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Unpacking Artificial Intelligence

By Tim O’Connor
Communications Manager

Exploring the ways distributors can use AI and machine learning to enhance their operations, reduce costs and respond more quickly to customers.

The seemingly limitless potential of artificial intelligence (AI) has been exhilarating science fiction fans for more than a century, but the real-world progress made in the technology over the last few years now has even C-suite distribution executives talking like Asimov and Bradbury devotees.

The technology may seem new, but practical versions of AI have been around since the early 1950s when the first computer programs were created to challenge humans at checkers, quickly establishing games as a barometer for AI advancement. The first big public breakthrough didn’t come until decades later when IBM’s Deep Blue beat chess grandmaster Garry Kasparov in 1997. In the years since, AI went on to conquer Jeopardy, Go and even Starcraft II.

Impressed by that progress, businesses began implementing AI in niche applications such as processing data for the banking industry and investment in AI technologies started to accelerate. The turning point came in late November 2022 when OpenAI made its ChatGPT AI available to the entire public. Suddenly, people had an AI tool they could use and experiment with on their own, and business leaders were soon formulating ways this exciting technology could benefit their bottom lines. Now, global spending on AI is projected to exceed $301 billion by 2026 — double the amount spent in 2023, according to International Data Corp., a global market research firm. Further, a study from Accenture shows that 67 percent of organizations have integrated AI into their business strategies and 38 percent of companies said their return on the AI investments have exceeded expectations.

The distribution industry is especially primed to benefit from AI technologies because many of the applications AI already excels at dovetail with how dealers operate. Distributors, including foodservice equipment and supplies dealers, can generate large data sets derived from information on their sales numbers, inventory, warehousing operations, and customer records — all of which can be used to effectively train machine learning AIs. According to a 2024 report from the Distribution Strategy Group, 53 percent of distributors believe AI will provide a competitive edge over the next three years and 15 percent believe it is vital to the success of their business. Despite the recognition of its value, the adoption of AI technology appears to be slow among distributors. The same report found that only 17 percent of distributors were using AI to support their sales efforts and even fewer were using it in areas such as their websites (12 percent), marketing (10 percent) and purchasing (2 percent).

Distributors that fall behind on implementing AI solutions risk leaving money on the table and hampering their growth. McKinsey predicts that early adopters of AI could increase their economic value by 122 percent by 2030. “Front-runners tend to slowly concentrate the profit pool of their industry in a winner-takes-all phenomenon,” the research firm said.

The expected business benefits are immense, but distributors must still be thoughtful in how they implement AI tools. When working in harmony across the entire organization, the results can be powerful as AI assists dealers in everything from automatically restocking products, optimizing pricing, selling to customers, scheduling delivery, managing invoices, and providing post-sale customer service. Understanding how AI can complement each of those areas of operations is a good place to start.

Forecasting and Inventory Management
The difference AI can make begins before the distributor even has its goods in hand. AI applications can use inventory, sales and customer data to identify seasonality patterns and estimate the shape and duration of product life cycles to determine optimal stocking levels. Further, it can identify popular equipment and supplies and automatically restock them once they fall below a certain inventory threshold, effectively reducing carrying costs and minimizing the likelihood of stockouts.

However, automating the procurement process doesn’t help much if the desired equipment can’t be found. Here, too, AI can help dealers. By tapping into information on weather patterns, production slowdowns and other disruptive events, AI applications provide real-time visibility into the entire supply chain, enabling dealers to make adjustments before a shortage hits. Some tools even identify alternative vendors by scraping websites for supplier information and scorecards.

The role of AI in procurement and forecasting will only expand as the technology becomes even more capable. ToolsGroup, a provider of AI-powered supply chain planning software, found that 41 percent of supply chain professionals expect more productive supply chain planners because of machine learning-augmented supply chain projects.

The warehouse is the backbone of many distributors’ operations, integral to meeting customer expectations for rapid delivery but costly to maintain. AI can help companies maximize their warehouse space and run their facilities more efficiently.

One way AI is impacting warehouses is through slotting optimization. Warehouses are complex environments that demand intense planning and foresight to make the most of their footprint. With slotting optimization, AI reduces wasted space by categorizing and organizing inventory across the entire facility. This process can happen automatically, eliminating the need for the manual warehouse mapping required by traditional slotting systems. Additionally, a properly trained AI will learn as time goes on and adapt its model as supply and demand conditions or forecasts change.

By continually feeding the system new data, AI algorithms can make dynamic recommendations on optimal warehouse layouts to further increase storage capacity and minimize travel distances for faster pick times. This alone would make it a worthy investment for some dealers, but when paired with other innovations, such as robotic pickers and sorters, AI can transform warehouses into nearly autonomous operations — an especially lucrative possibility in an era of rising costs and labor shortages.

The foodservice equipment distribution and supplies industry is often seen as a relationship business, so it’s unlikely that top-performing salespeople need to worry about being replaced by a savvy chatbot. But that still leaves a lot of room for AI software to serve in supportive roles and identify ways to expand the customer base. Forty-nine percent of organizations are now using machine learning and AI to identify sales prospects, according to the Harvard Business Review, and 67 percent believe those technologies will be critical for future competitive advantages in marketing and sales.

Many dealers already have extensive customer data that could serve as the basis for training machine learning algorithms on how to improve insights into customer behaviors and needs. AI software can analyze that information to alert dealers to foodservice operator purchasing patterns, preferences and trends. Dealers can then use those improved insights to personalize marketing efforts or tailor product offerings.

Going forward, many of those marketing messages may be AI-authored based on patterns the software recognizes in customers. Gartner predicts that by 2025, 30 percent of outbound marketing messages from large organizations will be synthetically generated, up from less than 2 percent in 2022. That will pave the way for companies to reach larger customer pools more quickly while providing their human salespeople with actionable leads to follow.

Advancements to voice search are another area where the Distribution Strategy Group believes AI will help dealers drive sales. As AI gets better at understanding natural language requests, it will be more effective and accurate at generating responses and finding the right products for customers. It can then automatically suggest cross selling items related to the original request or identify substitutes if something is out of stock.

Finally, access to sales and delivery data enable AI applications to automatically generate invoices, freeing employees from menial but time-consuming tasks so they can focus more on customer service. Reducing the number of man-hours spent invoicing speeds up the entire process by 72 percent, according to the Distribution Strategy Group. Handing invoicing over to AI is also meaningful cost savings, as Adobe has estimated the cost to process a single invoice at between $15 and $40 for most businesses.

Once an order is placed, dealers must contend with one of the most sensitive parts of the sales process: delivery. A delivery crew stuck in traffic in the morning can have ripple effects throughout the rest of the day, frustrating customers who are depending on a timely arrival. Delays, even short ones, can be especially problematic when the equipment is part of a larger installation, such as new restaurant construction, as it can hold up the next stage of work.

Most drivers are used to following the guidance of the GPS in their cars. When it comes to deliveries, AI acts as a sort of super GPS. Beyond just optimizing the route to the destination, AI solutions create an entire delivery plan based on real-time traffic conditions, the vehicle’s capacity and customer preferences. This leads to more on-time arrivals and greater customer satisfaction.

Customer Service
Speaking of customer satisfaction, today’s AI frequently act as the first point of contact for many buyers. A Forbes Advisor survey found that 73 percent of businesses use or plan to use AI-powered chatbots for instant messaging and 61 percent use AI to optimize emails.

It’s easy to understand why AI has quickly taken over some customer-facing roles. AI can automate transcription, accurately converting speech into text to provide a written record of the customer’s interaction that distributors can use when following up on their requests. Additionally, the large language models used by modern chatbots mean that AI is usually able to generate a relevant answer more quickly than its flesh-and-blood counterparts. Because of this, deploying AI in customer service may soon be seen not as an enhancement but a necessity. According to research from Oracle, AI-powered enterprises are projected to respond to customers 50 percent faster than their peers by 2024.

Looking Ahead
These are just some of the ways distributors are using AI and machine learning today. The number and quality of available solutions will only improve as time goes on and the technology matures. Based on simulations, McKinsey has projected that 70 percent of companies will adopt at least one type of AI technology by 2030, increasing global economic output by $13 trillion. To ensure they are part of that economic boon, dealers can start by assessing their current capabilities and identifying areas where AI can help them operate more quickly, efficiently and profitably.   

Understanding AI Terms

Getting a grasp on the artificial intelligence applications that are making their way into the business and distribution worlds starts by understanding the vocabulary. This brief guide explains some of the most common terms related to this rapidly advancing technology.

Artificial Intelligence
As defined by John McCarthy, a computer scientist known as one of the “founding fathers” of AI, artificial intelligence is “the science and engineering of making intelligent machines, especially intelligent computer programs.” In the current sense of the term, AI is not intelligent in the same way humans are — it doesn’t understand the meaning behind the data it processes, is totally dependent on humans feeding it information and cannot operate outside its defined logic. However, it is very good at ingesting large amounts of data and recognizing complex patterns. This enables it to perform tasks that previously required human intelligence, such as speech recognition, language translation or decision making.

Machine Learning
Machine learning is a subset of AI that consists of computer programs constructed to improve as they gain experience working with data. Machine learning algorithms target a specific goal and are then fed a large amount of data they can use to rapidly brute force test millions of possible solutions. Over time, the AI narrows down the most optimal pathways to maximize the outcome, effectively learning how to deliver the best result for its defined goal.

Deep Learning
Deep learning is basically machine learning on steroids. Instead of needing structured, well-defined data to make predictions, deep learning uses neural networks — models that mimic how biological neurons work in unison — to process unstructured data. This means that less human intervention is needed, as a deep learning algorithm can determine the defining features of a data set on its own and use that information to perform much more complex tasks such as facial recognition or autonomous driving.

Large Language Model
Large language models (LLM) are why ChatGPT sounds so life-like. Using deep learning, LLMs are trained to recognize human speech or writing and generate relative responses in plain language. For businesses, the ability to replicate human language makes LLMs useful for applications like chatbots or customer service, but they can also bridge the knowledge gap for using other AI tools. Anyone can write a natural language prompt that directs the underlying AI to perform a task, such as creating specialized code for an e-commerce website or generating a list of prospective customers based on past purchases. Thanks to LLMs, AI is becoming more accessible to a wider range of businesses, no computer science degree required.

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