A faster claim. A clearer answer about cover. Less time repeating your story. Support that arrives before a health problem becomes a hospital admission. These are tangible ways AI could improve private health insurance. They also offer a more useful starting point for investment decisions than a broad ambition to “use more AI”.
In responses from collected through our PHI report registrations between September and November 2025, leaders saw opportunities across claims, member experience, analytics and prevention. Their confidence in implementing AI responsibly was more mixed.
Those responses provide a useful baseline for a question worth revisiting: how do funds turn enthusiasm for AI into outcomes members can actually feel?
Where leaders expected the biggest impact
Claims automation and fraud detection led the responses, selected by 61% of the people who answered this question. Member experience followed at 56%, then data analytics and risk stratification at 49%. Wellness and prevention attracted 32%, while care delivery and hospital substitution attracted 29%.
These expectations span two connected opportunities: improving how insurance works today, and expanding how funds support members’ health.
The first offers practical places to begin. Reducing manual processing, helping staff find accurate information and making cover easier to understand could remove friction for members and employees alike.
The second asks more of the organisation. Identifying someone who may benefit from early support is only useful if the fund can offer an appropriate service, explain why it is relevant and help the member access it.
An insight becomes valuable when someone can act on it.
What would funds do with the capacity they release?
One leader described the opportunity as:
“Making PHI more affordable by reducing processing/sales overheads.”
Another highlighted:
“Productivity for the workforce, empowered consumers with their choices”
These comments point to an important strategic choice. When AI releases capacity, leaders need to decide how that capacity will improve the business and its service to members.
Some benefits may flow through lower operating costs. Others may come from giving employees more time to resolve complex enquiries, support vulnerable members or address recurring problems in the member journey.
That connection needs to be designed. Automating a task does not, by itself, establish what happens to the time saved or who benefits.
For each use case, a fund should be able to explain the path from a better process to a better outcome.
Confidence needs to be tested against a specific use case
Among the leaders who answered the responsible implementation question:
- 45% were somewhat confident in their organisation’s ability to implement AI responsibly over the next three years.
- 43% were very confident.
- 13% were not confident.
These figures describe confidence, rather than demonstrated readiness. They do not tell us whether an organisation has reliable data, clear accountability or effective controls for a particular application.
An internal tool that helps employees locate approved information requires different safeguards from a system influencing claims decisions or recommending health support.
Leaders need to ask specific questions. What happens when the system is wrong? Who checks its performance? Can an employee or member challenge the result? Who is accountable for fixing the problem?
Answering those questions early helps teams choose an appropriate starting point and understand what it will take to expand.
Stop guessing where AI fits in PHI
Get a clear view of where to start, what matters, and what comes next.
10 practical use cases. Real implications for PHIs. Exclusively free for Innovation Insiders
Start with a member problem and a measurable result
My view is that the most useful AI conversations begin with a problem the fund already understands. Perhaps members struggle to get a clear answer about a benefit. Perhaps employees spend too long navigating disconnected systems. Perhaps a care program is reaching people too late.
Choose a bounded problem, establish the current performance and define what improvement would look like. Measures might include resolution time, accuracy, repeat contacts, member understanding or uptake of appropriate support.
Cost savings matter too. But a cheaper interaction that leaves a member confused can create more work elsewhere.
The aim should be to demonstrate a result, understand the limitations and use that evidence to decide what comes next.
What should we look for next?
The 2025 responses show where participants saw potential. They leave an important question open: which applications will deliver enough value to become part of everyday operations?
That is what I’ll be looking for as we develop the 2026 PHI Innovation Report. Where is experimentation producing measurable results? What is helping organisations progress? And where are the same barriers continuing to slow promising ideas?
For members, the benefit will show up in everyday moments: getting an accurate answer, resolving a problem with less effort or accessing useful support sooner.
Those are outcomes worth building towards.
Explore what AI could mean for your fund
Where could AI create the greatest value for your organisation, and what would it take to put it into practice? Download our AI Whitepaper to explore the opportunities and implications for private health insurance.
For broader industry insights, you can also register for the 2026 PHI Innovation Report and its free insights webinar.
About the responses: This article draws on self-selected responses collected through Accelerated Innovation’s PHI report registration forms between September and November 2025. 41 responses were available for the AI impact question and 40 for the responsible implementation confidence question. The findings describe these respondents’ views and should not be treated as representative of all Australian health insurers. Multiple selections were permitted for the impact question.