The distribution industry is undergoing a profound transformation driven by supply chain disruptions, fluctuating demand and the need for operational efficiency. While AI offers promising solutions—such as predictive analytics, digital twins and automated decision-making—many organizations struggle with effective implementation.  

This is because their AI solutions often focus on technology first, rather than solving real-world distribution problems. Design Thinking provides a structured, human-centered framework that ensures AI initiatives are aligned with business objectives, adaptable to disruptions and capable of delivering measurable value. 

By integrating customer empathy, iterative prototyping and cross-industry insights, Design Thinking helps distributors optimize inventory, enhance supplier collaboration and improve decision-making processes. And as I’ve experienced in the semiconductor and pharmaceutical supply chains, Design Thinking can turn Applied AI into a powerful tool for overcoming distribution challenges. 

The Role of Design Thinking in AI-Driven Distribution 

AI is revolutionizing distribution by enhancing demand forecasting, optimizing procurement and automating logistics. However, AI initiatives often fail due to: 

• Lack of alignment with actual business needs. Many AI solutions are designed without understanding the human and operational factors affecting distribution. 
• Undefined objectives. Organizations invest in AI without clearly identifying the specific challenges it should address. 
• Inability to adapt to real-time disruptions. AI models based on historical data often struggle to handle unexpected supply chain shocks. 

Design Thinking provides a strategic approach that ensures AI solutions are designed to solve practical distribution problems rather than just introducing more data-driven complexity. As I see it, the Design Thinking process has five stages, each of which can be applied to AI: 

• Empathizing with stakeholders 
• Defining the problem 
• Ideating solutions 
• Prototyping 
• Testing and iterating 

Empathizing with Supply Chain Stakeholders 

The empathy stage  is about understanding the challenges faced by distributors, suppliers and customers. AI adoption often fails when solutions are developed in isolation without considering real-world behaviors and operational constraints. 

During the COVID-19 pandemic, semiconductor supply chains faced unprecedented disruptions due to fluctuating demand for consumer electronics and automotive components. Traditional forecasting models, which relied solely on historical sales data, were ineffective in anticipating demand shifts. 

By applying Design Thinking, I developed an AI-driven Forecast Supply Allocation Model that combined real-time supplier constraints with demand signals. This approach led to: 

• Dynamic reallocation of raw materials to critical manufacturing lines. 
• AI-powered scenario planning to anticipate demand shifts and mitigate stockouts. 
• Improved supplier collaboration through automated constraint-based recommendations. 

This AI-human hybrid approach enabled more effective decision-making and improved supply chain resilience. 

Defining Distribution Problems AI Can Solve 

A critical step in Design Thinking is clearly defining the problem before developing a solution. Many organizations attempt to apply AI broadly without specifying what operational inefficiencies it should address. 

In the pharmaceutical industry, supply chain constraints during the pandemic led to critical drug shortages. Initially, the focus was on improving demand forecasting, but through Design Thinking workshops, the real problem was redefined: 

• The challenge was not demand prediction but optimizing supply allocation based on dynamic constraints. 
• Buyers and planners lacked visibility into supplier lead times, making AI-generated forecasts ineffective. 

By shifting focus, the AI implementation was adjusted to: 

• Combine demand forecasting with supply allocation models to provide actionable insights. 
• Predict supplier reliability and adjust procurement strategies dynamically. 
• Implement AI-powered “what-if” scenario planning to test different supply chain disruptions. 

This problem-first approach ensured AI adoption delivered tangible benefits rather than just producing more data-driven forecasts. 

Ideating AI Use Cases by Learning from Other Industries 

For distributors looking to implement AI, the ideation phase of Design Thinking should involve exploring AI applications beyond traditional distribution models and drawing insights from other industries. 

For example, financial markets use AI-driven dynamic pricing algorithms to adjust stock prices in real-time based on supply and demand. Inspired by this, I led the development of an AI-powered inventory allocation model for semiconductor and pharmaceutical distribution. This model included: 

• AI-powered prioritization of inventory based on real-time demand shifts. 
• Dynamic supplier contract adjustments to optimize restocking frequency. 
• Customer segmentation modeling to allocate inventory efficiently and prevent shortages. 

This new AI-based allocation strategy improved product availability and reduced overstocking risks—and we couldn’t have achieved it without Design Thinking. 

Prototyping AI Solutions Before Scaling 

The prototyping phase of Design Thinking focuses on developing small-scale models before full-scale deployment. This is especially important when it comes to AI, as AI-driven forecasting models need to be tested in a controlled environment before being trusted with real-world tasks. 

When my team was developing our solution, the company wanted to simulate different supplier disruptions to evaluate AI response strategies. So, we built a prototype that allowed us to identify flaws in the model’s generated recommendations and correct them before full rollout. We also discovered ways to improve supplier lead-time accuracy and validate dynamic inventory reallocation strategies. As a result, implementation was low-risk, and the tool was optimized before company-wide adoption. 

Testing and Iterating AI for Continuous Improvement 

AI models require ongoing refinement based on real-world feedback. The testing phase of Design Thinking ensures that AI systems remain adaptable and aligned with evolving market conditions. 

For example, when my team was implementing AI-driven supplier allocation models, our initial iterations: 

• Over-prioritized suppliers based on historical data, ignoring recent performance trends. 
• Failed to adapt to unexpected lead-time fluctuations. 

By incorporating real-time supplier performance data and human decision-making feedback, we were able to achieve: 

– 30% improvement in AI-driven procurement efficiency.
– Dynamic adjustment of supplier selection criteria based on changing market conditions.
– Alignment of AI recommendations with business needs, reducing supply chain disruptions. 

This iterative approach ensured that AI solutions remained relevant, adaptable and impactful. 

Design Thinking Powers Applied AI in Distribution 

Many distributors struggle with AI adoption because they treat it as a technology investment rather than a strategic business tool. Design Thinking ensures that AI is developed with a clear business purpose, aligned with human insights and continuously optimized for success. 

To apply Design Thinking to your AI strategy, follow these steps: 

– Empathize with end-users to ensure AI adoption meets their needs.
– Define AI problems precisely to maximize business impact.
– Ideate AI solutions inspired by cross-industry best practices.
– Prototype AI solutions before full-scale implementation.
– Test and iterate AI strategies continuously for ongoing optimization. 

By embracing Design Thinking, distributors can unlock the full potential of Applied AI, ensuring that AI-driven strategies enhance supply chain efficiency, resilience and profitability.