Exploring the AI-driven future of freight forwarding
In this strategic design project I helped a leading nordic freight forwarding company to explore how AI could enhance their global service production of 12,000 annual orders. Through an audit and focused stakeholder engagement, I identified key inefficiencies and opportunities, and proposed a future vision for AI-driven centralized service model, supported with a solution prototype.
Background
Company handles around 12,000 annual global freight forwarding orders via air and sea, acting as an agent that organizes shipments with logistics partners. We explored how AI could improve the efficiency and scalability of their service operations. At the time, their service production was highly manual, with around 50 people in multiple teams, handling orders with various ways of working. Work was often reactive and unpredictable, leading to reduced autonomy and low engagement among employees.
Approach
With limited resources, I led and executed the project mostly solo with key goals:
• Understand the current state and root problems
• Identify development opportunities
• Craft a tangible future vision and roadmap for measurable change
I audited the current operations, interviewed key stakeholders and service specialists, and then mapped the service, customer and tech landscape. I instructed a custom GPT to help research global foresight and created a high-level service blueprint of the current state. I then presented my findings and facilitated a co-creation workshop with stakeholders to identify opportunity areas and to generate solution ideas.
Findings and opportunities
On of the key findings was that the constant task-switching and re-orientation between orders and customers, created significant inefficiencies. Processing a single shipment typically involved 5–15 tasks, each lasting from a few seconds to a few minutes, with a theoretical average at around 20 minutes of working time per order. Tasks were performed with varying methods, tools and partner services. Teams and individuals had their own ways of working, which made it tough to share tasks or balance the load, especially with complex orders. Without a clear, shared process, the whole operation struggled to scale, even affecting the customer experience, putting pressure to down-scaling at times.
One of the key opportunities we found was that many of the partner tools and consolidating systems offered flexible and modern APIs giving access to data and functionalities. This could enable an AI-driven solution.
Delivery and solution preview
I proposed a centralized service production model to break silos, harmonize workflows, and streamline operations, with the projected outcomes:
• Increased happiness, engagement and autonomy among specialist
• Increased operational efficiency, scalability and predictability
• Improved customer experience
• Strengthened market position and competitive advantage
I delivered a vision brief including measurement model and designed a prototype of the service specialist tooling to communicate the vision. I also prepared a initial validation and implementation plan to support investment decision.
Design evidence & outputs
