January 23, 2026
Transforming shipping claims: Stena Line's journey with AI
Muhammad A. Samad, Group Head of Digital and Artificial Intelligence at Stena Line
This conversation explores how a traditional industry uses AI to streamline claims, reduce backlog and improve customer experience. Viewers will gain insights into how learning systems, trust building and practical experimentation enable real world operational impact.
Modernising claims processing
A traditional shipping company set out to challenge legacy processes by applying AI and automation to high volume claims handling. With inconsistent documentation, regional variations and rising payouts, the team focused on early progress to test whether modern technology could create meaningful improvements.
Operational impact and customer experience
The rollout delivered clear results. Rejections were cut by 50 percent, payouts aligned better with real damage values and backlogs decreased significantly. Customers also noticed faster processing times, providing positive feedback and signalling that the new approach created tangible value in everyday operations.
Building trust and navigating resistance
Introducing AI into a legacy heavy organisation triggered resistance, particularly where no digital footprint existed. The team found that success depended less on proving the technology and more on building trust, understanding local nuances and enabling people to see how the tools supported their work.
Learning systems and sustainable adoption
Rather than implementing a fixed system, the process relied on constant learning. Models improved over time as data context was built, and teams adapted their ways of working. Upskilling became essential, supported by natural curiosity and external exposure to AI tools, helping employees engage with the new solutions.
Organisational evolution and future readiness
To run AI at scale, business development and digital development were brought closer together. Regional teams collaborated with tech functions to refine processes, address data gaps and maintain momentum. The key learning was to avoid rigid standardisation and instead use the technology to create space for trust, flexibility and continuous improvement.
January 23, 2026
Transforming shipping claims: Stena Line's journey with AI
Muhammad A. Samad, Group Head of Digital and Artificial Intelligence at Stena Line
This conversation explores how a traditional industry uses AI to streamline claims, reduce backlog and improve customer experience. Viewers will gain insights into how learning systems, trust building and practical experimentation enable real world operational impact.
Modernising claims processing
A traditional shipping company set out to challenge legacy processes by applying AI and automation to high volume claims handling. With inconsistent documentation, regional variations and rising payouts, the team focused on early progress to test whether modern technology could create meaningful improvements.
Operational impact and customer experience
The rollout delivered clear results. Rejections were cut by 50 percent, payouts aligned better with real damage values and backlogs decreased significantly. Customers also noticed faster processing times, providing positive feedback and signalling that the new approach created tangible value in everyday operations.
Building trust and navigating resistance
Introducing AI into a legacy heavy organisation triggered resistance, particularly where no digital footprint existed. The team found that success depended less on proving the technology and more on building trust, understanding local nuances and enabling people to see how the tools supported their work.
Learning systems and sustainable adoption
Rather than implementing a fixed system, the process relied on constant learning. Models improved over time as data context was built, and teams adapted their ways of working. Upskilling became essential, supported by natural curiosity and external exposure to AI tools, helping employees engage with the new solutions.
Organisational evolution and future readiness
To run AI at scale, business development and digital development were brought closer together. Regional teams collaborated with tech functions to refine processes, address data gaps and maintain momentum. The key learning was to avoid rigid standardisation and instead use the technology to create space for trust, flexibility and continuous improvement.
