Thinking
Notes on AI, evaluation, and building things that work
Perspectives on AI strategy, adoption, and implementation for business owners, not developers.
AI in property valuation: the evidence, the design rules, and what it could become
The best Australian evidence on vision AI in valuation measures a different task than the one vendors demo. The findings, and the design rules that follow.
Eleven cells moved. Here is what they mean for your business.
Reading the September 2026 State of AI verdict table: what improved, what declined, and what to do differently this quarter.
Competitor intelligence for small business: what AI can and cannot see
What AI-assisted competitor intelligence really is for a small business: the public sources worth watching, what they cannot tell you, and the legal line.
AI in regulated professional work, Mid-2026
One structure links family law, valuation, and building inspections: a signed document others rely on. How each field's regulator answered the AI question.
The business knowledge base: evidence, risks, and how to build one
What a business knowledge base actually is, what the evidence says it delivers, the security and privacy realities, and how we build one that holds up.
What AI can see in your customer data (and what it cannot)
What AI can genuinely find in the customer records an SME already holds, what it cannot, and when a spreadsheet honestly beats a model.
What an AI quoting engine actually does
What an AI quoting engine takes in, what it drafts, what the evidence says about accuracy and speed, and why the final price stays with a human.
Australia's AI adoption gap is bigger than the 12% headline suggests
ABS says 12% of Australian businesses use AI. The real story is 35% of large businesses against 11% of small ones, and the barrier isn't the technology.
Why we let AI run the interviews (and why we never let it pretend to be human)
AI-conducted interviews compress weeks of stakeholder discovery into days, standardise what gets asked, and lower the guard that distorts honest answers.
How AI capability actually moves through a business
The decisive variable in SME AI adoption is the human absorption sequence, not the tooling. A working framework from observation across WA businesses.
AHPRA advertising rules for psychologist websites
Recovery stories, 'specialist', 'clinical psychologist', and endorsement titles are where psychology sites breach the National Law. A practical read-through.
Customer service AI has finally grown up
Chatbots and AI receptionists earned their bad reputation. What changed, and how the mature version answers every call without replacing anyone.
Who can use the titles 'Dr', 'Specialist', and 'Surgeon'?
AHPRA restricts 'specialist' and 'surgeon' to specific registrations, and 'Dr' has its own rule. What health practice websites can and cannot claim.
Your best people hate writing reports
The operators you promote are brilliant at the work and allergic to reporting. A scheduled AI call interviews them, drafts the briefing, they approve it.
Your website isn't just for humans anymore
How to build a chatbot that keeps itself up to date, can't leak client information, and won't answer beyond what you've published.
Can you show Google reviews on your health practice website?
AHPRA bans clinical testimonials, even true ones, but service reviews are fine. What that means for the Google reviews widget on your practice site.
What AHPRA's advertising rules mean for your website
Your practice website is advertising under the National Law. What AHPRA's rules prohibit, who is responsible, and how to check your own site.
Is it safe to paste client data into ChatGPT?
Short answer: it depends on one setting, and most people have it wrong. What ChatGPT, Claude and Copilot do with your data, and what the Privacy Act expects.
What a good AI audit actually delivers
The audit report named one recommendation specific enough to check, and what the Build that followed looked like: one real engagement, generalised.
AI and video, Mid-2026: the models can watch now, not just listen
AI could always transcribe video. It can now read the frames as well, and every hour of footage a business owns becomes something it can question.
Case study: a 119-page AML/CTF program in three days
How we built a seven-document AML/CTF compliance pack for a small accounting practice in three days, working from 31 confirmed assumptions.
From evidence base to delivery: a production AI methodology
How we delivered 34 evidence-anchored AI briefings to a WA peer-advisory chapter: fact-checked literature review, multi-agent verification, one method.
The six functions of a working AI system
A working AI system is six functions doing six jobs. When all six connect, hallucinations get caught, outputs hold steady, and models become swappable.
Supervised autonomy: the middle path for AI architecture
Between drafts you approve and agents you hope about sits the middle path: an envelope of authorised routine work, supervised, audited, and yours to widen.
The state of applied AI in Mid-2026
Our literature review of applied AI in mid-2026: ten capability categories, three fact-check passes, written for operational leaders.
How to design a PHI redaction system for clinical AI
PHI redaction is part of a clinical AI tool's architecture, not a feature you add. What the literature says it should look like, and how we built it.
How we built on-device de-identification so AI never sees real names
Most AI privacy is a policy. Ours is architecture: an NER model runs in the browser and strips names before anything leaves the device.
Your agency's clients are about to ask why this costs so much
A solo consultant built in three weeks what your agency quoted twelve for. The client doesn't know why yet. The agencies that survive change what they sell.
What do you love doing? What do you hate doing?
Ask people what they love doing and what they hate doing, then show them AI is coming for the second list. Why the reframe works, and how it fails.
Why I don't use n8n (and what I do instead)
n8n demos well. But a compelling demo and a reliable production system are different things, and the distance between them is where businesses get hurt.
Your codebase was not built for AI. That's the actual problem.
Amazon's mandatory meeting about AI breaking production is an architecture story: codebases built for human maintainers only, now maintained by AI.
Your team has AI licences. You don't have an AI system.
Fifteen people, fifteen separate AI accounts, no shared context. The problem isn't the tool; it's the architecture around it. Here's the fix.
Your $2,000 day starts the night before: our system keeps you on the tools, not on the phone
Optimised routes overnight, automatic customer notifications, and promises the system keeps or corrects. A scheduling system that protects your daily rate.
The fastest way for an executive to get across AI
AI moves faster than any executive can track. One focused conversation, one written report, and a decision you can act on: your time stays on the business.
Your IT department will take 18 months. You need this working by next quarter.
Senior leaders know what they need built; the gap is time. A prototype gets the tool working now and hands IT a validated blueprint for later.
We built an AI invoice verifier. Here's where it hits a wall.
We built an AI invoice verifier and watched a fake beat a real invoice. Why document analysis alone cannot stop fraud, and the five layers that can.
How to build an AI chatbot that doesn't lie to your customers
Woolworths scripted its AI to talk about its mother. The business fix is honesty; the technical fix is architecture that prevents fabrication by design.
Why AI safety features are load-bearing architecture, not political decoration
The 'woke AI' label came from real failures, but they were engineering failures, not safety failures. The difference matters wherever errors have consequences.
Woolworths' AI told a customer it had a mother. That's a problem.
Woolworths' AI assistant Olive was scripted to talk about its mother and uncle. When callers realised, trust broke instantly. The fix is honesty.
Google is no longer the only way your customers find you
Customers now find businesses through ChatGPT, Perplexity, and Gemini. The sites AI cites are structured differently to the sites Google ranks.
The personal workflow analysis: what watching a real workday reveals about automation
People describe the work they value, not the work that eats their time. Recording a real workday reveals the automation opportunities interviews miss.
An AI audit that starts with your business
How an operations-first AI audit works: what it looks for, how the evidence is collected, what the report contains, and what it tells you to skip.
What production AI teaches you that demos never will
The gap between a demo and a working system is where the useful lessons live. Architecture, framing, privacy, adoption: the patterns repeat every time.
The psychology of why your team won't use AI
You buy the tool, run the demo, and three months later nobody is using it. Five predictable psychological barriers, each with a strategy that works.
Stop telling AI what NOT to do (and what to say instead)
Instructions built on prohibitions make AI cautious and generic. Describing what you want instead transforms the output, and the reason comes from psychology.
How we turned generic AI into a specialist: and what that means for your business
Mediocre AI output is rarely the model's fault. Three structural changes that turn the same model from generic to specialist-grade.
Your business has 9 customer touchpoints. AI can fix the 6 you're dropping.
You pay to get customers to your door, then lose them to missed follow-up. AI can handle the six touchpoints most businesses drop.
What happens to your data when you press 'Send' on an AI tool
Businesses send customer data to AI tools without knowing what happens during processing. The spectrum of AI privacy is wider than you think.
Questions about PAC?
Instant automated answers, drawn from this site.