Echo's bottom-up AI strategy: how operations teams actually get 70% gains
Most teams add AI to old processes and hope for a lift. Echo Global Logistics learned that barely moves the needle. The bigger wins show up when you redesign the work, not just the tool.
Echo's CIO, Zach Jecklin, put it plainly: pointing AI at decade-old workflows gives you small gains; rethinking the workflow can drive productivity jumps up to 70%. That insight now anchors how Echo runs AI across the business.
The dual strategy: top-down efficiency + bottom-up initiative
Echo runs AI on two tracks that feed each other.
- Top-down automation: AI handles repeatable, high-volume tasks: quoting from shipper emails, building loads from email content, requesting tracking updates, collecting documents, and managing billing. These are the time sinks that scale across teams.
- Bottom-up enablement: 3,000 employees use AI to solve the nuanced, messy work that never makes it into a roadmap. This is where the hidden value lives-customer quirks, carrier preferences, and edge-case tasks that vary by lane and account.
Why bolt-on AI under-delivers
Legacy workflows were built for yesterday's constraints. Automating them as-is often cements inefficiency. Echo found that the real gains come from changing task structure-what's done, by whom, and in what order-then letting AI do the heavy lifting inside that new flow.
That's the difference between shaving minutes and removing steps entirely.
Inside Echo's bottom-up engine
Echo treats internal AI adoption like a portfolio. Many small bets, fast cycles, scale the winners.
- AI champions: Hundreds of trained "enthusiasts" spot opportunities inside their own books of business. They know the work, so they find the friction.
- Rapid prototyping: Hackathons turn ideas into working demos in a day. If it works in the wild, it moves forward.
- Incentives: Monetary rewards and recognition keep the pipeline full. Wins are shared across similar teams to multiply impact.
- Developer collaboration: Engineers shift from requirements wrangling to code review and hardening. They start with prototypes and clear business intent, not vague tickets.
What this changes for operations leaders
You don't need to centralize every AI decision to get results. You need clear guardrails, a fast lane for experiments, and a way to spread what works. Think "enablement and standards," not "command and control."
Echo's approach turns every employee into a process engineer with a toolkit. That's how the long tail of work gets system-level improvement.
Practical playbook: your first 30 days
- Week 1: Pick two high-volume tasks for top-down automation. Define done: accuracy target, average handle time, first-pass yield.
- Week 2: Appoint AI champions in each ops pod. Give them a short training path and a simple safety checklist.
- Week 3: Run a one-day hackathon. Focus on email parsing, exception handling, and document requests-fast ROI areas.
- Week 4: Promote two prototypes to pilot in live accounts. Build a one-page playbook so other teams can copy-paste the win.
Examples that tend to hit fast
- Turn shipper emails into structured quotes and draft replies.
- Create loads from unstructured email content with auto-validation.
- Trigger carrier tracking pings and consolidate responses.
- Collect PODs and invoice docs, then reconcile to TMS records.
- Flag exceptions by account rules instead of generic thresholds.
Cleaner systems, fewer edge-case builds
Echo expects logistics systems to get simpler. As AI handles nuance at the edge, you don't need to hard-code every exception into the TMS. Keep the core clean; let AI manage variability where it actually happens-at the task and account level.
How developer work gets easier
Instead of starting from a ticket and guessing the business need, developers review working prototypes with real inputs and real users. Requirements are tighter, rework drops, and time goes into quality and scale instead of churn.
KPI set for AI in operations
- Cycle time: Quote-to-book and book-to-pickup.
- First-pass yield: Percent of tasks completed without human edits.
- Exceptions per 100 loads: By customer and lane.
- SLA hit rate: On quotes, updates, and docs.
- Automation coverage: Share of volume touched by AI.
- Employee NPS: Especially in pods adopting bottom-up tools.
Governance that doesn't slow you down
- Keep a short approved-tool list and usage guidelines.
- Log prompts, outputs, and data access for audits.
- Review models for bias and accuracy on key accounts.
If you need a reference framework, see the NIST AI Risk Management Framework for practical controls and processes. NIST AI RMF
Want to skill up your AI champions?
A focused training path helps non-technical ops talent ship working prototypes fast. Explore role-based options here: Complete AI Training: Courses by Job
The takeaway
Top-down automation pays the bills. Bottom-up innovation finds the hidden wins. Echo's bet is simple: teach everyone to build, then scale what proves itself under real freight, real customers, real deadlines.
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