AI in Companies: Operational Adoption and Its Challenges

In recent years, transformations involving the use of artificial intelligence have made it a corporate priority, with many companies seeking ways to incorporate this technology to optimize their processes. In this context, the first major wave of AI adoption in organizations has been predominantly operational, focusing on task automation, efficiency gains, and cost reduction, driven primarily by the need to justify AI investments with concrete and, ideally, short-term results. 

The operational adoption of AI can be observed on various fronts, each with its technological complexity. In customer service, perhaps the most common example, companies implement chatbots and virtual assistants, which, technologically speaking, can range from simple decision trees to sophisticated natural language models, such as ChatGPT. Depending on how they are built, these systems can automate responses, personalize interactions, reduce waiting times, and resolve a series of predictable situations without requiring human interaction, directly impacting the customer experience. 

Another recurring example lies in the automation of repetitive administrative processes, where RPA (Robotic Process Automation) solutions can be combined with AI to handle tasks such as invoice processing and contract analysis. In the logistics and supply chain sectors, AI is applied to optimize demand forecasting and inventory management. Predictive algorithms analyze consumption trends, weather conditions, and historical data to suggest optimal stock levels, minimize waste, and ensure product availability. 

Beyond these examples, artificial intelligence is also strongly impacting other operational areas. In Human Resources, its algorithms are used for candidate screening and recruitment, automating resume analysis, and reducing the time required for the selection process. Some companies even use chatbots to conduct initial interviews and evaluate candidates based on predefined responses.  

In Marketing, it can be applied to personalize advertising campaigns, helping optimize ads based on consumer behavior analysis, audience segmentation, and trend predictions. In Production and Manufacturing, computer vision systems assist in quality inspection by identifying product defects with precision superior to human capabilities, while predictive models prevent machinery failures by identifying wear patterns. 

The aforementioned AI uses are some examples of how it is utilized at an operational level within companies. Such solutions directly "operate" in functions that, until recently, depended solely on manual efforts. These are traditionally repetitive and high-volume processes that require a significant workforce to execute. With the introduction of artificial intelligence, they have been optimized, allowing teams to direct their efforts to higher-value tasks within companies. 

However, although its operational adoption is an almost intuitive first step—and essential for AI to prove its value and justify investments while increasing executive confidence in technology-based solutions, there is a significant issue to consider when this implementation occurs in isolation within companies. Especially in the current "AI urgency" context, where everyone wants "their AI," it is common for different business units to adopt their solutions without a central strategic alignment, which, inevitably, results in systems that do not communicate, duplicated efforts, and resource waste.

If each department in an organization implements its own AI tools without an integrated vision, it is possible (and likely) to face difficulties in the exchange between sectors, overlapping solutions, and misaligned decisions about infrastructure, leading to redundant investments and a lack of governance. This can result in fragmented solutions that, despite being effective individually, create long-term complexity and inefficiency, compromising their evolution within the organization. 

To avoid this, a path would be to structure processes to identify and implement AI solutions in a coordinated manner. This would involve a mapping effort to identify where AI could add the most value, evaluating return on investment before implementation, establishing robust governance to ensure interoperability and regulatory compliance, and testing solutions before scaling them. Additionally, gradual and strategic adoption allows companies to achieve immediate operational gains while planning for AI evolution toward more tactical and strategic use. 

Finally, the operational-level adoption of artificial intelligence is undoubtedly a natural path for companies, as it enables quick and measurable gains. However, organizations that limit AI solely to automation risk fragmenting their efforts and missing opportunities for strategic impact. 

The true value of AI in companies lies not only in cost reduction but in its ability to transform business models, create new revenue streams, and strengthen competitive advantages. The next stage of this journey requires a more tactical and strategic view of technology, ensuring it is used in a cross-functional and coordinated manner to generate long-term impact.