Why are AI projects failing in freight brokerages and logistics companies?

According to Quijano, companies without existing technology teams invested heavily in AI without the foundational infrastructure needed to deploy it effectively. The gap between access to AI tools and the ability to use them well is substantial. Many organizations built fragile systems that worked in testing but caused 'pretty important damage' in production, and lacked the project management, change management, and cost controls required for successful implementation.

What is 'vibe coding' and why is it relevant to logistics technology?

Vibe coding refers to using AI to generate software with minimal traditional development oversight. An independent analysis found that 99% of vibe-coded applications failed commercially and were unpublished from infrastructure platforms. The lesson for logistics: speed of creation does not equate to business value. As Quijano stated, 'garbage in, garbage out'—even faster creation doesn't produce viable businesses.

How should logistics companies approach AI implementation going forward?

Rather than pursuing AI-first strategies, organizations should treat automation as the primary goal, with AI as a component only where it adds measurable value. Quijano recommends three elements: (1) treating AI like a new employee requiring clear job descriptions and training, (2) implementing documented SOPs with exception handling and continuous training, and (3) establishing closed-loop feedback systems with cost tracking and governance oversight. AI should be 'present but not talked about' to avoid hype-driven poor decisions.

What staffing changes are needed to manage AI responsibly?

Quijano advocates for 'cross-pollination' teams that blend operations, business, and technology expertise. Organizations need professionals who understand metrics that demonstrate business value, implementation requirements, and technology architecture. This represents a shift from siloed roles toward integrated pods with broader coverage capacity that can evaluate when AI genuinely solves problems versus when simpler rules-based solutions suffice.

What is the current challenge with AI agent deployment in logistics?

Leading AI companies report hundreds of thousands of agents deployed across organizations. Many companies are building more agents than needed and facing runaway token spend. The article suggests companies are over-automating with AI when disciplined evaluation of where AI genuinely adds value (versus where rule-based systems suffice) would be more cost-effective and reliable.

Technology & Innovation

High Impact

AI Experimentation Phase Ends: Logistics Firms Face Reality Check

Jul 17, 2026

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The logistics industry is entering a sobering phase after months of AI-driven experimentation. According to Alfonso Quijano, CTO at Lean Solutions Group, companies without established technology infrastructure have burned substantial capital testing AI solutions that ultimately failed to deliver business value. The shift represents a maturation in how the industry thinks about artificial intelligence—moving away from AI-first branding toward treating AI as one component within disciplined automation strategies. ) has not automatically created capable AI-deploying organizations.

Companies are discovering that success requires the same foundational elements as any major technology initiative: rigorous project management, change management discipline, cost controls, and documented processes. The rise and collapse of "vibe coding"—where AI generated software with minimal traditional development oversight—illustrates the danger of speed without substance. According to industry analysis cited, 99% of such hastily-built applications failed commercially. For supply chain leaders, the implications are significant.

Organizations that rushed into AI deployments without operational discipline are now facing the consequences: brittle systems causing business damage, runaway token spend from over-deployed agents, and abandoned projects requiring reversal. The path forward demands treating AI implementations like onboarding new employees—with clear job descriptions, documented SOPs, exception handling, and continuous training. Lean Solutions Group's expansion of governance and cost visibility tools signals market recognition that accountability and measurable outcomes, not adoption speed, now define success.

#ai-implementation

#freight-brokerage

#logistics-automation

#ai-governance

#cost-management

#technology-adoption

#operational-efficiency

#change-management

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Simulation Suggestion

immediate

What if your organization abandons half of its AI projects due to poor ROI?

Simulate the operational and financial impact of discontinuing underperforming AI automation projects, including re-training staff on manual processes, reallocating technology resources, and recovering from process gaps created by partially-deployed systems that must be unwound.

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this month

How would implementing AI governance and cost controls reduce token spend?

Evaluate the potential cost savings and service level improvements from implementing centralized AI governance, cost visibility dashboards, and disciplined agent deployment policies that reduce unnecessary agent proliferation and optimize token usage across your organization.

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Simulation Suggestion

strategic

What if you shifted from AI-first to automation-first strategy?

Model the transition from pursuing AI solutions for every problem toward identifying where rule-based automation suffices versus where AI genuinely adds value. Assess the impact on project success rates, implementation timelines, cost predictability, and production system reliability.

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