In this guest post, Sam Riley, Co-Founder and CEO of AI-powered software platform Drova, examines the growing role of artificial intelligence in aged care, arguing that while AI can free staff from administrative burdens, its success depends on strong governance, human oversight and clear accountability to protect residents and improve care outcomes.
No sector carries a heavier weight of responsibility than aged care. The people working within it – nurses, carers, therapists, support workers – chose this path because of what it means to show up for someone at their most vulnerable. To hold a hand. To notice when something is wrong. To be the person a family trusts with their parent, their grandparent, their loved one.
My mother spent fifty years as a registered nurse. When she started, she told me the job was almost entirely direct care and nursing. By the time she retired, more than half of it was records management and administration. The work she loved – the reason she became a nurse – had been slowly buried under a mountain of paperwork that served compliance, not people.
That tension sits at the heart of where aged care finds itself today. The sector is under enormous pressure: workforce shortages, rising complexity of care needs, tightening regulation and increasing demand. And now, arriving with considerable promise and considerable risk, comes AI.
The opportunity is real
AI is already playing a role in the sector – fall detection through CCTV systems, tools designed to address loneliness, clinical decision support, transcription and note-taking for residents who can no longer manage it themselves. The possibilities extend further: AI-enabled companions, pain management applications, behaviour monitoring, operational systems that reduce the invisible labour that consumes so much of a carer’s day.
Most importantly, AI has the potential to give caregivers something back. To lift the administrative burden that has slowly displaced the human work they came to do – and return them to it. Not to replace them, but to clear the path in front of them.
But this is where the story gets complicated.
The weight of getting it wrong
In a sector built around dignity and human connection, the consequences of AI deployed carelessly are not abstract. They are felt by real people, in real rooms, at the most vulnerable moments of their lives.
Consider what happened at one mid-sized residential aged care provider. Facing the growing documentation demands of the Strengthened Aged Care Quality Standards, they deployed an AI tool to classify and prioritise incoming incident reports. The intent was sound – reduce the manual review burden, make sure nothing slipped through. Instead, something quietly broke. Quality staff, trusting the automated classifications, pulled back from their own review routines. No control existed to verify what the system was producing. No one was clearly accountable for escalation decisions. No mechanism confirmed that mandatory reporting obligations were being met.
A pattern of falls in one wing was repeatedly classified as low-severity. No human escalated it. The reporting window to the Commission closed. The issue surfaced only during a scheduled audit – not in management reporting, not at the risk committee, not at board level. By then, there were formal compliance findings. And a question that couldn’t be answered cleanly: could earlier human intervention have prevented harm?
“This was not a failure of technology. It was a failure of orchestration.”
The gap nobody is talking about
Across the sector, AI is being adopted the same way: process by process, function by function, each initiative in its own silo. Documentation here. Compliance tracking there. Rostering optimisation somewhere else. Each one well-intentioned. None of them connected.
And at the board and executive level, nobody is confident they are seeing the full picture. Risk committees are making decisions without the visibility they need. Critical issues stay buried in operations. Leadership discovers problems through audits – not through their own systems. The question every board member should be asking – are our AI implementations being governed properly, and would I know if they weren’t? – often has no good answer.
This is the governance gap that matters most. And in aged care, where the stakes are measured in human wellbeing, it cannot stay open.
Where AI is already earning its keep in governance, risk and compliance
Beyond the risk cases, AI is already delivering real efficiencies for the teams that carry aged care governance.
Incident classification is the clearest example. Under the Aged Care Quality and Safety Commission’s Serious Incident Response Scheme, Priority 1 incidents must be reported within 24 hours, and the pressure on quality teams to triage volume without missing detail is unrelenting. Bounded AI classification, paired with mandatory human review, is helping process that volume faster, and stopping the 24-hour clock running out on incidents that sat in the wrong queue.
Compliance evidence collection is another. Rather than staff chasing artefacts before an audit, AI can link evidence to the right control as it is generated. In broader GRC settings, organisations using AI-powered evidence automation have already reported significant reductions in audit-preparation time.
Standards mapping is starting to move too. Under the Strengthened Aged Care Quality Standards that took effect in November 2025, providers must demonstrate continuous compliance across seven quality domains. AI-powered platforms can now read a provider’s policies and operational data, map them against the standards, and flag exactly where obligations are not fully covered.
What is coming next
The next wave of AI in aged care governance will move from point tools to connected assistance across the compliance and risk stack.
Continuous compliance monitoring, watching operational data alongside regulatory feeds and flagging emerging non-compliance. Regulatory intelligence, reading standard updates as they publish and pushing remediation tasks to the right owner. Predictive compliance risk, spotting which controls are trending toward failure before the audit finding lands.
Above those tools, expect coordinated governance layers. AI-native platforms that give risk and executive teams a single, real-time view of every risk and control, who owns it, and whether it is performing as it should. This is the layer most providers do not yet have.
Design your governance foundation now, so that when these tools arrive they land in an environment that can hold them safely.
How to start well
For providers starting out, three principles are worth locking in early.
Start with objectives, not technology, and let the AI selection follow from what you are actually trying to achieve. AI software like Drova exists to help organisations cascade governance from objectives down.
Insist on traceability by design. Every action should be linked back to the objective it serves, the risk it manages, and the person accountable for it. Without that link, you have fragmented activity rather than cohesion, control, and confidence.
And give the board visibility from day one, so strategic decisions are made on what is happening now, not what was true last quarter.
The caregivers in this sector came here to care. The leaders in this sector carry that responsibility on their behalf. Getting AI governance right is how both of them and their organisation are protected, and how the work that matters most gets the space it deserves.
