The Role of AI and Emerging Technologies in Personalising Digital Mental Health Services

Digital mental health services are rapidly evolving, driven by the transformative potential of artificial intelligence (AI), data analytics, and emerging technologies. As mental health needs rise and digital government services become the norm, the opportunity—and responsibility—to personalise support for diverse user groups has never been greater. This page explores how AI and emerging technologies are shaping the future of digital mental health, the opportunities they unlock, and the challenges that must be addressed to ensure responsible, ethical, and inclusive implementation.

The Promise of Personalisation in Digital Mental Health

Personalisation is at the heart of effective mental health care. Every individual’s journey is unique, shaped by their life events, cultural background, financial situation, and personal preferences. AI and data analytics enable digital platforms to move beyond one-size-fits-all solutions, offering tailored interventions that meet users where they are.

Recent research shows that over half of Australians have sought treatment for a mental illness, with younger generations, those experiencing major life events, and individuals with precarious finances most likely to reach out for help. Importantly, 72% of people with a mental health condition said that online consultations would make them more likely to seek support—rising to 80% among Millennials and 72% among Gen Z. This appetite for digital support underscores the need for services that are not only accessible, but also relevant and responsive to individual needs.

How AI and Emerging Technologies Enable Personalisation

1. Chatbots and Virtual Assistants

AI-powered chatbots can provide immediate, confidential support to users at any time of day. These tools can triage queries, offer evidence-based self-help resources, and guide users to appropriate services. For those hesitant to seek face-to-face help—due to stigma, privacy concerns, or geographic isolation—chatbots offer a low-barrier entry point to care.

2. Predictive Analytics for Early Risk Detection

By analysing patterns in user behaviour, engagement, and self-reported data, predictive analytics can identify individuals at risk of mental health crises or deterioration. This enables proactive outreach and early intervention, potentially preventing escalation and improving outcomes. For example, automated messages can recommend additional screenings or support based on age, life events, or risk factors—an approach supported by 81% of surveyed Australians.

3. Digital Identity and Seamless Access

Digital identity solutions, such as myGovID, streamline access to mental health services while maintaining security and privacy. They enable personalised experiences by remembering user preferences, securely storing health information, and facilitating cross-agency support during critical life events. As digital identity adoption grows, it becomes easier to deliver coordinated, tailored support across the mental health ecosystem.

4. Data-Driven Content and Interventions

AI can dynamically tailor content, resources, and interventions based on user profiles, engagement patterns, and feedback. This ensures that users receive information and support that is relevant to their cultural background, language, and stage of recovery. Continuous feedback loops allow services to adapt in real time, improving both engagement and outcomes.

Opportunities: Tailored Support, Efficiency, and Equity

Expanded Reach: Digital platforms, powered by AI, can extend mental health support to rural and regional communities where face-to-face services are scarce. Telehealth, online support groups, and automated self-help programs break down geographic and logistical barriers.
Automated Triage and Resource Allocation: AI streamlines patient assessments, triage, and scheduling, freeing up clinicians to focus on complex cases. This operational efficiency was demonstrated in the transformation of Stride, an Australian mental health charity, where digital solutions freed over 15,000 hours annually for patient-facing care.
Continuous Improvement: Data analytics enable providers to monitor outcomes, identify service gaps, and refine interventions. This supports a cycle of continuous improvement, ensuring services evolve to meet changing needs.
Inclusivity Through Co-Design: Co-designing digital mental health services with diverse user groups ensures that solutions are culturally relevant, accessible, and trusted. Engaging communities in the design process addresses barriers such as language, stigma, and digital literacy.

Challenges: Privacy, Trust, and the Digital Divide

While the benefits are significant, integrating AI into mental health services raises important challenges:

Privacy and Data Security: Mental health data is highly sensitive. Concerns about loss of control, data breaches, and misuse of information are top barriers to trust. Over half of Australians have lost trust in government data security following high-profile breaches. Transparent communication, robust security protocols, and clear governance are essential.
Trust in AI and Automation: While 55% of Australians support the use of AI in government services, 94% express concerns about risk management and want clear regulation. Trust is especially fragile among those with recent mental health struggles or precarious finances. Building trust requires transparency, ethical AI practices, and user empowerment.
Digital Literacy and Access: The digital divide persists, with lower-income households, rural residents, and older Australians less likely to access or benefit from digital mental health services. Financial stress, lack of digital skills, and poor infrastructure are key barriers. Targeted digital inclusion initiatives and omnichannel support are critical to ensure no one is left behind.
Cultural and Demographic Relevance: One-size-fits-all solutions risk alienating culturally and linguistically diverse populations, Indigenous communities, and those with unique needs. Co-design and flexible service models are necessary to ensure relevance and equity.

Responsible, Ethical, and Inclusive Implementation

The path forward is clear: AI and emerging technologies must be harnessed responsibly, with a focus on ethics, transparency, and inclusion. Best practices include:

Human-Centric Design: Engage users from diverse backgrounds in the design and testing of digital mental health services.
Robust Data Governance: Prioritise privacy, security, and user control over personal information.
Transparency and Communication: Clearly explain how AI is used, how data is protected, and how decisions are made.
Continuous Feedback and Improvement: Use data and user feedback to refine services and address emerging risks.
Omnichannel Access: Maintain alternative channels (phone, in-person) to ensure accessibility for all.

Conclusion: Shaping the Future of Digital Mental Health

AI and emerging technologies offer unprecedented opportunities to personalise digital mental health support, making care more accessible, efficient, and effective. However, realising this potential requires a commitment to responsible innovation—one that places privacy, trust, and inclusion at the centre. By co-designing with communities, investing in digital literacy, and upholding the highest standards of data governance, governments and providers can ensure that digital mental health services deliver meaningful, lasting benefits for every individual, regardless of their background or circumstances.

At Publicis Sapient, we are committed to helping governments and organisations harness the power of AI and digital innovation to create a more inclusive, responsive, and compassionate mental health ecosystem. The future of mental health care is digital, personalised, and—above all—human.