Capture a Minute: Why Human-in-the-Loop Is Becoming Non-Negotiable in Engineering

Across engineering, construction, and asset-heavy industries, we're seeing an enormous push toward AI-driven workflows. Vendors are racing to embed AI into design review, document generation, compliance checking, and decision support. But there's a fundamental disconnect that many organisations are only now starting to recognise:

AI is probabilistic. Engineering must be deterministic.

And that gap is where things start to go wrong.

The Core Problem: Probabilistic AI vs Deterministic Engineering

AI excels at generating likely outcomes. It predicts, infers, and recommends based on patterns in data. That's incredibly powerful but it's also inherently non-deterministic.

Engineering, by contrast, lives in a world of:

• Standards and compliance
• Traceability and auditability
• Quality gates and approvals
• Clear accountability

When an engineering decision is made whether it's approving a drawing, accepting a model change, or issuing documentation "probably correct" is not good enough.

This is where many AI initiatives begin to falter. Organisations deploy AI too aggressively, assume correctness, and remove human oversight too early. The results can be embarrassing at best and catastrophic at worst.

High-profile examples, such as publicised cases involving one of Australia's largest accounting firms producing reports with unreviewed AI-generated content, have made this risk impossible to ignore. The industry has taken notice.

The Industry Shift: Human-in-the-Loop by Design

The lesson is clear: AI cannot operate blindly inside engineering systems.

Instead, AI must operate with people, not instead of them. This has led to a growing industry focus on Human-in-the-Loop (HITL) architectures:

• AI can recommend
• AI can assist
• AI can prepare
• Humans must decide, approve, and accept responsibility

This is exactly the gap that Capture in MinuteView is designed to fill.

What Is Capture?

Capture is a dedicated human-in-the-loop interaction layer inside MinuteView.

It provides a structured, auditable way for:

• People
• Automated workflows
• Deterministic processes
• AI agents

…to pause, request input, and require explicit human interaction before proceeding.

Whether the trigger is:

• A user requesting a formal review
• A workflow requiring approval
• An AI agent proposing a document modification

Capture ensures that nothing moves forward without the right person seeing it, understanding it, and approving it.

The Capture Inbox: Focus Where It Matters

At the heart of Capture is the Capture Inbox.

Instead of relying on scattered emails, chat messages, or buried system notifications, Capture centralises all required interactions in one place and prioritises them intelligently.

The inbox:

• Automatically ranks high, medium, and low-priority interactions
• Brings critical approvals to the top
• Ensures nothing time-sensitive is missed

Examples include:

• Autodesk Construction Cloud reviews requiring sign-off
• Engineering document approvals
• AI agents requesting permission to modify or publish content
• Workflow checkpoints waiting on human confirmation

Everything that needs a human decision lands exactly where it should.

Deep Review, Not Blind Acceptance

Capture doesn't stop at simple approval buttons.

When deeper scrutiny is required, users can:

• Open documents directly from the interaction
• Compare old vs new versions
• Review AI-suggested changes side-by-side
• Validate compliance, standards, and intent

This makes Capture suitable for real engineering review, not just superficial acceptance.

The system supports engineers doing what they already do review, assess, and approve but in a way that is structured, traceable, and scalable across teams and projects.

Why This Matters More Than Ever

The engineering industry is waking up to a hard truth:

Blind trust in AI is not innovation, it's risk.

As AI becomes more capable, the need for human checkpoints increases, not decreases. The more powerful the automation, the more important it is to control where and how decisions are finalised.

Human-in-the-loop systems like Capture are quickly becoming:

• A governance requirement
• A quality assurance mechanism
• A risk-mitigation strategy
• A competitive advantage

Capture: AI With Accountability

Capture represents a fundamental shift in how engineering organisations should think about AI adoption.

It's not about replacing human expertise it's about amplifying it.

By ensuring that AI recommendations always flow through structured human review points, organisations can:

• Maintain compliance and traceability
• Preserve accountability
• Reduce risk
• Enable confident AI adoption

In a world where engineering decisions carry real consequences, Capture provides the framework to harness AI's power without compromising the rigor and accountability that engineering demands.