AI for Distributors

Buzz, hype, mania—it’s been surrounding artificial intelligence for a couple of years. As a distributor, the benefits of AI (artificial intelligence) may seem both tantalizingly close and vaguely defined. How can you separate myth from reality, meme from substance?  

First, recognize that the commercial deployment of AI will require major organizational transformations as processes become AI-enabled. The technological aspect of those transformations is the easy part; the hard part is answering the WIIFM (What’s In It For Me?) questions from your team, with no BS. How will adopting AI tools help your team thrive? 

In this blog, we’ll go through a quick crash course in the basics of AI and how distributors should think about it. 

Operations Management and AI: Ignore the Hype 

As the tech writer Alberto Romero observed, AI has gotten a lot of lofty headlines, but it hasn’t come close to changing the world yet. In fact, the most talked-about use cases so far are frivolous, unsavory, or both. Spam farms, essay generators, and non-consensual pornography aren’t going to help your distribution business. 

This lack of practical application doesn’t mean that AI isn’t useful or that people aren’t aware of its power. The real problem is that most people simply don’t know how to properly harness that power in the real world.  

As a distribution decision-maker, you’d be wise to tune it all out and instead find a rich flow of valuable information about real world business applications of AI that exist. You’ll need to be properly equipped with practical knowledge if you have any hope of changing entrenched workflows into explicit ones with feedback loops and KPIs that can be optimized over time.  

Potential AI Applications for Distribution 

With a roadmap established and your team bought into the benefits of adoption, the potential applications of AI are myriad. For example, you could automate pricing negotiations so that more than 80% are approved on the first pass, allowing your team to focus on the more intricate 20%.  

Task Management and AI 

AI can be used to quickly build a complex quote from a provided list, or automatically schedule deliveries and optimized truck loading sequences.  

Sales Enablement and AI 

Lights Out Order Processing, or LOOP, can take email orders and enter them as they come in, even outside business hours. AI can help your sales team evaluate incoming leads to determine if they’re worth pursuing and help your delivery team expedite delayed shipments. Many leads are recurring business and can be managed by your inside sales force, freeing up time for outside sales to generate new business.  And simplified workflows can increase your CRM adoption rates.  

Inventory Management and AI 

AI-enabled tools can help gain insight into real-time inventory levels. By analyzing purchasing and ordering patterns, these tools can identify trends and patterns to make accurate predictions on inventory stocking levels. And by identifying customer buying patterns, distributors can pinpoint which customers are most likely to buy these items again. 

Customer Service and AI 

Distributors can design a chatbot specifically to meet their business needs. Taking it a step further, you can use ChatGPT and email bots to help those with dyslexia or non-native English speakers compose more cohesive emails for business. 

Some vendors are already offering a Synthetic Sales Rep, or SSR, that can incorporate all your team’s existing knowledge to help your reps make more sales and manage larger territories. Gartner estimates that with the right AI tool automating tasks like prospecting and sales planning, your reps could free up 27% of their work time and invest it in targeted growth.  

Marketing and AI 

Many companies are already using CRM. However, Mac Mcintosh, president of AcquireB2B, compares using CRM to building a car from a kit: “All the parts are there, but you need the time and skill to put it all together.” AI-powered marketing automation, however, “is like buying the car you want or need, with all the features you want already installed and some gas in the tank, ready to drive. In either case, you still need to know how to drive and where you want to go.”  Adding AI capabilities can significantly power the data from your CRM, allowing you to increase the number of leads that engage a field rep with a meaningful prospect.  

Remember that most growth in industrial markets is share shift; AI is simply the most powerful tool for making those shifts to date.  

What Makes AI Different from Current Coding Practice? 

 Having a practical understanding of AI is to recognize how it’s different from traditional coding.  

For decades, coding has been logic-based: if A is true, then B, or else C. The program only knows what it has been specifically written to know. 

AI algorithms learn directly from the data. Instead of using the old LISP model to, say, directly identify cats, AI learns by reviewing many cat images so it can infer an answer from those examples. This is why AI needs Internet access and massive data sets to succeed. (It’s also why OpenAI is being sued by the New York Times for training ChatGPT on their content without permission.) 

However, AI can’t make any inferences without a human structuring a prompt for them. It needs an architect to define how it should infer for a specific situation—such as distribution processes and marketing. 

That is your job as a business leader with AI: figuring out how to drive it, choosing a destination, and plotting the best route.  

The AI Advantage: What AI can do for business 

Recent research by McKinsey shows the value of having a roadmap. They found that, despite persistent skepticism among business leaders, “building up digital and AI capabilities adds up to real value, a trend that holds true for every sector analyzed.” Companies that have leading digital and AI capabilities “outperform laggards by two to six times on total shareholder returns.” 

The spread between those leaders and laggards is only getting bigger, having increased 60% since the last period McKinsey studied AI adoption (2016-19). As they put it, this effect is the result of how leading companies “rewire their organizations,” providing them with “hard-to-copy capabilities” that let them “better identify where their business model could be improved with technology, to develop digital solutions, and to effectively drive their adoption and scaling.” 

This is why it’s important to ignore the broad-based AI hype, which can make it sound like you’ll be able to do anything you dream up with the push of a button. To target, pursue, and capture more value, more quickly, you first need to transform your organization’s technological business practices. And as with any journey, that requires taking a first step.  

Adopting AI: Deciding Where to Start 

Before determining the directions to your desired destination, figure out where you are as a business. What are the work activities that your team performs, and how feasible is it to automate those tasks? Tasks like people management and applying expertise are unlikely to be automated any time soon, as are stakeholder interactions and unpredictable physical work. But data collection, data processing, and rote physical work are all well within the realm of automation. Start by targeting those areas so you can build a solid foundation where the benefits of AI are immediate and obvious.  

From there, Gartner’s RevTech maturity scale shows how the speed of adoption increases as progress is made. Invest in marketing automation to improve quality opportunities from the outset, being sure to answer the WIIFM question and take design input from your sales team so that it works the first time. Then, integrate the tech more into the selling process over time.  

Making AI Work for You 

According to McKinsey, the digital transformation necessary for proper AI adoption is far more likely to succeed if a company makes progress in six key areas: strategic road map, organization and talent, operating model, technology, data, and adoption and scaling. Don’t invest money in an AI tool until you’ve invested time and effort in research, then building up those areas and improving your tech knowledge base. 

AI isn’t just an IT department matter. It’s all about strategy and executive leadership. The best way is to involve your team throughout the design and implementation process. With their involvement, you’ll get more buy-in, greater adoption, and therefore a much higher probability of success.