AI in business: how Artificial Intelligence can drive competitive advantage in 2026

Artificial intelligence (AI) has moved beyond being a promise of the future to become a present and essential tool for businesses. More than just a technological trend, AI begins to reveal its full potential when strategically integrated into business processes, becoming increasingly accessible thanks to cloud platforms, more efficient models, and low-code programming tools.

Adopting AI in a company should be viewed as a multi-phase journey. The starting point lies in identifying real business challenges: areas where time is most wasted, where complex data accumulates, or where productivity bottlenecks exist. A typical roadmap recommends starting with a data audit and defining key performance indicators, experimenting with ready-to-use solutions, measuring outcomes, refining processes, and only then moving on to more advanced integrations. For example, it is advisable to pinpoint a small operational challenge, pilot an AI-driven solution (such as automated scheduling or demand forecasting), measure tangible results within a matter of weeks, and then scale up to other areas, building a culture of experimentation and rapid adaptation.

Training is fundamental to this process. Technology implementation is not enough; it must be accompanied by cultivating a digital culture and empowering teams to evolve their workflows. Today, a range of public and private programs are available to boost AI skills, fostering environments of continuous learning and openness to new experiences and incremental innovation.

Despite greater technological accessibility, many business leaders face practical challenges. These include keeping up with the pace of innovation, administrative overload, and difficulties finding technology partners that match their company’s digital maturity. To accelerate AI adoption, most businesses rely on specialized technology partners, like Armis, and value solutions that are easy to implement, secure, and tailored to their specific reality. Successfully integrating AI requires strategy, ongoing adaptation, and a focus on sustainability to ensure lasting results and competitiveness at both national and international levels.

There are many possible approaches to implementing AI. The autonomous model delegates full tasks or decisions to AI systems, such as automatic customer request handling, document categorization, or inventory adjustments. This liberates time and eliminates repetitive processes, proving especially useful in back-office operations, customer service, or logistics. Alternatively, the Human in the Loop approach enables active collaboration. AI provides suggestions, forecasts outcomes, or presents options, but final decisions stay with employees. This is ideal for sensitive areas like marketing, preliminary financial analysis, or strategic decision support, where the human context and experience are crucial.

In both cases, robust AI governance practices are essential. These should include outcome monitoring, ensure algorithmic transparency, and always have space for human oversight when necessary.

AI delivers the most immediate impact in areas tied to operational efficiency and informed decision-making, but innovative cases are emerging in sectors such as healthcare, energy, mobility, manufacturing, and services, where AI is now used to anticipate needs, adjust processes in real time, or create new business models. Success lies not only in the technology itself but in its alignment with business objectives, impact metric analysis, and continuous improvement.

In 2026, AI will become as commonplace as the internet in competitive businesses. The call to action is clear: start small, learn from the outcomes, invest in team training, and choose the approach, autonomous or collaborative, that best fits each area. The greatest value comes not only from immediate gains but from the ability to adapt and continuously innovate that AI brings to the business ecosystem.

Not adopting AI across various sectors and systems within an organization results in a competitive disadvantage and potential challenges adapting to market volatility. Companies that maintain manual processes and make decisions solely based on intuition and past experience ultimately lose agility, efficiency, resilience, and responsiveness to market shifts. In an increasingly digital world driven by automation and data, falling behind in AI integration translates directly into reduced relevance, slower growth, and a higher risk of losing customers to more innovative competitors.

Joel Carneiro | AI Specialist