Attendees spent two and a half days at this year’s Applied AI for Distributors conference immersed in the latest on AI and how it can help distributors grow sales, drive productivity, improve customer experience and provide better services.
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Following the event, Ian Heller hosted a panel with technology leaders Kristen Thom, Vice President of Product at White Cup; Nelson Valderrama, founder of Intuilize; and Dush Ramachandran, president and CEO at ORS Group.
Here’s what stood out to them and where they believe AI is going in distribution:
Ian Heller: When word about AI first hit the distribution world, people were left dazed and confused. However, there was a very different response at our recent conference. People seemed ready to make use of these tools in their companies. Have you seen that transition among your customers and prospects over the past nine months?
Kristen Thom: Absolutely. Nine months ago, distributors were still in the early phases of AI and evaluating what big tech companies were doing with AI. They were keeping an eye on it, were interested, curious and maybe a little bit scared. Fast forward to today, that initial fear is over. Now, we are looking at what the big tech companies are doing and asking, “How do we use this? How do we benefit from this in our businesses?”
That’s definitely what we’re hearing from the distributors we work with.
Nelson Valderrama: There’s still a big bucket of people kicking the tires and trying to understand what it is, what to do with it and learn the lingo. Many owners don’t want to feel stupid, so they wanted to learn more before they start talking. A growing group of practical and pragmatic people is trying to figure out how to match a problem with a solution. This is entirely different from what I witnessed both at the fall event and with prospects and customers.
Heller: What did you share at the event that resonated or surprised the audience?
Dush Ramachandran: At the conference, I presented specific case studies of three customers that included their results, return on their investment, reduction of their overall inventory and accuracy of their demand forecast. The attendees scribbled furiously in their notebooks because they could see the specific benefits and case studies that they could apply to their business. You could see the cogs in their minds turning, thinking if they can reduce inventory by 8%, that’s $1.6 million.
Several people came up to me after the presentation, and they all had very much the same thing to say: many statements about how AI is important, it’s coming, you need to be a part of this, and so on. However, some say the most impactful thing I shared was how specific results and numbers can be measured. AI is important, it’s here and it will revolutionize the industry. But it would be great to know the extent of payback and return on investment.
Thom: Building on what Dush is saying, one thing that we shared and continue to share in our messaging about AI to distributors is that technology is there for us to make this workable. The barrier to entry using this technology is much lower than a year ago.
We hear it all the time. One, “I don’t think people can adopt this. They’re not going to understand it.” And two, “My data isn’t there. My data isn’t what I need it to be.”
Technology has risen to the occasion. It’s there to help us overcome those barriers to entry. There’s a myth that your data will be clean at some point. There’s always going to be pieces that we wish were better. And that adoption is so much easier when you look for tools built for users and not for a developer or data scientist. There are many of those out there right now. It is pretty easy to get your foot into the door.
Valderrama: There were conversations exploring AI, and we told those people AI is just another technology. You have been buying technology for decades, and AI is exactly the same process. That makes things simple for them.
I would say it’s a challenge to choose one. You have a problem; you want to find a solution and put a dollar sign on it to see if that’s what you want to do as a business. But it’s a journey. It’s not a silver bullet. It’s not like you buy AI and it will change the world for you. That’s the conversation I have to make sure people understand it.
For instance, I talked with a gentleman during a launch, and I asked him genuinely, what are you doing with all this? He said, I’m looking forward to my annual bonus and that his bonus is focused on profitability.
After talking with him about all the different solutions, he decided the solution he wanted was the one that would give him the half a million dollars that he was looking for this year. He said he knows it’s applicable; he knows the risk is there, but the risk is a lower one. He created a matrix in his head in order to make that trade-off.
Heller: Is there a difference in the technical skill sets that distributors need to evaluate and adopt AI technologies? For example, if you were running a distribution business, would you need to reskill your IT department, your workforce and your executive team so that you understand this new technology and can adopt it more effectively?
Ramachandran: Not necessarily. It depends on the application. Right now, if you have a data science organization and are home brewing your own AI solution, yes, you need to have a deeper understanding. And that is almost the exclusive preserve of very large companies like Grainger. They have hundreds of people and data scientists creating their applications. However, for the vast majority of distributors, they ought to be focused on increasing their fill rates, optimizing their inventory, forecasting their inventory and thinking about what to keep in stock in what location, what time and when.
Boil it down. They’re not in the business of developing applications; they’re in the business of “boxes in and boxes out.” The best applications hide all the complexity underneath and allow them to use the application to leverage the power of AI to answer business questions. Simulate business problems and get ‘what if’ scenarios and answers. That’s the key.
Heller: Some distributors struggle to find an AI integration that works with their current systems. They wonder if they should find a system that is easy to integrate or one that fills most of their needs, even though it may take time to fine-tune. If their ERP can’t simply bolt on with these new solutions, are they better off adopting whatever AI they can right now or should they pause and replace the system?
Valderrama: There is no one right answer. It depends on where they are and their scale of business. Consider a $30 million annual revenue distributor versus a $500 million one. Legacy systems will be very hard to do, but they are doable. It’s going to come down to cost. If a company realizes that their ERP system will not take them where they need to be in two, three, five or 10 years, maybe that’s a decision they need to make now and bite the bullet. It’s not that AI is necessarily the solution; they need to identify the problem they want to solve and figure out which technology will provide that solution.
Heller: When I talk to distribution executives, they tell me they are getting pressure from their board, PE group and shareholders to demonstrate that they are investing in AI. They’re not getting pressured to make sure technology capabilities match up with the requirements. They’re getting pressure to adopt AI. Are you hearing that from your customers and prospects, and what do you tell them?
Thom: Absolutely. We hear it all the time. Frequently, it’s matched with bewilderment. They say, I don’t know how I need AI, but I know I need AI. Like you said, they’re getting this pressure both from internal and external forces. We’re all talking about it all the time and feeling the pressure from all angles. Every news article is popping up about AI.
Our tactic goes back to what Nelson said. AI should be a tool to solve a problem. Rather than say we need AI, let’s figure out what pain points in the business AI is particularly well-suited to address. AI is advancing rapidly and is good at looking at large data sets and finding patterns. Use that as a building block to start solving those problems in your business.
Heller: Do you have customers using your technologies to differentiate their customer experience?
Thom: Customer relationship management, or CRM, is at the core of many of our conversations about how AI is helping businesses. It helps distributors understand their customers’ business and ordering patterns and puts them in a position to be that trusted advisor and consultative salesperson. It brings all the different data sets between emails or ERPs (enterprise resource planning) together for a better view. AI makes that easier to do at scale.
Ramachandran: The solutions we offer to the distribution industry have a direct impact on customer experience. If a customer walks into a retail location or a branch location of a distributor, they come in looking for a certain product. If they don’t find it, they walk away mad. The challenge with that is that lost business is not captured. Nobody knows how much of that business was actually lost.
If it’s a large purchase order that’s not filled fully, you have fill rates and you can measure that. You might say we need to improve our fill rates. However, at the retail branch level, having the right inventory in the right place makes all the difference. That’s the difference between walking into the retail location and saying, I know I’ll find it here versus I don’t know if I’ll find it. And if I don’t find it, I have to make another stop somewhere else and find what I want. So, that’s a very direct impact on the customer experience. This influences customer loyalty. If it happens three times, you walk into a retail location of a particular distributor and don’t find the thing you’re looking for, the chances you’re going back there a fourth time is infinitesimally smaller.
Valderrama: An example I think a lot of the small and mid-size guys will appreciate, for instance, is the respond to a quote. In the old days, responding to a quote may take a couple of days depending if you have the stock, the inventory and the price. Today, the user expects immediate price and delivery through your website, app, phone or email.
Distributors need to have everything set up in their CRM or ERP system to price within 24 hours or 48 hours. When they receive a quote with one line or 50 lines, they know that they can turn it around in four to eight hours. That makes a big difference. Big KPIs that can be hidden in user experience, or it could be smaller ones, like turnaround time, set price or product availability. It depends on what you’re shooting for, but it definitely is a huge change in user experience.
