Fractional Talent · Multi location field services
From an AI pilot to an accountable operating plan.
Move a promising AI workflow into regular operations with defined ownership, quality evaluation and a workable support process.
Discuss a similar challenge01 / The challenge
Where the work began.
A field services company had demonstrated an AI workflow that prepared internal job summaries from approved operational records. The pilot showed potential, but extending it across the business raised practical questions.
Branches recorded information differently. Failed runs had no clear owner. Staff were uncertain about which outputs needed review. The company needed an operating plan before teams depended on the workflow.
02 / The approach
What we did.
RFA combined a fractional AI delivery lead with an automation engineer, working alongside branch operations, IT and the workflow’s business owner. The team defined a common input schema, required source fields and exceptions for individual branches.
The processing design separated incoming work from execution through a queue. It included bounded retries, a queue for failed jobs and supported integrations with the existing workflow. These controls formed the recovery and support approach, with unsuccessful runs assigned a visible place for review.
RFA defined a versioned evaluation set covering normal jobs, incomplete records, conflicting notes and examples from different locations. The change process required prompt and model revisions to be recorded and tested before release. Staff review, correction and escalation requirements reflected the consequences of an incorrect output.
The rollout framework connected expansion with checks on failed runs, processing time, review rates and cost per completed item. It kept support ownership and output quality within the same operating discussion.
03 / The scope
What the work covered.
- Workflow input and processing scope
- Evaluation and controlled change scope
- Operational monitoring and rollout scope
04 / The outcome
What the work established
The engagement established an operating framework covering input standards, processing and recovery, quality evaluation, controlled changes, staff review and a staged rollout.
It also established a framework for assessing adoption, output quality and support effort. The time assessment framework included the effort required for review, corrections and maintenance.
The demo made the opportunity clear. Running it across the business raised a different set of questions: who checks the output, who handles a failed run, and who approves a change? We needed clear answers before asking teams to depend on it.