Diesel Prices Are Falling: How AI Helps You Win

WTI and Brent both slipping under $60 per barrel (for the first time since early 2021) is more than an oil-market headline—it’s a budgeting problem and a planning opportunity for transportation and logistics teams.

Here’s the frustrating part: your fuel surcharge and retail diesel benchmark don’t move at the same speed as futures. So even when the market sells off hard, most fleets and shippers feel the benefit later—and unevenly. That lag can create pricing whiplash, missed procurement windows, and bad routing decisions that look “fine” until you see the month-end fuel line.

This post is part of our AI in Supply Chain & Procurement series, and I’m going to take a clear stance: fuel volatility isn’t the enemy—operating blind is. If you’re still managing fuel costs with weekly averages and spreadsheets, you’re donating margin to competitors who’ve instrumented their network with predictive analytics.

What the diesel drop really means for logistics budgets

The practical takeaway: diesel is falling, but the signal you’re probably using is delayed. That’s not a theory—it’s visible in the current spread between wholesale/futures and the retail benchmark used in many surcharge programs.

According to the reported market data this week:

• The DOE/EIA average retail diesel fell 5.8 cents to $3.607/gal, the fourth straight weekly decline, for a 26.1-cent drop over four weeks.
• Over roughly the same period, ULSD futures fell far faster—down about 21.2% from the Nov. 18 settlement ($2.7011/gal) to around $2.1286/gal.
• The “catch-up gap” matters because many contracts, surcharge tables, and customer negotiations are tied to retail benchmarks rather than daily rack or futures.

The lag is where margin gets lost (or found)

When futures collapse and retail benchmarks drift down slowly, three things happen:

1. Carriers can end up under-recovering fuel if their real cost doesn’t match the surcharge index.
2. Shippers can overpay on surcharges relative to actual market conditions.
3. Brokers/3PLs get squeezed in the middle if pricing commitments don’t reflect the timing mismatch.

A simple one-liner you can repeat internally:

Fuel volatility doesn’t just change your costs—it changes who has the better timing.

Why the oil market is selling off (and why it’s not just geopolitics)

The direct answer: the dominant bearish driver is a multi-year supply-demand imbalance. Talk of a Russia–Ukraine truce can move sentiment, but what’s pushing prices lower is the growing belief that the world is headed into an oil surplus that persists into 2026.

The key numbers shaping expectations:

• The International Energy Agency outlook implies demand growth in the ~830,000 b/d range for 2025, and ~860,000 b/d for 2026.
• Supply growth is projected to outpace demand materially, pointing to a 2026 picture where global supply (~108.6 million b/d) exceeds global demand (just under ~104.8 million b/d).

That implied gap is why crude benchmarks breaking below $60 feels “structural,” not “headline-driven.” And structural moves are exactly what logistics procurement teams struggle with if they’re only looking in the rearview mirror.

The freight reality: fuel isn’t just a line item

Fuel prices touch:

• Linehaul and last-mile unit economics (especially in dense metro zones with stop-and-go inefficiency)
• Mode decisions (truckload vs intermodal inflection points shift)
• Bid strategy (who eats what risk, and how quickly can you re-rate)
• Service promises (routing, buffer time, and exception handling)

If your organization treats fuel as “finance’s problem,” you’re missing the operational lever: fuel-aware planning is network planning.

Where AI actually helps: turning fuel swings into decisions

The direct answer: AI helps when it converts messy fuel signals into timed, local, operational actions—not just prettier dashboards.

Think of fuel volatility as a forecasting problem with three layers:

1. Market layer: futures, crude benchmarks, macro outlooks
2. Price layer: rack-to-retail pass-through, regional taxes, station networks
3. Operational layer: routes, dwell time, driver behavior, empty miles, customer constraints

Most companies only work layer 2 with a weekly index. The advantage comes from connecting all three.

1) Predictive fuel cost management (beyond weekly averages)

A useful AI approach is a probabilistic forecast of your effective fuel cost by lane/region, not a single national number.

What that enables:

• Surcharge guidance: when to renegotiate, and how to set triggers
• Budget accuracy: finance gets ranges (P50/P90), not fantasy certainty
• Procurement timing: opportunistic buys and hedging decisions become data-driven

Practical model inputs I’ve found deliver value fast:

• ULSD futures + crack spread indicators
• Regional rack prices (where available)
• Lane mix (miles by geography)
• Seasonality (winter blend effects, holiday demand distortions)
• Dwell/idle data (because gallons burned at zero mph still count)

2) AI route optimization that responds to fuel and congestion

When diesel drops, many teams relax. That’s backwards.

Lower fuel cost reduces the penalty for miles—but it doesn’t eliminate the penalty for time. Congestion, detention, and failed delivery attempts remain expensive. AI routing earns its keep by minimizing total cost-to-serve, including:

• miles
• time
• tolls
• probability of delay
• stop density impacts

And here’s the nuance: during fast fuel moves, the “best” route can change because the balance between fuel and labor shifts.

A concrete example:

• If diesel is high, you may favor fewer miles even if the route is slower.
• If diesel is falling and labor is your dominant cost, you may favor faster routes even if they’re slightly longer.

AI can recompute that tradeoff daily (or intraday) using real constraints—not “rules of thumb” that worked last quarter.

3) Better supply chain forecasting when energy signals are noisy

Fuel is a proxy signal for broader economic expectations. When crude sells off hard, it often coincides with shifts in:

• manufacturing sentiment
• consumer demand expectations
• inventory strategies

In the AI in Supply Chain & Procurement context, this matters because demand planning and transportation planning can’t be separated anymore. If you’re forecasting shipments without accounting for macro-driven cost and capacity changes, your forecast is incomplete.

The best setups I see combine:

• demand forecast outputs (shipments by region)
• transportation network simulation
• fuel price scenarios

So your S&OP meeting stops being “what do we think will happen?” and becomes “what do we do if scenario A or B happens?”

A practical playbook for fleets, shippers, and 3PLs

The direct answer: treat falling diesel as a chance to tighten your system—contracts, models, and operating rhythms—before the next swing.

Step 1: Audit your fuel exposure (you can do this in a week)

Create a one-page exposure map:

• What % of spend is tied to a retail index?
• How often do rates reset (weekly, monthly, quarterly)?
• What’s the lag between your actual paid price and your surcharge recovery?
• Where are you most exposed geographically?

If you can’t answer these quickly, AI won’t save you yet—you need the basics instrumented.

Step 2: Add “fuel sensitivity” to lane economics

For your top 50 lanes (or customers), calculate:

• baseline cost per mile
• gallons per mile (or mpg distribution)
• fuel share of total cost

Then build a sensitivity table: what happens to margin at ±$0.25/gal and ±$0.50/gal?

This is where AI forecasting becomes actionable: you’ll know which lanes deserve attention when the market moves.

Step 3: Use AI to set triggers, not guesses

Good trigger examples:

• If futures drop X% in Y days and your retail index hasn’t moved, flag contracts for review.
• If a region’s expected retail pass-through is slow, adjust surcharge guidance by geography.
• If the forecasted fuel range widens, tighten routing constraints (reduce empty miles, reduce idle time tolerance).

AI works best as a decision engine with thresholds and workflows—otherwise it’s a report nobody uses.

Step 4: Don’t ignore last-mile efficiency when fuel is cheap

When fuel is cheaper, leadership sometimes stops caring about small inefficiencies. That’s a mistake.

Last-mile economics are dominated by:

• failed delivery attempts
• route density
• driver time
• customer time windows

AI that reduces exceptions by even a small amount can out-earn the benefit of a fuel drop—especially during peak retail weeks and post-holiday returns (yes, December pain is real).

Common questions teams ask when diesel starts sliding

Will retail diesel keep falling at the same pace as futures?

Not usually. Retail benchmarks typically lag futures moves. The lag length depends on regional dynamics, taxes, and how quickly wholesale changes pass through.

Should we renegotiate fuel surcharge programs when futures collapse?

If your surcharge index lags your real cost (or your customers’ expectations), yes—at least review it. The goal isn’t “win” the surcharge; it’s aligning recovery with reality to prevent relationship damage and margin surprises.

Is AI worth it if we’re a mid-sized fleet or a regional 3PL?

Yes, if you start narrow: forecast fuel exposure on your top lanes, connect it to routing and pricing triggers, and expand from there. The ROI comes from fewer bad decisions, not from building a perfect model.

What to do next while diesel is trending down

Diesel price declines feel like relief, but they also hide risk: they tempt teams to stop paying attention. The operators who come out ahead are the ones who use this window to harden their playbook—pricing discipline, fuel-aware routing, and scenario-based forecasting.

If you’re following our AI in Supply Chain & Procurement series, this is the recurring theme: AI pays off when it’s connected to procurement and operations at the same time. Fuel is one of the cleanest places to prove it because the dollars are big, the signals are frequent, and the “before vs after” is easy to measure.

When the next fuel swing hits (and it will), will your team be reacting to a weekly average—or acting on a daily forecast tied to real decisions?