Thinking
Notes on AI, evaluation, and building things that work
Perspectives on AI strategy, adoption, and implementation for business owners, not developers.
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, why the trick is in the data, 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, and they approve it. No ego, no politics, no blank page.
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. The answer was sitting in plain sight.
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?
What ChatGPT, Claude, and Copilot promise about your data, what the Privacy Act requires, and the honest answer for professionals handling client files.
What a good AI audit actually delivers
The audit report named one recommendation specific enough to check. What the Build engagement that followed looked like, shown through 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.
Why the privacy case against cloud AI memory isn't paranoia
An AI knowledge base concentrates everything sensitive a business holds. Dated 2026 incidents show what cloud custody means once legal process gets involved.
Your AI knowledge base is an attack surface
A knowledge base an AI agent can read and write is a productivity tool, and dated 2026 incidents show it is also somewhere an attacker can plant instructions.
The real asset in an AI knowledge base isn't the notes
In every AI-maintained knowledge base, one file carries the owner's judgement and compounds. The wiki pages are the least valuable part.
The missing measurement in the AI second-brain boom
Every claim about AI knowledge bases saving time is self-reported. The one controlled experiment measured token economics, not benefit.
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.
AI in building inspections, Mid-2026
AI defect detection is strong on obvious defects and weak on the subtle ones where liability lives. Which capabilities fit inspection work in 2026.
AI in property valuation, Mid-2026
AVMs are reliable enough for triage, not for the final word on contested property. What has shifted in valuation work by mid-2026, and what has not.
AI in family law, Mid-2026
Federal Court practice note GPN-AI makes AI verification a professional obligation. What the courts now require, and what the evidence says about legal AI.
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.
What if you had perfect memory across every client?
Every practice captures more than it can recall. AI gives practitioners perfect memory across every client, so preparation becomes thinking time.
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.
Two types of AI audit: and how to know which one you need
Where do we start with AI? It depends on whether you need to find the opportunities or reclaim the time. Two audits, two perspectives, one goal.
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.
AI audit that starts with your business
An operations-first AI audit starts with how your business actually runs, and only recommends AI where the evidence says it will work.
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.