2025 broke the speed-first myth in last-mile

2025 put a spotlight on something last-mile teams have felt for a while: fast is great, but predictable is what scales. The past year didn’t magically make delivery simpler, it made expectations sharper. Buyers still want speed, but they’re increasingly optimizing for reliability, cost control, and fewer “where is it?” moments across the full journey (dispatch > delivery > exception handling > recovery).

Last-mile is entering a new phase: not “more tools,” but better decisioning. The teams winning aren’t the ones stacking carriers or dashboards—they’re the ones building an operating model that reduces variance, recovers fast when reality hits, and learns from every delivery.

What you’ll get in this post:

• The assumption that quietly died in 2025
• What we said “no” to and why it mattered
• The operator mistake that creates hidden cost
• Where AI is genuinely useful today vs. where it’s still hype
• Why orchestration is an operating model (not a feature)
• The root causes behind failed deliveries—and how to reduce them
• When adding providers makes outcomes worse
• A contrarian bet for 2026

What’s one assumption buyers had 12 months ago that’s no longer true?

Salman

Claim: “Speed wins” used to be the default. In 2025, buyers started treating reliability and cost control as equally important.
Why it matters: A missed delivery costs more than a slower one: reattempts, support tickets, refunds, and brand damage.
What to do about it: Design your delivery promise around consistency: tighter confidence in ETAs, fewer exceptions, and faster recovery when something goes off-plan.

Shaban

Claim: Buyers assumed adding more delivery options automatically reduces risk. Often, it increases variance.
Why it matters: More providers can mean more systems, more edge cases, more exception pathways unless you’re orchestrating with rules, scoring, monitoring, and recovery.
What to do about it: Treat provider growth like a product rollout: add providers alongside controls (performance scoring, clear SLAs, exception playbooks, and automated recovery paths).

What did we say “no” to this year that made everything else possible?

Salman

Claim: No to one-off custom builds that don’t compound; yes to reusable building blocks that scale.
Why it matters: One-off logic creates “tribal knowledge” software. It slows every future launch and multiplies maintenance.
What to do about it: Standardize the primitives (rules, preferences, triggers, exception categories) so you can adapt quickly without rebuilding everything from scratch.

Shaban

Claim: No to shipping features without instrumentation.
Why it matters: If you can’t measure success rate, exception rate, and recovery time, you can’t improve; you end up debating opinions instead of operating a system.
What to do about it: Make measurement part of the definition of “done.” Every workflow should have a baseline, a target, and a feedback loop.

What’s a mistake we’d warn other operators about?

Salman

Claim: Overpromising the delivery experience before the exception-recovery muscle exists.
Why it matters: Tight windows and aggressive ETAs amplify complexity. Without recovery, the system breaks under normal variability (traffic, access issues, recipient availability).
What to do about it: Build recovery first: define exception categories, escalation rules, customer comms templates, and time-to-recovery targets, then tighten the promise.

Shaban

Claim: Treating exceptions like a support queue instead of a product surface.
Why it matters: If exception handling is manual, every growth bump becomes a headcount problem.
What to do about it: Productize exceptions: detect early, route to the right resolution path, and automate the first response wherever possible.

Where AI helps vs. hype in last-mile

Salman

Claim: AI earns its keep when it improves specific operational decisions, not when it’s treated like magic.
Why it matters: Last-mile is a decision engine. The best outcomes come from better choices at the moments that matter: who to dispatch, how to route, when to reroute, and how to recover.
What to do about it: Use AI where it can add leverage: dispatch/provider selection, dynamic routing, ETA adjustment, early exception detection, and recommending the best recovery path before the customer complains.

Shaban

Claim: AI is strong at prioritization and prediction; weak when it’s expected to replace fundamentals.
Why it matters: If your inputs are messy (addresses, instructions, scan events), AI can’t “intelligence” its way out.
What to do about it: Pair AI with operational hygiene: clean address data, clear SOPs, consistent events, and disciplined provider performance management.

AI helps today:

• Dispatch / provider selection
• Dynamic routing + stop sequencing
• ETA adjustment (based on real-time signals)
• Early exception detection
• Recovery recommendations (reroute, reattempt, refund, comms)

AI is still hype when it claims to:

• “Set it and forget it” fully autonomous delivery ops
• Replace clean data, SOPs, and disciplined provider management
• Fix broken processes without changing the underlying operating model
  
  

The biggest misconception about “orchestration” in last-mile

Salman

Claim: Orchestration isn’t a feature; it’s an operating model.
Why it matters: If outcomes aren’t feeding back into future decisions, you’re not orchestrating, you’re broadcasting orders.
What to do about it: Build a loop: decide > execute > learn, and make “learn” change tomorrow’s dispatch.

Shaban

Claim: Orchestration isn’t “one API to many providers.” That’s table stakes.
Why it matters: Real orchestration is controlling variance: normalizing SLAs, measuring performance, managing exceptions, and protecting the customer promise even when the network is chaotic.
What to do about it: Define orchestration in terms of outcomes: lower failure rate, faster recovery, tighter ETA accuracy, and less manual touch.

The 3 most common root causes of failed deliveries (and what to do about them)

Root causes

1. Recipient unavailable
2. Incorrect/incomplete addresses or poor geocodes
3. Tighter promised windows + operational complexity (more constraints, more edge cases)

Shaban

Claim: Many “failed deliveries” are actually exception handling failures; the issue was recoverable, but the system detected it too late or escalated it too slowly.
Why it matters: Late detection converts small issues into expensive outcomes (reattempts, refunds, support burden, churn).
What to do about it: Treat recovery speed as a core metric, not a nice-to-have.

What to do about it:

• Address validation: normalize formats, validate unit numbers, confirm geocodes
• Better instructions: collect access notes, gate codes, and delivery preferences upfront
• Proactive comms: set expectations early; notify when ETAs shift, not after failure
• Exception SLAs: define resolution paths by exception type (and automate first actions)
  

When does adding more providers make things worse, not better?

Salman

Claim: When it increases variance faster than it increases capacity.
Why it matters: Inconsistent scans, inconsistent SLAs, and inconsistent support paths equal more ways to break trust.
What to do about it: Score providers, enforce rules, and design fallback logic. More providers without controls is just more unpredictability.

Shaban

Claim: When volume gets fragmented across too many options.
Why it matters: You lose leverage, performance becomes noisy, and your ops team becomes the human adapter between half-integrations.
What to do about it: Consolidate intelligently: route volume to the best-performing providers, keep backups, and avoid spreading orders so thin that performance data becomes meaningless.

Our contrarian bet for 2026

Salman

Claim: The next wave isn’t “AI everywhere.” It’s AI that’s accountable, tied to outcomes.
Why it matters: Buyers don’t need prettier dashboards. They need fewer reattempts, faster recovery, tighter promise windows, and better ETA accuracy.
What to do about it: Evaluate AI by operational metrics: success rate, exception rate, recovery time, and customer experience outcomes, not novelty.

Shaban

Claim: The best last-mile stack won’t be the one with the most features; it’ll be the one that reduces operational decisions while improving the promise.
Why it matters: Complexity is the quiet tax on growth. The goal isn’t to do more work faster; it’s to need less work to get reliable outcomes.
What to do about it: Invest in systems that remove manual touches through better decisioning, clearer workflows, and reliable recovery paths.

If 2025 taught anything, it’s that last-mile doesn’t reward optimism, it rewards operating models. Speed still matters, but the winners are building for predictability: fewer failures, tighter confidence in ETAs, and faster recovery when reality hits.