Artificial intelligence already analyzes thousands of proposals, predicts scenarios, and suggests the best purchasing conditions. And yes, many companies in Brazil already do this daily. But the truth is that the barrier today is no longer technology. It's us.
When AI suggests rejecting an offer without explaining why, do you trust it? When it gets it right, but nobody understands how, would you still proceed? Negotiating with AI is less about data and more about decisions.
In theory, using AI in negotiations should bring immediate gains. But in practice, the results depend on the professionals' trust in the system. If the AI's suggestion doesn't make sense to the buyer, they will ignore it. If the system seems like a black box that cannot be questioned, the team may use it out of obligation, without understanding the impacts.
In practice, everything depends on trust. And trust doesn't just come from being right. It comes from predictability, clarity, and a sense of control. In some cases, AI may be technically correct but fail in how it communicates its decision. And without understanding, the recommendation is unconvincing.
More mature platforms already offer reliability indicators, use clear language, and allow for questions. But this requires time, culture, and practice. It's not enough to install AI. It needs to become part of the daily lives of decision-makers.
The risk of trusting too much, or too little.
We are facing a new type of bias: that of automation. Some accept everything AI suggests without question. Others reject everything, simply because it came from a system. In both cases, who is really deciding? Nobody.
A manager might approve an unfavorable proposal simply because the system indicated it. Another might ignore a risk warning because "it's not quite like that in practice." And both compromise the potential of automation.
Negotiating with AI isn't about automating the conversation. It's about enriching the conversation.
Neither co-pilot nor autopilot
As systems evolve, the desire to automate everything grows. After all, if AI gets 90% of decisions right, why not hand over control to it?
Because not every negotiation fits into an algorithm.
Non-standard situations, verbal agreements, subjective clauses, and long-term relationships still challenge algorithmic logic. AI can recommend excluding a supplier for below-average performance without considering that the failure was caused by a climate crisis in the region. It can suggest renegotiating prices without knowing that the contract is tied to a global strategic partnership.
Therefore, the most effective model is still the hybrid one: AI proposes, humans validate.
In this model, trust needs to go hand in hand with governance. The company needs to know who authorized what, based on what criteria, and whether the decisions can be traced.
A system that automatically rejects proposals from small businesses due to low reliability scores may, in fact, be reproducing historical distortions. And if this leads to business exclusion or biases, the responsibility will not lie with the AI, but with whoever authorized its use without adequate oversight.
Blaming AI for a loss is like blaming the compass for a shipwreck. Systems need to be auditable. Their suggestions need to be explainable. And professionals need to be prepared to correct flaws before they become the norm.
Augmented intelligence begins with the relationship.
The term "centaur negotiators" originated in the world of chess, where humans began competing in pairs with algorithms. The result? Partnerships that surpassed both the best players and the best individual machines. Something similar is already happening in corporate procurement.
Professionals use dashboards during meetings. They simulate conditions. They receive automatic alerts about risks and opportunities. They compare what is being said with the history of contracts and market standards. And most importantly: they know when to listen to the system and when to trust their own experience.
This relationship requires something new: emotional intelligence applied to interpreting AI. It's necessary to understand when it's confident, when it's merely estimating, and when the data is insufficient.
In Brazil, where a large part of negotiations are still done face-to-face, this adaptation is even more challenging. There are political contexts, informal historical contexts, and agreements of trust that don't appear in the systems. AI doesn't see what goes on behind the scenes. It doesn't sense the hesitation. It doesn't perceive the tension.
Therefore, trusting too much is costly. Always being suspicious costs time. And finding the balance depends on training, internal controls, and organizational maturity.
The next move is not technical.
More than developing algorithms, the real leap lies in knowing how to direct them. This begins before the negotiation with the definition of criteria, objectives, and priorities, and continues afterward with the analysis of the real impact of the decisions.
AI helps make decisions. But it's the human who defines what should be negotiated, why it matters, and what compromises make sense in each scenario.
The future of negotiation isn't about replacing people with systems. It's about creating productive alliances between complementary intelligences. It's about developing teams that know how to leverage the best of automation without sacrificing strategic judgment.
AI is already on the table. The question is whether companies are ready to engage with it with maturity, responsibility, and a long-term vision.
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