Distribution Strategy Group and Modern Distribution Management both highlight specific use cases for AI technologies in the distribution sector to drive revenue by transforming the sales process. From uncovering opportunities hidden in your data to improving cross-selling and customer retention, a few specific examples include:
Using AI in Distribution To Drive Revenue:
A Simple Guide to Getting Started
Artificial intelligence offers tremendous promise for companies across all industries. AI in distribution is no exception.
Discover the new agentic AI built for distributors
Imagine a customer buys electrical connectors. A month later, one of your sales reps logs in to the CRM and sees a list of the most popular related accessories to recommend to that customer when they pick up the phone.
A few weeks later, your rep receives a notification that, based on their past order history, the same customer is likely ready to reorder another product.
Your rep uses a generative AI assistant to draft an email reminding them to place their order.
Now imagine similar activities happening five times a day, with 10 different sales reps. Halfway through the month, you’ve already exceeded your sales goals.
This isn’t the distant future — It’s already happening.
Using artificial intelligence in the distribution industry isn’t as common as it is in other industries yet, but it’s quickly gaining traction as leaders discover its potential. If you haven’t used AI, you certainly aren’t alone or falling behind, but now is the time to get ahead to gain a competitive edge.
You might be most familiar with examples of how AI and machine learning are eliminating manual data entry and analysis, streamlining warehouse operations, or improving customer satisfaction. However, the more significant opportunities go beyond simply improving operational efficiency. Smart distributors are also incorporating AI into sales and marketing activities to drive revenue growth. AI’s ability to provide deep insights into customer buying habits, optimize upsell opportunities, and offer real-time product performance data makes it more than just a shiny new tool; it’s an entirely new way of working.
Adoption of AI in Distribution
Most organizations are still exploring the potential to use AI in distribution.
Only about one-third of distributors are using AI at all, according to recent research from Modern Distribution Management. Of those who are using it, about 25% have only implemented it for one specific function.
Yet the latest research by the Distribution Strategy Group shows clear interest — 55% of distributors recognize the potential of AI to offer them a competitive edge. Fewer than a third have actually implemented it in some way, but those who are using it are focusing on opportunities that drive revenue rather than only saving time.
Sales and marketing, including improving the website and eCommerce experience, are the most common use cases for AI in distribution cited in the survey.
Progress Deploying AI
This represents a shift from viewing AI as a behind-the-scenes tool to leveraging it as a strategic asset for customer-facing teams. In an industry with shrinking margins and fierce competition, AI offers a pathway to not just survive, but thrive.
AI in Distribution: 8 Quick Wins
Cross-selling and Upselling
We all know if we click on a certain type of product while browsing online, we’ll likely see ads and recommendations for similar products based on browsing that product page.
This type of advertising is known as retargeting, and it most often occurs through digital ads. AI models can enhance retargeting by drawing conclusions from large volumes of data in a fraction of the time it would take an experienced analyst and making recommendations easily accessible to sales reps as they engage with customers. This opens up significant opportunities to improve the sales process for customers and increase order value by strategically recommending related products or suggesting substitutes.
According to McKinsey, about 35% of what consumers buy on Amazon come from personalized recommendations.
Predicting When Customers Are Likely to Reorder
As consumers, we have the ability to save grocery orders and re-submit them each week.
We receive discounts and special offers when we’re most likely to need to reorder common household items. AI enables distributors to make similar predictions about their customers based on their purchasing history. Combining AI with automation makes this functionality especially powerful.
Rather than writing dozens of reminders to follow up with customers at specific times, distribution sales reps can simply receive these recommendations from an AI model that integrates with customer order data and is visible within their CRM. They can even receive automated notifications via email or text.
AI enables distributors to make similar predictions about their customers based on their order history.
Matching Slow-Moving Inventory With the Right Buyers
Excess inventory is a significant problem for distributors.
Many struggle with a lack of real-time visibility into product performance, which means by the time they realize they have inventory liabilities, it might be too late to sell those products and still turn a profit.
AI can give distributors insight into slow-moving stock by analyzing purchasing and ordering patterns, while simultaneously identifying customer buying patterns to pinpoint which customers are most likely to buy these items again.
White Cup uses AI to analyze customer and product data, displaying slow-moving products within its BI dashboard. Distributors can then create a list of customers who purchased those products within a certain period of time and send them an email in minutes.
Many distributors struggle with a lack of real-time visibility into product performance. By the time they realize they have excess inventory, it might be too late to sell those products at a profitable price.
“For us, AI is about providing the right suggestion at the right time to the right person to drive real results for your team.”
Kristen Thom
VP of Product
White Cup
Voice and Image Search and Ordering
Natural language processing tools like Alexa or Siri allow customers to ask about specific products in terms they are familiar with and quickly find them via a mobile device or smart speaker.
These applications are already used frequently to order household products, but they offer the potential to improve ordering in B2B eCommerce sales and wholesale distribution, as well.
For customers who may not know how to describe a complex or unfamiliar product, such as an engine valve in an older piece of equipment, image-based AI can be particularly helpful. Google Gemini is the first AI technology designed to be multi-modal from its inception, allowing customers to receive a detailed description of a photo they take from their phone. In the near future, AI applications could also provide recommendations on where to find a specific part.
In the near future, AI applications could also provide recommendations on where to find that specific part.
Simplifying Product Configuration and Quote Creation
Many distributors sell complex products that require sourcing, engineering, and project management just to develop a quote.
This process is time-consuming for both the customer and the sales, engineering and customer service teams. Consider a distributor selling custom industrial robotics equipment that may require dozens of different components. For this type of sale, it can take months and many hours of the team’s time just to prepare a quote.
AI-powered solutions have the potential to simplify this process, making it easier for customers to find the parts they need and for distributors to compare pricing.
AI can also help distributors increase revenue for complex products by helping them quickly compare the costs of parts among manufacturers or suppliers.
Expanding Customer Value and Customer Base by Improving Marketing Performance
In the same ways that distribution AI improves sales performance, it can also drive revenue by enhancing the ROI of marketing campaigns.
Sending targeted emails to customers most likely to buy top related products or slow-moving inventory are just two specific examples.
Using AI in marketing can help distributors draft higher-converting emails, write more compelling website copy and sales collateral more quickly and develop more enticing offers.
For instance, Google’s Performance Max campaigns use AI to analyze the content of a landing page and automatically generate headlines, descriptions and images.
Using AI in marketing can help distributors draft higher-converting emails, write more compelling website copy and sales collateral more quickly, and develop more enticing offers.
Driving Revenue by Elevating Customer Support
AI technologies like chatbots and virtual assistants can provide instant support to customers, addressing routine questions so your customer service reps and inside sales teams can spend time on more revenue-generating activities.
For instance, an AI-powered chatbot can help a customer find standard offerings quickly on your website so your team can spend more time asking questions that help them identify other opportunities to support them. You might only be shipping products to a few East Coast locations currently and discover that the company actually needs similar products on the West Coast; they just didn’t realize you also serve that market.
An AI-powered chatbot can help a customer find standard offerings quickly on your website.
Evaluating AI Technologies for Distribution
With so many emerging AI technologies and a lack of understanding of what sets them apart, it can be difficult to know where to begin.
To cut through the hype, start with your company’s specific goals and determine how using AI technologies in distribution can support them.
A few key questions to consider:
How can AI support your company’s top priorities?
Think about your company’s best growth opportunities.
Is it maximizing the value of every order? Expanding your customer or dealer base? Identifying more successful partnerships with suppliers?
What are the biggest roadblocks to overcome in that area, and where can AI bring the most value?
If it’s difficult to increase order value because your customers tend to buy the same products consistently and without fail, consider how you can incorporate more upselling and cross-selling into your processes.
It might be a matter of using AI to analyze existing data within your ERP and CRM and making it more accessible to your sales team as they interact with customers.
How robust and accurate is our historical data?
Consider what data your organization has available now. Is it comprehensive, accounting for your full mix of products and factors like seasonality over several years?
Has it been updated within the past year?
Who has access to the data, and how many steps are involved in gathering it?
Many distributors have developed complicated workarounds to compile sales and product data into a report someone can analyze. They either need to have someone with expertise manipulating data in their ERP, use a collection of Excel spreadsheets or third-party tools, or hire a data analyst to get the insights they need.
This is a fundamental problem if they want to introduce new AI technologies.
“Data analytics are not as sexy as AI. But without a clear data strategy, you won’t be able to leverage AI as well as competitors with a cleaner and healthier data infrastructure,” said Tom Gale, CEO of Modern Distribution Management.
How is the AI model trained?
Effective AI models continuously learn from select sets of data.
The quantity and quality of the data directly impact the quality of the output. Consider OpenAI, the creator of ChatGPT. The company states its large language models are trained on information that’s publicly available, licensed from third parties and provided by human trainers.
While this makes it extremely capable of generating the most predictable responses to questions or drafting original emails that follow widely accepted practices, the sources are too general to help distributors draw specific conclusions about what their customers want.
AI models trained on data from your organization will be much more meaningful and actionable if you have the right type of data and a large enough quantity of it.
It’s important to note that AI model training is an ongoing process. While the initial predictions from a new AI model might not be exactly what you expected, models learn and improve quickly as you continuously feed them data.
How do we communicate the value of AI to our employees?
It’s normal to encounter some resistance to introducing any new technology to your team, especially if they’ve been successfully managing customer relationships for years.
Just remember that seeing is believing — the sooner you can show them simple, practical ways they can use AI to accelerate sales and marketing performance, the easier it will be to bring them onboard. Showing them how easy it can be to identify the best upsell opportunities is a great start. Make it clear your company isn’t investing in AI tools to replace people, but to help them maximize their efforts by showing them their next best actions. AI is good at replacing tasks and making recommendations, but it can’t address a customer’s complex product needs or put their mind at ease.
How do we set aside time for training with a new solution?
As MDM research found, there’s a significant knowledge gap in the area of artificial intelligence technologies. 86% of employees want AI training, but only 14% have participated in training programs, according to “Distribution AI: A Playbook to Accelerate Success.”
In addition to making AI training available, bring conversations about AI to the forefront at company meetings and one-on-one conversations. Share success stories whenever possible so everyone understands the real impact AI can have on their sales and marketing goals and your company’s revenue.
What are the technical requirements for successful AI integration?
To avoid adding complexity, look for AI solutions you can easily integrate with your existing technology and processes.
Most distributors already use an ERP, and many also use a business intelligence solution to manage reporting and monitor your progress on key performance indicators. Many sales teams also have a CRM to manage customer outreach. Your marketing team might use different platforms to manage advertising, email communications, and eCommerce, while the customer service team uses helpdesk software.
An integrated CRM and BI software with built-in AI features can replace many single-point solutions. In addition to being easier to implement, it will also be easier for your team to use if they can log in to just one platform.
Consider the scalability, security, and support provided by any vendors you are evaluating as well.
Here are a few questions to consider as you evaluate AI technology providers:
- How well-established is the vendor?
- Do they use a closed or open source AI model?
- Do they have specific expertise in distribution?
- What type of support is available?
- Do they have a documented data security and data privacy policy?
- What is their pricing model?
“Data analytics are not as sexy as AI. But without a clear data strategy, you won’t be able to leverage AI as well as competitors with a cleaner and healthier data infrastructure,”
Tom Gale
CEO
Modern Distribution Management
Maximize Every Move With White Cup's
AI-Powered Solutions for Distributors
White Cup CRM + BI is designed to help distributors maximize every move with AI-powered recommendations that drive revenue.
From the moment your team logs in, they’ll see their next best actions, including:
- Which related products to recommend to customers for the best upsell opportunities
- When customers are likely to reorder based on their past history
- Which products are slow to sell and which customers are most likely to buy them
- Your team can also save hours each month with predictive sales forecasting that uses AI to analyze your historical sales data, accounting for seasonality and other factors.