The Five Questions

We use these questions to evaluate every workflow before recommending automation or AI.

1

Is the process stable enough to standardize?

Automation requires consistency. If the workflow changes weekly, automation becomes a maintenance burden.

We look for processes that have settled. The steps are known. Exceptions are rare. Operators agree on how it should work.

If the process is still evolving, we stabilize first. Automation comes second.

2

Where is time actually being lost today?

Most teams guess where time is lost. Operators know. We ask them.

We map the workflow step by step. We time each step. We identify bottlenecks. We separate real delays from perceived ones.

We automate where time is actually lost, not where it feels slow.

3

What data exists and where does it break down?

Automation depends on data. AI depends on better data. If the data is incomplete or inconsistent, automation fails.

We trace data from source to destination. We identify gaps. We find where manual entry introduces errors. We document what is reliable and what is not.

If data quality is poor, we fix that before building on top of it.

4

What controls are required if something goes wrong?

Automation removes human oversight. That creates risk. We identify what could go wrong and how to catch it.

We build monitoring. We set thresholds. We create fallback procedures. We ensure operators can override the system when needed.

Automation without controls is reckless. We do not deploy recklessly.

5

Does AI add measurable leverage here or just complexity?

AI is useful when decisions require pattern recognition at scale. Most workflows do not require this.

We evaluate whether AI provides measurable improvement over simpler automation. If a rule-based system works, we use that instead.

We deploy AI only when it adds clear value. We do not use it to justify budget or create impressive demos.

How we apply this framework

We ask these questions for each workflow. One at a time. The answers determine whether we automate now, stabilize first, or recommend a different approach entirely.

The result is a roadmap built on evidence, not assumptions. Implementations that match the workflow. Progress that can be measured honestly.