Published: 22 December 2025

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In our earlier post, we talked about AI's transformative potential for Fleet Management Systems - from better usability to predictive maintenance. But here's the thing: potential and practical implementation are two very different beasts.

Let's dig into what actually happens when you integrate AI into FMS in the real world.

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The Low Stakes vs. High Stakes AI Dilemma

Consumer AI can launch when it's "good but not great." When errors are relatively harmless - a chatbot gives a clunky answer, an image generator makes weird hands - users are pretty forgiving. Annoyance is the worst-case scenario.

But FMS operates in high-stakes territory. A dispatch decision that sends equipment to the wrong location doesn't just inconvenience someone - it costs tens of thousands of dollars in lost production. AI that misinterprets maintenance data doesn't just frustrate a user - it risks equipment failure or safety incidents.

When AI errors can be catastrophic rather than merely annoying, "good enough" takes on a completely different meaning. So the question becomes: what does "good enough" actually mean for high-stakes AI in mission-critical operations?

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What Actually Matters for AI Deployment

Here's what we've been thinking about as we develop AI capabilities:

Performance parity isn't enough. Your AI needs to perform at least as well as human operators - but there's a catch. Remember how self-driving cars crash less than human drivers, yet every incident makes headlines? Same deal here. AI in FMS doesn't just need to match human performance. It needs to exceed it, consistently, to earn trust.

Risk management gets complicated fast. Help desks and software assistants can hand off to humans when they hit a wall. Pretty straightforward safety net. But what happens when AI is guiding a client through complex system configuration?

Think about it: if the AI misinterprets what a client's trying to achieve, or gives incorrect advice, we're talking real financial losses. And guess who's potentially liable? The vendor.

Client perception matters more than you'd think. Even when AI performs well, clients might still trust human expertise more. You can't just deploy the technology and hope for the best - you need transparency, robust support options, and clear communication about both benefits and how you're managing risks.

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Taking It Step by Step

Given all this, we're taking a measured, sequential approach at Wenco. Start with lower-risk applications, build trust, then move toward more complex decision support.
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AI-Powered Documentation and Support

This is where it makes sense to start. AI-driven chatbots and support systems can handle routine questions 24/7, with seamless handoffs to human agents when things get tricky. It builds confidence in what AI can do while keeping that safety net in place.

These systems can answer questions about operational behaviour and guide users through features. Basically, making expertise accessible around the clock - not just during business hours in a specific time zone.
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Dispatching Decision Support

Now we're getting into more sophisticated territory.

Sure, mathematical optimization for standard dispatch operations is largely solved. Wenco Dynamic Dispatch handles that. But the real world doesn't operate in a vacuum. Dispatchers constantly juggle competing objectives: production targets, equipment availability, operator preferences, maintenance schedules, unexpected conditions. It's messy.

We're conducting research into new dispatching technology driven by simulation, where AI provides critical decision support. Using AI-driven scenario analysis, the system could help dispatchers navigate complex trade-offs and make better-informed real-time decisions when they're balancing multiple objectives.

The key word there? Helps. This augments human judgment rather than replacing it. Humans stay in control of final decisions - they just get better insights to work with.

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Future Directions: Extending Dynamic Dispatch

Looking ahead, the next step for AI in FMS is to extend the existing dynamic dispatch to decrease the number of haul cycles that require human intervention. Here are the key opportunities:

  • Adding support for corner cases that prevent the use of automated dispatch on certain haul routes today (e.g. customized blend at dump points)
  • Improving handling of dynamically changing mine conditions (e.g. rerouting due to shovel down)
  • Improving operator trust, freeing dispatchers to go "hands-off" and focus on other aspects of their job
  • Demonstrating consistent reliable automated dispatch over time
  • Improving FMS feedback as to why certain decisions are being made
  • Becoming more proactive to alert operators to situations that do require human intervention

Side note: AI is also making serious waves in predictive maintenance through Asset Performance Management solutions. That’s another exciting application we’re scoping to address the critical challenge of reducing unplanned downtime.

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The Bottom Line

Integrating AI into Fleet Management Systems isn't about rushing to deploy the flashiest technology. It's about careful consideration of both opportunities and risks.

Start where AI delivers clear value with manageable risk, build trust and capability, then tackle the more complex challenges.

At Wenco, that means developing intelligent support and dispatching decision support that deliver real value while maintaining the reliability our customers expect from mission-critical systems. As AI technology evolves, its role in FMS will absolutely grow - but success comes from thoughtful deployment that keeps human expertise at the centre while amplifying what's possible.

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Published: 22 December 2025
Last Updated: 22 December 2025

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