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What successful AI implementation really looks like in aged care
A lot of organisations are using AI. Very few are getting real value from it.
Research from McKinsey shows that 88% of organisations use AI in some form. But only 6% are high performers, meaning they can actually measure the value AI brings to their business.
Last year, 42% of companies abandoned most of their AI initiatives before reaching production. That’s up from 17% the year before, according to S&P Global Market Intelligence.
So what separates the 6% of high performers from everyone else? AlayaCare’s pilot programs give us some real answers, and helps give an insight into how to implement AI most effectively.
Learn how to implement AI for your team in our recent webinar, Implementing AI: Moving your team from ideas to impact:
What good actually looks like
Across AlayaCare’s beta pilots, organisations that invested time upfront, in data readiness, clear scoping and supporting the right cohorts, consistently achieved better outcomes.
Results from those implementations include:
- 80% reduction in missed incidents
- 50% faster reporting of serious issues
- 50% reduction in service refusals
- 90% accuracy in AI risk modelling
- 60% improvement in note review accuracy
- 15 hours saved per clinical supervisor, per week
These are outcomes from real providers who did the groundwork to make AI implementation stick.
“Customers that actually put the emphasis and the time into getting their data readiness right consistently achieved significantly better outcomes,” says Thilan Perera, SVP, Customer Success at AlayaCare. “That kind of accuracy builds trust, and that trust then enforces adoption — which leads to greater benefit realisation.”
The step most organisations skip
Most organisations stop at what Ali Kaabi, Director of Client Solutions at Rohling, calls Tier 1: the system is live, the project was delivered on time, staff logged in.
That’s not nothing. But it’s not success.
“Tier 1 is your typical project completion,” says Ali. “AI is not going to be that. The fact that the system is live and operational,that’s the foundation. That’s not enough to call this a successful adoption.”
As Rohling outlines, the organisations that achieve results like those above invest across all three tiers:
- Tier 1 — The system is live and operational
- Tier 2 — Staff are genuinely changing how they work (measured at 30–90 days post go-live)
- Tier 3 — The value promised in the business case is actually being delivered
The gap between Tier 1 and Tier 3 is where most organisations quietly stall. High performers take a different approach, they define success upfront across all three stages and measure it from the start
Data readiness isn’t optional
One of the most consistent findings from AlayaCare’s pilots was the impact of data quality, not just on outcomes, but on trust.
“We often talk about garbage in, garbage out,” says Thilan. “It is no different when we talk about any AI implementation.”
When AlayaCare ran pilots of its client intelligence suite, organisations that hadn’t defined consistent data capture processes got inaccurate results. That led to false positives, which led to a loss of trust, which led to staff abandoning the tool altogether.
The organisations that got it right worked with AlayaCare upfront to define which data entities the AI should work from and how that data should be captured consistently. The payoff was 90%+ accuracy, and the confidence that comes with it.
Thilan Perera speaking about the elements for a successful implementation at the recent webinar, Implementing AI: Moving your team from ideas to impact
Before you roll out, it’s worth asking: is your data clean enough, and consistent enough, to give your AI tools a fair chance?
Start focused, then scale
Another pattern that separated high performers: scope.
“In order for you to truly embed AI into your teams, you need a very clear scope of the problem you are trying to solve or the process you are trying to automate,” says Thilan.
The temptation with AI is to do everything at once. The organisations that got the best results resisted that. They picked a small cohort, defined clear success measures before they started, ran a focused pilot, proved the value — and then scaled.
They also chose the right pilot group. People who were genuinely curious about AI, less fearful of change, and who could become internal advocates once the rollout worked.
“Run a pilot with a group that can be your AI champions,” says Thilan. “Make sure that this group of individuals are well supported, both during implementation and post-implementation.”
The questions worth asking now
Here’s a simple check:
- Do you know what AI success looks like, not just at go-live, but at 30, 60 and 90 days?
- Have you benchmarked your current state so you can measure what changes?
- Is there someone in your organisation who owns adoption beyond project completion?
If you’re not sure, you’re not alone. But the organisations building something that lasts are the ones who can.
AI in aged care is no longer a future conversation. The question now is whether your implementation is built to deliver real value, or just to get to go-live.
Want to see how AlayaCare’s AI solutions are already delivering results across home, community and residential care? Book a demo and we’ll show you what’s possible.
Most organisations stop at getting the system live but that’s only the starting line. Research shows 88% of organisations use AI in some form, yet only 6% can actually measure the value it delivers. The gap between a successful go-live and real business outcomes is where most aged care AI initiatives quietly stall.
Organisations that invest time upfront in data readiness and clear scoping consistently achieve better outcomes — including an 80% reduction in missed incidents, 50% faster reporting of serious issues, and 15 hours saved per clinical supervisor per week. The common thread isn’t the technology, it’s the groundwork done before go-live.
According to Rohling, AI success should be measured across three tiers. Tier 1 is the system being live and operational. Tier 2 is staff genuinely changing how they work, measured at 30–90 days post go-live. Tier 3 is the value promised in the business case actually being delivered. Most organisations only reach Tier 1 and call it done, while high performers define and measure success across all three from the start.
Data quality is critical. Organisations that haven’t defined consistent data capture processes before their rollout get inaccurate AI results, which leads to false positives, a loss of staff trust, and tool abandonment. Organisations that define which data entities the AI should draw from — and how that data should be captured consistently — achieve 90%+ accuracy and the staff confidence that comes with it.
Start small. Providers achieve the best results when they resist the temptation to do everything at once — picking a focused cohort, defining clear success measures before starting, proving the value, then scaling. Choosing the right pilot group matters too: people who are genuinely curious about AI and can become internal advocates once the rollout works.
Look for staff who are less fearful of change and genuinely interested in how AI could help their work. This group becomes your internal AI champions — so it’s worth investing in their support both during and after implementation. Their experience shapes how the rest of the organisation sees and adopts the technology.
Building on Rohling’s implementation advice, three questions are worth answering first: Do you know what success looks like at 30, 60 and 90 days post go-live? Have you benchmarked your current state so you can measure what changes? Is there someone in your organisation who owns adoption beyond project completion? If the answers aren’t clear, the implementation isn’t ready to scale.
