Artificial intelligence (AI) has shifted from a trendy buzzword to a serious business tool, but for wholesalers and distributors, widespread adoption of this technology is still in its early stages. Only 21% of B2B commercial leaders surveyed by McKinsey reported that their companies have fully enabled AI for buying and selling.
Many merchants understand the huge potential of AI, but are hesitant to integrate and apply these tools until their data feels “ready”. That hesitation makes sense, because in distribution accuracy is everything. You can’t automate what you don’t trust.
On the flip side, merchants that have already adopted AI are finding ways to make every part of their sales and operations engine run smarter, with more accurate forecasting, faster quoting, and deeper customer insights.
For distributors facing tightening margins and rising customer expectations, AI is quickly becoming the differentiator between those who grow and those who lag behind. In this article we’re looking at how AI can be used to improve data management and drive sales efficiency.
What AI adoption looks like in 2025
AI adoption across the wholesale and distribution sector is happening, but unevenly. Some companies haven’t started, some are exploring small pilot projects, and others have already embedded this technology into their core operations. The difference often comes down to data maturity, leadership vision, and how clearly the business use case is defined.
According to a Modern Distribution Management survey, 34.9% of distributors are now using AI for multiple activities, while 25% say they still aren’t using AI in any of their workflows.
AI adoption typically begins in three core areas:
- Sales enablement and forecasting: Predictive models can analyze transaction history and seasonality to help sales teams anticipate demand and prioritize outreach. For companies that have already implemented AI systems, 87% say it has positively impacted the efficiency of their sales teams.
- Pricing optimization: AI tools can recommend real-time adjustments based on costs, competition, and customer behavior, ensuring margin protection while staying competitive.
- Inventory and supply planning: Machine learning can improve forecasting accuracy by detecting patterns in orders, returns, and regional demand, helping reduce both overstock and shortages.
Early adopters are already reporting measurable results — from sharper forecasting accuracy, to faster quoting and more efficient inventory management. McKinsey notes that these early movers stand to increase their cash flow by 122%, while late adopters could lose up to 23% of their cash flow.
How AI turns sales insights into revenue
Sales success in the wholesale and distribution industry has traditionally depended on intuition. Reps know their customers and can sense when a reorder is coming, but that knowledge often lives only in their heads. Artificial intelligence is helping turn that instinct into something more measurable and repeatable.
By analyzing historical transactions, AI models can identify factors like:
- Which customers are most likely to reorder
- Which customers are at risk of churn
- Which products are most profitable to promote
- Which products tend to be purchased together (cross-sell potential)
- Which accounts respond best to flexible financing or extended terms
A restaurant supply distributor, for example, might discover that customers who buy a specific type of disposable packaging tend to reorder within three weeks. An AI-enabled system can automatically flag these accounts or trigger personalized follow-ups exactly when buyers are ready. This precision leads to higher conversions and fewer missed sales opportunities.
Quoting engines powered by AI can merge pricing, inventory, and contract terms to produce accurate quotes in seconds. The result is what many B2B leaders describe as augmented selling. These are conversations that stay human, but are guided by data-driven intelligence.
AI can also act as a co-pilot for sales. Natural language models can scan CRM notes, emails, and purchase histories to find the next best customer to call and the most profitable product to upsell to them.
When every interaction is informed by patterns across past customer behavior, AI can turn sales from reactive to proactive, helping distributors capture revenue that might have otherwise slipped through the cracks.
Why clean data is the foundation for success
Automations based on bad data lead to bad business decisions (only much faster!). AI learns from the information it’s given, so without clean data, even the best AI models fail. Many wholesalers and distributors operate from siloed product catalogs alongside fragmented ERP and CRM systems where duplicates, errors, and outdated contacts are common.
Across the distribution industry, leaders consistently cite data quality as the biggest obstacle to realizing AI’s full potential. Only 35% of sales professionals say they completely trust the accuracy of their organization’s data.
Before integrating AI technology, merchants need to first ensure that their product, pricing, and customer data are clean, structured, and unified across systems. The good news is that automations can help here too.
Machine learning models are already being used to scan large product catalogs, detect duplicate SKUs, standardize product descriptions, and flag anomalies in pricing or inventory data. What once required months of manual spreadsheet work can now happen in days, allowing teams to focus on more of the work they enjoy, instead of messy data cleanup. Every hour saved on data cleanup or quote corrections is an hour spent selling, which is where AI’s return truly compounds.
Aside from inaccurate data problems delaying wider AI adoption, many companies face skills gaps, with sales and operations teams not yet being fluent in interpreting AI-generated insights or understanding how to use AI in their daily decision-making. According to Salesforce, 49% of sales leaders say they’re not sure how to safely use generative AI to improve efficiency.
AI is only helpful if teams trust and use the output. Transparency also matters. When reps can see why the system recommends something to them, they’re more likely to act on this data.
How automation is transforming the sales process
Time is the most valuable currency in wholesale and distribution, and it’s also the one that’s most wasted. Sales reps can spend hours each week on manual quotes, chasing approvals, and cross-checking inventory instead of talking to customers. Salesforce reports that non-selling tasks such as admin work and meeting preparation can consume 70% of reps’ time.
AI tools can change this dynamic completely, by:
- Synthesizing vast customer datasets to extract insights and develop suggestions to improve sales time allocation and messaging.
- Generating customer-facing materials with customized messaging based on data.
- Deploying a chatbot for instant customer service on priority topics.
- Processing customer meeting notes to identify next actions, inputting this data into a CRM, and identifying insights and patterns.
- Creating natural language summaries and reminders that identify sales opportunities for reps to pursue, plus talking points for customer communications.
The goal isn’t to replace human sales reps, but to free them from repetitive administrative tasks so they can focus on relationship-building and strategy. This time saving translates directly into revenue, with higher conversions and stronger customer retention rates.
According to a McKinsey report, 30% of all sales-related activities can now be automated. Early adopters of sales automation report increases in customer satisfaction and greater time spent with customers, with efficiency improvements up to 15% and sales uplift up to 10%.
Automations also improve accuracy. When a buyer requests a quote, the system can pull real-time pricing, freight, and tax data directly from unified systems. Instead of waiting days for a manual response, they can get an accurate quote in minutes. In competitive industries like building materials or restaurant supply, this level of speed and convenience can be the deciding factor between winning and losing a deal.
Beyond quoting and approvals, machine learning models can scan transaction histories, open quotes, and even email sentiments to flag which customers are most likely to buy (or most at risk of churn).
For example, a sales rep logging into their CRM can now see a ranked list of next-best steps telling them who to call, what to pitch, and when to follow up. Reps don’t have to guess where to spend their time or which account to pursue, the system tells them and data backs it up.
A Salesforce report found that AI is making the biggest difference to sales teams by:
- Spotting errors, updating records automatically, and keeping sales data accurate.
- Analyzing customer behavior and feedback to surface needs before a rep even picks up the phone.
- Using predictive analytics to help forecast reorders, suggest upsells, and tailor recommendations to each buyer.
- Flagging pipeline risks and predicting deal outcomes, helping teams plan with confidence.
- Creating personalized emails and follow-ups in seconds.
As AI and automations continue to get smarter, the biggest efficiency gains will come from doing the right work, faster. Merchants that successfully automate their key processes will be able to deliver the seamless buying experience that B2B customers expect.
Building a successful roadmap for AI sales efficiency
For distributors ready to bring AI into the sales process, success depends less on the technology itself and more on having a clear, disciplined roadmap to follow. That means moving in deliberate phases where each step builds confidence in the technology.
Data preparation
As we mentioned earlier, any AI initiative relies on the quality of its underlying data. Most merchants discover early on that their customer, pricing, and product records are inconsistent or incomplete.
This is the time to audit data sources, fix duplicates, and automate cleaning wherever possible. Machine learning tools can flag errors and standardize product names, but human oversight still matters. The faster you can make your data AI-ready the sooner this technology can start adding value.
Pilot projects
Rather than trying to deploy AI across the entire business, start with one or two use cases tied directly to measurable results. Many distributors begin by automating quote generation, forecasting reorder timing, or pinpointing the next-best customers to contact.
These early projects build trust in the technology and generate internal proof points for stakeholders, as well as proving to sales teams that AI can make their jobs a lot easier.
Integration
Once initial pilots show value, the goal is to connect AI tools with existing ERP, CRM, and eCommerce systems so insights can flow naturally across the business.
An isolated AI model that lives in a vacuum doesn’t scale, but a connected system that feeds data back into sales workflows does. This is where the gains in sales productivity start to compound.
Continuous learning and iteration
AI programs improve the more they’re used, so tracking KPIs and collecting user feedback is essential. Teams should measure indicators like adoption rates, forecast accuracy, and conversion lifts, and use those insights to fine-tune models and workflows over time. As teams begin to trust the outputs, AI adoption will accelerate organically.
Companies that succeed with AI share a few common traits. They start with realistic scopes and don’t chase perfection. They move quickly, learn fast, and adapt as needed. When distributors follow this path, AI can eventually become an indispensable part of how their sales and operations engine runs.
In summary
AI won’t replace the human touch that defines the B2B wholesale distribution industry, but it will definitely raise expectations for speed and precision.
Buyers will increasingly expect distributors to anticipate their needs, not just respond to them. Sales reps will rely on automation so they can focus on strategy and relationship-building rather than data entry. And leaders will rely on real-time data to guide smart, fast decisions.
The companies that start adopting AI across their sales processes now will have a huge competitive advantage once this technology reaches mainstream adoption.