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business-streamlining-playbooks

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Business-Streamlining Playbooks

Rachel Woods runs a 10-person AI operations agency where zero humans do sales outreach and follow-up, manage the inbox, report on marketing campaigns, or build marketing funnels — the work still gets done, on time, every week, because every recurring process is a playbook (see playbooking-method). This note is a working list of playbooks worth building for a solo or small AI consulting practice, plus prompts for what to define before writing each one.

Why this matters

Sales, marketing, ops, and reporting are where most consulting practices lose hours that should be billable or invested in delivery. Each item below is a candidate to remove from the human todo list. The pattern is always the same — define the four components (playbooking-method: trigger, inputs, steps, outputs) and pick a host (Claude Skill, custom GPT, Copilot agent, Gemini Gem, Zapier).

Rachel’s Stack (Use as Templates)

1. Inbox-zero playbook

  • Trigger: every morning at 8:00 AM.
  • Inputs: incoming emails since the last run.
  • Steps: categorize by urgency → decide response posture → draft replies → check calendar and propose/send meeting times.
  • Output: inbox triaged; drafts ready to skim and send.

2. Campaign launch playbook

  • Trigger: a new offer is greenlit.
  • Inputs: the offer description, audience, dates.
  • Steps: draft landing page → draft email sequence → draft lead magnet outline.
  • Output: launch-ready assets in one pass.

3. Post-campaign performance analysis

  • Trigger: campaign end-date.
  • Inputs: campaign metrics dump.
  • Steps: run analysis against historical baseline → extract key takeaways → recommend next actions.
  • Output: campaign post-mortem with a “do next” list.

4. Sales-lead personalization

  • Trigger: new qualified lead.
  • Inputs: lead profile, source, expressed interest.
  • Steps: research lead → match to relevant offer → draft personalized outreach.
  • Output: personalized first-touch ready for review.

5. Post-call client coaching

  • Trigger: end of any client call.
  • Inputs: call transcript + the coaching rubric (“how we want to show up for clients”).
  • Steps: score the call against the rubric → flag misses → suggest concrete improvements for next call.
  • Output: a self-coaching note before the next session.

6. Project-planning playbook (Rachel’s favorite — the one she contributed as a bonus)

  • Trigger: a new project (idea, brief, or request).
  • Inputs: just a description of the project. Low-friction on purpose.
  • Steps: clarify with questions → brainstorm approaches → pick the best → build timeline and tasks → start executing the AI-owned tasks.
  • Output: planned project + work already underway. Credited with 3× project throughput.
  • Why it matters: this is the keystone playbook — it spawns the tasks every other playbook in your stack executes (campaign launch, inbox-zero, sales personalization).

→ Full breakdown with phase-by-phase detail, question lists, success criteria, and the client-work adaptation lives in project-planning-playbook.

These can be stacked into a personal operating system — playbooks calling playbooks (morning routine → strategy → projects → ideas → tasks → life admin). That’s the end state to work toward, not the starting point.

Candidate Playbooks for an AI Consulting Practice

Inspired by Rachel’s set, biased toward client-facing work:

  • Discovery-call playbook — trigger: scheduled discovery call. Inputs: prospect’s company, role, expressed problem. Steps: pre-call research → tailored question list (driving toward the four playbook components) → post-call summary → proposal-shaped notes. Output: a discovery brief + a draft proposal angle.
  • Proposal playbook — trigger: discovery brief approved. Inputs: discovery brief, pricing tiers. Steps: scope → deliverables → timeline → SOW → cover note. Output: send-ready proposal.
  • Client-onboarding playbook — trigger: signed SOW. Inputs: client details, project type. Steps: kickoff agenda → access/credentials checklist → intro email → calendar holds. Output: client onboarded in <24h.
  • Weekly client status report — trigger: every Friday. Inputs: this week’s commits, calls, decisions. Steps: summarize progress → flag risks → list next-week commitments. Output: same-format report each client expects.
  • Playbook-discovery playbook (meta) — trigger: new client engagement. Inputs: client’s calendar, SOP folder, todo list, wish list. Steps: scan each of Rachel’s four hunting grounds → score candidates by hours/month and reversibility → return a prioritized backlog. Output: the engagement’s roadmap.
  • Content-from-client-work playbook — trigger: end of a notable engagement. Inputs: anonymized case notes. Steps: extract the lesson → draft post / newsletter / case study. Output: marketing artifact built from delivery work (turns delivery into top-of-funnel without dedicated marketing time).
  • Lead-qualification playbook — trigger: inbound lead form. Inputs: form data + LinkedIn/website. Steps: score fit → route (book call / nurture / decline) → draft response. Output: every lead handled in minutes, consistently.

How to choose what to build first

Apply the architect lens before writing any of these:

  1. Simplicity — what’s the smallest version of this playbook that could work end-to-end? Build that, not the full thing.
  2. Cost of change — playbooks are cheap to revise; bias toward shipping the v1 and editing in production.
  3. Blast radius — start with playbooks where a bad output is recoverable (drafts you review) before automating anything that sends to a client unattended.

Threshold rule from Rachel: anything recurring that costs >4–6 hours/month is worth playbooking now.

  • playbooking-method — the four-component framework these all sit on top of.
  • architect — prioritization lens for which playbooks to build first.

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