Generative AI is indeed useful for automating repetitive and verifiable cognitive tasks , such as understanding documents, assisting people in processes, or generating workflows.
Generative AI is not reliable for automating irreversible decisions or critical actions without control , because it can produce plausible and difficult-to-detect errors.
What does “automating with generative AI” really mean?
Automating with generative AI doesn't mean "putting a model to do things on its own." It means integrating a probabilistic system into a process that was previously completely deterministic. And that difference, though it may seem subtle, changes everything.
For years, traditional automation has relied on explicit rules: if A occurs, then B is executed. Generative AI breaks this paradigm because it doesn't operate with fixed rules, but rather with learned probabilities . It doesn't "know" what to do; it estimates the most plausible course of action based on the context. This makes it extraordinarily flexible… and potentially dangerous if not properly managed.
Therefore, when we talk about automation with generative AI, we're really talking about designing hybrid processes , where deterministic components (rules, validations, limits) coexist with generative components (interpretation, language, context). The common mistake is treating the model as if it were an intelligent rule. It isn't. It's more like a very fast, highly educated, and very self-assured worker… who also makes mistakes.
Understanding this is key to avoiding automating the wrong things or demanding that AI assume responsibilities that, as of today, it cannot reliably handle.
One of the biggest mistakes today is calling any use of ChatGPT, Copilot, or an internal LLM "automation."
It's not the same:
- Use generative AI to generate text
- Using generative AI to help a person
- Use generative AI to automatically perform actions
Automating with generative AI means that the model is part of a process , not that it "responds well".
The key question is not how intelligent he seems , but:
→ What part of the process am I delegating and who controls the outcome?
From a process engineering perspective, generative AI is a probabilistic component within a workflow. It doesn't replace rules, controls, or responsibilities; it complements them.
What DOES make sense to automate with generative AI
1. Understanding disordered information (documents, text, human language)
Document automation is currently the most fertile ground for generative AI because it addresses a very specific and widespread problem: organizations are full of critical information that is not structured for automatic processing. Invoices, contracts, reports, emails, work orders… documents designed for humans, not machines.
Generative AI doesn't replace OCR or traditional rules; it fills the gap between them . Where OCR sees text, AI sees meaning. Where a rule fails because the format changes, AI interprets the intent. This allows for the automation of tasks that previously required constant human review, not because they were complex, but because they were ambiguous.
The reason this use case works so well is simple: the result can be verified. If the AI extracts an amount, date, or reference, the system can validate it against other sources. When there's verification, the risk is controlled. And when the risk is controlled, automation ceases to be a gamble and becomes a rational decision.
Therefore, when implemented correctly, document automation not only reduces costs, but also frees up the time of qualified people for tasks where human judgment is irreplaceable.
Generative AI is especially good when the problem is not "calculating", but interpreting :
- Scanned documents
- Technical reports
- Unstructured forms
Here, automation is not about "deciding", but about:
- Extract relevant information
- Normalize it
- Prepare it for later systems
That's why the combination of OCR + generative AI + validations is working so well in back office, purchasing, finance, and operations.
→ It works because the result can be verified .
2. Copilots for people who execute processes
Another use with clear results is assisted, not autonomous, automation .
Examples:
- A co-pilot guiding a technician step by step
- An assistant who helps fill out a form
- A system that suggests the next action in a complex process
Here's the AI:
- Reduce cognitive load
- Speeds up tasks
- Avoid errors of omission
But it does not replace human responsibility .
This approach is key in real industrial and business environments, where the question is not "can AI do it?", but "who is responsible if something goes wrong?".
3. Generate “controlled” workflows and automations
Generative AI is also useful for designing automations:
- Propose steps
- Orchestrate system calls
- Connect tools
Provided that:
- The steps are defined
- The shares are limited
- There must be control before execution
In practice, AI works well as a flow architect , not as an unsupervised executor.
What you should NOT automate with generative AI (or only partially)
1. Irreversible decisions without verification
It's not a good idea to fully automate with generative AI:
- Payments
- Critical Approvals
- Changes in production systems
- Actions with legal or security impact
Because?
Because generative AI can make convincing mistakes .
It doesn't fail like a calculator (an obvious error), but like a tired human who "sounds confident".
In critical processes, that is unacceptable.
2. Autonomous actions in physical or industrial systems
In OT, robotics or infrastructure environments:
- Language is not action
- A small mistake can have big consequences
Academic research is clear:
Language models do not understand the physical world , they only describe it.
Here, generative AI must:
- Assist
- Simulate
- Explain
But not to act without strict layers of control .
The real risk: the “executable hallucination”
When discussing hallucinations in generative AI, we usually think of incorrect or fabricated responses. But in automation, the real risk isn't the wrong response itself, but rather the wrong action taken based on a plausible response .
Executable hallucination occurs when a system not only generates an incorrect conclusion but also translates it into an automatic action. At that point, the error ceases to be merely informative and becomes operational. And the more fluid and "human" the system, the harder it is to detect that something has gone wrong.
This phenomenon is forcing us to change how we design automation. It's no longer enough to ask, "Is the answer correct?" but rather, "What happens if this answer is incorrect and no one checks it?" The difference between good and bad automation with generative AI lies not in the model itself, but in how the system handles the possibility of error.
Minimum controls before automating with generative AI
If you're thinking about automating with AI, these rules are non-negotiable:
1. All results must be verifiable
If you can't verify it:
- Don't automate it
- Or keep him/her assisted
2. The flow must be explicit
None of this "AI decides" nonsense.
Each step must be clear:
- What goes in
- What comes out
- What happens if it fails?
3. The context must be controlled
AI shouldn't "remember", it should consult :
- Documentation
- Processes
- Real data
4. Human control must be graduated
Not everything is black and white:
- Review at the beginning
- Sampling after
- Alerts to anomalies
How to start automating with generative AI (without costly mistakes)
Starting to automate with generative AI shouldn't be a leap of faith, but a gradual process. Organizations that achieve sustainable results aren't those that automate the fastest, but those that best sequence the delegation of responsibilities .
The assisted phase is not a preliminary step to be "overcome," but rather a learning mechanism. It allows us to understand where AI falls short, where it provides real value, and what controls are necessary before increasing the level of autonomy. Skipping this phase often generates a false sense of initial success followed by problems that are difficult to justify internally.
Automation with generative AI matures when it becomes a well-designed extension of the process, not a shortcut. And that requires time, measurement, and a clear understanding of what part of the work belongs to the machine and what remains human responsibility.
A realistic approach works in phases:
-
Assisted : AI helps, the person decides
-
Semi-automatic : AI operates with validations
-
Self-employed : only for very limited and controlled tasks
Skipping steps usually ends up in:
- Loss of trust
- Systems disconnected from reality
- Cancelled projects
Conclusion: automate less, but better
Generative AI is not magic.
It is a powerful, probabilistic, and useful tool if designed well .
The correct question is not:
→ “What can AI do?”
The really relevant question is:
→ “What part of my process am I willing to delegate… and under what controls?”
At Brain, we see it every day: the automations that work aren't the most sophisticated or the most flashy, but the best governed . Those where AI doesn't replace the process, but reinforces it.
That's precisely why we designed the AI Automation Expert Program at n8n . It's not training for "doing things with AI," but for learning to automate with sound judgment , understanding when to use generative AI, when not to use it, and, above all, how to integrate it into real-world workflows without compromising reliability, security, or accountability.
If you're considering automating processes with AI—or are already doing so and want to improve—this training covers exactly the principles you've read in this article, put into practice step by step:
