When to Automate

Automation creates value when the work is stable, rules are clear, data is trustworthy, and risk stays controlled. Design the process first, then choose the pattern: workflow, integration, RPA, or AI. Measure results on a clock.

Overview

Not every task should be automated. Look for high-volume, rule-driven work with low variance and clean handoffs. Avoid automating broken designs. Fix the flow, then automate the stable parts.

Decision framework (suitability tests)

Work profile

  • Volume: large and predictable
  • Variance: low; limited paths
  • Latency: faster response matters

Logic & data

  • Rules clear; inputs structured
  • Data quality sufficient (complete/accurate/timely)
  • Systems expose stable APIs or screens

Risk & value

  • Failure cost acceptable; rollback exists
  • Control posture maintained or stronger
  • Net value positive after run/maintain costs

Process & data readiness

Process checklist

  • Current map (BPMN L2/L3) with variants and exceptions
  • RACI and decision catalog with SLAs
  • Controls placed in-flow; evidence locations defined

Data checklist

  • Event timestamps defined; stable IDs
  • Master/reference data owners named
  • Input rules (format, range, lists) documented

Design before automate

Remove rework loops and unclear approvals first. Automation will lock defects in place if the design stays broken.

Pattern selection (fit-for-purpose)

Workflow / Case

  • Route tasks, enforce steps, capture evidence
  • Use for human-centric coordination
  • Model with BPMN/CMMN

Integration (API/ETL)

  • Move/transform data between systems
  • Use APIs or ELT/ETL; prefer APIs over screen steps
  • Document contracts and error handling

RPA

  • UI steps only when APIs absent
  • Stable screens; limited UI change
  • Strong logging; small surface area

AI (LLM/ML)

  • Classify, extract, summarize, draft, or rank
  • Guardrails: policy, prompts, retrieval, redaction, logging
  • Prefer agent-assist over full autonomy for high-risk work

Pattern picker

  • Rules + APIs → integration
  • Rules + no APIs (stable UI) → RPA
  • Human routing + evidence → workflow
  • Unstructured text/images → AI with guardrails

Human-in-the-loop & exceptions

Design

  • Define thresholds for auto-approve vs. review
  • Escalation path; time to cure; ownership
  • Evidence captured at decision time

AI guardrails

  • Usage policy and role scope
  • Input redaction; retrieval from approved sources
  • Logging for prompts, responses, overrides

Risk, controls & policy

Controls to keep

  • SoD and access reviews
  • Approvals at thresholds; audit trail with user and timestamp
  • Change control for bots, APIs, and models

References

  • NIST AI Risk Management Framework — nist.gov
  • ISO/IEC 27001 (security controls) — iso.org
  • COBIT (governance of IT) — isaca.org

ROI & measurement

Value model

  • Time saved × loaded rate (labor)
  • Error reduction × cost of error (quality)
  • Latency gains → revenue or service lift
  • Run & maintain: bot/API/model ops, licensing, support

Proof

  • Baseline 4–12 weeks before change
  • Track lead time, FPY, backlog, and exception rate
  • Publish deltas and keep SPC on at least one KPI

Pitfalls

Automating a bad design

Fix the process first. Automation will harden defects.

Screen scraping over stable APIs

Prefer APIs. Use RPA on UI only when APIs do not exist and UI is stable.

No owner, no rollback

Every automation needs an owner, a kill switch, and a rollback plan.

90-day starter

Days 0–30

  • Pick one flow; map L2/L3; list exceptions
  • Score suitability (work, logic, data, risk, value)

Days 31–60

  • Choose pattern (workflow/integration/RPA/AI)
  • Define HITL thresholds and controls
  • Baseline KPIs; draft ROI

Days 61–90

  • Pilot; track cycle time, FPY, exception rate
  • Install monitoring and change control
  • Publish deltas; plan scale-out

References

  • NIST AI Risk Management Framework — nist.gov
  • ISO/IEC 27001 (information security management) — iso.org
  • COBIT (governance) — isaca.org
  • Lean value-stream mapping (for latency/queue economics) — lean.org
  • OMG BPMN/DMN/CMMN (process, decisions, cases) — BPMN · DMN · CMMN

Automate the stable parts. Guard the rest with clear rules and evidence.

If you want a suitability scorecard and pattern picker, ask for a copy.

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