AI for
Critical Assets
AI solutions for the systems society depends on.
An interdisciplinary hackathon in Linz developing practical AI solutions for the critical systems society relies on — energy, mobility, healthcare, water, public infrastructure, industrial equipment and other critical assets.
Applications are open. Final challenge tracks and supporting partners will be announced as the program develops.
Countdown
25–26 Sep 2026
Not another LLM demo — real operational impact for the systems society depends on.
Choose How You Want to Get Involved
Apply as a Participant
Work in an interdisciplinary team on a practical AI challenge, supported by mentors and connected to real operational needs from industry.
- Build a prototype or solution concept
- Receive mentoring and domain input
- Work on applied AI challenges
- Present your result at the final pitch
- Connect with industry and other ambitious participants
Support as a Challenge Partner
Help define one of the final challenge tracks, contribute relevant operational context and explore AI-enabled ideas for your own asset-management challenges.
- Sponsor a selected challenge track
- Contribute mentors or jury representatives
- Provide business and operational context
- Ideally provide dummy or anonymized data
- Explore potential follow-up pilots or collaborations
From Physical Assets to Better Decisions
Asset management is the coordinated management of physical and technical assets across their lifecycle. These assets may include production machinery, energy infrastructure, buildings, transport systems, vehicles, equipment or critical operational systems.
The goal is to make better long-term decisions by balancing three dimensions:
Performance
Improve reliability, availability and operational outcomes.
Cost
Optimize lifecycle investments, maintenance planning and resource use.
Risk
Identify vulnerabilities earlier and improve safety and resilience.
Artificial intelligence can support this work through forecasting, anomaly detection, document analysis, computer vision, decision support, optimization and clearer communication of complex asset information.
Real Challenges. Applied AI. Industry Context.
For applicants
This is not a purely theoretical competition. Participants will work on challenge areas derived from real asset-management needs and develop practical prototypes, AI assistants, dashboards, analytical models or solution concepts.
For companies
For supporting organizations, the hackathon offers a focused way to bring practical challenges into an innovation setting, connect with emerging talent and explore solution directions without requiring an immediate full-scale pilot.
Practical AI Prototypes
Interdisciplinary Teams
Expert Mentoring
Industry-Relevant Challenges
10 Potential Tracks. 3 Will Be Selected.
We are currently developing a pool of ten potential AI asset-management challenge tracks. The final hackathon will focus on three tracks selected together with supporting sponsors and challenge partners, based on relevance, available mentoring, feasible data access and prototype potential.
Companies can help shape one of the three final tracks by contributing operational context, mentoring and, where possible, dummy or anonymized example data.
Predictive Maintenance
Forecast failures and reduce unplanned downtime using maintenance, operational or sensor data.
Risk Prioritization
Improve asset-criticality scoring and support more effective mitigation decisions.
Lifecycle Costing
Estimate whole-life costs and optimize replacement, maintenance or investment timing.
Compliance and Audit Automation
Support evidence preparation, documentation reviews and audit-readiness processes.
Asset Data Integration
Connect fragmented information across systems, spreadsheets and operational data sources.
Visual Inspection
Use image-based AI to identify damage, wear or early degradation.
Workforce and Task Scheduling
Improve maintenance planning, crew allocation and operational resource use.
KPI and Performance Monitoring
Detect trends, leading indicators and emerging performance issues.
Spare Parts Optimization
Forecast inventory needs and reduce both stockouts and unnecessary overstocking.
Stakeholder Communication
Translate technical asset information into targeted reports, alerts and management insights.
Final challenge tracks will be announced once supporting companies and implementation contexts are confirmed.
Build Something That Solves a Real Operational Problem
We are looking for motivated students and young professionals who want to apply AI in a meaningful, practical context. Participants will collaborate across disciplines, choose one of the final challenge tracks and build a solution over two intensive days.
Who should apply
- AI and data science students
- Computer science and software engineering students
- Business informatics students
- Engineering students
- Product, UX and innovation-oriented participants
- Young professionals interested in industrial AI and infrastructure
You do not need prior asset-management expertise. Relevant context will be introduced through challenge briefings and mentoring.
Participation is subject to organizer approval through the registration process.
- 01
Apply
Submit your registration through Luma.
- 02
Join a Team
Collaborate with participants from different backgrounds.
- 03
Build
Develop an AI-enabled prototype or solution concept.
- 04
Pitch
Present your result to partners, mentors and a jury.
Quick Facts & Application Pipeline
Team size
2–5 builders
Cost
Free to apply
Eligibility
Students & young professionals
Invites
Rolling, via Luma
Application Pipeline
- 01Open
Now
Early interest & waitlist on Luma
- 02Soon
Q2 2026
Public applications open
- 03Locked
Aug 2026
Review & confirmations sent
- 04Locked
25–26 Sep 2026
Hack weekend in Linz
Shape an AI Challenge Relevant to Your Operations
We are looking for asset-intensive organizations interested in supporting one of three final challenge tracks. Relevant organizations may operate or rely on infrastructure, industrial equipment, buildings, energy systems, transport networks, fleets, production facilities or other technical assets.
Companies can get involved by:
- Supporting or sponsoring one of the selected challenge tracks
- Helping refine the practical challenge statement
- Providing mentors with operational or technical expertise
- Contributing to the final jury or feedback session
- Providing dummy data, synthetic datasets or anonymized example data where feasible
- Exploring promising prototypes after the event
Realistic Data Makes Better Prototypes
Dummy data, synthetic datasets or anonymized example information can help teams build more realistic and valuable solutions without requiring access to confidential production systems.
Shape Practical Innovation
Bring a relevant challenge into a focused AI development format.
Meet Emerging Talent
Connect with motivated interdisciplinary participants.
Explore Solution Potential
Gain early insights into AI-enabled approaches for operational issues.
Enable Follow-up Projects
Identify ideas that may lead to pilots, student projects or further collaboration.
From Challenge to Prototype in 36 Hours
Friday
25 September 2026- 16:00Arrival & registration
- 17:00Opening keynote
- 17:45Track reveal & briefings
- 18:30Team formation
- 19:30Dinner & hack start
- 23:00Evening checkpoint
Saturday
26 September 2026- 08:30Breakfast
- 10:00Mentor sessions
- 12:30Lunch
- 14:00Pitch coaching
- 16:00Submission deadline
- 16:30Final pitches & jury
- 18:00Awards & networking
- 19:30Close
Expected Team Output
- Prototype, demo or structured AI solution concept
- Short final pitch
- Explanation of AI approach and required data
- Expected operational value
- Limitations, risks and responsible AI considerations
- Potential next implementation steps
Preliminary schedule — final times, venue and partners will be confirmed closer to the event.
Linz · Venue To Be Announced
The hackathon will take place in central Linz, Austria. Final venue and on-site logistics will be confirmed in summer 2026.
Working space
Team desks, power, fast wifi, whiteboards and breakout rooms for focused build time.
Meals provided
Breakfast, lunch, dinner and snacks across both days. No off-site runs.
Mentor desk
Direct access to asset-management and AI mentors throughout the weekend.
Location
Linz, Austria
Coordinates
48.3069° N · 14.2858° E
Announcement
Summer 2026
The People Around the Build
Speaker, mentor and jury line-ups will be announced as partners and contributors are confirmed.
Speakers
Industry leaders and AI practitioners sharing insights at the opening and closing ceremonies.
TBA
Mentors
Experienced engineers, asset-management experts and founders supporting teams throughout the weekend.
TBA
Jury
A panel from industry, academia and innovation evaluating final pitches on impact, technical depth and execution.
TBA
Connecting AI Talent with Asset-Management Expertise
Young AI Leaders Linz
Local AI for Good Hub
Young AI Leaders Linz is part of the global Young AI Leaders Community within the AI for Good initiative. Based in Linz, the hub connects emerging talent, academia, industry and institutions around responsible AI innovation, education and applied projects.
Founded
2024
Active members
20+
Network
Global YAIL
Adam Lea-Bischinger
Asset Management Expert
Adam Lea-Bischinger is an experienced asset-management practitioner with expertise in maintenance, reliability, condition monitoring and asset management across asset-intensive industries. He brings the domain perspective behind the hackathon's practical challenge focus.
Learn More About AdamChallenge Partners to Be Announced
We are currently speaking with companies and organizations interested in supporting the final challenge tracks through sponsorship, mentoring, operational expertise and suitable example data.
Challenge Track Partner
Challenge Track Partner
Ecosystem Supporter
Frequently Asked Questions
Take Part in AI for Critical Assets
For Applicants
Build AI-enabled solutions for real operational challenges with an interdisciplinary team.
For Companies