# AI’s Trojan Horse - Mexico Business News

*Источник: Mexico Business News*
*Дата: 2026-10-02*
*Язык: en*

**Кратко:** STORY INLINE POST
Artificial intelligence continues to expand across organizations at a pace few technologies have reached before. But behind the enthusiasm, an uncomfortable warning is beginning to emerge: without a clear strategy for governance, security, and business application, AI can become a true Trojan horse — a tool that enters with promising efficiency and ends up exposing sensitive information, increasing costs, and failing to generate measurable financial returns.

STORY INLINE POST
Artificial intelligence continues to expand across organizations at a pace few technologies have reached before. But behind the enthusiasm, an uncomfortable warning is beginning to emerge: without a clear strategy for governance, security, and business application, AI can become a true Trojan horse — a tool that enters with promising efficiency and ends up exposing sensitive information, increasing costs, and failing to generate measurable financial returns.
For the logistics and transportation sector in Mexico and the United States, this is not an issue limited to IT departments. Every route, rate, freight manifest, contract, customer, and incident is part of the knowledge that makes a company competitive.
That is why the question should not simply be “Are we using AI?,” but rather: “Are we using the right AI, with our data, to solve specific problems and under our control?”
The Hidden Cost of the Cloud
One of the challenges of adopting tools such as Claude, ChatGPT, or Gemini is cost. The problem is not necessarily in subscribing to a tool, but in using it indiscriminately, paying for increasingly advanced models without having a clear understanding of the return.
The industry is already talking about “token maxing” and the tendency to use the most advanced and expensive models for any task, regardless of its complexity.
The case of Uber is illustrative. The company had to establish limits on the use of AI-based coding tools after consuming its budget sooner than expected. In 2026, it established a $1,500 monthly limit per employee and per tool for some of these solutions.
Marcos Grilanda, vice president and general manager of Databricks for Latin America, has pointed to another problem: Many companies encouraged employees to use AI without establishing clear limits or “guardrails,” causing costs to grow without adequate governance.
The first lesson is simple: Not every task needs the most powerful AI available. It needs the right AI for that task.
What the MIT Report Reveals
MIT’s NANDA project published "The GenAI Divide: State of AI in Business 2025," a study that analyzed more than 300 public AI implementations, interviews, and responses from business leaders.
Its most striking conclusion was that, despite estimated investments of between US$30 billion and US$40 billion, approximately 95% of the organizations analyzed were not achieving measurable financial returns, while a small group was achieving significant results.
The authors describe this as “The GenAI Divide,” the gap between the enormous adoption of generative AI and its limited real-world business transformation.
It is important to note that this is preliminary research and not a peer-reviewed academic study. Therefore, the 95% figure should be understood as a finding from that sample, not as a universal statistic applicable to every company.
But the message remains relevant: Adopting AI does not guarantee value creation.
The Other Price: Data
The financial cost may not be the most important one. There is another price: the company’s own data.
Satya Nadella, CEO of Microsoft, has warned about what he calls the “Reverse Information Paradox:” As companies use AI, they need to provide increasingly more internal knowledge to obtain genuinely useful answers. That knowledge can include processes, documents, instructions, corrections, and decision-making criteria accumulated over years. Each interaction can provide context about how an organization actually operates.
And there is an even simpler risk: An employee may copy confidential information into a public AI tool in order to solve a task quickly.
A rate table.
A contract.
A manifest.
A profitability report.
A customer database.
The intention may be to save time. The result may be taking strategic information outside the company’s control perimeter.
What Does This Mean for Logistics and Transportation?
Logistics is an industry built around information: routes, rates, fuel, transit times, unit availability, drivers, customers, maintenance, border crossings, customs documentation, and demand patterns.
Imagine an operations manager who copies a cost table into a public AI tool to identify which routes are less profitable.
The answer may be correct.
The question is different: What information had to be provided to obtain that answer, and under what conditions is that information being processed?
In an operation between Mexico and the United States, the risk is even greater. Shipment, customer, carrier, customs, and border-crossing data are subject to different regulatory and contractual frameworks.
The absence of an internal policy governing AI use does not only represent a risk of information leakage. It can also become a compliance issue.
The Alternative: Specialized AI
Does this mean companies should stop using ChatGPT, Gemini, or Claude? No.
General-purpose tools are excellent assistants for writing, research, programming, analyzing non-sensitive information, and solving a wide range of tasks.
The problem arises when we try to turn a general-purpose tool into the operational brain of a company.
One thing is using AI to help you work. Another is building AI around the way your company works.
Specialized AI can start from a completely different context. It does not need the user to explain from scratch what a route is, what variables affect a transportation operation, or what information is relevant to making a particular decision.
That knowledge can be part of the system by design.
The difference is not necessarily which model is “smarter.” It is how relevant it is to the problem we are trying to solve.
What Should Transportation Companies Do?
There are three fundamental areas:
1. Digitize and structure their information.
Routes, rates, incidents, maintenance, fuel, unit availability, and historical results must be converted into structured information.
2. Define where data is processed.
Companies must establish what information can leave their infrastructure, what must remain under internal control, and which external services can be used safely.
3. Apply AI to specific problems.
Instead of asking, “How can we use ChatGPT?,” the question should be: “What specific problem in our operation do we want to solve with AI?”
Route optimization, demand forecasting, predictive maintenance, profitability analysis, unit assignment, visibility, and process automation are concrete problems. This is where AI can generate value.
The Conclusion for the Industry
Artificial intelligence will continue to be a decisive technology for logistics and transportation in North America. But the competitive advantage will not necessarily belong to whoever uses the most AI, but to whoever integrates it most effectively into their operation.
The question is no longer: “Does our company use AI?”
The question is: “What problem are we solving, how much value are we generating, and who has control over the data we need to do it?”
Using ChatGPT, Gemini, or Claude without context can be an excellent way to start experimenting with AI. But a transportation company that wants to turn AI into a competitive advantage needs something different: Artificial intelligence that knows its industry, understands its processes, and is specifically designed to solve transportation and logistics problems.
That is precisely the path we are building at LIS: AI applied to transportation, not generic AI for transportation.
The difference may seem small. In day-to-day operations, it can be enormous.
Sources:
- MIT NANDA, The GenAI Divide: State of AI in Business 2025.
- Expansión, analysis of costs and governance in enterprise AI use.
- Los Angeles Times, Uber case and limits on the use of AI tools.
- Satya Nadella, statements on the “Reverse Information Paradox,” as reported by TechCrunch.
- Analysis of the methodological limitations of the MIT study.

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