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SCADA Engineer

HMI display development and updates

Enhances◐ 1–3 years

What You Do Today

Design and maintain Human-Machine Interface displays for control room operators. Follow ISA 101 high-performance HMI standards to ensure operators can quickly assess system status and respond to abnormal conditions.

AI That Applies

AI evaluates existing HMI displays against ISA 101 standards, identifying information overload, poor color usage, and missing situation awareness elements.

Technologies

How It Works

The system tracks learner progress, competency assessments, and engagement patterns across the learning environment. The automation engine executes each step in the process sequence — validating inputs, applying business rules, generating outputs, and routing exceptions to human review queues. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

HMI redesign projects get accelerated with AI-generated improvement recommendations based on standards analysis.

What Stays

Understanding operator workflows, designing for the specific operating conditions of each utility, and the iterative process of testing displays with actual operators.

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 hmi display development and updates, understand your current state.

Map your current process: Document how hmi display development and updates works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Understanding operator workflows, designing for the specific operating conditions of each utility, and the iterative process of testing displays with actual operators. 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 GE iFIX 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 hmi display development and updates 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 engineering manager or VP Eng

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

They're deciding which AI developer tools to adopt team-wide

your DevOps or platform team lead

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

They manage the infrastructure that AI tools depend on

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.