming more valuable as uniquely human traits that AI cannot yet replicate. Thus, while some paths close, others open up, offering new possibilities for career and development. AI is also changing the nature of work, introducing more flexible and dynamic environments. With

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THE LIMITS OF ARTIFICIAL INTELLIGENCE: WHAT AI CAN’T DO FOR YOUR BUSINESS (…YET)

When we think about the role of Artificial Intelligence (AI) in business, we often focus on its capabilities, such as process optimization, service personalization, and decision-making improvements. However, it is equally important to recognize the limitations of AI and what it cannot do for your business. In this article, I seek to share some critical aspects that AI, no matter how advanced, cannot accomplish in a business environment. Firstly, AI cannot completely replace the human element, especially in areas that require empathy, moral judgment, and a deep understanding of human nuances. Creativity, innovation, and leadership are aspects deeply rooted in human experience and intuition, which machines still cannot replicate. While AI can help with decision-making, the ultimate responsibility and strategic insights still lie with human leaders. Moreover, AI cannot guarantee business success on its own. It is a tool that, if used properly, can lead to significant enhancements. Nevertheless, without

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MACHINE LEARNING: FOR DIFFERENT NEEDS, DIFFERENT WAYS OF LEARNING

Machine Learning innovations are reshaping the business landscape, offering practical solutions and driving operational efficiency. This article explores how recent developments in deep learning, reinforcement learning, and unsupervised learning are being applied in the corporate world. In deep learning, Convolutional Neural Networks (CNNs) – a specific type of artificial neural network inspired by the human visual process and widely used in image and video recognition tasks – are revolutionizing retail and security through image recognition. Companies use these technologies to analyze consumer trends via social media images and for intelligent surveillance systems. In addition, models such as the Vision Transformer (ViT), which analyze images from a broad perspective rather than pixel by pixel, are being employed to improve the quality of visual inspection on production lines by detecting product defects with enhanced accuracy. Reinforcement learning has significant practical applications in logistics and supply chain management. Algorithms like Deep Q-Networks (DQN)

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