Why capable AI still produces average work

The High-Value AI Blueprint

I ran into GPT-3 almost by accident in 2020. I had just moved to Queensland for a director role when COVID hit. We were down 50% headcount, under a hiring freeze, and facing a shrinking market. The number still had to be delivered.

Our first real use was market qualification and business development. A rep showed me the output. We had generated $1.2M in revenue from a segment we had never cracked. That year the business grew 160%.

Six weeks after we rolled the same approach across the sales floor, lead volume increased fivefold. The team slowed down. Leads were queuing for manager approval. AI had increased volume at the front of the process. The approval path had not changed.

We rebuilt the workflow the next day. Three steps were removed. Two were automated. The manager moved from approving every lead to coaching reps on the opportunities that were converting. Cycle time dropped 60%. Conversion recovered.

The same capability created $1.2M in revenue and later created a larger queue. The workflow determined the result.

Six years later, AI runs through almost every serious part of my work: research, commercial analysis, content, systems, decisions and execution.

Five disciplines consistently produce the strongest results.

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Why generic output keeps appearing

Generic output usually comes from three gaps:

• The task has been named without a clear outcome
• The model has too little business context
• Nobody has defined what success or failure looks like

A team asks for "a strategy", "better content" or "a more executive response". The model fills the gaps with the most common patterns from its training data. The result can look polished, but it often belongs to any executive in any company.

What does not change

An LLM is a pattern-matching engine. It predicts the most likely useful continuation of the tokens you give it, based on its training and the information in your prompt.

It does not understand your business. It has no taste, no judgment, and no skin in the game. It cannot feel the cost of being wrong.

That is why better models alone rarely solve the problem. They get better at following weak instructions. They still need you to define the purpose, supply the relevant context, apply judgement, and own the outcome.

The five disciplines organise that division of work.

1. Define the outcome with brutal clarity

Vague requests are the fastest way to get generic output.

When you ask for "a strategy" or "review this proposal", the model fills the gaps with the most common patterns from its training data.

The single highest-leverage move is to define the outcome so clearly that the model has almost no room to guess.

For any important piece of work, get explicit on three things:

• What exact decision or action this must drive
• Who must be able to act on it and what they need to understand or feel
• What would make the output completely unusable

Example: Determine whether this proposal is ready for executive approval. Test the commercial assumptions, implementation ownership, and evidence of value. Separate established facts from forecasts. Recommend: Approve, Revise, or Stop.

If you cannot write the outcome clearly enough that success or failure can be verified in two sentences, the brief is still too vague.

The model has no understanding of your business or standards. It will default to the most common patterns it has seen before. Extreme clarity on the outcome is the only way to stop it.

2. Load the right context

Vague context forces the model to default to the statistical average of everything it has seen before. An executive works with specific, often unspoken knowledge: past board pushback, real operational constraints, risk tolerances, and which numbers actually move the P&L.

The model cannot access any of that unless you make it explicit.

For high-stakes work, load context in five categories:

• Commercial pressure: Current budget constraints, margin targets, and P&L priorities
• Decision history: What has already been approved, rejected, or left open
• Audience reality: Who will read or act on this and what they already know or believe
• Operating constraints: The workflows, systems, and bottlenecks that will deliver the outcome
• Evidence rules: Approved data sources, strict boundaries, and confidentiality requirements

Store your core operating context once at the workspace level. Add workstream-specific context inside the relevant project. Attach task-specific facts directly to the brief.

Before the model starts drafting, instruct it to review these files and ask three clarifying questions. This surfaces hidden assumptions before you invest time correcting a weak first pass.

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3. Structure the execution

Complex work usually contains several separate jobs: research, challenge, synthesis, and drafting. Dumping all of them into a single prompt forces the model to handle everything at once. It will often move to polished writing before the underlying logic is solid. That is why weak assumptions frequently survive into the final output.

Break the task into four explicit stages before any formatting begins:

• Understand: Extract the objective facts and list the real operating variables.
• Test: Act as a hostile auditor. Surface gaps, optimistic forecasts, and weak assumptions.
• Decide: Form the core recommendation, list the rejected options, and state the confidence level.
• Communicate: Turn the isolated decision into the required format (board paper, email, framework, etc.).

This creates a clear checkpoint. You can review and correct the reasoning before a flawed assumption is carried into the final asset.

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4. Apply sharp human feedback

The first output is only raw material. Real value is added when you remove specific defects.

Vague commands achieve nothing.

"Make it better" gives no direction. "Make it more executive" produces corporate language. "Make it punchier" breaks flow into awkward fragments.

Effective feedback names the exact defect and tells the model what to keep, cut, and strengthen.

Use this structure:

The core recommendation is sound. The introduction spends too long on background. Lead with the decision. Keep the commercial evidence. Remove the generic context. Expand the two assumptions that could change the outcome. Do not add alternative options.

Review every important draft against three questions:

• Did the model make a clear call, or only organise information?
• Does the certainty match the strength of the evidence?
• Is the commercial consequence stated clearly?

One focused round should remove most structural defects. Repeated prompts to fix voice or logic mean the original outcome or context was not clear enough.

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5. Make the final decision and reallocate the capacity

The model can research, test, and recommend. Only the executive owns the consequence.

Before release, run the final gate:

• Is the content factually, legally, and reputationally sound?
• Does the evidence support the certainty in the language?
• Would I put my name on this?

Once quality is locked, redirect the time saved. If the method frees 10-12 hours per week, move that capacity into higher-value work such as customer relationships, negotiation, or decisions previously too slow to attempt. Do not let it disappear into more meetings.

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The limitation

AI performance varies sharply by task. Confident language gives little warning when the model is outside its reliable range.

Public statements, legal interpretations, customer commitments, and capital decisions still require source verification and named human approval. Accountability always stays with the executive.

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Before Friday

Pick one live piece of work with real commercial, customer, or operational weight.

Write the outcome in one paragraph. Load the five context blocks. Run it through Understand, Test, Decide, and Communicate. Record the change in time, assumptions surfaced, decision quality, and verification effort.

Name where the released capacity will go.

By the end of the week you will have one completed high-value asset, a reusable brief, and a clear decision on whether to scale the method.

Ramon.

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Listen to the conversations behind the work

Ifthis kind of practical thinking resonates, you'll get a lot from the Applied AI Australia podcast.

This Tuesday episode drops with the Senior Vice President and Chief Marketing Officer for Salesforce Australia and New Zealand.

Next week: CEO of Compare Club.

Also coming up: Vice President and Managing Director for Workday Australia and New Zealand, and the CEO of Coca-Cola.

We go deep on how senior leaders are using AI inside their organisations - what's working, what isn't, and what decisions move the needle.

Watch or Listen here.

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About Applied AI Australia

Applied AI Australia helps executives turn AI into measurable business outcomes. We filter every initiative through one question: Does this increase revenue or decrease costs? If not, it's noise.

Leaders do not need more AI hype. They need to know which decisions AI should change, what risk it creates, and how to get value without creating chaos.

I am not a career consultant. I am a leader with 15 years of P&L experience, most recently as an executive at News Corp.

Following our acquisition by Acquire Intelligence, we combine this approach with a 10,000-strong global team that delivers enterprise execution.

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Disclaimer: Nothing in this newsletter is legal, financial, or professional advice. It is research, pattern recognition, and practical operating observations for Australian boards and executives. Before acting on any of it, speak with your own adviser.