Stop digging for answers, just ask your fleet data.
AI is everywhere, but for fleet managers, the real question isn’t “Is AI the future?”
It’s “How can AI actually help me run my fleet better today?”
In this educational webinar, we’ll break down artificial intelligence in plain language, separate hype from reality, and show how fleets are using AI right now to reduce busywork, improve decision-making, and gain faster insight into maintenance and cost data.
You’ll also see a live demo of how Squarerigger uses AI to answer your most important fleet questions instantly, accurately, and without digging through reports.
What you’ll learn
AI in plain English
What AI actually is, what it isn’t, and why it matters for fleet maintenance.
What’s real vs. what’s hype
Where AI is already delivering value for fleets today (and where the industry is still over-promising).
Why data quality matters
Why “garbage in, garbage out” is very real with AI and how fleets can prepare their data to get trustworthy insights.
The truth about predictive maintenance
Why “fully predictive maintenance” isn’t realistic yet for most fleets, and what does work today.
Live demo: Ask your data a question
See how Squarerigger’s AI tools let you ask natural-language questions like:
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Which trucks are overdue for PMs?
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What’s my parts spend this month?
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Show me downtime by location.
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How many of [this part] do we have in stock?
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Which units are costing me the most to maintain?
…and get answers in seconds, using your own maintenance and parts data.
A realistic take on predictive maintenance (no buzzwords)
Predictive maintenance is one of the most talked-about applications of AI... And also one of the most misunderstood.
The reality?
Most fleets don’t yet have enough clean, centralized, consistent data to support true, fully automated predictive maintenance across their entire operation.
That doesn’t mean predictive maintenance isn’t possible; it just means it has to be approached the right way.
What does work today:
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OEM-based preventive maintenance recommendations
Using trusted OEM standards (like MOTOR) to define service intervals and maintenance requirements.
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Fleet-specific context
Applying your operating conditions, asset usage, service history, and parts data, not generic assumptions.
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AI-powered insight on top of real data
Identifying trends, exceptions, cost drivers, and risk areas based on your fleet’s actual performance.
With Squarerigger, fleets combine OEM recommendations with their own maintenance and usage data to build a realistic, practical path toward predictive maintenance without relying on guesswork or black-box AI.
This webinar will explain what that looks like, what’s possible today, and how fleets can move toward smarter, more proactive maintenance over time.
