Workflow pilots
Build one narrow AI workflow only after the decision is clear.
A pilot is not an open-ended AI project. It is a small implementation around one workflow, real users, real examples, human approval rules, and a measurable operating improvement, so the team can prove value before expanding spend or scope.
Most pilots begin after the Workflow Audit, once the workflow path, data inputs, risks, and success metric are clear.
Narrow scope
One workflow, real inputs, real users, and a clear owner.
API-friendly systems
Start with the systems the team already uses where access, APIs, and permissions allow.
Human checkpoints
Customer-facing, sensitive, or uncertain outputs can wait for review before action.
Measurable outcome
Define before/after metrics such as response time, manual steps removed, review accuracy, and adoption.
Pilot lifecycle
A pilot should create evidence, not momentum for its own sake.
The goal is to decide whether the workflow should expand, change, stay limited, or stop.
Scope
Choose the workflow, user group, inputs, review rules, and metric.
Build
Prototype or build the AI-assisted workflow around real examples and API-friendly systems where feasible.
Test
Run the workflow with real users, edge cases, and human approval checkpoints.
Measure
Compare output quality, time saved, exception rate, and adoption against the starting point.
Decide
Expand, maintain, revise, or stop based on evidence instead of enthusiasm.
Example pilot shapes
Pilot scorecard
Before / after
What changes in a good pilot.
The useful version does not remove judgment. It prepares the work, flags uncertainty, and leaves the right decisions with people.
Before
Requests are read, summarized, routed, and followed up by hand.
After
The system prepares the summary, checks for missing details, recommends a route, and waits for approval.
Before
Weekly updates are rebuilt from scattered systems, files, messages, and notes.
After
The first draft is assembled consistently, with missing inputs flagged for a manager.
Before
Senior staff answer the same internal questions repeatedly.
After
Staff get cited answers from approved sources, with escalation when confidence is low.
How we decide whether the pilot worked
- Did it reduce repeated manual steps?
- Did reviewed outputs meet the agreed quality threshold?
- Did users trust it enough to keep using it?
- Were exceptions and edge cases handled safely?
- Is the workflow worth expanding, maintaining, or stopping?
What we will not pilot
- • No vague chatbot without a defined workflow
- • No unowned process that no one can approve
- • No sensitive automation without review controls
- • No replacement-first framing
After a successful pilot: Systems Partner support
If the pilot works, HighTide can continue as a Systems Partner: monitoring performance, documenting the workflow, training users, tuning prompts and rules, maintaining integrations through available APIs, and expanding carefully where the evidence supports it. If the pilot does not work, it should stop or be redesigned based on evidence.