AI has been hailed as the next big leap for distribution businesses, promising unprecedented efficiencies, customer insights and profitability. The polls White Cup conducted in recent webinars on the topic show more than 60% of distributors plan to implement AI soon, while 10% are already using it.  

Yet early research on AI projects in development at businesses just two years ago found 60-80% of AI initiatives failed to launch. 

This staggering figure raises an important question for distribution leaders: How can you ensure your company beats the odds and succeeds with AI? 

Here’s a closer look at what distributors can learn from failed attempts to integrate AI into business models and what elements are crucial to developing a sustainable strategy.

PREMIUM – MDM Research: Industry’s AI Participation Jumps from a Year Earlier (July)

Why AI Projects Fail 

Many companies rush to adopt AI without a clear understanding of what they want to achieve. This results in projects that lack focus, drift off course and ultimately fail to deliver value. 

In the Forbes article referenced above, author Ron Schmelzer, who developed a methodology for AI building project management used by companies and government agencies, said he often sees companies assigning AI projects to development teams without addressing one of the most critical factors: data. 

AI relies on high-quality data to generate accurate insights. At the same time, many organizations struggle with data that is incomplete, inconsistent or siloed across systems. This problem is especially prevalent in distribution businesses that rely on ERPs to manage inventory, purchasing, accounting and many other functions. While these systems contain large quantities of valuable data, most sales teams don’t have access to them. ERP administrators or IT leaders can extract this data, but it isn’t typically organized in a way that’s useful for understanding sales trends or customer purchasing habits. 

Sales reps and other customer-facing team members can’t see which accounts bring in the most revenue for the organization, for instance. They’ll just see a long list of “ship to” contacts they’ll need to spend time sorting through to understand which ones are associated with each account. 

Unless your company has purchasing data sorted by both account and contact, it’s difficult for AI to make meaningful recommendations for target accounts and customers, creating a business intelligence blind spot for your organization. 

The Critical Role of Business Intelligence

Even the best AI model imaginable is only as accurate as the data you’re feeding it, and you’ll get the best results if that data is being updated in real-time. By the time many distributors organize their customer and purchasing data into a comprehensive report or hire a consultant to assemble one, that report is likely outdated.  

Business intelligence solutions organize raw data into clear dashboards that drive action. Instead of reviewing a large volume of order data from the past quarter, your sales team can easily see whether sales are trending up or down, which products are most popular, and which customers or accounts purchased specific products.  

They can have more proactive conversations with customers when they have a complete order history, payment history, and any recent activities at their fingertips. If a customer added a specific product to their cart but failed to complete the purchase, the rep can follow up to ask if they have questions, making them more likely to close a deal that would have otherwise been overlooked.  

With this level of business intelligence, your team can use AI to analyze purchasing data and predict which products a specific customer is likely to order next, based on the products most commonly purchased together. They can even predict when that customer is most likely to place the next order, based on their past order history.  

Your leadership team can also set more specific priorities when they can see which products, customers and suppliers bring in the most revenue.  

Technology and Processes to Make AI Actionable 

A strong data strategy is fundamental to the success of any AI initiative, but it’s not enough. Your team needs to be empowered to act on the insights they see and use automation to accelerate their efforts. If they’re relying on spreadsheets with outdated customer information or sending emails that aren’t visible to anyone else within the organization, they’re likely losing track of potential opportunities or open quotes.  

It may take several reminder emails before a customer explores a new product or signs a quote. Purpose-built CRM software makes it easy to automate these communications or assign tasks to specific team members to follow up, ensuring nothing falls through the cracks. An effective CRM for distributors integrates with ERP systems, eCommerce platforms and other sales channels to bring in real-time data, then transforms it into insights your team can act upon.  

For instance, if your sales reps notice a certain product is becoming increasingly popular among your customers, they could recommend it in future emails from your CRM. AI can take this several steps further, recommending relevant products to specific customers based on their individual order history.  

Your team can easily add these products to emails or quotes to increase the potential value of every order.  

Set a Strong Foundation for AI-Assisted Sales 

The distribution industry is evolving quickly. AI is fueling this change while holding immense potential for the future — but only if it’s approached with a clear strategy and a strong foundation in data. By focusing on data quality, integrating AI with business intelligence and a CRM and aligning your projects with strategic goals, you can ensure your AI initiatives not only survive but thrive.