Dr Finlay McCall and Adrian Weir are combining research into human-AI collaboration with more than thirty years of frontline IT experience. In this four-part series, they will examine AI readiness in public, learn from Australian businesses and develop a practical readiness tool from what they find.
AI readiness is often presented as a checklist - choose a tool, write a policy, train the team and start using it. Real organisations rarely experience change so neatly.
A business may be ready to use AI for one bounded internal task but not for a decision that affects a customer. A team may have access to capable tools but lack the time, confidence or shared routines needed to use them well. Leaders may see an opportunity while remaining unsure about privacy, security, accountability or whether the proposed use solves a worthwhile problem.
These are not signs that a business has failed an AI-readiness test. They are the questions a useful readiness process should help it answer.
That is why we are launching AI Readiness for SMBs, a research-translation and learning series from Milnsbridge. We will bring together two forms of knowledge that are too often separated - research into how people and AI systems can learn to work together, and practical experience of how technology is selected, supported and lived with inside real businesses.
We are not beginning with a finished assessment or a predetermined answer. We are starting with a working idea, testing it against published evidence and practitioner experience, listening to businesses, and showing how our thinking changes as we learn.
A conversation more than thirty years in the making
This collaboration did not begin with the current wave of generative AI.
For more than thirty years, we have been thought partners - friends who return to difficult ideas, test each other's assumptions and keep conversations going long after a particular technology has stopped being new. Over that time, our discussions have moved through philosophy, economics and other leading-edge technologies. More recently, they have centred on artificial intelligence and what it will ask of people, organisations and education.
That history matters because AI readiness is not only a technical question. It involves judgement, values, work design, learning, responsibility and the practical limits of organisations. Our conversations have often approached the same problem from different directions. This series turns that long-running exchange into a shared public inquiry.
Research and practice as equal partners
Finlay comes to AI through education and research. His PhD in Education at the University of Canberra examined curriculum and course design for human-AI collaboration - how people and AI systems might be prepared to work together deliberately rather than simply being placed together and expected to succeed. His wider work on machine education and human-machine teaming asks what each participant needs to learn, how roles should be understood and how collaboration can improve over time.
Adrian comes to the same questions from the front line of technology delivery. As founder and Managing Director of Milnsbridge Managed IT Services, he brings more than thirty years of experience across organisations including Telstra, Citibank and Unilever, as well as direct work with Sydney small and medium-sized businesses. Since founding Milnsbridge in 2002, he has seen technologies move from promise to implementation - and has dealt with what happens after the launch, when systems must remain secure, reliable, supportable and useful to the people doing the work.
Neither perspective is an ornamental addition to the other. Theory helps us ask better questions, recognise hidden assumptions and avoid mistaking activity for capability. Practice tests whether those ideas make sense under the constraints of actual businesses - limited time, uneven experience, existing systems, customer obligations, security requirements and competing priorities.
Our aim is to work as equal partners across that boundary. Finlay will lead the human-AI collaboration, evidence and inquiry framing. Adrian will lead the business, customer and managed-service perspective. We will develop the argument and recommendations together, and both of us will approve every article before it is published.
What we mean by AI readiness
Our starting position is simple
AI readiness is the capability to decide where, why and how AI should participate in the business - and what people, evidence and safeguards are needed before its use expands.
This definition leaves room for more than one responsible answer. A ready organisation can say yes to a well-bounded experiment. It can say not yet when its data, skills or controls are inadequate. It can say not here when a proposed use conflicts with customer expectations, legal responsibilities or the organisation's values.
Readiness is therefore not a badge awarded to an entire organisation. It is better examined through a real use case - a particular person or team, doing a particular task, using particular information, with an intended result and a clear account of who remains responsible.
This is our working hypothesis, not our final verdict. The purpose of the series is to test and improve it.
Why we are building - and learning - in public
Many technology articles present the polished answer and hide the uncertainty, revision and disagreement that came before it. We want to take a different approach.
Building in public means showing the stages of the work. We will explain the evidence behind our starting position, the questions we take to businesses, the limitations of what we hear, and the changes we make before producing a practical tool. Readers will be able to see which ideas survived contact with practice, which needed adjustment and which questions remain unresolved.
It is also an educational commitment. We do not want the audience to receive another framework without seeing how it was formed. By making the process visible, we hope business owners, leaders and teams can use the same questions to examine their own assumptions about AI.
This is a bounded practitioner inquiry, not a large representative study or an academic research project. A small number of customer conversations cannot tell us what every Australian business thinks or needs. They can, however, help us test whether our language is understandable, whether our questions reflect real decisions and whether a proposed readiness tool is useful outside a research document.
We will not name participating people or businesses, record conversations or publish attributed material without clear permission. We will also distinguish what the published evidence supports from what our participants report and what we infer from the combination.
The next three articles
Article 1 - the background and working hypothesis
Article 1 will ask what it means for an Australian small or medium-sized business to be ready for AI. It will examine why access to tools is not the same as organisational capability and set out our working readiness model.
We will also introduce a question we want to test - whether readiness changes with organisational size, existing AI experience, role structure and the consequences of the proposed use. The aim is not to sort businesses into winners and laggards. It is to understand what different organisations may need in order to make sound decisions on their own terms.
Article 2 - what businesses told us
Article 2 will take the inquiry into practice. Subject to consent and participation safeguards, we plan to speak with three Milnsbridge customers representing different organisational sizes and levels of AI experience.
We will publish the common questions, explain how the conversations were conducted and report bounded observations rather than pretending that three cases represent the market. Where participants agree to be named or quoted, they will be able to review the relevant wording and organisational context before publication.
Article 3 - what we learned and built
Article 3 will bring the published evidence, practitioner experience and customer conversations together. We will explain what changed in our thinking, what remained consistent and what businesses can do next.
Only then will we release the first version of the AI Use-Case Readiness Profile, our working name for a practical tool that helps a business examine one proposed use of AI. It will not issue an "AI-ready" certification or reduce readiness to a single score. Its purpose will be to help people identify strengths, unresolved questions, reasons to pause and practical next steps.
The tool will be an output of the inquiry, not a prop attached to a conclusion we had already reached.
How we will use AI in producing the series
A series about human-AI collaboration should be open about its own production method.
AI tools will support drafting and production. We - the human authors - will develop the concepts, direct the work, select and check the evidence, challenge the output, make the editorial decisions and approve the final text. We remain responsible for what is published under our names.
That method is part of the inquiry. We are interested not only in what AI can produce, but in the judgement, learning and accountability required to use it well.
Come along as the work develops
We are beginning with a proposition rather than a product - businesses need the capability to decide how AI should participate in their work, not pressure to adopt it everywhere.
Across the next three articles, we will put that proposition under scrutiny. We will move from research, to conversations with businesses, to a practical tool shaped by what we learn. We will show our working, acknowledge the limits and explain what changes along the way.
If your organisation is trying to decide where AI belongs, where it does not or what your people need before adoption expands, this series is for you.
Background sources
University of Canberra - doctoral thesis record, Anticipating curriculum and course design for human-AI collaboration (2025). University of Canberra thesis record
Adrian's published Milnsbridge team profile. Milnsbridge team profile
Finlay's educator and research profile. Finlay McCall website
Follow the opening release
Read Article 1 - AI Readiness on Your Terms
Articles 2 and 3 will follow the consented customer conversations. The practical tool has not yet been released.
About the authors
Dr Finlay McCall is an educator and researcher specialising in human-AI collaboration. His PhD at the University of Canberra examined how curricula and courses can prepare people and AI systems to work together deliberately over time. Drawing on more than twenty years of teaching, he helps people and organisations build the judgement, routines and learning capability needed to use AI well. For Milnsbridge, he translates research into practical guidance for Australian businesses. Learn more at Finlay McCall website.
Adrian Weir is the founder and Managing Director of Milnsbridge Managed IT Services. He brings more than thirty years of technology experience across Telstra, Citibank and Unilever, as well as hands-on work with hundreds of Sydney businesses. For the Milnsbridge AI-readiness series, Adrian contributes practical knowledge of how organisations assess, adopt and manage technology, grounding the discussion in day-to-day business and service experience. Learn more at Milnsbridge team profile.
Finlay contributes the human-AI collaboration and evidence perspective; Adrian contributes business and IT support experience. Both authors are responsible for the final published work.
Hero image is an AI-generated editorial illustration and does not depict the authors or research participants.
