Artificial Intelligence in Practice: How Companies Are Transforming Their Operations and Strategies

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21 de August de 2025

Artificial Intelligence (AI) has evolved from a distant technological promise to become a strategic pillar in the transformation of companies worldwide. In recent years, the adoption curve has accelerated impressively, driven by computational advances, the democratization of access to tools, and the increase in the volume and quality of available data. Today, discussing AI is no longer a conversation limited to technology or research departments. It is an agenda that involves financial directors, operations leaders, marketing managers, and CEOs, as it directly impacts the way companies compete, innovate, and generate value.

According to recent surveys, more than 50% of global companies already utilize Artificial Intelligence to some extent. This adoption, however, is not uniform. Some sectors have advanced more aggressively, while others move at a slower pace, whether due to regulatory barriers, infrastructure limitations, organizational culture, or digital maturity. The common point is that, regardless of the segment, AI is shaping new standards of efficiency, personalization, and decision-making.

In the second post of the series “Artificial Intelligence in Practice”, we will explore the vast potential of AI. Learn how different sectors are applying AI to solve concrete challenges, optimize processes, and create new business opportunities. We will analyze the factors that accelerate or hinder this adoption, bring real examples, and project trends that should guide the next phase of this technological revolution.

The paradigm shift: from automation to strategic intelligence

Artificial Intelligence has undergone various phases of development and application in the corporate world. Initially, it was seen as an extension of traditional automation: a way to reduce manual tasks, accelerate calculations, and standardize operations. With the advancement of machine learning algorithms, increased computing power, and the development of more sophisticated models, AI has evolved into a much more strategic function.

Today, it is not just about “automating” something that was already done, but about rethinking entire processes based on new analytical and predictive capabilities. Companies are using Artificial Intelligence to anticipate behaviors, personalize experiences at scale, forecast demand with greater accuracy, and identify risks before they materialize.

For example, in supply chains, intelligent systems can automatically adjust orders based on consumption forecasts, taking into account variables such as weather, regional trends, and consumer behavior. In the financial sector, AI models are capable of identifying subtle patterns in transactions that indicate fraud, something that is practically impossible for human analysts to do at scale.

This paradigm shift necessitates a new approach to thinking about technology: Artificial Intelligence is not merely an operational tool, but a strategic agent of transformation.

Global overview of AI adoption

Studies reveal that AI is already present at various levels within organizations. Some companies incorporate AI in a way that is invisible to the end user, for example, through dynamic price adjustments or delivery routing. In contrast, others use it as an explicit differentiator, such as in virtual assistants or visible personalization features for customers.

According to The State of AI report by McKinsey, the most common areas of application today include:

Customer service and sales: chatbots, virtual assistants, and recommendation systems;
Operations: predictive maintenance, process optimization, and inventory management;
Marketing: audience segmentation, personalized content creation, and campaign analysis;
Finance and risk: fraud detection, credit analysis, and risk modeling;
Research and development: scenario simulation, design optimization, and discovery of new materials.

Whatfix, in turn, highlights that sectors such as healthcare, manufacturing, and financial services are among the most advanced in AI integration. In contrast, areas like education and the public sector still face significant barriers, despite their great potential.

Challenges in implementing Artificial Intelligence

Despite the enthusiasm, implementing AI is neither immediate nor straightforward. Among the main challenges identified by companies in different sectors, we can highlight:

Data quality and availability: Artificial Intelligence depends on data to learn and deliver accurate results. In many sectors, this data is fragmented, outdated, or protected by privacy rules that require careful handling;
Organizational culture and internal resistance: The adoption of AI can generate internal resistance, particularly when it involves automation that replaces human tasks. Successful companies communicate benefits and invest in training so that teams see AI as an ally.
Talent shortage: Professionals specialized in AI, data science, and machine learning engineering are in high demand and short supply, causing many companies to seek external partnerships or “off-the-shelf” solutions in the market.
Technological infrastructure: Sectors with low digital maturity face difficulties in integrating AI into legacy systems, which requires significant investments in modernization.
Regulation and ethics: Areas such as healthcare and finance operate under strict compliance and privacy rules, which makes implementation more complex.

Integrated examples of application

Instead of segmenting by sector, it is worth examining a set of examples that demonstrate how AI is being applied in a transversal manner, combining different areas to create a real impact.

Imagine a global logistics company that integrates AI into its operations. The system analyzes meteorological data, traffic conditions, vehicle availability, and order priority to recalculate routes in real time, reducing delays and fuel consumption. At the same time, predictive maintenance algorithms monitor the fleet’s state, preventing failures and enhancing safety. In the back office, AI systems adjust the allocation of human resources based on the volume of deliveries, thereby optimizing costs.

Another example is a hospital network that utilizes AI not only for image diagnostics but also to predict peaks in hospitalizations based on seasonal trends, disease outbreaks, and mobility data, which allows for more efficient planning of beds, medical staff, and supplies.

In retail, companies combine predictive analytics for inventory management with recommendation engines that personalize offers for each customer, integrating data from previous purchases, website behavior, and campaign engagement. The result is an increase in conversions and a significant reduction in losses due to excess or insufficient products.

Impacts on performance and ROI

Measuring the return on investment (ROI) of AI is a challenge, as the impacts go beyond immediate cost reduction. In many cases, the most outstanding value lies in revenue growth or risk mitigation.

For example, a bank that implements AI for fraud detection can avoid multimillion-dollar losses. At the same time, a manufacturing company that adopts predictive maintenance can reduce unplanned downtime and extend the lifespan of its equipment. These gains, although not always directly visible in the quarterly balance sheet, accumulate and strengthen competitiveness in the long run.

Reports indicate that companies leading in AI achieve productivity gains up to 40% higher than competitors, in addition to a greater ability to adapt to market changes.

Future trends

The future of AI is poised to yield increasingly specialized and integrated solutions. Some trends that should shape the scenario in the coming years include:

Generative AI applied to business: content creation, design, and accelerated prototyping;
Sector-specific models: trained with data and terminology specific to each segment;
Integration with the Internet of Things (IoT): connection between AI and smart sensors for real-time monitoring;
Explainable AI: focus on transparency and interpretability to facilitate compliance and acceptance;
Cognitive automation: a combination of AI with process automation for more complex decisions.

Conclusion

The adoption of AI is not a question of “if” but of “when” and “how”. Companies that manage to align technology, organizational culture, and business strategy will have a greater chance of extracting real value from this transformation.

The current scenario shows that, although each sector has its own maturity curve, the possibilities are practically unlimited. From logistics to healthcare, from retail to finance, AI is redefining not only the way of operating, but also what is possible to achieve.

In the next post, learn how Artificial Intelligence is revolutionizing consumer electronics retail, with practical examples and guidelines on pricing and performance for those who want to get started, as well as some forecasts. Keep following ASM’s content and discover how to turn data into strategic decisions.

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