AI in companies: from a one-off tool to an applied strategy

Use of AI in companies: current situation

In most companies, the use of AI is still scattered. Some employees use text generation tools to prepare emails, proposals, or descriptions; they use AI models to translate documents, structure ideas, or summarize long reports. Many of these practices bring immediate gains: they save time, help overcome obstacles, and reduce the effort required for administrative tasks.

At the same time, this use tends to be unstructured. Each employee chooses the tool they prefer, there are no clear guidelines on what type of information can be used, nor criteria for evaluating the results. AI is useful, but it is disconnected from internal processes, from the systems where company data is centralized, and from the objectives defined for each area.

This reality creates a paradox: on the one hand, there is a growing sense that AI “can help,” but on the other, it is difficult to transform this potential into measurable value. Without a connection to business data, without integration with existing platforms, and without a strategic framework, companies run the risk of using AI in a limited way, only in specific actions, with little visible impact on operations.

From a one-off tool to an applied strategy

Having an applied AI strategy means no longer viewing these tools as an occasional resource and instead integrating them as part of how the company works, makes decisions, and evolves. Instead of starting with technology, the starting point becomes the business: what challenges exist today, which decisions are most time-consuming, which processes depend on manual analysis, where there is underutilized information.

From there, AI is thought of as an additional layer on top of an already built foundation: organized data, integrated systems, and defined processes. Companies that have already made progress in digitizing and organizing information have a clear advantage here. When there are reliable data sources—such as a well-fed CRM, a consolidated ERP, or specific platforms for each area—AI no longer works on scattered information and instead supports the reality of the business.

From this perspective, AI does not appear as a replacement for what already exists, but as a reinforcement. It can support the analysis of large volumes of customer requests, synthesize information that previously required hours of reading, help identify patterns in commercial or marketing results, support teams in decision-making, and reinforce automations already in place. The focus shifts from “where can we use AI” to “where can AI improve what we already do today.”

Where AI can start to make a difference

When these conditions begin to be met, AI can be applied in specific areas with real impact, such as customer service and support. Here, the volume of requests, emails, and interactions is usually high, and there is not always time to analyze the complete history of each case. AI tools can help summarize previous conversations, suggest responses based on previously resolved examples, identify recurring themes, and assist in prioritizing requests. The goal is not to eliminate human contact, but to make responses more consistent and informed.

AI also has a role to play in internal operations. Teams that spend much of their time reading reports, consolidating information from various sources, or preparing documents can benefit from models capable of summarizing extensive content, extracting key points, organizing documents by topic, or drafting initial versions of reports that are then adjusted by people. In contexts where the volume of information has grown with digitalization, AI acts as a filter that makes this information usable on a daily basis.

In sales and marketing, AI can support lead analysis, opportunity tracking, and campaign results reading. When there is sufficient history, it becomes possible to identify behavior patterns, group contacts with similar characteristics, or help teams focus on segments with a higher probability of evolution. At the same time, it can support content preparation to ensure that it is adapted to different audiences and channels, provided that there are well-defined lines of communication.

At the management level, AI emerges as a decision support tool. Instead of replacing analysis, it helps prepare the ground: it aggregates data from different systems, identifies relevant variations, synthesizes information in more accessible language, and speeds up access to a global or detailed view. In companies where there is already a major effort to collect and report data, AI can transform that effort into faster and more informed decisions.

AI as a continuation of digital maturity

The adoption of AI in companies is not an isolated starting point. It is the logical continuation of a path that begins with digitalization, moves on to data organization and systems integration, and ends with process automation. AI adds a new layer to this journey: it allows for better interpretation of available information, supports decisions with more context, and reduces the effort required for analysis and synthesis tasks.

The difference between using AI as a one-off tool or as an applied strategy lies in how it is integrated. When it is seen only as a curiosity or occasional resource, the results tend to be sporadic and difficult to measure. When it is thought of in conjunction with processes, data, and business objectives, AI becomes a structural part of how the company responds to daily challenges.

In the end, the question is no longer just “if” AI should be used, but ‘how’ and “where” to apply it meaningfully. Companies that manage to make this transition from scattered experimentation to integrated use will be better prepared to take advantage of the technology, keep up with market developments, and build a solid foundation for the next steps in their digital maturity.