TL;DR
If your team is “using AI” but nothing feels faster, cleaner, or more profitable, you don’t have an AI problem—you have an implementation problem. This 90-day plan gets you from experiments → repeatable systems.
Most teams aren’t behind on AI because they don’t know what tools exist.
They’re behind because they’re trying to duct-tape “AI outputs” onto a messy business.
If your ops are chaotic, AI doesn’t fix that. It scales it.
What you want is an AI implementation plan that turns your most repetitive, expensive, or error-prone work into standardized workflows your team actually uses.
Here’s a straightforward 90-day plan that works for lean teams.
Week 0: The “Stop Doing Random Stuff” Audit
Before you automate anything, you need clarity on what’s worth automating.
Make a list of the top 15 recurring tasks across:
- Marketing production (content, ads, email, creative briefs)
- Sales ops (lead triage, follow-ups, proposals)
- Customer success (onboarding, FAQs, support tickets)
- Internal ops (reporting, meeting notes, documentation)
Then tag each task:
- Frequency: daily / weekly / monthly
- Pain level: annoying / expensive / risky
- Inputs: what triggers it, what data it needs
- Output: what “done” looks like
Pick 3 candidates with:
- High frequency + clear inputs/outputs
- Low legal/compliance risk
- A human review step (for now)
That’s your AI implementation backlog.
Days 1–30: Standardize Before You Automate
AI can’t “follow the process” if the process only exists in someone’s head.
Deliverables in the first 30 days:
- Workflow maps (simple is fine)
Trigger → steps → owner → tools → output - Definition of Done (DoD) for each workflow
Examples:- “Email draft is done when it matches brand voice, has one CTA, and includes 3 subject lines.”
- “Report is done when it includes spend, CPA, ROAS/ROI, learnings, next actions.”
- Prompt + template library
One place your team can copy/paste:- Brand voice rules
- Offer positioning
- FAQs and common objections
- Approved claims language
- Content and ad frameworks
This is the part teams skip.
And then they wonder why AI “doesn’t work.”
Days 31–60: Build the AI Workflows (Human-in-the-Loop)
Now you can implement AI like an operator—not a hobbyist.
Pick your first 3 workflows and create:
- A standard prompt (with examples + constraints)
- A review checklist (what humans verify)
- A handoff step (where it goes next)
Example workflow: Content → Email → Social
- AI generates: outline, draft, hooks, CTA variations
- Human edits: accuracy, tone, positioning, legal claims
- System outputs: publish-ready post + repurposed assets
If you’re using automations (Zapier/Make/n8n), start with “assistive automation”:
- Trigger: new idea added to content board
- Action: generate outline + briefing doc
- Output: draft appears in a doc for review
The goal isn’t full autonomy yet. It’s reliability.
Days 61–90: Operationalize (So It Doesn’t Die on a Tuesday)
This is where implementation becomes adoption.
What to lock in:
- Governance rules
- What AI can do without approval
- What requires human review
- What is prohibited (privacy, customer data, etc.)
- Training + documentation
- “How we use AI here” SOP
- Where prompts/templates live
- Who owns updates
- Metrics that matter
Pick 2–3 per workflow:
- Time saved per task
- Cost per output (or labor hours reduced)
- Error rate / rework rate
- Cycle time (idea → published, lead → proposal, ticket → resolution)
AI ROI is rarely one big number. It’s a pile of small operational wins that compound.
Common mistakes (so you don’t step on rakes)
- Automating a broken process
- Letting everyone “do their own thing” with prompts
- No review checklist → quality drifts fast
- No owner → workflows rot
- Measuring “AI usage” instead of business outcomes
If you want help implementing this fast
If you’d rather not spend 6 months “testing tools,” I do Done Day builds where we pick 1–3 workflows and implement the system end-to-end (templates, prompts, automations, governance, training).