Generative AI in wholesale distribution

Generative AI in wholesale distribution: Opportunities abound

Wholesale distribution companies have a significant opportunity to increase the productivity of their workforce by focusing their GenAI efforts on sales enablement, quote generation and order entry and post-sales customer support.

GenAI has the potential to generate trillions of dollars in gross domestic product (GDP) for the global economy and drive new levels of worker productivity.¹ Even if a fraction of that impact is realized, the opportunity for many organizations is significant and potentially transformative. For wholesale distribution companies, one of the largest opportunities to unlock new value is by using GenAI in internal sales and customer service operations. Most lines of trade in wholesale distribution—ranging from food service to electrical to medical—are headcount-intensive businesses, with 5% to 7% of expenses typically tied to sales and service labor.²

According to a Deloitte analysis, applying GenAI to three domains within sales and customer service—sales enablement, quote generation and order entry and post-sales support—has the potential to generate 75 to 100 basis points of earnings before interest and taxes (EBIT) improvement for the average wholesale distributor.³ Understanding the scope of what GenAI can do and potential use cases for implementing it is a first step to helping your organization better leverage this powerful technology.

Putting it all together: Key considerations

To successfully implement Generative AI and unlock new value in sales and customer service, wholesale distributors need to think critically about the right combination and sequencing of use cases, the specific technologies to deploy, how to up their game in data and knowledge management, and finally what changes need to be made to their operating model and workforce planning.

To get started, we offer some questions organization leaders should ask themselves:

• Does the use case add value and support the strategic goals of our organization? What’s the return on investment (ROI)? How should multiple GenAI use cases be sequenced to maximize time to value and ROI?
• What technologies and vendors should be used? How do they fit into our organization’s overall technology road map and enterprise architecture? What overarching policies and governance need to be in place?
• Is the right data foundation in place for the effort? How should data and knowledge management practices evolve in preparation?
• Who will lead these efforts? What roles should our functional leaders in sales and service play versus information technology (IT), data, analytics and AI leaders?
• How fast should the sales and service organizations evolve? Who will lead these efforts and manage the tension between running the business and transforming the business?

These questions take time to answer, but leading organizations typically don’t resolve every open item before getting started. Proofs of concepts and pilots accelerate learnings and help bring clarity to the specific choices that must be made to scale Generative AI effectively. Learn more in our full report.

Contributors: Sanjay Agarwal, Guy Blissett and Celia Ludwinski

Endnotes

¹ Goldman Sachs, “Generative AI could raise global GDP by 7%,” April 5, 2023.
² Deloitte analysis includes marketing spend, typically significantly smaller than sales and service related spend.
³ Deloitte analysis.