How generative AI is disrupting distribution

Generative artificial intelligence (gen AI) is set to unleash a wave of disruption to businesses in all sectors. McKinsey analysis suggests that the technology could be worth $2.6 to $4.4 trillion to the global economy every year, with the biggest impact seen in customer operations, marketing and sales, software engineering, and R&D.

The distribution sector stands to benefit from the gen AI revolution as much as any other. The exhibit offers a glimpse of what it might be like to work in a gen-AI-enabled distribution business.

Generative AI will unlock next-level growth and productivity at an industrial distributor in several areas.

It’s already happening

Hypothetical scenarios for 2030 and beyond are no longer enough. Leaders are already using gen AI in their distribution businesses today. Here are five examples of distribution companies that are creating value with gen AI technologies:

  1. Sales and marketing: A building material distributor is using gen AI to create personalized marketing emails based on customer attributes. The company uses an off-the-shelf marketing content generation tool, which takes data from a lead generation engine and generates a custom message according to the customer’s details, such as their trade and current project, all in the appropriate brand voice.
  2. Procurement: An industrial and electronics player is using gen AI to automate the prescreening of supplier bid documentation, resulting in a 90 percent reduction in review time and a two-month acceleration of the timeline from tender to project start. The AI model provides structured summaries and actionable insights from supplier data, improving efficiency and decision making in the bidding process.
  3. Inventory management: A large semiconductor and electronics distributor is using gen AI to accelerate the tariff code classification of products. The company applies a large language model (LLM) that can link product descriptions to tariff guidelines. The model helps assign Harmonized Tariff Schedule (HTS) codes for millions of SKUs, improving the accuracy and speed of the process. In a pilot project, gen AI chose the correct tariff code around 95 percent of the time.
  4. Logistics: A large industrial distributor is using gen AI to reduce shipment lead times by managing shipping documents more efficiently. The company is increasing the share of documentation that is automatically generated from end to end, and harnessing gen AI to proactively identify missing documentation and propose actions to address issues and avoid risks. Ninety to 95 percent of trade documentation in sea transport is not automated, leading to multiple interfaces and information exchanges between stakeholders, with many opportunities for mistakes and inconsistencies. The new system has cut the average lead time for producing documentation by 60 percent, improved shipment lead times, and reduced the share of shipments held up due to missing documents.
  5. Customer service: A global logistics company is implementing gen AI functions to improve its customer service offerings. This logistics player uses a gen AI solution to increase the accuracy of chatbot responses to customer search queries. The system also automatically generates frequently asked questions pages, assisting customers who search for help using the company’s app and website.

Ready to scale?

While these examples have delivered significant value, the use of gen AI by distributors is currently at an early phase, with companies focusing on a narrow range of specific use cases. That’s in line with the experience of other industries. In a global survey on AI adoption, McKinsey found that one-third of organizations are regularly using gen AI in at least one business function, and 60 percent of organizations with existing AI capabilities are now using gen AI. Only a few companies are prepared for the widespread adoption of the technology, however, with just 21 percent of respondents reporting established policies governing employees’ use of gen AI technologies in their work.

That needs to change. The adoption of gen AI is proceeding rapidly, and its application across the full distribution value chain is likely to become commonplace. We expect that every role will use gen AI technologies to improve efficiency and boost output. Distribution industry leaders can take five steps to stay ahead of this trend:

  1. Strategic road map: Organizational leaders should develop an overarching transformation road map that can help sequence the steps of implementation, including addressing contemporary industry threats such as increased disintermediation and nontraditional disruptors like pure e-commerce players.
  2. Use case selection: To ensure that the adoption of gen AI is directed toward an organization’s greatest needs, leaders should prioritize a list of use cases within their organization and focus on applications with the greatest impact and feasibility. For example, distributors should prioritize using gen AI as a generator of bespoke customer solutions to address higher customer expectations for convenience and services.
  3. Use case execution: It’s important that organizations implement the different use cases they’ve prioritized by adapting existing enterprise-grade solutions to their specific needs. Leaders should build robust ecosystems around their gen AI tools, ensuring those tools fit into the current enterprise architecture and have been tested against anticipated customer use.
  4. Governance: Leaders should develop centers of excellence to govern the utilization of gen AI tools. These centers should be staffed by cross-functional teams of engineers, data scientists, and experts in machine learning, ethics, and risk. Additionally, a risk management plan that details specific actions to identify and address security, privacy, and fairness concerns is essential to gen AI governance.
  5. Change management: Finally, leaders wanting to adopt gen AI must prepare their organizations for change and support them through it. This is done via positive role modeling of gen AI utilization, fostering understanding and conviction in others, and reinforcing the use of the technology through incentives and goal setting. Companies will also need to invest in capability building to ensure their organizations have the skills to develop, maintain, and generate maximum value from gen AI tools.

Generative artificial intelligence is helping distributors offer more responsive, personalized, and efficient service. To deploy this exciting new family of technologies at scale, leaders need to seize the opportunity now. That will require a robust strategy, new capabilities, and effective governance and change management mechanisms.