Choosing What to Automate (and When)

Most operational debt doesn’t come from the things we failed to automate.
It comes from the things we automated too early.

The last few years turned “automation” into an unquestioned virtue. Every SaaS demo promises fewer clicks and more flow. Every team has a Notion page of “automation ideas.” The problem is that most of those ideas describe friction, not failure. They target human annoyance rather than system instability.

Before you connect another webhook or drop an AI agent into production, stop and ask the one question that decides whether automation creates leverage or chaos:

Does this workflow already behave reliably when a human runs it?

If the answer is no, automation will only hide the mess behind faster code.

The Automation Mirage

Organizations often automate for optics, not for outcome.
It’s an understandable impulse — speed looks good on a dashboard. A new trigger firing every minute feels like progress. But an automated mess is still a mess; it just runs on schedule.

In RevOps, this shows up as:

• Lead routing rules that duplicate or drop records.
• Auto-syncs between CRM and marketing tools that overwrite human corrections.
• LLM “assistance” drafting follow-ups from dirty data.

Automation amplifies whatever already exists — accuracy or confusion. That’s why “instrument before you automate” isn’t a slogan; it’s survival.

The Two-Layer Test: Impact and Reliability

Before automating any workflow, check two independent layers:

You need both layers green.
High impact but low reliability? Instrument it first.
Low impact but reliable? Keep it manual or delete it.
Automation only pays off in the quadrant where work is reliable and material.

Step-Zero Instrumentation

Automation is measurement’s reward, not its substitute.
Before you touch a workflow tool, capture these four numbers manually:

1. Volume – how often does this process run per week?
2. Effort – how many minutes of focused human time per run?
3. Error rate – how often does it break or require rework?
4. Lag – average time from trigger to completion.

Collect this for a week or two. You’ll know exactly where automation buys time instead of just moving it.

If a step is rare, quick, or brittle, it’s a bad candidate.
If it’s frequent, boring, and predictable, it’s a perfect one.

The Decision Ladder

Think of automation as a five-rung ladder. Each level carries different risk and visibility.

1. Manual – Humans do it end-to-end.
2. Assisted – A tool helps but requires judgment (templates, macros).
3. Triggered – Fully automated start; manual review mid-flow.
4. Managed – Full automation with logging, owner, rollback.
5. Autonomous – Automation that adapts to context (AI or dynamic rules) with self-audit.

You only climb one rung when you can prove the previous rung’s reliability.
Skipping rungs looks efficient but costs more later when you debug an invisible robot.

The ROI Matrix

You don’t need a finance model; you need a 2×2 grid.

Axis 1: Frequency × Effort
How often and how long does the task take?

Axis 2: Pain × Risk
What’s the cost of error or delay?

Low Frequency / Low Pain   → Ignore or delete
High Frequency / Low Pain  → Automate early
Low Frequency / High Pain  → Add guardrails, not automation
High Frequency / High Pain → Automate carefully with owner & rollback

Overlay a simple scorecard:

Anything over 45/60 is a strong candidate; below 35 stays manual.

Failure Modes: Bad Candidates That Look Good

1. Data creation tasks
   Automating input creation (summaries, notes, follow-ups) before you trust your data quality just spreads noise faster.
2. Multi-owner steps
   If success depends on three teams updating the same record, automation will become an endless blame router.
3. Low-frequency, high-variance work
   Example: quarterly account clean-ups. Every cycle brings new exceptions — automation dies on the first edge case.
4. Undefined triggers
   If you can’t state exactly when the clock starts, you can’t trust the automation.
5. No rollback
   Anything touching customer or revenue data must have a rollback plan. If your workflow doesn’t, it’s a grenade with a cron job.

The Governance Loop

Automations, like people, need performance reviews.

1. Runbook-First: Every workflow gets a one-page Notion entry — owner, SLA, trigger, inputs, outputs, failure modes, rollback link.
2. 3T Gate: Target, Trigger, Trace — the sanity check before deployment.
3. Prune Friday: Weekly 30-minute audit to delete, merge, or simplify rules.
4. Metrics: Track SLA hit rate for each automated step. If it falls below 90%, investigate or roll back.

Governance doesn’t slow you down. It keeps you from scaling entropy.

A Walk-Through (Generic Example)

Let’s apply the framework to a common RevOps scenario: assigning inbound leads.

Step 1: Instrument

• 320 leads per week
• 3 minutes of manual triage each
• 5% error rate (mis-owner or delay)
• SLA: respond within 15 minutes

Step 2: Assess layers

• Impact: High — directly affects speed-to-lead.
• Reliability: Humans succeed 95% when done on time.

Step 3: Ladder & ROI

• Volume × Effort × Pain = 320 × 3 × high = 960 “pain units.”
• Stable inputs: yes (form data).
• Clarity: yes (region, product line).
• Score: 48/60 → automation candidate.

Step 4: Runbook-First

Owner: Ops lead
Trigger: Form submission
Inputs: region, product line
Outputs: CRM owner field
Failure modes: missing data, duplicate entry
Rollback: revert owner via change log

Step 5: Governance

• SLA metric: auto-assignment within 1 minute; human confirmation within 10.
• Weekly exception report: leads without owner after 2 minutes.
• Prune check every Friday to remove rules for inactive reps.

Result: a simple automation with clarity, accountability, and rollback.
If the data schema changes tomorrow, you have one runbook and one switch to flip.

The Hidden Cost of “Set-It-and-Forget-It”

Every automation creates a new dependency surface.
When your CRM schema, API, or tool permissions change, those silent automations break first — often without error messages.

Treat every new automation like an employee:

• It has a job description (runbook).
• It needs onboarding (testing).
• It requires 1:1s (Prune Friday).
• It can be fired (rollback).

If you can’t name who owns an automation, you don’t own the risk.

AI, Autonomy, and the Next Rung

The final rung of the ladder — autonomous automation — is where large language models enter. They promise context awareness, fuzzy matching, and decision support. But they also add opacity and probabilistic behavior.

Before introducing an LLM into your workflows, the rest of your system must already meet three criteria:

1. Instrumentation: Every data path is logged and timestamped.
2. Governance: Every automation has an owner and rollback.
3. Boundaries: Clear separation between suggestion and execution.

If you can’t audit your existing automations, adding a model only makes the mess more eloquent.

That’s the next topic — evaluating large language models for operational use — but here’s the teaser:

Automation is a scalpel.
LLMs are a surgeon.
Before you hire one, you’d better have a clean operating room.

The Automation Readiness Checklist

Ask these before every build:

If you can’t tick all six, keep it manual, instrument it further, or kill it.

Automation is leverage only when it amplifies something healthy.
Otherwise, it’s just faster entropy.

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