What counts as "AI automation"
Traditional automation follows fixed if-this-then-that logic. AI automation adds a layer where an LLM makes judgment calls — classifying, extracting, generating, or routing — that traditional rules can't reliably handle. The result: automations that survive messy real-world inputs and don't break every time the format changes.
30 workflows worth automating with AI
Sales & marketing
- Speed-to-lead replies (under 60 seconds, 24/7)
- Lead enrichment and scoring
- Personalized outbound sequences (Clay + Apollo + Claude)
- Inbound chat qualification and routing
- Content repurposing (video → blog/social via YTCrafty)
- Newsletter writing from podcast or video sources
- Meeting preparation briefs (CRM history + LinkedIn + news)
- Call summary + CRM update after every sales call
Customer support
- Tier-1 ticket deflection (40–65% no-human-touch)
- Voice intake for after-hours calls
- Sentiment-based escalation routing
- Self-service knowledge base with semantic search
- Agent-assist (real-time response suggestions)
- Post-call coaching from call recordings
Operations
- Invoice processing and AP automation
- Contract clause extraction and review
- Expense categorization and policy enforcement
- Inventory reconciliation
- Vendor onboarding document collection
- Compliance-document gap detection
Internal productivity
- Slack / Teams Q&A from internal docs
- Meeting transcription + action-item extraction
- Weekly status reports auto-generated from CRM + ticket data
- HR FAQ deflection and policy questions
- Internal-comms repurposing (announcement → email + Slack + intranet)
Finance / billing
- Dunning sequences that respond to context (not just timers)
- Subscription save-saves when customers cancel
- Revenue recognition document review
- Investor-update drafting from KPI dashboards
- FX hedging analysis
- Anomaly detection on transactions
Picking a platform
- Make.com — visual, fast to build, fine for low-volume linear flows
- n8n — same UX, self-hostable, no per-execution fees, our default
- Zapier — simplest, most plugins, breaks at AI scale
- Code (Python + LangGraph) — for complex agentic flows where visual tools hit a wall
- Inngest / Temporal — for durable, long-running workflows that need true reliability
How to sequence your rollout
- Quarter 1: Pick the one workflow with highest volume × highest pain. Build it well. Measure ruthlessly.
- Quarter 2: Add 2 more workflows in the same business function (so you're stacking expertise).
- Quarter 3: Branch into a second business function. Build a small internal ops team to maintain.
- Quarter 4: Audit ROI of everything shipped. Kill the lowest 20%. Double down on the rest.
Most failed AI automation programs failed by building 8 things badly instead of 2 things well. The math favors depth.
Want help sequencing? Run our free AI audit.