Two brains, one AI decision: what we've learned about AI in care
Not long ago, a healthcare provider came to us with a list.
It was a thoughtful list. Every item represented something that took time, created frustration, added pressure to the team or felt harder than it needed to be. They wanted to explore whether AI could help automate parts of it and, on the surface, that made complete sense.
As we worked through the list together, something interesting emerged. The opportunities themselves were real, but they weren't actually the problem the organisation was trying to solve. The list had become a collection of solutions before anyone had fully stepped back to understand what was driving them.
That experience has stayed with me because it captures a conversation Tom Leyden and I find ourselves having regularly.
Tom comes at this work from the technology side. He understands what AI can do, how quickly things can now be built and where organisations can create leverage through automation and intelligent systems. My work sits closer to strategy, service design and operating models. I'm interested in how work happens, how services are delivered and what people need for change to actually stick.
We're looking at the same challenge from different angles and, increasingly, we've realised the best outcomes happen when those perspectives come together early.
Most organisations don't struggle with AI because the technology isn't capable. The challenge is usually that the conversation starts with the tool rather than the outcome.
In aged care and disability services, that's an important distinction.
Leaders are navigating workforce pressures, funding constraints, compliance requirements and increasing expectations from the people and communities they support. Every investment matters. Every project competes for time, attention and resources. Getting clear on the problem you're trying to solve before choosing a solution becomes incredibly important.
Over time, we've found ourselves thinking about AI initiatives through three connected stages: understanding the opportunity, designing the solution and supporting adoption. At every stage, the technology lens and the people lens need to move together.
Understanding the opportunity
The earliest stage of any AI initiative is often the most valuable because it's the point where organisations still have space to explore, challenge assumptions and think strategically about what they're trying to achieve.
This is where we encourage leaders to start with the service rather than the technology. Before discussing platforms, pilots or automation opportunities, it's worth spending time understanding where the pressure points sit and what outcome would make the biggest difference.
Some of the questions we find ourselves asking are:
What experience are we trying to improve?
Where is the pressure in the system today?
What is creating frustration for staff, customers, participants or families?
What outcome are we hoping to achieve?
How does this connect back to our strategy and organisational priorities?
If this problem disappeared tomorrow, what would be different?
The strongest projects are anchored in a clear understanding of the problem. They connect directly to organisational priorities and are grounded in how work happens today.
That understanding becomes even more important in care environments where work is highly relational, often complex, and shaped by hundreds of small decisions made by frontline teams every day. Technology can support that work brilliantly, but only when it understands the reality it's being introduced into.
So, is AI any different from every other technology change?
AI does bring a new and interesting capability that has not been available to many organisations. For the first time it is now feasible to introduce new ways to interact with your clients through using everyday language as the interface - be it through interpretation of emails, report writing, language translations and even voice interactions. The combination of use cases are large. On the surface this sounds like an amazing capability that could help your business scale, connect to our clients and provide a much better experience for all.
However there are risks that every organisation must be aware of when embedding AI into their projects. These risks are unique to AI and must be understood by those involved in its implementation.
Model drift - AI may produce unexpected results over time
Cost controls - when left uncontrolled, AI tools can incur huge costs.
Hacking / security - AI may expose another cyber security risk that could expose your client's data
Of course there are more risks - but these 3 provide a flavour of what you must be concerned about.
Quick wins matter. So does the bigger picture.
One of the tensions we talk about most with leadership teams is the balance between creating momentum and creating sustainability. Quick wins are important. They help people see what's possible, build confidence and demonstrate value. They create energy around a project and help organisations learn quickly.
At the same time, focusing only on individual use cases can create a collection of disconnected improvements that don't add up to a better system.
Many organisations have experienced this before. A new tool solves one problem. Another tool solves another. Over time, complexity grows rather than reduces, and teams are left managing multiple systems that were never designed to work together.
The organisations seeing the greatest benefit from AI are usually doing two things at once. They're identifying opportunities that can deliver value quickly while also maintaining a clear view of the broader service, data and operating model they're building towards.
Questions worth considering at this stage include:
How does this initiative fit within our broader operating model?
What data will this rely on, and how confident are we in its quality?
Could solving this problem create new challenges elsewhere?
What capabilities will we need to build internally?
How will we know whether this has genuinely improved outcomes?
Keeping one eye on today's opportunity and the other on tomorrow's organisation helps ensure short-term wins contribute to a stronger long-term system.
Design and build
One of the most significant shifts we've seen in recent years is how quickly solutions can now be built.
What once took months can often be prototyped in weeks or even days. That's exciting because it creates opportunities to test ideas, gather feedback and refine solutions much earlier than we could in the past. What it also means is that the value has shifted.
Technology is no longer the biggest constraint. The thinking behind it has become far more important. We've seen organisations move quickly into development only to realise later they hadn't fully understood the process, the people involved or the impact the change would have on service delivery. Building the solution wasn't the challenge. Understanding what needed to be built was.
The questions we encourage teams to explore are:
Have we involved the people closest to the work?
Do we understand how work actually happens today?
What assumptions are we making?
How will this affect customers, participants and frontline teams?
What does success look like from their perspective?
The faster technology becomes, the more important good design becomes. Speed is valuable, but only when it's moving in the right direction.
Adoption starts at the beginning
One of the biggest misconceptions about technology projects is that adoption happens at the end. Our experience has been the opposite. Adoption begins the moment people are invited into the conversation.
When teams understand the problem, contribute to the design and can see how the solution connects to their work, change feels very different. It becomes something they're helping create rather than something being implemented around them.
This is particularly important in aged care and disability services where people are already balancing significant responsibilities. Any new way of working needs to fit into the reality of service delivery rather than compete with it.
Some useful questions here are:
Who needs to be involved early?
What concerns might people have about this change?
How do we create ownership rather than compliance?
What support will people need to adopt new ways of working?
How will we gather feedback and adapt as we go?
The projects that gain traction over time are usually the ones that respect people's reality from the beginning.
The partnership is the point
If there's one lesson we've taken from this work, it's that successful AI projects are rarely technology projects alone. They're service projects. They're people projects. They're operating model projects. The technology matters, but its role is to support a broader outcome.
When organisations bring together a deep understanding of the problem, a clear view of how work happens and a practical understanding of what technology can enable, something powerful happens. The conversation shifts away from "How do we use AI?" and towards "How do we create better outcomes for our people, customers and communities?"
That's usually where the best ideas begin.
