AIRON

AI is already changing research support, but across the sector we are still at an early and uneven stage of implementation.

Some professionals are experimenting with tools and workflows. Others are waiting for clearer guidance. Many are trying to understand what is useful, safe, ethical, and realistic while managing already busy roles.

This is exactly why we need to come together as a community.

If we do not understand where we are now, it will be difficult to see where we are going. If we work in isolation, we risk duplicating effort, repeating avoidable mistakes, and missing opportunities to learn from one another.

That is the purpose of AIRON Initiatives: to help the research operations community move from uncertainty to direction through shared evidence, practical experimentation, and accessible resources.

Understanding where we are

The State of AI in Research Operations 2026 Survey will help build a clearer picture of how AI is being used, tested, and understood across research operations.

This matters because decisions about AI should be informed by the people closest to the work. Research-supporting professionals understand the realities of their roles, the pressures on institutions, and the areas where AI may genuinely help or create risk.

By taking part in the survey, professionals can make their experience visible. Institutions can better understand how the sector is changing. Together, we can build a stronger evidence base for responsible AI adoption.

Take the survey:
State of AI in Research Operations 2026 Survey

Turning ideas into practice

Evidence is important, but we also need practical examples.

The AIRON AI Use Case Hackathons will create focused spaces where professionals can work together on real challenges, explore possible AI-supported workflows, discuss risks, and develop examples others can learn from.

The aim is not to create perfect solutions overnight. The aim is to help people learn by doing, build confidence, and turn individual experimentation into shared community knowledge.

For professionals, this is a chance to develop practical capability. For institutions, it creates examples that can support more informed local conversations and adoption plans.

Sign up for a hackathon:
Register your interest in AIRON AI Use Case Hackathons

Sharing what already exists

Across the sector, people are already creating useful guidance, templates, examples, and learning materials. Too often, this work remains hidden inside teams or institutions.

The AIRON Resource Library is designed to bring practical knowledge together in one place, so people do not always have to start from scratch.

By sharing resources, we can reduce duplication, save time, learn faster, and help more professionals and organisations access support as AI continues to become part of research-supporting work.

Contribute a resource:
Submit a resource to the AIRON Resource Library

Why this matters

AIRON Initiatives are designed to support professionals, institutions, and the wider research ecosystem at the same time.

For professionals, they offer a way to stay informed, build confidence, share experience, and contribute to the future of research support.

For institutions, they provide insight, examples, and resources that can support more responsible and practical AI adoption.

For the wider research ecosystem, they help ensure that AI adoption is shaped by real practice, not only by technology trends or external pressure.

Most importantly, they help make sure that the people closest to the work have a voice in shaping what comes next.

A practical way forward

The three initiatives work together.

The survey helps us understand where we are.
The hackathons help us explore what responsible practice can look like.
The Resource Library helps us share what we are learning.

Together, they create a practical foundation for a more confident, informed, and responsible approach to AI in research operations.

This is the invitation: take part, share your experience, contribute what you can, and help build the evidence, examples, and resources the sector needs.

Take the survey. Join a hackathon. Contribute a resource. Share the initiatives with colleagues.

The future of AI in research operations should be shaped by the people who understand the work.