Start Small, Think Big.

SSTB.ai native learning

Make one workflow better.

Practical, owned learning for operators who want to use AI with clearer boundaries, human judgment, and an observable finish line.

Public learning path

AI Workflow Foundations

A short public path for choosing one repeatable workflow before adding tools or automation.

01

Course overview

AI Workflow Foundations

A short public path for choosing one repeatable workflow before adding tools or automation.

Start with one recurring job, not a shopping list of tools. This path helps you choose work that is frequent enough to improve, bounded enough to test, and important enough to measure.

You will leave with a workflow statement and a first-pass division of responsibility between people and AI.

02

Module

Workflow Triage

Narrow a broad automation idea into one repeatable workflow with an observable finish line.

Good candidates happen repeatedly, have recognizable inputs and outputs, and can tolerate a staged rollout. Avoid starting with rare crises, unclear ownership, or work where a plausible-looking mistake creates immediate harm.

The two lessons move from selection to responsibility mapping.

03

Lesson

Choose One Repeating Workflow

Use frequency, clarity, consequence, and ownership to select a useful first workflow.

Score each candidate from one to three on four questions:

  • Does it happen at least weekly?
  • Can you recognize when it is finished?
  • Can a person review the result before consequences occur?
  • Is one person accountable for the workflow?

Prefer the candidate with the strongest total and the clearest owner. Then write:

When [trigger] happens, [owner] turns [input] into [result], and we know it worked when [signal].

Keep the first version small enough to run manually. Automation comes after the workflow is legible.

04

Lesson

Map Human and AI Steps

Separate judgment, generation, verification, and approval before automating the workflow.

For each step, assign one role:

  • Human judgment: chooses goals, exceptions, tradeoffs, or consequences.
  • AI assistance: drafts, classifies, summarizes, or transforms within explicit bounds.
  • Human verification: checks facts, permissions, tone, and policy before use.
  • System action: records or sends an approved result with durable identity and recovery.

Mark sensitive inputs before testing. Replace them with synthetic data until the workflow has an approved data boundary. Stop when ownership is unclear, a required source is unavailable, or an output cannot be verified.

Use the companion checklist to review the complete map.

05

Field checklist

Workflow Triage Checklist

A compact review for workflow scope, responsibility, data safety, verification, and recovery.

  • [ ] The workflow has a recognizable trigger and finish line.
  • [ ] One person owns the result and exceptions.
  • [ ] AI tasks are bounded; judgment and approval remain explicit.
  • [ ] Synthetic data is sufficient for the first test.
  • [ ] Sensitive inputs and prohibited uses are named.
  • [ ] Every generated result has a verification step.
  • [ ] Failure is visible and does not silently advance the workflow.
  • [ ] The team can stop, retry, or return to the manual path.
  • [ ] A useful result and one measurable signal are defined.