Artificial intelligence has taken an increasingly significant role within companies. The advancement of models and, especially, agents capable of executing tasks and interacting with different systems has greatly expanded what can be done with technology. Activities that once depended exclusively on people working at a computer can now, in many cases, be automated or performed with the support of digital employees.
This transformation did not just begin now. Experience with artificial intelligence applied to business has been evolving for several years, initially in activities more related to customer service and public relations. From these applications, it became clear that agents could take on broader functions, also participating in sales, support, and other company processes.
This is how the idea of the digital employee gained ground: instead of using artificial intelligence only to answer questions or generate content, it can be embedded within operations to perform specific functions, using the necessary tools and systems to complete the work.
From conversation to process
A digital service employee can receive a request, guide a customer, and forward a demand. But as this agent gains access to other tools, its role can extend to different stages of the customer relationship.
In a commercial context, for example, artificial intelligence can be involved from the initial contact to later stages of the journey. It can handle conversations, track information in the CRM, support lead management, and participate in automations related to customer relationships.
This represents an important shift in how companies can use technology. The goal moves from merely automating a specific task to building an operation where different activities are interconnected.
The same logic can be applied to other areas. Communication is an example. Communication operations involve much more than publishing a post. There is research, market analysis, competitor monitoring, benchmarking, positioning definition, production, testing, and evaluation of results. Part of this work can be executed by agents working in parallel at a scale that would be difficult to achieve with an exclusively human operation.
This does not mean the entire process should be handed over to artificial intelligence. Some activities rely on analysis, context, experience, and human authorship. The point is to understand which parts of the process can be taken over by technology and how people can focus their work where their participation is most relevant.
The perspective shift: thinking about the entire process
One of the most common challenges in adopting artificial intelligence is viewing technology through isolated tasks. A company might consider using AI to write a post, answer messages, or organize specific information. All these applications can be useful, but they do not necessarily represent a significant operational transformation.
Greater value appears when the company begins to analyze the entire process. Instead of thinking only about creating a post, for example, it is possible to look at the whole communication operation: what topics need researching, how the market is positioning itself, which content should be produced, how it will be tested, and how the results will be used to guide future content.
The same applies to sales, service, or any other area. Artificial intelligence can perform a task, but its potential increases when it is part of a larger flow where one activity feeds the next and information is used throughout the entire process.
This kind of design also helps avoid a common problem: investing in automations that work technically but have little impact on business results.
Artificial intelligence is also changing how it appears on the internet
The change is not only happening within operations. The behavior of people seeking information is also evolving. For a long time, companies worked to appear on search engines through pages, keywords, and links. With the popularization of AI tools, some searches now happen directly in environments able to interpret a question and provide an answer.
This creates a new concern for companies: besides being found by traditional engines, they need to be present in the answers generated by these systems.
This scenario has led to new practices regarding how a company structures its content and digital positioning. Again, artificial intelligence can assist in this task by analyzing large volumes of information, identifying patterns, and structuring content at scale. It is an example of how technology can end up being used to manage the transformation caused by technology itself.
There is no identical starting point for all companies
With so many possibilities, it is natural for a company to want to start implementing many things at once. The problem is that this approach can lead to scattered investments without a clear relationship between the automations developed and the expected results.
That’s why, before choosing a tool or developing an agent, it is important to understand how the company operates. an assessment allows identifying existing processes, the systems used, how data is organized, and key operational points. From this assessment, it becomes possible to define which initiatives should be prioritized.
This stage also helps differentiate what is merely interesting from what can truly deliver results:
- An automation can be technically sophisticated and yet have little importance for the business;
- Another automation, seemingly simple, can eliminate a significant bottleneck or reduce a substantial amount of operational work.
Therefore, the starting point should not be the available technology but the company’s need.
The problem of scattered data
The assessment often reveals another issue: many companies have information spread across different systems, spreadsheets, and tools that do not communicate properly. This creates difficulties both for operations and for using artificial intelligence.
When data is scattered, processes become harder to control and information might not be available when needed. A message might be sent to the wrong person, a piece of data might fail to update, or a team may work with different information about the same customer.
Implementing AI can be an opportunity to reorganize this structure. In some cases, this means integrating existing systems. In others, it may make sense to develop a custom solution that centralizes the information and processes needed for that operation. Software development has also changed considerably in this context.
Custom software has gained a new dimension
Traditionally, when a company needed a system for a specific process, there were two main options: develop it internally or buy a ready-made solution. Market tools solve common problems for many companies but rarely can exactly replicate the operation of a specific organization. That is why often the company's process needs to be adapted to the tool while also configuring the tool to fit the process's needs.
Artificial intelligence is making a third option more feasible: developing systems from the start based on how that company works.
Instead of starting by choosing a tool, development can begin with existing processes and the specific needs of the business. This allows building solutions closer to the actual operation and, in many cases, significantly speeding up development. The combination of modern development tools and AI has made custom software more accessible than it was a few years ago.
When software also gains a digital employee
A custom system can be used directly by company teams. But it can also be connected to an agent able to interact with this system. In this scenario, the digital employee is not just responsible for performing a specific task but can also help the manager use, monitor, and modify the structure developed for the company.
For a non-technical user, this can greatly simplify the interaction with software. Instead of always relying on a specialized person to make certain changes, the manager can explain what is needed in natural language and use the agent as an interface to the system.
Naturally, there is important technical work behind this. It is necessary to structure the architecture, develop the software, configure access, and correctly define what the agent can or cannot do. Once this structure is ready, however, interaction can become much simpler for those who use the system daily.
Security remains part of the implementation
The expansion of AI use has also increased concerns related to security and data access. This is a legitimate concern, but it does not mean that artificial intelligence must be treated as a technology that inevitably exposes company information.
An agent, like any other user or system, needs to have permissions and roles defined within the technological architecture. It should only access what is necessary to perform its functions. Security therefore depends on how this structure is built and managed.
Furthermore, many vulnerabilities that appear in companies are not new and did not arise because of artificial intelligence. Misconfigured systems, exposed credentials, improperly accessible files, and service providers without the necessary controls have posed risks for a long time.
The experience of Redigir itself, a group company, illustrates this point. The company suffered an attempted breach related to a vulnerability in a configuration file left exposed by a service provider. The problem was quickly identified by existing security mechanisms, and a backup structure was prepared for this type of situation. The episode was not related to the use of artificial intelligence; the vulnerability already existed.
What changes with the evolution of AI is that increasingly powerful tools can also be used to find vulnerabilities more quickly. Therefore, companies need to keep pace with this movement and strengthen their own security structure. Instead of simply avoiding the technology, it is necessary to be prepared to use it properly and protect operations in a scenario where attacks can also become more sophisticated.
Frequently asked questions about AI in companies
Is an internal technical team necessary to implement artificial intelligence?
Not necessarily. Many companies start with strategic partnerships or specialized consulting to create the initial architecture and digital employees, training the internal team only to monitor daily operations.
Where should a company start implementing AI?
The ideal first step is a process assessment. Instead of hiring isolated tools, the company should map where the biggest bottlenecks are and which area (such as sales, service, or communication) will bring the quickest return.
Does using digital employees put company data at risk?
Only if the structure is poorly designed. When agents are configured with well-defined access permissions and integrated into a secure architecture, they operate with the same levels of control as any corporate system.
Conclusion
The range of possibilities may make adopting artificial intelligence seem more complex than it really needs to be. A company does not need to start by trying to automate all areas at once. The path can be simpler: understand how the operation works today, identify processes that consume the most resources, analyze where bottlenecks exist, and establish a priority order.
From this assessment, it is possible to define which tools make sense, which processes can be automated, and where developing a proprietary solution can bring more results. In some cases, the first project will be a digital service employee; in others, it may relate to sales, communication, internal operations, or data integration.
There is no single recipe. There is each company's process and, from it, the possibility of finding where artificial intelligence can take over work, integrate information, and increase operational capacity.
If you want to move beyond experimentation and start using AI as an effective part of your business, the first step is to understand your operation. Schedule a strategic assessment and discover where artificial intelligence can have the greatest impact on your business model.