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Symposium Theme and Sub-Themes

Symposium Theme

AI in Research Management: Agents, Governance, People, Scale

Artificial intelligence is rapidly reshaping the way research is conceived, managed, funded and translated into impact. From intelligent assistants that streamline everyday tasks to sophisticated models that support discovery, AI is changing the research enterprise at every level. At the same time, it raises important questions about governance, integrity, workforce capability and organisational readiness.

This symposium brings together research leaders, research managers, technology specialists, policymakers and practitioners to explore how AI can be adopted responsibly and effectively across the research ecosystem. Through practical case studies, expert insights and interactive discussions, delegates will examine not only what AI can do today, but how research organisations can build the capability, governance and confidence needed to realise its long-term value.

Rather than focusing on technology alone, the symposium considers AI through four interconnected themes that together define successful implementation: Agents, Governance, People and Scale.

Sub-Themes

Agents

AI agents, automation, practical tools, research lifecycle applications

AI is moving beyond simple chatbots to intelligent agents capable of undertaking increasingly sophisticated tasks. From literature reviews and grant development to project management, research administration and reporting, AI agents are beginning to augment work across the entire research lifecycle.

This stream explores emerging AI capabilities, practical applications already delivering value, and the opportunities and limitations of autonomous and semi-autonomous AI systems.

Sessions will showcase tools that improve productivity, reduce administrative burden and enable researchers and research managers to focus on higher-value work.

For example:

  • - AI agents and digital assistants
  • - Research administration automation
  • - Grant writing and funding support
  • - Literature discovery and synthesis
  • - Data analysis and research workflows
  • - Human-AI collaboration
  • - Emerging AI tools and future applications

Governance

Ethics, integrity, policy, security and responsible AI

As AI becomes embedded within research processes, strong governance becomes essential. Research organisations must balance innovation with integrity, ensuring AI is used transparently, responsibly and in ways that maintain public trust.

This stream examines the governance frameworks, policies and risk management approaches required to support responsible AI adoption.

Discussions will consider research integrity, privacy, cybersecurity, intellectual property, regulatory developments and institutional accountability.

For example:

  • - Responsible AI frameworks
  • - Research integrity in the age of AI
  • - Ethics and transparency
  • - Policy development
  • - Data governance and security
  • - Privacy and intellectual property
  • - Risk management and assurance

People

Future of work, psychosocial impacts, capability, change management

AI is transforming not only how work is performed, but the skills, roles and organisational cultures that underpin research. Successful AI adoption depends on preparing people as much as deploying technology.

This stream explores the human dimensions of AI implementation, including workforce capability, leadership, wellbeing and organisational change.

It considers how research organisations can build confidence, support staff through transition and create environments where people and AI work effectively together.

For example:

  • - Future research workforce
  • - Capability development and AI literacy
  • - Leadership in an AI-enabled workplace
  • - Change management
  • - Psychosocial impacts of AI
  • - Trust, acceptance and adoption
  • - Building human-centred AI organisations

Scale

Pilots to adoption, business cases, enterprise, data curation, implementation

Many organisations have experimented with AI, but relatively few have successfully embedded it across the enterprise. Scaling AI requires more than technology. It demands strategy, governance, investment, quality data and organisational alignment.

This stream focuses on moving beyond isolated pilots to sustainable organisational capability. Delegates will explore implementation strategies, business cases, data readiness, adoption models and lessons learned from organisations successfully deploying AI at scale.

For example:

  • - Enterprise AI strategy
  • - Moving from pilots to production
  • - Building the business case
  • - Data curation and readiness
  • - Implementation roadmaps
  • - Measuring value and impact
  • - Scaling innovation across research organisations

Acknowledgement of Country

ARMS acknowledges the Traditional Custodians of the lands from which we connect. We recognise their continuing connection to Country, culture and community, and pay our respects to Elders past and present. We extend that respect to all Aboriginal and Torres Strait Islander peoples.

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