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JOURNAL — 009 · AI & Automation

# Zapier vs Make vs n8n: An Automation Engineer's Honest Take

AK Areeba Khan, Automation Architect · July 13, 2026 · 4 min read

We build on all three every week. Pricing cliffs, error handling, self-hosting maths and which platform fits which team size — no affiliate links, just invoices.

Choosing an automation platform is choosing what your operations cost at 10x volume — decided today, by accident. Zapier wins on ecosystem and forgiveness, Make wins on branching logic per dollar, n8n wins on self-hosting and data privacy — and the wrong pick costs five figures at scale.

## AI where it's strong, never where it's sued

Generative AI is superb at drafts, summaries, extraction and research compression. It's reliably wrong about facts, citations and anything that must be true. Design workflows around those physics, not around the demo.

In production that means: AI proposes from your data, humans dispose before anything faces a customer or a court. Speed with a gate — not autonomy with a prayer.

The teams getting ROI are unsexy: briefs, reports, triage, first drafts. The teams in trouble automated trust itself.

## The automation readiness audit

1. Name the three tasks your best people do that a template could
2. Field audit: one format per data type, required where it counts
3. Every client-facing automation gets a human checkpoint
4. Logs and failure alerts on — silence is where errors breed
5. Each workflow has an owner who isn't the person who built it
6. Quarterly kill-review: expand, fix, or unsubscribe

## Measure hours, not vibes

Automation ROI is concrete or it's fiction: hours returned, errors caught, response-time improvements, revenue touches that wouldn't have happened. Baseline before, measure after — then decide to expand or kill.

Every workflow logs its runs; every quarter we tally. The survivors of that review are the stack; the rest are subscription archaeology.

If a vendor can't express value in your hours and dollars, the value is their recurring invoice.

> Automate like a pessimist, measure like a CFO.ALIFY automation practice

## Automate the boring, guard the human

The automation projects that fail try to replace judgment; the ones that pay remove transfer work — copying, checking, chasing, formatting. Judgment stays with people; logistics go to machines.

Our intake question is always the same: show us the task your best person hates. That's the first workflow — high frequency, low creativity, and instantly measurable in hours returned.

## Integrations before intelligence

The highest-leverage 'AI project' for many businesses is a boring integration: quotes that write follow-ups, forms that create CRM deals properly, stock that syncs before it oversells. Plumbing before prophets.

Once data moves reliably, the clever layer — scoring, drafting, forecasting — has something real to chew on. Intelligence on top of disconnected tools is hallucination with a budget.

Sequence it: connect, then collect, then compute. Ordering matters more than vendor choice.

> The best bot is the one nobody notices because nothing went wrong.Workflow review, 2026

## Where to start this week

Price your top five workflows on all three platforms at current volume AND at 10x. The gap between those two numbers is your real decision input.

Then keep it honest with a short list:

- Bots that pretend to be human to customers
- Workflows nobody can switch off without the consultant
- Seventeen tools when four would cover the map
- Put the date on the calendar — playbooks without Fridays are just reading

And when the scope outgrows the spreadsheet, that's precisely what our team is for.

## Straight answers

### Is our data safe with these AI tools?

Depends on architecture — we default to processing that keeps client data out of model training (API tiers with zero-retention terms, self-hosted where justified) and we document every tool's data terms in your runbook.

### Will automation replace our staff?

In our deployments it replaces their least favorite hours — the copy-paste, the chasing, the retyping. The humans get redeployed to judgment and customers. Teams that use automation to shrink usually shrink their service quality identically.

### Where should we start with automation?

The worst repetitive task — the one your best person does weekly and hates. It should be high-frequency and low-judgment: intake, routing, reminders, reporting. One clean win funds and justifies everything after it.

AI & Automation ALIFY Journal 4 min read
AK
Areeba Khan
Automation Architect · ALIFY

Builds the workflows that give small teams enterprise leverage. Allergic to manual data entry since 2019.

Keep reading

## More from the journal.

AI & Automation May 28, 2026 9 min read AI Automation Playbook: 7 Workflows Small Teams Should Steal Forget moonshots. These are the seven automations that return 20+ hours a month to teams under 25 people — with the stack, the guardrails and the order to build them in. Read article AI & Automation July 19, 2026 4 min read AI Workflows for Marketing Teams: What's Actually Working Beyond the demo reels: the production AI workflows — brief generation, variant testing, research mining — that survived six months of real client use. Read article AI & Automation July 4, 2026 4 min read Chatbots vs Live Chat: The Conversion Data Nobody Shows You Vendors promise chat lifts conversion 40%. Across our client installs the truth is narrower: where bots beat humans, where they tank trust, and the hybrid that wins. Read article
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