The failure patterns we see
- Vague problem. No acceptance criteria or success metric.
- Bad/hidden data. Missing APIs, messy fields, no retrieval plan.
- No owner. Nobody accountable for decisions and unblockers.
- Scope creep. Ten workflows at once instead of one.
- Weak guardrails. No refusal rules, approvals, or audit logs.
- Zero evals. No golden sets or regression checks.
- No change management. Teams don’t know how to operate the AI.
Counter-playbook we deploy
Scope one workflow: Clear inputs, outputs, SLAs, and success metric.
Data readiness: Expose APIs, retrieval indexes, and QA gates.
Guardrails: Refusal rules, approvals, rate limits, audit logs.
Evals: Golden + synthetic tests, regression runs every change.
Rollout: Shadow → supervised → partial autonomy.
Ownership: Clear RACI and weekly checkpoints.
Timeline we recommend
- Week 0–1: Define workflow, metrics, guardrails, and data access.
- Week 2: Build + instrument logging and evals; run shadow mode.
- Week 3: Supervised runs with approvals; tighten prompts/tools.
- Week 4–6: Partial autonomy with alerts; expand after meeting SLAs.
Signals of success
- Automation/deflection rate improving weekly.
- Error and rollback rates trending down.
- Time-to-resolution and unit economics better than baseline.
- Operators trained and comfortable with handoffs.
Shipping AI is an execution and governance exercise, not a model demo. Define outcomes, add guardrails, and prove value fast.
FAQ
What should we automate first?
High-volume, rules-based tasks with clear inputs/outputs and measurable impact.
How do we measure quality?
Golden tests, eval suites, human review on critical paths, and trend dashboards.
What about compliance?
Least-privilege credentials, logging, redaction, and refusal rules for sensitive actions.
How do we avoid vendor lock-in?
Abstract tools/models, keep prompts/evals versioned, and design for swap-ability.