When it comes to artificial intelligence applied to business, most people almost automatically think of customer service chatbots, sales automation, or customer support. This is understandable: these are the most visible and widely promoted uses of AI in everyday business life. But this is only a fraction of what the technology is capable of — and perhaps not even the most transformative.
On the XMACNA podcast, Gustavo Fechus spoke with Daniel Valadares about a field still little explored by most managers: the use of artificial intelligence within industry, going far beyond sales and after-sales. The conversation brought concrete examples of how AI is already being used to monitor operations, prevent accidents, and accelerate responses — often leveraging infrastructure the company already has.
AI is not just chat: thinking of artificial intelligence as a central brain
One of the main points raised by Daniel Valadares is a mindset shift. Instead of seeing AI merely as a conversation interface — where someone types a question and waits for an answer — it's more productive to understand it as a large data processing brain, capable of supporting decisions and, in many cases, making decisions and triggering actions autonomously.
This perspective shift opens the door to industrial applications. When a company already has cameras, sensors, monitoring systems, or any other kind of data capture, these resources can become the "eyes and ears" of an artificial intelligence. What changes is precisely the capability to process that information in real time and turn it into action.
The Smart Sampa example: how AI already works in accident prevention
To illustrate the concept in an accessible way, Daniel used a publicly known case: Smart Sampa, a monitoring project using cameras spread throughout the city of São Paulo. These cameras are connected to AI systems able to identify situations like traffic accidents in real time.
He shared a personal example: his wife had an accident on a regional highway — a truck hit the rear of the car, which spun on the road. Even before anyone manually called for help, the highway support team was already on the way because the control center identified the accident through cameras and dispatched assistance automatically.
The central point of this example is not the case itself, but what it reveals: this level of automation and rapid response is not exclusive to large public projects with unlimited budgets. It is much more accessible to companies than most imagine.
Practical applications of AI within the industry
According to Daniel, industries that already have camera monitoring systems can "plug" this monitoring into artificial intelligence and configure it to identify specific risk or process situations. Some examples mentioned in the conversation include:
- Automatic identification of workers without safety equipment (PPE), with immediate alert to the responsible team;
- Signaling anomalous behavior in machines or equipment, allowing preventive maintenance before a failure;
- Remote monitoring of hard-to-reach structures — such as oil platforms — using drones equipped with AI instead of high-risk manual inspections, like rappelling.
These applications are already a reality in large companies like Petrobras, which uses drones for inspections on offshore platforms. But according to Daniel, this level of technology is no longer exclusive to large corporations: today it is increasingly accessible to industries of any size.
You don't need to overhaul your entire operation to use AI
One of the biggest blocks Daniel and Gustavo observe among business owners is the feeling that adopting artificial intelligence requires a complete business restructuring. This perception, according to them, is one of the main reasons managers procrastinate on implementing AI solutions — even while recognizing their importance.
In practice, XMACNA's experience shows the opposite: in most cases, the solution is to leverage the structure the company already has by connecting it to a different "brain." As Daniel summarizes, often the process is simply "unplug from 1 and plug into 2" — that is, redirecting existing data and processes to an artificial intelligence layer without discarding what already works.
This also means that, in most cases, adopting AI does not require an increase in headcount. Instead of hiring a new team, the company begins to extract more value from the human and technological resources it already has in operation.
Why every AI solution needs to be customized
Gustavo underscores an important point in the conversation: every business problem is specific, and that is precisely why generic solutions often fail to deliver real results. XMACNA's work always starts with a consulting phase — deeply understanding how the operation works today, what the bottlenecks are, and where artificial intelligence can generate the greatest impact.
According to Daniel, this development is collaborative and gradual. There is no "magic solution" that solves everything at once; there is a process built together with the client that evolves as the operation also evolves. This work model has already been applied by XMACNA both in franchises and small to medium-sized businesses and in more complex industrial projects, always respecting the structure and people who are already part of the business.
Frequently asked questions about AI in industry
Is artificial intelligence in industry only for large companies?
No. Although examples like Smart Sampa or drone monitoring on oil platforms involve large structures, the technology behind these solutions is increasingly accessible. Today, small and medium-sized industries can also apply AI to monitoring, safety, and preventive maintenance processes.
Do I need to replace the entire company system to implement AI?
In most cases, no. The most efficient approach is usually to connect artificial intelligence to the systems and equipment the company already uses — such as security cameras and sensors — instead of replacing the entire existing infrastructure.
Does implementing AI mean reducing the company's team?
Not necessarily. Many AI solutions are designed to enhance existing human resources by automating repetitive or constant monitoring tasks, not to replace people.
Conclusion
The conversation between Gustavo Fechus and Daniel Valadares sends a clear message: artificial intelligence in industry is not a distant promise nor a project exclusive to large corporations. It is already available, adaptable to the reality of each operation, and in most cases can be implemented using the structure the company already has.
If your company faces challenges in safety, monitoring, or operational efficiency, there is likely an AI-based solution capable of solving this problem without a complete business overhaul. Schedule an assessment with XMACNA and discover how to apply artificial intelligence to your operation in a customized and strategic way.