Skip to content

Manufacturing Engineer

Work Instruction Development

Enhances◐ 1–3 years

What You Do Today

Write and maintain work instructions that operators follow — step-by-step procedures with photos, specs, and quality checkpoints. They need to be clear enough that a new operator can follow them on day one.

AI That Applies

AI that generates work instruction drafts from process videos, CAD models, and BOM data. Computer vision that creates step-by-step visual guides from production footage.

Technologies

How It Works

The system ingests production footage as its primary data source. A language model processes the input by identifying relevant context, generating appropriate responses, and structuring the output to match the expected format and domain conventions. The output — work instruction drafts from process videos — surfaces in the existing workflow where the practitioner can review and act on it. The critical details — the 'be careful here because.

What Changes

Work instructions draft themselves from your process video. The AI captures each step, generates annotated images, and creates the document structure you'd build manually.

What Stays

The critical details — the 'be careful here because...' notes, the quality check that catches the defect before it leaves the station, and the tribal knowledge that no video captures.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for work instruction development, understand your current state.

Map your current process: Document how work instruction development works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: The critical details — the 'be careful here because. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support Computer Vision tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long work instruction development takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your VP Operations or COO

Which training programs have the highest completion rates, and which have the lowest — what's different?

They're prioritizing which operational processes to automate

your process improvement or lean lead

How do we currently assess whether training actually changed behavior on the job?

They understand the workflow dependencies that AI tools need to respect

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.