eScience 2026 Workshop

AI tools, models, and workflows are evolving faster than most organisations can adapt to their people, processes, technology stacks, and cultures. Technical training alone (for example, learning a specific model or tool) rarely delivers lasting productivity gains unless it is embedded into real workflows, decision making, governance, leadership, and organisational culture.

This workshop examines what actually works in AI and data capability uplift programs—and what does not—through real world case studies from universities, government (at all levels), small and medium enterprises, the not-for-profit sector, and different religious congregations with examples spanning Australia, and Europe this workshop provides a truly international perspective.

The aims of this workshop will be to explore the gap between what leaders think their teams are doing with AI and what staff actually feel confident and supported to do. This will include the cultural and psychological conditions that make uplift efforts succeed or fail, the reusable lessons from building these programs across sectors so we stop reinventing the wheel, measure what matters, and better understand how the stories leaders and colleagues tell during AI transformation shape engagement, culture, and outcomes.

This workshop is suited to:

Researchers and research software engineers leading AI and data-intensive eScience projects.
Data, AI, governance, and digital transformation leaders in government, NGOs, and SMEs collaborating with research organisations.
Practitioners and teams using, adopting, implementing, or building AI tools and capability uplift programs inside organisations.
Leaders interested in the cultural, ethical, and institutional implications of AI adoption.

The workshop will include presentations, posters and discussion on topics including:

Design and evaluation of AI/data capability uplift programs: wins, failures, unintended consequences, what to measure and why.
AI adoption and workflow automation for high-impact eScience: using shared, linked, collaborative data assets, facilities, and open-source tools/ resources in real projects.
Human readiness, AI governance, and trust: leadership, policy, and organisational conditions that enable safe and effective AI use and foster trust and productivity.
Human–AI collaboration and leadership: understanding which tasks can be effectively delegated to AI systems and which require human judgement, responsibility, and meaning-making.
Culture, narrative, and meaning-making in AI transformation: how organisations across industry, government, and mission-driven or faith-based institutions shape collective understanding and engagement with AI.
Scaling what works: patterns, templates, and re-usable playbooks for capability uplift without “reinventing the wheel” in each new project

Date: Monday 28th September 2026
Location: TBC
Time: TBC - 1/2 day workshop

The workhsop structure is still a work in progress, but the workshop will combine:

Invited talks from practitioners and researchers with deep experience deploying AI in complex organisational settings.
- Georgia Atkinson, Senior Data Scientist at the National Innovation Centre for Data (NICD)
- Carlos Muñoz Novo, partner at The Way Over
- Rhetta Chappell Data Scientist & Partnerships Lead Griffith University’s Relational Insights Data Lab (RIDL) & Griffith Data Trust (GDT)
1–2-minute lightening talk for authors to advertise their work for the poster session.
Poster session for extended abstracts that have been accepted on to the workshop.

A closing interactive session to co-develop a simple, shared “AI Capability Uplift Playbook” for eScience partners.

Call for Papers

We are looking for anyone interested in the above topics in the "What will the workshop involve?" section to submit an extended abstract (2 - 4 pages) to the workshop, which is part of the IEEE eScience conference.

Authors of accepted works will be expected to present a poster at the workshop and give a 1-2-minute presentation to highlight their work.

Authors should use the EasyChair submission web page to submit their extended abstracts. Authors should select "make a new submission" and then select the track "AI Capability Uplift That Actually Works: International Case Studies from Academia, Government, Industry, and Faith-based Organisations".

All submitted extended abstracts will undergo single-blind peer review by two committee members.

Accepted, camera-ready extended abstracts will be submitted to IEEE for inclusion in the eScience 2026 workshop proceedings, in accordance with the conference formatting guidelines.

Important Dates

Paper submission: TBC
Notification of acceptance: TBC
Camera-ready extended abstracts due: TBC
Submission Link

IEEE eScience 2026

AI Capability Uplift That Actually Works:
International Case Studies from Academia, Government, Industry, and Faith-based Organisations

Call for Papers

Important Dates

Programme Committee