Proof-of-Concept Is Dead: Try Business-in-Concept Instead

Australia, Apr 3, 2025

For years, the Proof-of-Concept (PoC) was seen as the safe way to test new technology.

Low investment
Clear scope
Fast timelines
Technical validation

It made perfect sense in the traditional IT project lifecycle.

But when it comes to AI, the classic PoC approach is fundamentally broken.

Here’s why: AI doesn’t just need to work — it needs to create value. And value doesn’t come from technical validation. It comes from solving real business problems in real business environments.

The Problem with the Classic AI PoC

In many AI PoCs, the same pattern plays out:

A model is built in a test environment
Synthetic or small sample data is used
The use case is selected for simplicity, not strategic importance
The business is kept at arm’s length to avoid “slowing it down”

What happens next?

The PoC technically works — but has no path to operationalisation
The use case has no meaningful value story
The business wasn’t engaged — so no one owns it
The initiative dies in the “value gap” between IT and the organisation

It’s no wonder so many AI pilots never make it past the pilot phase.

AI Needs to Be Grounded in Business from Day One

AI is not a tool you plug in. It’s a capability you build. That means the success of an AI initiative depends on:

Having a real business sponsor
Aligning the initiative with an actual pain point or opportunity
Using real, messy data — not curated samples
Understanding the downstream process and decision implications
Measuring success through business outcomes, not technical performance

In other words, AI needs to be business-in-concept, not just proof-of-concept.

What Business-in-Concept Looks Like

A business-in-concept approach flips the traditional PoC model on its head.

Instead of asking:

“Can the model work?”

You ask:

“Can this improve how we operate, decide, or compete?”

And instead of building tech in a vacuum, you:

Engage business leaders early
Define the value hypothesis clearly
Map the user journey or workflow the AI will impact
Build around a real-world process with real-world data
Ensure it’s embedded into how decisions are made

This approach makes AI real — fast. It creates momentum. And it starts to build the internal capability and change muscle your business actually needs.

Avoiding the Governance Trap

Business-in-concept also forces you to confront your information landscape early:

Do you trust your data enough to make AI-informed decisions?
Are your governance policies clear enough to allow responsible use?
Can your team actually act on what the AI recommends?

Instead of hiding these issues under the hood of a “controlled test,” you address them up front — and make progress where it counts.

Why This Matters Now

The organisations making real progress with AI aren’t doing more pilots. They’re doing better, more integrated experiments.

Experiments that are tied to real outcomes
Experiments that elevate internal knowledge
Experiments that drive cross-functional collaboration
Experiments that uncover and fix foundational blockers

Because at this stage in the AI journey, the goal isn’t to test the tech. The goal is to build the business muscle to scale it.

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