Despite the surge of interest in artificial intelligence (AI) in spring 2023 that continues today, AI is not a new-fangled technology. We’ve been exposed to it for years – whether we noticed it or not. But it was ChatGPT-4 and the much more human output we got back that launched AI to the forefront of our collective consciousness in March 2023.
Now, a full year following the release of ChatGPT-4, the onslaught or simply newfound awareness of available technologies and functionalities that leverage AI have distributors wondering: Should I be doing more with AI, and what exactly should I do?
My take? Distributors will need to leverage AI in their businesses. Research shows distribution has both a high need for digitalization, a high percentage of workers likely to see high exposure to AI and a high percentage of jobs at risk due to digitalization. Thus, there’s an economic need and understood implications – as well as opportunities.
However, before you choose a handful of point solutions to solve this or that issue, know the following facts about AI for distributors.
Fact #1: AI Capabilities are a Team Effort
As you assess your next steps with AI, it’s important to avoid looking at the technology in a vacuum. Why? Because it’s not just about AI. It’s about being digitally ready, leveraging multiple technologies fueled by a foundation of clean, standardized data.
Digital readiness involves dozens of technologies working together – with data at the center – to truly drive meaningful capabilities and opportunities. Those technologies include enterprise resource planning (ERP) systems, Internet of Things (IoT) devices, mobile devices, the internet and more.
This journey to digital readiness isn’t new. It’s been progressing to its current state for decades, from the introduction of the World Wide Web to our first ERP systems, which transformed how we can gather, structure and use data. From there, technologies have continually emerged to further improve how we leverage data.
With this in mind, as you face the conundrum of “what to do about AI,” it’s imperative to broaden the scope of achieving digital readiness.
Fact #2: AI Can Address Real Distributor Challenges
It often seems as if distributors get the crumbs of innovation. The latest and greatest technologies aren’t always designed with their unique needs in mind. However, with the data and technologies available to you today, AI can help you manage the realities of distribution operations, all while staying profitable and competitive. It can make a significant difference, as its core benefit is allowing you to do more with less – a game distributors have been playing for ages.
On the one hand, you need to reduce overall costs and maintain – or grow – a healthy margin. You must also manage market pressures, customer expectations, growing market volatility and more. On the other hand, you must battle skill and labor challenges. You’re likely dealing with low brand visibility, a seemingly less attractive work environment, a decentralized location, an aging workforce and difficulty providing premium salaries for the skills you need on deck.
This is where intelligent technologies and automation come in, helping you get smart to stay ahead.
Fact #3: Distributors Have Two Types of AI to Leverage
There are two main categories of AI for distributors to consider.
1. Discriminative AI
With discriminative AI, the learning models only have access to certain data pools. It’s discriminating against any information outside of this box. Distributors already use this type of AI in a lot of ways.
With discriminative AI, you can more readily tie quantitative KPIs to the AI application. You could say:
- Price optimization led to a margin increase of one point.
- Inventory optimization led to a two percent service level increase, with consistent cash flow.
- Opportunity buying led to a lower cost of goods sold due to the realization of economies of scale across different products benefiting from the same supplier inventive programs.
AI Application Examples
Leave less money on the table. Discriminative AI, along with machine learning and other intelligent technologies, can be used in pricing optimization. For instance, it can analyze various factors that impact buying decisions for specific customers to determine whether it’s logical to round off to the next dollar on a discount.
Stock with accuracy. It’s difficult to make correlations across categories without technology. AI can take data related to a flu wave across the United States and how that affects categories, such as increases in Advil and Kleenex purchases, and make game-changing correlations. With this, you can make improved inventory decisions.
Keep customer shelves on trend or bins replenished. If your distribution business also manages shelves or bins for customers, you can use AI, machine learning and related technologies to better manage layout, inventory and compliance. The AI can forecast trends and how assortment and stocking levels need to change over time, and it can help ensure the right pacing, locations, pricing and more.
Improve opportunity buying efforts. You can use AI, machine learning and predictive analytics to optimize what you buy and lower the cost of goods sold, as it will look ahead and identify what you could add to your purchase to optimize buying based on your contractual agreements.
2. Generative AI
Generative AI use cases are more qualitative than Discriminative AI cases. In these instances, you’re dealing with human-like output, such as chatbots, co-pilots and digital assistants. For example:
- Human Resources can use Generative AI to screen resumes and create job postings.
- Distributors can use Generative AI to create products descriptions for their e-commerce pages.
- You can use Generative AI to help sketch out documents for a bidding process to accelerate progress.
- Project managers can use chatbots and co-pilots to ask intuitive questions about deliveries, timelines and tasks using Natural Language Processing (NLP).
Fact #4: When You’re Ready, You Can Freestyle AI to Level Up
The examples we’ve discussed thus far are transformative and can make a major difference for your business, but they’re available to everyone, so they’re not necessarily differentiators. Powerful differentiation can come when you’re comfortable enough to freestyle around AI, combining AI services like NLP, pattern recognition, deep learning and others to problem-solve and innovate for your unique needs. There is a great deal of potential for distribution-specific AI use cases.
For instance, you could develop an AI capability around predictive customer loss management. It’s difficult to report on elements you don’t have data for, such as lost sales, and it’s difficult for salespeople to notice risks of churn when customers are still buying and profitable. AI and other intelligent technologies can take multiple elements into consideration and make correlations so you can understand why you’re losing customers in certain product categories or regions and take action to prevent loss. For instance, you might change your rebate programs, promotions, prices or way of serving them. This way, you can get ahead of the curve and address customers specifically to maximize their lifecycle value.
Distributors typically lag behind with technology. My advice to you is: Get smart, stay ahead. At the end of the day, whether you’re just starting out with Generative AI use cases or you’re at a point you’re ready to innovate and freestyle, there is high value in leveraging AI in distribution.
Magnus Meier is the Vice President and Global Head, Wholesale Distribution Business Unit for SAP and a senior lecturer at Texas A&M, where he teaches the Digital Distributor course in the Master of Industrial Distribution Program. Magnus started with SAP in 1998 and acquired extensive cross-industry, solution, services and support knowledge throughout his career. He has held a variety of positions internationally, from consultant in Germany, to Manager of Industry Business Development teams in Japan and the U.S. Magnus has a Master’s degree in economics from the Gerhard Mercator University in Duisburg with a special focus on East-Asia.