We all want to implement AI — but many companies are still operating on paper and don’t even have the data to understand how their business actually behaves.

According to McKinsey, even in industries experiencing a slowdown, logistics and distribution companies continue to invest in technology and digitalization. They know that failing to incorporate these solutions puts them at a disadvantage compared to competitors. However, many still lack a clear strategy for implementing these tools. They know they should “be more digital,” but they don’t really understand why. And when the purpose isn’t clear, prioritizing change becomes even harder.

Operations teams are busy. Stopping to define, document, and digitize processes is a real sacrifice for people who are already stretched thin. Add to that several cultural challenges — limited digital skills, resistance to change, siloed departments, and the lack of time to register key operational information — and you quickly see why transformation is much harder in practice than it sounds on paper.

Everyone Wants to Transform, Few Know Why

Throughout my career, I’ve seen companies rush to adopt new tools or trends without a strategic reason behind them. Years ago, when I was more focused on marketing, I witnessed how everyone wanted to be on social media simply because it was popular. Today, many companies want dashboards, automations, or AI, even if they haven’t yet defined the processes they want to digitize or the decisions they want those systems to support.

Some leaders know they need to modernize, but they can’t clearly articulate what success would look like. And that’s exactly where the work becomes most interesting for us. Understanding how our clients operate, identifying the real problems they face, and guiding them step by step through their digital journey has been one of the biggest factors behind our success in recent years.

Digital transformation has become a “must.” But the truth is that many companies want AI without having the basic operational data required to feed it. In my experience leading B2B SaaS implementations, the reasons for digitization are often too general:

“We need to modernize.”

“We want to be more efficient.”

But rarely do we hear more specific, actionable problems such as:

“We don’t have visibility into operator responsibilities, and that’s causing delays.”

“We need better metrics on fuel efficiency to define policies that save us money.”

When companies don’t have clarity, transformation is harder to prioritize. When they do, they’re able to turn a system implementation into a true business transformation.

When Change Meets Reality: Operational and Cultural Barriers

Every transformation comes with challenges, and in companies where vehicles are fundamental to value creation, the obstacles often lie in the day-to-day realities of operations.

1. Time and bandwidth
Operations teams are so focused on execution that stopping to document processes or create historical records feels impossible. But systems rely on good data, and many companies don’t even have inventories or traceable histories. These must be built collaboratively.

2. Cultural resistance
In Mexico, many logistics and operations areas have been managed the same way — and often by the same people — for years. A lack of digital skills or fear of technology can create resistance among the teams who will ultimately use the new tools.

3. Siloed structures
Fleet management touches multiple areas, and when departments don’t communicate, integration becomes difficult and technology’s impact remains isolated. Mapping the process, identifying key people, and communicating the benefits across teams are essential for adoption.

At the end of the day, digital transformation is not a technology project — it’s an organizational change process. Tools are rarely the hardest part. The real work lies in aligning teams, ensuring everyone understands the value, and helping them build the data foundation needed for the system to work.

The Leap from Data to Insight: A Step Too Far for Many Businesses

Once information is centralized and teams start recognizing the value of the system, it may seem like the hardest part is over, but in reality, the real challenge is just beginning.

Companies want dashboards, KPIs, and AI predictions, but without sufficient and reliable data, none of that is possible. Deloitte’s industry surveys confirm that data problems are the primary barrier to adopting AI in industrial environments.

That’s why automation and system integrations are no longer a luxury, they’re essential. Integrating information from fuel cards, GPS, maintenance systems, and internal tools allows fleets to validate data, monitor performance, plan services, and control expenses more intelligently.

But even with strong buy-in and good integrations, one of the most persistent obstacles is capturing all the information that still depends on people. Having catalogs, standardized parameters, and automated triggers helps, but operational environments will always face risks like lack of connectivity, forgetfulness, or rushed workflows.

This is why the next step is not just digitization but intelligent detection. Systems must increasingly be able to “sense” vehicle movement, identify behavioral patterns, and record data automatically, minimizing human intervention.

And that is exactly the challenge we are working on today.

Digital transformation doesn’t start with AI. It starts with understanding the business, capturing the right data, and creating the conditions for technology to actually deliver value.

The companies that will lead the next decade in logistics and fleet management won’t be the ones with the newest tools, but the ones with the strongest data foundations and the ability to turn that data into action.

The future belongs to organizations that can automate information capture, connect their systems, and leverage technology to make better decisions — faster, smarter, and with less effort from their teams.

We’re not just building systems. We’re building the infrastructure that will enable the next generation of intelligent operations.

And this is only the beginning.



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