AI Poses a Make-or-Break Challenge for Distributors

AI Poses a Make-or-Break Challenge for Distributors

The mindset of many distributors on the topic of artificial intelligence today is worrisome. “Terminator” jokes abound, as individuals imagine that machines are coming to destroy distribution. Change is coming, but it’s not that sort of change. Far from dehumanizing distribution, AI will personalize customer experiences like never before and drive tremendous growth for AI-equipped distributors. 

Before discussing the benefits of AI, it's helpful to quickly dispatch the “Terminator” myth. We aren’t anywhere near achieving generalized AI of the sentient, Schwarzenegger variety. In fact, academics disagree about whether it’s even theoretically possible. For the foreseeable future, we’ll be stuck with narrow, task-specific AI. 

Narrow AI

This type of AI analyzes mass quantities of data, finds patterns and performs specific tasks. Narrow AI needs tons of data to learn, but is highly effective when given adequate information. 

For example, human teenagers are expected to learn how to drive a car with just a few hundred miles behind the wheel. On the other hand, Waymo’s self-driving car, which began a decade ago as the Google Self-Driving Car Project, needed millions of miles to become operational. As countless hours of machine learning accumulate, however, those self-driving cars can surpass human performance. 

Even if you drive stick, you still probably use narrow AI every day without realizing it. Spam filters, GPS navigation apps and voice recognition are just a few examples of narrow AI in action. These programs take in data (emails, driving routes and voice samples), analyze it and perform specific tasks (i.e. remove spam, calculate quick routes, or translate speech into text). If you already trust AI to censor your mailbox and direct you through traffic, then why not use it to grow your business?

Distributors can make massive gains with AI by analyzing data, predicting customer purchases and making targeted sales. This sort of predictive technology is proven to work, and has fueled the biggest disruptors of the decade; Amazon uses AI recommendations to drive 35% of revenue. Netflix uses algorithmic predictions to keep 75% of viewers engaged, and Facebook uses AI to haul in $17.4 billion from advertising

These tech-savvy giants may be outliers by traditional standards, but they’re not actually that atypical in an AI world. The more data AI has, the more effective it is. In time, the best AIs attract more customers, and thus more data, which makes them even better in the future. This phenomenon is known as a Data Network Effect, and suggest that Amazon is not big just because it’s good at predicting customer purchases, it’s also good at predicting customer purchases because it’s big. 

More Data Points, More Points of Sale

In the past, businesses had to trade scale for personal service, but AI has flipped that formula on its head. Today, large companies grow with AI and continually use the technology to provide superior customer service. As companies acquire more customers and data, they get even better at personalizing each buyer’s shopping experience. Amazon’s “recommended for you” feature — a prime example of personalization in action — continually gets more accurate as customers keep buying. 

AI features like these are especially impactful in distribution because distributors operate across multiple channels. More channels mean more data points to feed AI, and more points of sale to use it. Features like “recommended for you” and “complete the cart” originated online, but can also be used for customer service, sales and marketing. 

Here’s a specific example: Customer service reps typically cannot pitch items to callers because they don’t know what each individual would want to buy. AI-assisted customer service reps, however, are able to tap into systems that comprehensively track and analyze data to reveal what each customer is most likely to purchase. This synthetic knowledge lets reps pitch the exact item that each caller is most likely to want, even if they personally know nothing about that caller. 

With this kind of system in place, each of a distributor’s channels complements the other ones. When distributors use AI to personalize interactions, they don’t only increase the size of that sale, they also prepare themselves to sell even more in the future.

The good news for distributors is that they already have tons of customer data, and are well positioned to cash in on AI.  Now, they need to start realizing the value of that data. Emphasis on “now.”

Now or Never?

AI is an investment, and the timing of AI adoption is critical. A recent McKinsey simulation predicts that AI will create $13 trillion of business value by 2030, but that value won’t be shared evenly. Early adopting businesses will grab almost all of those profits and more than double cash flow. Non-absorbers will lose out. In short, customers will preference distributors who offer AI-personalized shopping experiences at the direct expense of those who don’t.

Distributors have a golden chance to win big on AI. However, if they don’t start using their data, and fast, this opportunity will quickly vanish. Once customers leave and take their data with them, businesses will struggle to catch up. 

In that sense, those who think of AI as “The Terminator” are right. Not because AI is dangerous, but because fearful distributors are dangerously close to enacting a self-fulfilling prophecy. Businesses that refuse to adopt AI will forfeit the chance to personalize customer service, and start losing customers. By the time they realize what has happened, it may be too late. No, AI is not the Terminator. It’s an invaluable tool (actually a $13 trillion tool), but it just might end up terminating those who don’t use it. 

This article was originally published with the mdm here: https://www.mdm.com/blog/technology/ai-poses-a-make-or-break-challenge-for-distributors/

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