Question 1

What does the AI Opportunity Scanner actually look at?

Accepted Answer

The scanner looks at the work around a workflow: what repeats, where information gets re-entered or re-explained, which handoffs slow down, and where people lose time or context. It uses your answers to surface opportunities by likely value, friction, and feasibility. It is a directional readout for a conversation, not an automated decision about your organization.

Question 2

What data does a scan touch?

Accepted Answer

A scan starts with the context you choose to provide in the questionnaire: a description of the work, its inputs, its bottlenecks, and the outcome you want to improve. It does not require access to your systems or a broad sensitive dataset. Leave out credentials, secrets, and unrelated personal information; use a reduced, redacted, or de-identified example when detail is useful.

Question 3

What happens after the scan?

Accepted Answer

We review the readout with you, choose the smallest useful opportunity, and define what success and risk look like. If the fit is right, the engagement can move through workflow design, a bounded pilot, implementation, documentation, training, and managed support. Each stage has a decision to continue, adjust, pause, or stop before the next one begins.

Question 4

Do you stay involved through implementation and training?

Accepted Answer

Yes, when the engagement calls for it. We can help turn a promising workflow into a dependable one by configuring the handoff, documenting how it works, training the people responsible for it, and supporting refinements after launch. Managed support keeps the workflow reviewable as the work, tools, and risks change.

Question 5

Where do humans stay in control across the workflow?

Accepted Answer

We name an accountable owner and place review checkpoints around the moments that matter: checking sources, correcting or rejecting an output, handling exceptions, and approving anything consequential before it moves forward. AI may prepare, summarize, draft, or surface options, but it does not silently make a high-impact decision or remove the person responsible for it.

Question 6

How are privacy, retention, access, and model use governed?

Accepted Answer

Before production use, we agree on the smallest useful data boundary, where workflow data lives, who can access it, how long it is retained, and how it is deleted. Inputs and outputs are not silently repurposed to train general models; a different use requires explicit agreement. We also define incident handling and a pause path so governance is part of operating the system, not paperwork left beside it.

Clear answers for careful work.

Good questions make better systems.