AI and Railways: Taking asset performance to new heights
Discover insightful perspectives and findings about the impact of AI on rail asset management gained from in-depth interviews with key industry stakeholders. Document downloaden
Rail Asset Management | Whitepaper
AI and Railways: Taking asset
performance to new heights
AI and Railways: Taking asset
performance to new heights
3 | 17AI and Railways: Taking asset performance to new heights
AI holds transformative potential for the
railway industry, offering improvements
across various domains. One domain
ripe for AI-enabled transformation is rail
asset management. With the right mix of
insight, expertise, and tools, their use of
AI can become incredibly transformative –
building a foundation that will reshape rail
asset management.
AI holds transformative potential for the
railway industry, offering improvements
across various domains. One domain
ripe for AI-enabled transformation is rail
asset management. With the right mix of
insight, expertise, and tools, their use of
AI can become incredibly transformative –
building a foundation that will reshape rail
asset management.
4 | 17AI and Railways: Taking asset performance to new heights
Transforming rail asset management with AI
AI evangelists, data scientists, enterprise architects,
business process owners, project managers, and
developers already understand the value of AI.
Meanwhile, other stakeholders might be less
enthusiastic because adopting AI is considered
challenging – requiring careful planning, cross-
departmental collaboration, and significant
organizational changes including shifts in work-
flows, processes, and job roles.
Railway organizations are no strangers to this
reality. However, they also know that resistance to
such change – from cultural barriers to lack of
stakeholder buy-in – can hinder successful
implementation. The future of their industry relies
on building a workforce that thoroughly under-
stands AI’s capabilities and limitations, clearly
articulates business needs, and quickly adopts
new technologies and innovations.
SAP commissioned a survey to unravel the true
value of using AI in rail asset management.
Conducted in December 2023, a series of
in-depth interviews with key industry stakeholders
uncovered insightful perspectives and findings
on the following topics:
• Current business practices in AI adoption
• Areas of improvement for asset management
• Perceived value of AI and strategies for
realizing them
• Business and IT skills necessary for effective
AI-enabled practices
• Technology considerations for data
management, integration, and protection
• Future rail industry vision for AI in asset
management within the next 5–10 years
Transforming rail asset management with AI
AI evangelists, data scientists, enterprise architects,
business process owners, project managers, and
developers already understand the value of AI.
Meanwhile, other stakeholders might be less
enthusiastic because adopting AI is considered
challenging – requiring careful planning, cross-
departmental collaboration, and significant
organizational changes including shifts in work-
flows, processes, and job roles.
Railway organizations are no strangers to this
reality. However, they also know that resistance to
such change – from cultural barriers to lack of
stakeholder buy-in – can hinder successful
implementation. The future of their industry relies
on building a workforce that thoroughly under-
stands AI’s capabilities and limitations, clearly
articulates business needs, and quickly adopts
new technologies and innovations.
SAP commissioned a survey to unravel the true
value of using AI in rail asset management.
Conducted in December 2023, a series of
in-depth interviews with key industry stakeholders
uncovered insightful perspectives and findings
on the following topics:
• Current business practices in AI adoption
• Areas of improvement for asset management
• Perceived value of AI and strategies for
realizing them
• Business and IT skills necessary for effective
AI-enabled practices
• Technology considerations for data
management, integration, and protection
• Future rail industry vision for AI in asset
management within the next 5–10 years
5 | 17AI and Railways: Taking asset performance to new heights
Staying on the right track to
high-performing rail assets
High-performing rail assets are vital to achieving
railway performance excellence. Railways face
two unique asset management complexities:
• On-time delivery: Freight and passengers get
to their destination in good condition and within
agreed timelines.
• Capacity planning: The utilization of rolling
stock and network assets is maximized.
Stir in safety and an increasing number of govern-
ment regulations that require compliance, and
the overall picture becomes clear. There is a
relentless drive to do more with less in an increas-
ingly risk-averse private and public environment.
Interviewed rail organizations consider AI as a
key technology enabler to improve customer
experience and asset utilization.
In our exploration, industry experts shed light
on specific asset management focus areas and
scenarios where AI is poised to make a significant
impact (see Figure 1). The consensus is that AI
will greatly enhance the generation of dynamic
maintenance demand and maintenance planning,
approval, and orchestration.
Asset performance management, maintenance
scheduling, and dispatch and asset register
management will also benefit significantly.
Mobile execution is expected to yield medium
to high benefits from AI.
Figure 1: Top rail asset management focus areas for AI-enabled transformation
Low Med High Very High
Generation of dynamic
maintenance demand 9 9 45 36
Maintenance planning,
approval, and orchestration 0 27 36 36
Asset performance
management 25 13 25 38
Maintenance scheduling
and dispatch 11 44 44 0
Asset register management 33 22 44 0
Mobile execution 29 29 43 0
IMPACT SCORE
34
34
22
21
19
15
Response Ranking (%)
Staying on the right track to
high-performing rail assets
High-performing rail assets are vital to achieving
railway performance excellence. Railways face
two unique asset management complexities:
• On-time delivery: Freight and passengers get
to their destination in good condition and within
agreed timelines.
• Capacity planning: The utilization of rolling
stock and network assets is maximized.
Stir in safety and an increasing number of govern-
ment regulations that require compliance, and
the overall picture becomes clear. There is a
relentless drive to do more with less in an increas-
ingly risk-averse private and public environment.
Interviewed rail organizations consider AI as a
key technology enabler to improve customer
experience and asset utilization.
In our exploration, industry experts shed light
on specific asset management focus areas and
scenarios where AI is poised to make a significant
impact (see Figure 1). The consensus is that AI
will greatly enhance the generation of dynamic
maintenance demand and maintenance planning,
approval, and orchestration.
Asset performance management, maintenance
scheduling, and dispatch and asset register
management will also benefit significantly.
Mobile execution is expected to yield medium
to high benefits from AI.
Figure 1: Top rail asset management focus areas for AI-enabled transformation
Low Med High Very High
Generation of dynamic
maintenance demand 9 9 45 36
Maintenance planning,
approval, and orchestration 0 27 36 36
Asset performance
management 25 13 25 38
Maintenance scheduling
and dispatch 11 44 44 0
Asset register management 33 22 44 0
Mobile execution 29 29 43 0
IMPACT SCORE
34
34
22
21
19
15
Response Ranking (%)