The AI Experimentation Phase Is Over

The AI conversation in logistics has shifted. on s.

In the latest installment of Lean Quarterly Dive with FreightWaves and the team at Lean Solutions Group (LSG), Thomas Wasson sat down with Alfonso Quijano, chief technology officer and co-founder at LSG, to talk about how AI and automation are actually playing out inside freight brokerages and logistics companies.

For much of 2025, frontier AI labs promised agentic workflows would remake entire job categories. Quijano said that promise didn’t land the way it was pitched, and companies without existing technology infrastructure are discovering the gap between access to AI and the ability to use it well.

“I’m seeing that the experimentation phase is mostly over,” Quijano said. “You tried it, you went into it without a lot of experience. Companies that didn’t have technology teams found themselves investing a ton of money into AI to see if it worked for them.” Broad access to AI platforms didn’t eliminate the need for fundamentals, though.

“Even if the technology had democratized access and everybody could download OpenAI and Claude, it doesn’t mean that you’re an AI company,” Quijano said. “You actually need good project management. You need good change management. You need to have good control of your costs. Who knew?”

The rise and fallout of “vibe coding” (using AI to generate software with minimal traditional development) was a cautionary tale for the industry, according to Quijano. “An independent publisher put out that 99% of vibe coded apps are in the garbage,” Quijano said. “[These apps] are not making any money.

They get published and then get unpublished from the infrastructure platform.” The lesson, he said, is that speed of creation doesn’t equate to business value. “Garbage in, garbage out. Even if it’s faster to create technology, that doesn’t mean that you’re going to create more businesses,” Quijano said.

According to Quijano, applications built hastily during the height of the AI wave were often fragile once deployed. “It seems to work, but in reality it’s very brittle,” he said. “When you apply it in real life and in production, it can cause some pretty important damage within your business.”

Rather than chasing AI-first branding, Quijano argued the more mature approach treats AI as one component within a broader automation strategy, deployed only where it adds value. “It’s better for AI to be present but not talked about than the other way around,” Quijano said.

“You kind of have to make it invisible for it to be adopted as it should within organizations.” Many companies have wrapped AI around problems that once had straightforward, rules-based solutions.

“You’re taking something that would have otherwise been pretty simple and now using AI for it, and the unpredictable nature of the outcome is causing issues,” Quijano said. “You’re kind of better off saying, ‘I’m going to bring automation into my company’ rather than being an AI-first or AI-native solution.

That automation can have AI components when it’s necessary, but no more than that.” According to Quijano, organizations should conceptually treat the technology not as software to configure, but as personnel to onboard. “I don’t think it’s a tool. I think it’s an employee,” he said. “What do you do with a junior employee that joins your company?

You train them. You ensure that you give them a very defined job description so that they know exactly what they need to do.” That means documented processes, exception handling and ongoing training. “You need a very defined SOP,” Quijano said. “You need to ensure that your process is well-documented and that you account for potential errors in the process.

Exception management. You need to have continuous training. You need to close the loop.” Lean has been building that closed-loop infrastructure directly into its own technology stack.

The company recently expanded its LeanTek platform with new AI governance, workforce intelligence and cost visibility tools, giving organizations execution-level cost tracking, workflow feedback loops and centralized governance dashboards designed to move AI deployments past experimentation and into accountable, measurable production use.

“Organizations are increasingly focused on how to manage, measure and scale AI responsibly,” Quijano said of the update. “These new capabilities give leaders the confidence to scale AI while ensuring transparency, accountability and measurable business outcomes.” Skipping the groundwork produces the same outcome as a mismanaged hire.

“You’re eventually going to have to fire this new employee, which is another thing that I’m seeing very, very frequently now,” Quijano said. “There was a wave of AI projects that started earlier in ‘25 that companies are suddenly finding ways to get out of.” Quijano also pushed back on the idea that AI can fix broken processes or underperforming teams.

“You can actually increase the output of errors into your system, potentially,” he said. LSG was