Navigating the “AI Fog”: What Fleet Managers Need to Know About Long-Term Planning
If you've felt unusually hesitant lately about committing to a five-year fleet strategy, a major infrastructure investment, or a long-cycle vehicle procurement plan, you're not alone. Harvard Business Review has a name for that feeling: AI fog.
UC Berkeley-Haas professor Toby Stuart coined the term to describe a growing inability to plan confidently for the future as artificial intelligence reshapes industries faster than most organizations can adapt. And while the concept applies broadly, it has sharp implications for fleet operations.
The Problem With Long-Term Commitments
Fleet management has always been a long game. Vehicle lifecycles, contracts, driver training programs, infrastructure upgrades for electrification — these are multi-year, sometimes decade-long commitments. They're built on an implicit assumption: that tomorrow will look more or less like today, only slightly improved.
AI is eroding that assumption at an accelerating pace.
Consider a few questions that fleet decision-makers are already wrestling with: How will autonomous vehicle technology change our workforce? Will AI-powered route optimization render our existing platform irrelevant? Will the regulatory landscape shift dramatically as governments respond to AI-driven changes in transportation and labor?
That's AI fog in action — not pessimism, but a genuine difficulty in underwriting decisions that assume a predictable future.
What Fleet Leaders Can Do
HBR's recommended antidote is a meaningful shift in strategic mindset: move away from rigid long-term planning and instead optimize for optionality. For fleet professionals, that translates into several practical postures:
Stay modular. Where possible, favor flexiblity, scalable platforms, and technology investments that can be unwound or redirected if the landscape shifts.
Reskill continuously. Your team's value — and your own — increasingly lies in adaptability. Invest in training that builds transferable skills around data analysis, AI tool usage, and change management, rather than role-specific expertise that may narrow over time.
Build strategic pivots into your planning. Rather than treating a change in direction as a failure, normalize it. Scenario planning that explicitly accounts for AI disruption — including "what if we need to abandon this approach entirely?" — is prudent.
Monitor, don't predict. Early signal detection matters more than long-range forecasting. Establish regular checkpoints to reassess technology trends, competitive dynamics, and workforce realities rather than committing to a single roadmap and staying the course regardless.
The Bottom Line
AI fog doesn't mean paralysis. It means the old model of "plan far ahead and execute faithfully" is being replaced by something more dynamic — and frankly, more demanding. Organizations that build flexibility into their strategies now, rather than doubling down on certainty, will be far better positioned to absorb whatever disruptions come next.
The fog isn't lifting anytime soon. The advantage goes to those who learn to navigate it.
3 Reasons Your Fleet's Fuel Costs Are Too High — And What to Do About It
Fleet managers face relentless pressure to reduce operating costs, and fuel is almost always at the top of that list. Cutting fuel spend is about understanding where the actual waste is coming from. The data points to three root causes that, left unaddressed, will continue to inflate your fuel bill regardless of what diesel costs at the pump. Here's what they are, why they matter, and exactly what to do about each one.
There's a Better Way to Think About Fleet Safety
Fleet safety is one of those things that's easy to underestimate until you can't anymore. For most organizations, the approach looks something like this: pull records periodically, review anything flagged, handle issues one at a time. It's a reasonable system. But as fleets grow and roads get busier, that approach starts to show its limits — not because anyone is doing anything wrong, but because the complexity of managing driver risk has quietly outgrown the tools most teams are working with.