Generative AI in wholesale distribution
Elevating sales and customer service through AI
How is artificial intelligence (AI) technology changing today’s distribution landscape? Discover what’s possible across sales, quotes, orders and customer service when organizations harness the cutting-edge capabilities of Generative AI (GenAI).
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.
Transformation in whole distribution with Generative AI
GenAI can help wholesale distribution companies identify and leverage significant opportunities for increased workforce productivity in their sales enablement, quote generation and order entry, and their post-sales customer service and support.
There are multiple ways Generative AI can bolster sales efforts:
- Selling strategy support: Synthesizing vast amounts of customer data and market research to extract insights and develop suggestions to guide sales time allocation and messaging.
- Selling collateral development: Generating a first draft of customer-facing materials in a company template with customized messaging based on customer data, including the ability to easily replicate and build on past successful collateral.
- Personalized learning: Using a chatbot to identify relevant learning materials quickly, serve as a “digital tutor” on priority topics, and generate easy-to-understand synthesis of content related to products and applications in the customer’s business.
- Customer notes synthesis and insight mining: Processing customer meeting notes (handwritten, typed or voice recorded) to identify next actions, generate inputs into customer relationship management tools, and identify patterns and insights from across the entire sales force.
- Augmenting “next best action” recommendation systems: Using GenAI to create natural language summaries and reminders from “next best action” recommendation systems that identify sales opportunities for reps to pursue, campaigns to use and topics to address with customers.
- Advanced search: Creating a “super search assistant” that allows reps to apply natural language to search by product description, product application, technical specifications and other attributes across company data, vendor data and the web.
- Product comparison and substitution: Developing custom comparisons of products and identifying substitutes based on priority attributes.
- Batch order processing: Identifying product IDs from customer emails or provided documents to prepare data for electronic data exchange order processing or other forms of automated quote or order generation.
- Customer inquiry response: Drafting correspondence to customers to follow up with questions about an inquiry or to explain a quote (for example, comparison of products, options available).
- Augmenting product recommendation systems: Creating easier ways to search and interact with traditional AI- and machine learning-fueled recommendation systems (for example, cross-sell, upsell, substitution) using natural language.
- On-demand customer account insights: Serving up a synthesis of customer account information, past issues and active case summaries to customer service agents in their workflows.
- Agent-facing chatbot assistant: Creating an easy way to search company policies, past order activity and past cases to reduce escalations and improve first-issue resolution.
- Post-case closure summarization: Delivering automatic summarization and tagging of cases based on predefined rules, including sentiment analysis that indicates customer satisfaction.
- Success planning: Generating automated customer success and get-well plans that address areas for improvement with prepopulated goals and actions based on historical customer behavior and account priorities.
- Customer issue visibility: Synthesizing and mining case data at scale to identify patterns in customer issues and agent responses, including the ability to analyze all cases for a given agent and provide specific coaching.
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.
