Stop Doing Science Projects: The 90-Day Pilot-to-Program Playbook for Automation

Start fast with a clear objective and it keeps pilots from dragging out too long

Why pilots stall (and how to avoid it)

Great demos die when they don’t connect to business outcomes, people, or the existing stack. The usual culprits: fuzzy success metrics, “shadow IT” integrations, unclear safety/approval rules, and no plan to scale beyond the first lane or cell. You can fix all of that with a tight, time-boxed approach.

The 90-day playbook

Days 0–15: Aim small, aim true

• Pick one high-friction loop: dock-to-stock pallet moves, assisted picking, vision-QC at one station, or returns triage.

• Write a one-page success spec: goal metric, constraints (space, IT/security, shift patterns), safety rules, and who can approve escalations.

• Baseline KPIs: throughput/hour, errors per 1,000, labor minutes/order, incident rate.

Days 16–60: Prove it—people first

• Deploy a minimal slice: 1–2 AMRs, one cobot cell, or a digital-work-instruction station.

• Orchestrate, don’t bolt-on: connect to WMS/MES/WES so work releases automatically and status flows back.

• Instrument everything: auto-logs for cycle times, interventions, and downtime; daily huddles to remove blockers.

• Train for new roles: “flow lead,” “cell tech,” and “first responder” with clear handoffs.

• Trust layer: guardrails for tool/robot behavior, approval gates for higher-risk actions, and an incident playbook.

Days 61–90: Commit or cut

• Compare to baseline: publish the delta in a single slide (throughput, quality, safety, labor).

• Lock TCO/ROI: include spares, supervision minutes, charge/maintenance time, software, and integration.

• Prepare the scale kit: repeatable layout, traffic rules, SOPs, IT templates, training, and SLAs.

• Roll to 2–3 sites/areas with a standard change plan and fixed implementation calendar.

KPI tree you can steal

• Throughput & service: picks/hour, lines/hour, dock-to-stock, on-time order %

• Quality & safety: errors per 1,000, near-misses, first-pass yield

• Labor & cost: labor minutes/order, cost per resolved task, downtime minutes/shift

• Adoption: operator NPS, training completion, intervention rate

Governance that wins audits and hearts

• Safety & compliance: UL/OSHA-aligned checklists, e-stops, exclusion zones, signage, ADA awareness.

• Model & rules transparency (for AI flows): what the assistant/robot may do, when to ask, when to stop.

• Observability: dashboards for performance and cost with versioned configs so you can roll back safely.

For OEMs vs. end users

• OEMs: package outcome-based offers (cycle-time, error-rate commitments), publish API/reference architectures, and pre-bake safety docs to shorten procurement.

• End users: insist on multi-vendor-friendly orchestration so you can scale without lock-in; negotiate price breaks tied to uptime and service SLAs.

Common pitfalls (and the fix)

• “Pilot purgatory.” Fix with a decision date at Day 75 and prewritten “go/no-go” criteria.

• Under-instrumented pilots. Treat data capture as a deliverable, not a nice-to-have.

• Ignoring people. Budget time for role design, training, and a visible feedback loop.