The Distribution Blog

AI in Distribution: How Artificial Intelligence Can Drive Growth

January 26, 2022

Table of Contents

Artificial intelligence (AI) is a growing industry that has changed the way businesses function and interact with their customers.

McKinsey Global Institute estimates that AI will grow the global economy by more than $13 trillion by 2030. Additionally, up to 70% of companies will have adopted some AI technology by that time.

Other studies estimate economic growth to exceed $15.7 trillion, with $6.6 trillion in growth resulting from increased productivity and $9.1 trillion resulting from the side effects of consumption.

Why is artificial intelligence growing at such a rapid rate? And what implications does it have for B2B distributors? Below we will explore the basics of AI and why implementing this technology into your business model is essential for future growth.

AI and Machine Learning Basics

AI is a broad term that encompasses any system trained to consume information and use what it has learned to reason intelligently. Although basic AI systems have been around since the 1950s, it has only been through recent hardware advancements that this technology has progressed to its current iteration. By training on example data, AI models can perform various tasks, from playing chess to assisting doctors in surgery, to predicting customer behavior.

Techopedia describes artificial intelligence as “A branch of computer science that focuses on building and managing technology that can learn to autonomously make decisions and carry out actions on behalf of a human being.” In addition, “AI is not a single technology. It is an umbrella term that includes any type of software or hardware component that supports machine learning, computer vision, natural language understanding (NLU) and natural language processing (NLP).” According to McKinsey, there are five main categories of AI:

  • Computer vision
  • Natural language processing
  • Virtual assistants
  • Robotic process automation
  • Advanced machine learning

Machine learning models are a type of AI often used to support B2B business operations and improve with experience. For example, a machine learning model can analyze a buyer's purchasing behavior to make higher-quality recommendations. If the customer does not resonate with the system's suggestions, the model adjusts to recommend more relevant items in the future.

Deep learning models are an even more specialized subset of AI. Deep learning is a highly advanced form of machine learning that can absorb massive amounts of information to develop an acute understanding of data and customer behavior. Whether they are fed “clean” or “noisy” data, deep learning models consistently achieve nearly 99% prediction accuracy . As a result, distributors who use deep learning AI to enhance operations will see productivity improvements, enhanced relationships with customers and increased profits over time.

AI is everywhere. Email spam filters, GPS navigation, Netflix recommendations and virtual assistants like Alexa and Siri are all examples of artificial intelligence. As a result, AI plays a role in almost every aspect of our daily lives. As developers shift their focus to B2B, utilizing AI for business reasons will become more common. With it, companies can automate manual processes and understand customer behavior like never before.

How is AI Different from Other Tech Innovations?

Many past technical innovations, such as computers, have tremendously impacted business. However, whereas computers evolved gradually over decades, AI learns and grows exponentially. According to the Harvard Business Review, classical learning models plateau after a certain point, but deep learning models continue to advance as they absorb more data. As a result, AI models with five years of data are significantly more valuable than models with only a few months or a year of information.

For example, when you beta test a new website, the first few users do a lot to help improve the site because they find the bugs. But by the time you reach a thousand users, there’s not much they can do to add value. Contrast that with a search engine that uses an AI algorithm to predict and suggest search terms: with each new search, the AI adds another data point and uses all the data it has accumulated to improve its performance and, ultimately, the user experience. 

How is AI Changing Business?

AI has the potential to learn and grow exponentially. While classical learning models plateau after a certain point, deep learning models continue to advance as they absorb more data. For example, because these systems are continually improving, AI models with five years of data are significantly more valuable than models with only a few months or a year of information.

Businesses that adopt AI now will have a significant head start over companies that adopt it late or not at all. For example, Amazon's recommendation platform is responsible for approximately 35% of sales , making it a multibillion-dollar asset. Netflix uses its recommendation algorithms to keep 75% of viewers engaged while AI predictions alert the company when a customer is likely to unsubscribe. By sending proactive suggestions to these users, Netflix's AI saves $1 billion per year The value of AI comes from its ability to give companies a competitive advantage by automating manual processes, providing customer insight to sales teams and making highly accurate product recommendations. As a result, AI tools have the potential to transform every area of a company, including:

  • Sales: After processing customer data, models will provide your sales team with valuable insights about product demand and purchasing patterns. With it, your team will have a clear picture of each customer's order history, buying habits, and reorder needs, as well as relevant product pairings and cross-sell recommendations.
  • Marketing: Your team can integrate AI into every level of their marketing efforts, from optimizing digital ads and organic content to providing more relevant product recommendations to the customers browsing your website.
  • Customer Service: AI can enhance your customer experience by providing CSRs with real-time, relevant customer information. When a customer reaches out to your support team, CSRs will have all the information needed right at their fingertips, including order history and potential upsell opportunities.
  • Logistics: AI models can dive into your databanks to identify key performance metrics within your organization. Your C-suite can then review this logistical information through company-wide overviews or by department.
  • Inventory Management: With COVID-19 forcing businesses to implement stricter security protocols and lockdowns, many distributors take advantage of AI to monitor customer storerooms and optimize stock levels remotely. Specially designed apps and IoT weight sensors streamline inventory management and drive automated reordering.
  • Order-to-cash processing: One of the most valuable aspects of AI is its ability to automate manual and repetitive tasks, especially with the order-to-cash cycle. AI can automate order and invoice processing, so your team has more time to focus on sales and customer support.
Applications of AI in Distribution

Why Is AI Important for Distributors?

Although artificial intelligence is common in B2C, B2B businesses did not use it as regularly until recently. This slow integration is partly due to the fact that B2B organizations have unique challenges that require specialized AI solutions. For example, while the average B2C storefront may offer a few thousand products, most distributors sell hundreds of thousands or millions of SKUs across multiple channels. As a result, AI models designed for B2C are not powerful enough to handle the needs of most B2B businesses.

The more information a model has, the faster it can learn and perform advanced functions. Since distributors have more data available for deep learning models to analyze, they have a distinct advantage over companies in other industries when it comes to rolling out AI to their organization. This wealth of data puts distributors in a great position to effectively leverage AI to sell more and optimize their business processes. Order automation, remote inventory tracking, inventory management, instant data entry, product recommendations and operational insights can all be enhanced with this technology.

Distributors will see a high return on investment with AI integration due to:

  • More data: Distributors have years of transaction history data with millions of product combinations at their disposal. This information makes deep learning models more precise and effective.
  • Fewer ethical constraints: Distributors generally have fewer ethical constraints and regulations than healthcare or legal companies do. The lack of regulation means developers can create AI models which focus solely on product recommendations and operational efficiency.
  • More opportunities: Until recently, AI vendors weren't focused on solving problems in the distribution industry. However, now that more developers are creating applications for B2B use, distributors who adopt AI early will see a significant improvement in workflows, productivity and overall operations.

McKinsey found that by 2030, early adopters will likely see exponential growth resulting from their investment in artificial intelligence. “At one end of the spectrum, front-runners are likely to benefit disproportionately. By 2030, they could potentially double their cash flow. Front-runners tend to have a strong starting IT base, a higher propensity to invest in AI, and positive views of the business case for AI.

At the other end of the spectrum, non-adopters might experience around a 20 percent decline in their cash flow from today's levels, assuming the same cost and revenue model as today.” AI is essential for the future of distribution. Although the adoption of AI for things like data entry and sales order automation is growing, only 12% of distributors today use AI for sales and marketing.

Unfortunately, many distributors still rely on separate sales channels for marketing, internal sales, external sales, customer service, ecommerce and others. The division of data between these departments ultimately results in frustrated customers, inefficient sales teams and lost wallet share.

Multichannel vs. omnichannel

As the global economy becomes more reliant on ecommerce and digital buying, investing in AI now will give your business an advantage over your competition. To maintain growth, distributors must optimize processes and improve customer experiences. Thankfully, AI is helping. The distribution businesses we work with at Proton, frequently refer to AI as their “secret weapon” for driving sales, growth, and customer satisfaction. Early adoption of artificial intelligence will grow your business while enabling your team to be more productive, efficient and consultative.

Four Areas Where Distributors Can Apply AI Today

Despite many emerging AI solutions in development, distributors can already apply AI in several ways.

Distribution Sales

Distributors can already use AI to assist with cross-selling and upselling, prioritize accounts, identify churn risk, and optimize prices.

For cross-selling and upselling, Proton’s AI can look at what products similar customers are buying and how often they buy them to generate leads and suggest reorders. This can lead to an 8% increase in sales per sales rep. 

In this example, Proton’s AI identified a product the customer is due to reorder. This sales play highlights a potential opportunity for a sales rep to increase wallet share. It gives the rep a reason to reach out proactively with a strong conversation starter. The rep can say, “Hey, it looks like you might be running low on the Scotch tape you typically order. Would you like me to add some to your order?”

Customer churn has traditionally been tricky to deal with because it can happen so gradually. Over several months or even years, a customer may make fewer purchases, less often, until they finally stop being a customer altogether. It’s hard for busy humans with limited bandwidths to catch on before it’s too late, but AI can detect irregular purchasing patterns early on and alert the distributor to a potentially unsatisfied customer. 

Pricing is another area where AI’s ability to amass and use knowledge can really pay dividends. Using customer and pricing data, AI solutions from companies like PROS and Zilliant can determine the optimal price for specific customers — not just for a segment or the entire customer base. This is ideal for long-tail items that sales reps don’t touch often enough to confidently know how to price them.

Distribution Operations

Some of the most important tasks for a distributor’s operations are also the most monotonous. Unlike humans, AI doesn’t get tired of repetitive, boring tasks. Services like Conexiom can enter orders accurately and efficiently, freeing sales reps to do what they’re best at (and probably enjoy a lot more): sell product and build relationships. 

Automation is also useful in the warehouse, where robots from vendors like Autostore can increase net profit margins by reducing pick errors and making the fulfillment process faster and more efficient. And for human workers, distributors can prioritize their safety by using services like Modjoul to monitor behavior, ensure compliance, and identify potential hazards before someone gets hurt.

Distribution Operations

Order picking is another one of those time-consuming manual tasks that AI can automate and accelerate. Using AI-generated speech and voice recognition tech, it can tell an associate what product to look for and exactly how much of it they need to pick, and the associate can tell the AI when they’re done — with results that are 99.9% accurate. This can boost productivity by 30-50% and improve employee satisfaction and safety.

AI can also optimize inventory by using what it knows about customer behaviors, supplier status, and even the weather to predict demand and account for any disruptions in the supply chain. While humans are often playing catch-up when it comes to issues like dead stock, AI’s real-time insights can keep distributors ahead of the curve and help them avoid stockouts and their consequences.

Distribution Product Management

A distributor’s massive catalog makes it impossible for even the best sales rep to have a deep knowledge of every product. Proton’s AI-powered semantic search can help them quickly find information and spec sheets about any product they’re trying to sell. It can also help customers find the right product by correcting typos and suggesting other products they might be interested in based on their search history.

Categorization is another hurdle that AI can help distributors clear. Solutions like can harmonize product catalogs from different manufacturers and distributors. They can also use metadata to put products into the right categories.

What to Look for in an AI Partner

According to Deloitte, 65% of companies that adopt AI decide to use a vendor rather than build a solution in-house. Proven solutions with established training help distributors hit the ground running.

When looking for a partner, remember that not all AI is equal. It’s important to find a vendor who can cater to the specific needs of a distributor. A distribution-focused AI vendor should be able to centralize complex data, identify new sales opportunities, lower your cost of sales, and gain insights with deep reporting. After all, a sophisticated industry like distribution warrants a sophisticated approach to AI. 

As the global economy becomes more reliant on ecommerce and digital buying, investing in AI now will give your business an advantage over your competition. To maintain growth, distributors must optimize processes and improve customer experiences. Thankfully, AI is helping. The distribution businesses we work with at Proton, frequently refer to AI as their “secret weapon” for driving sales, growth, and customer satisfaction. Early adoption of artificial intelligence will grow your business while enabling your team to be more productive, efficient, and consultative.

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