WEBVTT

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Welcome to the Jason and Scot Show, your source for the latest news and trends

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in the e-commerce industry.

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Featuring host Jason Retail Geek Goldberg, Chief Commerce Strategy Officer of

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Publicis, and Scot Wingo, co-founder and CEO of ReFiBuy.

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Here are Jason and Scot.

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Before we start this episode, we have an important announcement for you.

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Hi, I’m Kiri Masters and I’m excited to introduce Adgentic, a new show about

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advertising in the age of AI.

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I’ve spent the last decade analyzing retail media and now I’m fascinated by

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what happens when more of the shopping journey moves into AI assistants and agents.

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What happens to advertising when there’s no search results page?

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What will ads in AI actually look like?

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And where should brands, retailers, and publishers be paying attention right now?

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Those are the kinds of questions that we’ll be exploring on Adgentic through

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video essays, conversations, and perspectives from the people actually building this new ecosystem.

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Adgentic is live now as part of the ReFiBuy Media Network. Find the show on

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YouTube or wherever you get your podcasts and subscribe to the Adgentic newsletter on Substack.

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Welcome to the Jason and Scot Show. I’m your host, Jason Retail Geek Goldberg.

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And as usual, I’m here with your co-host, Scot Wingo.

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Hey, Jason, and welcome back. Jason and Scot Show listeners and viewers.

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Don’t forget we have a YouTube channel, everybody. Check that out. Jason and Scot.

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Today, we continue day two of our live podcasting from Retail Club AI Festival

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in Huntington Beach, California.

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We are joined by our guest. His name is Ellie Anselm, and he is an investor

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at Ridge Ventures. Ellie, welcome to the show.

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Well, Jason Scot, thank you guys so much for having me on. Excited to be here. Yeah, yeah.

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You and I first met, I think you called, you inbounded to me and wanted to chat

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about jump to commerce, like a year.

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It was a long time ago, I looked and it was like in,

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It was like right when I started Retail Gentic. So I think it was like…

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Side note, be careful what you ask for. February of 25. Yes.

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And I was like, someone else wants to talk about this? I think it was like February

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or March of 25. It was super early in this journey that we’re on.

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And then you guys invested. So that was a… I’m glad I took the call. So that was a good one.

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Tell us a little bit about your background and then what you do at Ridge Ventures.

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And we’ll jump into it from there.

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Yeah. So Juan, I’m also very glad and happy we were able to connect back then.

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A little bit about myself. So I kind of non-linear trajectory into venture,

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started off on more of the math, data science side of things.

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So in AI before it got sexy, traditional machine learning in financial services

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capacity. So it was with the S&P predicting crashing markets, essentially.

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It didn’t last very long, given I have too much ADHD to say within a research

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team and as exciting as algorithms are, had to do something a little bit more

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oriented around people, jumped shift to the startup side of the world,

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ironically building software for venture capital and private equity firms.

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Some thick skin dealing with people like myself on the other side of the table for some time.

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So both Empathy for Founders did the whole zero to one and building both,

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you know, product and go to market and that building, you know,

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company is still off and running.

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And I shifted to, you know, Ridge roughly three years ago now.

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I’ve been on the investment team focusing on, you know, hopefully investing

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in the best of breed seed and series eight companies.

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A little bit about Ridge and kind of where we focus our time.

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So firm’s been around for a bit.

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We’re currently investing out of our fifth fund and primarily spend all of our

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time, like I mentioned, early stage. So really everyone from having an ADECAN

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idea up until Series A companies that might have like, you know,

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what we like to call early product market fit.

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All of that is within B2B enterprise software, really like our best of breed

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across the stack ever from infrastructure to the app layer.

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And the reason we both focus on enterprise, not from an investment perspective,

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but also the way we are able to support our companies is what we’ve coined really

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is the Ridge Revenue Network.

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So think of a group of two to three hundred CXOs in mid-market in like four

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to two thousand enterprises.

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And one of the main cohorts is actually our CMO council.

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So a group of, you know, CMOs from some of these companies where we,

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you know, wine and dine them, host events, stay very close, you know,

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to the nose in terms of what’s happening on the ground, where the CMOs want

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to be investing, what they’re looking for in terms of best of breed tools,

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where they kind of rely on us.

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And then that becomes a nice flywheel for our portfolio companies as well and

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kind of feeds our investment strategy.

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Investors have a lot of different ways they kind of approach markets.

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Sometimes they’ll just take pitches and whatever is interesting to them,

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you know, is what they’ll evaluate.

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Other times they more proactively go out with a theme Where do you guys fall in that spectrum?

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I’d say a mix of both, but specifically within commerce, historically have been

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big investors in like commerce and the MarTech stack.

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So invested historically in, you know, Braze and then on the commerce side,

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you know, across that companies like Good Day Software, which is AI native ERP.

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Loop Returns, helping kind of the return system.

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And even like the CPC brands like Chubbies, which is actually the former founder of Good Day Software.

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So have experience in this space and me from like a thematic perspective,

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you mentioned, you know, reached out to you relatively early in this world of

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agentic commerce. I think before the term really became a thing or I was calling

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it agentic shopping. Yeah, it was close. I got one word right.

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50%. It’s OK. You’re right. You know, I think you got both words wrong because

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a lot of it and a lot of what you do is not agentic. So it’s true. Oh, boy. Here we go.

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Let’s not open that can of worms. But yeah, no, I was lucky enough to kind of

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be excited about the space, have really good context from just where,

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you know, the value has been attributed historically.

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And the way I saw the world going was there is just a, and maybe I’ll define

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like agendic commerce for people in the way we view it.

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Because I think a lot of skeptics come in and say, Ellie, like this is a great

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idea, but I don’t imagine ever like giving over my credit card,

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banking information to an agent and like it go buying a house for me.

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And I’m like, sure, you know, I don’t know if we’ll ever get there.

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But the way we view the world is anything within the experience of researching,

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you know, discovering and then ultimately purchasing products from an AI powered

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and autonomous experience is really agendic commerce.

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So any part of that stack. So it could literally just be discovering new products

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is really agendic commerce. And then all the other aspects within that sequence

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are going to be developed over time. Yeah.

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I looked at the market and said, OK, for that vision to be realized of people

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coming in, having seamless experiences and really scaling with like agentic

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or AI shopping, what needs to be true?

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And the first thing is consumers are only going to use tools if they have good

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experiences. And the good experience isn’t going to come necessarily from better models.

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It’s going to come from the merchants jumping on this train and saying,

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what do I need to do for the AI side of the experience to actually be good?

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And that’s really where we landed within, I think, product cataloging.

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But I can talk more about the second kind of how we view it,

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if that’s helpful. Yeah, I think Jason has a question.

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I love your framing. I 100% agree. And you’re going to miss the market view,

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narrowly defined agentic as that autonomous transaction.

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But I always turn that question around on people to say, I can never imagine mine now.

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Because like, could you imagine buying a house without getting agents advice,

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like to buy? I mean, it’s the biggest investment that most people will make.

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And increasingly, an agent is going to play a role in that transaction. Yeah.

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So I certainly agree. I’m sort of curious, though. It’s funny in my world.

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One of the biggest problems with this whole agentic thing is the pace of change, like.

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Things are happening so fast that our old models of how we select software or

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how we craft solutions or how we do change management, they’re not designed for this pace.

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In my mind, and you may tell me I’m wildly wrong, you’re in the one industry

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where the pace of change is potentially an advantage, right?

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Because it’s like you find out if you had a good investment philosophy quickly or not quickly.

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So, yeah, I don’t know. Is it helpful for you that things are happening so fast?

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Funny enough, in venture, it’s probably a hard part of the job.

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You actually don’t really know for some time. So like, you know,

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maybe early stage, yes, we get a write up on a company pretty quick.

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But there’s plenty of companies, you know, like Groupon that goes off,

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raises hundreds of millions, and then isn’t a great outcome for all the venture

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capitalists involved. So hopefully, you know, all the companies I invest in

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have fantastic outcomes, but, you know, we’ll continue to see past initial fundraise.

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But yes, I’d say the rate of change actually creates an opportunity for investors

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like Ridge specifically.

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There’s kind of like bifurcation in the market right now with the mega deals,

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you know, seed rounds getting done in a billion and the mega funds really taking

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a large portion of where you could say rounds are in volume,

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capital is getting attributed to.

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But now, given that the rate of change is so quick within all these industries,

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retail and commerce, for example, it actually comes down to,

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funds like Ridge that are building expertise in this market can stay so close

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to the ground, understand what’s actually happening, and then we can underwrite

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and make investing decisions faster than like mega funds that are trying to

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capture the whole world. Sure.

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Although, side note, I think you did just price a refi by a C round. So just saying.

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What are some of the areas that you’re looking at now as you think about where things are going?

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You’ve kind of been thinking about this for two years now. What are some areas of interest to you?

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Yeah, so kind of like I mentioned before, I think of it as a sequence,

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like to get to this world where everything is natively agentic for consumers

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and businesses and, you know, shopping, procuring things, what needs to be solved for and be true.

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So the first layer I really found was to be the data layer where you guys are

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playing now and enabling all of the agents and AI services to properly capture

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the end products and SKUs and services that people might want to be purchasing.

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Then the next one and when you talk about all the companies and infrastructure

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that’s getting this intermediated by AI the first layer is actually also advertising

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and there’s a lot of hype going on with ads showing up within AI services so within chat gbt.

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Obviously they have to generate some revenue to make up for the compute costs

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and you know their funding rounds and the dynamics you know we don’t have to

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go off on a tangent about their IPO getting delayed for safety but I think everyone

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is like picking up on on the trend what’s really going on but.

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Actually, AI native ads and offers happening on other surfaces is something

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that I find super interesting.

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So travel, for example, has always been limited in mapping inventory,

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potential offers and promotions that they can give to consumers by one aspect of latency.

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So to be rendered on, let’s say, Google Search, for example,

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and all these other platforms, they have to have a set of, hey,

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this is what we’re able to offer. We’re going to push it out.

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Super low latency systems. You can’t actually dynamically, you know,

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offer anything given those limitations.

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And people end up going on the site. It’s like, oh, this inventory isn’t true.

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The listing for the, you know, room is essentially perishable there.

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Yeah. And I think because of agents and people’s aperture to wait a bit longer

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for results has now opened a door where, hey, how can we create really dynamic,

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more personalized offers for all these consumers?

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So going on to maybe generic site, a side ad, not just being,

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hey, you know, we know you’re looking for a hotel.

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Here’s a hotel for two with a hotel view. But they know that your search was

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oriented around a flight to, you know, this particular country or going with your family.

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And maybe, you know, they’re throwing in a few nights plus dinner or whatever

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it might be that’s oriented for you. So really dynamic experiences on offers

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and promotions and personalized ads, which I think is very interesting.

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So you guys have a really nice office right there in San Francisco,

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kind of in the heart of things.

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If you look at the wave of kind of companies coming at you, is it higher than ever?

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Is it like a little bit in the lull? Specifically, I guess, in the categories you’re looking for.

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Yeah. What are some of the trends you see on the inbound of the company formation side of things?

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I’d say in terms of volume of companies, probably within the same pace that

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it’s been historically. If you look at the number of deals that are going on,

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it hasn’t significantly changed at the CD&S A stages.

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Primarily seed series a and where things have really changed is the bar for

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rounds to get done now so what was historically maybe when i started in venture 2024

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you know the typical series a is a company going from zero to one million and

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they are in a year and that’s like you know

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right down the middle of the fairway for us very interesting let’s go look at

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the dynamics of the team and the product and market,

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now companies doing zero to three or you know 5x growth in the year is like baseline.

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And we’re, you know, really trending towards companies that are selling into

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kind of the AI stack, whether it be within inference, whether it be with indirectly

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to the labs, and the leading token consumers really going zero to 10, zero to 15 in a year is,

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on one side, like we’re really trying to spend a lot of our time and then traditional

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SaaS, and I’d say like focus AI companies for,

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call it niche verticals and sectors, you know, commerce, maybe historically

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So like legal, for example,

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is more of, hey, what dynamics do they have in terms of underlying data, underlying modes?

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It’s really hard as things evolve to pin what is the compounding value for some

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of these companies. And we continuously see that.

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Harvey, for example, put out their research work on a in-house model because

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they’re not allowed to use any of their customers’ data.

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You know, questionable about the results and what they’re doing there.

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So I think everyone is grappling around like, hey, even for the companies that

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are far along, what is really like resounding value?

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And for the most part, it’s actually coming down to just teams execution and

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speed, like building an expertise in a particular market, getting enough,

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you know, deeply penetrated products within the largest consumers of whatever it is this tool is.

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And, you know, getting far and wide as possible within the industry.

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Speed is the most. Yep. Yep.

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I know there’s a lot of listeners of Retailjantic that are entrepreneurs.

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I’m guessing when you get approached about potential funding,

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there’s some common mistakes that get made over it. Do you have any sort of

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stock advice for entrepreneurs that are looking to get funded?

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Hmm trying to think of like the most generalizable yeah yeah for for founders.

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I’d say on one hand coming into conversations,

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and first try to get an understanding of the investor’s view of the world and of the market,

00:15:05.390 –> 00:15:08.610
founders sometimes come in with like very deep thoughts and it’s great to be

00:15:08.610 –> 00:15:13.390
opinionated but try to address what the investor is looking to understand like

00:15:13.390 –> 00:15:16.640
if you see them leaning towards pay explain to me like the competitive landscape

00:15:17.080 –> 00:15:19.470
don’t just say oh you know these competitors they don’t know what they’re doing

00:15:19.470 –> 00:15:20.300
you’re completely different like,

00:15:20.760 –> 00:15:24.600
take a second go a quick tour deeper and in explaining like

00:15:24.980 –> 00:15:29.080
the components of either who you’re selling to what you’re building and how it’s differentiated

00:15:29.460 –> 00:15:34.820
so active listening and answering questions directly is something that surprisingly

00:15:34.820 –> 00:15:37.760
like a lot of founders struggle with because they’re just so focused on hey

00:15:37.760 –> 00:15:40.010
this is the pitch i practice and i need to kind of sell,

00:15:40.500 –> 00:15:45.550
the investor on, as opposed to what can I learn from the questions this investor is asking?

00:15:45.840 –> 00:15:49.160
And like maybe gaps and holes in our narrative. And it’s fine to have gaps,

00:15:49.160 –> 00:15:51.770
like no deal we’re going to underwrite is going to be a slam dunk.

00:15:51.770 –> 00:15:54.170
It just doesn’t exist. Like there’s always things we underwrite in terms of

00:15:54.170 –> 00:15:57.900
risk, but just like calling out the risk, being very direct about it.

00:15:57.900 –> 00:16:02.120
And then your ways of, you know, hindering that risk or hedging it in any way.

00:16:02.370 –> 00:16:05.080
Metal launched Muse, and then you have Instinct.

00:16:05.464 –> 00:16:10.160
There’s Camp in the Silicon Valley world. It doesn’t seem to have as much traction though.

00:16:11.809 –> 00:16:15.220
There’s another one coming that has a human in the loop i forget the name of

00:16:15.220 –> 00:16:19.240
that one what do you think about this new category of personal agents and are,

00:16:19.720 –> 00:16:22.849
you starting to see people building on top of that already because you guys

00:16:23.109 –> 00:16:26.779
silicon valley reacts so fast to these things and in some ways these agents

00:16:26.779 –> 00:16:29.279
are all kind of a reaction to open claw well they’re they’re all,

00:16:29.939 –> 00:16:33.709
they’re all derivatives of taking open claw in a different direction type thing

00:16:34.099 –> 00:16:39.629
yeah i think I think it’s all kind of like pulling back to the reality of like

00:16:39.629 –> 00:16:42.259
the sequence of things and what it’s going to take to get to

00:16:42.679 –> 00:16:45.459
like, you know, native agent of commerce kind of experiences.

00:16:46.039 –> 00:16:50.269
Everyone wants to jump on these ideas. Open claw and like the ability to build

00:16:50.269 –> 00:16:55.689
these tools and the personal agents with quasi shopping capabilities kind of existed some time ago.

00:16:55.879 –> 00:16:59.499
But the only thing that’s changed is with better models, better connectors,

00:16:59.499 –> 00:17:02.099
the experience is just better. So people are really jumping on it.

00:17:02.439 –> 00:17:05.869
I think it has some room to grow. Cute animated characters. Yeah.

00:17:06.659 –> 00:17:09.139
Those go a long way. Don’t underestimate the power of that. They do.

00:17:09.859 –> 00:17:14.009
Myself included bowing into it. But yeah, I think obviously people within the

00:17:14.009 –> 00:17:17.559
Bay Area and Silicon Valley jump on them much quicker than others.

00:17:18.029 –> 00:17:21.369
We do create a bit of an echo chamber in terms of, oh, everyone’s going to jump

00:17:21.369 –> 00:17:23.579
on personal agents tomorrow and the whole world is going to change.

00:17:23.579 –> 00:17:27.989
But you go to the middle of the Midwest and you mention Muse or even Claude

00:17:27.989 –> 00:17:31.739
and CIO from a random mid-market logistics company is going to say who?

00:17:32.700 –> 00:17:36.490
So I think it’s great, it’s a good signal for what we’re going to see over the

00:17:36.490 –> 00:17:40.890
next quarter over quarter growth in both technological advancements and then

00:17:40.890 –> 00:17:42.920
also go to market distribution.

00:17:43.260 –> 00:17:46.940
So people are going to start, I think, adopting these tools as they get better,

00:17:46.940 –> 00:17:50.970
as the experience gets better, and that will ultimately uplift the underlying,

00:17:51.580 –> 00:17:56.030
market for, hey, what’s the infrastructure needed to actually enable these experiences,

00:17:56.030 –> 00:17:58.860
enable shopping to be completely autonomous.

00:17:59.530 –> 00:18:02.440
A lot of the things I’ve seen pulling companies back within Agenda Commerce

00:18:02.440 –> 00:18:05.550
is actually like the lack of the full stack existing.

00:18:05.950 –> 00:18:09.520
Like you can build and you can see with a lot of the companies are on payments,

00:18:09.710 –> 00:18:12.710
we could build all the payment infrastructure for agents in the world.

00:18:12.710 –> 00:18:16.640
But if there isn’t enough transactions or overall like agents and,

00:18:16.640 –> 00:18:20.600
you know, personalized tools for doing the shopping, you don’t really have anyone to sell to.

00:18:20.733 –> 00:18:24.409
So again, coming back to like the sequence of things, what tools need to come

00:18:24.409 –> 00:18:28.229
into place first, what kind of consumer experiences need to exist and how is

00:18:28.229 –> 00:18:31.569
that going to scale out in terms of this market coming together and that’s why

00:18:31.569 –> 00:18:32.789
i think like really you need to just

00:18:33.109 –> 00:18:37.539
give it time someone during the event actually mentioned this is a great i think

00:18:37.539 –> 00:18:39.749
like analogy for what we’re looking at and he’s

00:18:40.069 –> 00:18:45.619
you mentioned like the comparison of mobile shopping to desktop and in reality

00:18:45.619 –> 00:18:50.389
like it took almost a decade for mobile to really surpass desktop or however

00:18:50.389 –> 00:18:52.729
you want to you know measure the start and end but,

00:18:53.269 –> 00:18:55.269
But I think that is like a similar way. But we can look at this.

00:18:55.519 –> 00:18:59.119
It’s not going to be overnight. It’ll be a slow growing, you know,

00:18:59.119 –> 00:19:01.579
segment of how people experience their shopping.

00:19:01.579 –> 00:19:04.349
But it’ll be significant. I think it’ll take a large share. Yeah.

00:19:04.989 –> 00:19:09.730
It’s funny because I’ve looked at that exact progression and the like,

00:19:09.730 –> 00:19:11.750
obviously, everything in agentic is happening faster.

00:19:11.750 –> 00:19:14.900
Right. So it’s fair to apply some acceleration factor.

00:19:14.900 –> 00:19:21.030
But there’s an argument that the jump from non mobile to mobile actually like

00:19:21.030 –> 00:19:25.570
should have enabled more usage than the jump from non agentic to agentic.

00:19:25.570 –> 00:19:29.650
Right. Because agentic lets you do things that people are already doing better.

00:19:29.780 –> 00:19:33.990
It doesn’t necessarily create a new audience that didn’t exist before.

00:19:35.010 –> 00:19:38.260
When you gave a bunch of people that like to shop online a mobile phone,

00:19:38.790 –> 00:19:43.880
they suddenly had way more hours a day where they had the opportunity to shop.

00:19:43.880 –> 00:19:48.619
Right. And so just to put that in context, the iPhone launched in 2006.

00:19:48.619 –> 00:19:51.899
There was mobile for like 10 years before that, but we’ll pretend that didn’t exist.

00:19:52.259 –> 00:19:56.799
The iPhone launches in 2006. Ten years later, 10 percent of e-commerce was mobile.

00:19:57.062 –> 00:20:05.359
And it was like 13 years to get to 30% mobile, which we’re about 50% now. Yeah.

00:20:05.809 –> 00:20:11.899
And I think you made a good point, which is as you compare the agentic to non-agentic

00:20:11.899 –> 00:20:14.779
shopping, you’re not necessarily getting a new cohort of shoppers,

00:20:14.779 –> 00:20:18.979
but you are getting a new segment of shopping you can do. 100%.

00:20:19.269 –> 00:20:22.739
Like there is just certain type of shopping I would not have been able to do

00:20:22.739 –> 00:20:25.499
previously or would not have done previously if it wasn’t for agents.

00:20:25.499 –> 00:20:29.249
For example i went skiing with our group we have like you know with rich has

00:20:29.249 –> 00:20:36.339
like a annual kind of founder event and i went up i’ve been snowboarding for some number of years the,

00:20:37.119 –> 00:20:39.799
you know guy who was taking us looked at the rental board i had he’s like man

00:20:39.799 –> 00:20:41.899
what are you doing the rental board like you’re clearly you know,

00:20:42.479 –> 00:20:45.239
like an experienced snowboarder yeah i’m like i don’t know i’ve never really

00:20:45.239 –> 00:20:47.629
bought a board like i never thought about it and he’s like you absolutely need

00:20:47.629 –> 00:20:50.829
to buy a board like you cannot go down this mountain doing that yeah and it’s

00:20:50.829 –> 00:20:53.399
always embarrassing to have the rental board on the hella trip. Yeah.

00:20:55.599 –> 00:20:59.949
Yeah, I was definitely singled out. And I just didn’t know what type to buy.

00:20:59.949 –> 00:21:03.599
I knew that I liked really tight straps. I was somewhere between intermediate

00:21:03.599 –> 00:21:06.709
and expert. All these nuances and I just don’t have the time to go do the shopping.

00:21:07.069 –> 00:21:11.139
But because of opening up a couple of agents, doing some deep search across

00:21:11.139 –> 00:21:14.489
hours of review videos and explanations for where you are and what you prefer,

00:21:14.959 –> 00:21:18.709
I was able to buy a board and bindings and boots in like 10 minutes.

00:21:19.849 –> 00:21:23.099
Otherwise, we’re not a purchase. Yeah. And to me, that that goes back to your

00:21:23.099 –> 00:21:27.280
very first hypothesis that agentic is this broad thing that includes discovery,

00:21:27.280 –> 00:21:28.580
not just the transaction.

00:21:28.960 –> 00:21:33.190
Because, you know, we always joke e-commerce saw buying but broke shopping.

00:21:33.190 –> 00:21:36.210
Right. So like traditional e-commerce got very good at transactions,

00:21:36.690 –> 00:21:41.310
not so great at like discovering new products and the joy of shopping.

00:21:41.310 –> 00:21:45.910
And so to me, the big excitement about agentic is that it potentially unlocks.

00:21:46.510 –> 00:21:48.740
Digital joy of shopping and discovery.

00:21:49.150 –> 00:21:52.520
Yeah. Yeah. Let’s we’ve talked a lot about the front end of shopping.

00:21:52.520 –> 00:21:54.440
Let’s kind of pivot to the back end.

00:21:54.750 –> 00:22:00.040
Have you seen any interesting AI companies on supply chain or store management

00:22:00.040 –> 00:22:01.150
or any of those types of areas?

00:22:01.470 –> 00:22:05.480
Is that even interesting to you? Yeah, for sure. So we’ve actually looked at

00:22:05.490 –> 00:22:08.959
it like two, one being actually demand forecasting.

00:22:09.361 –> 00:22:14.329
So in terms of I’d say like the teams that are really using traditional machine

00:22:14.329 –> 00:22:17.729
learning algorithms that they would have taking in, you know, data points.

00:22:17.729 –> 00:22:20.249
That I didn’t even know existed in these types of forecasts,

00:22:20.249 –> 00:22:23.679
like weather and, you know, foot patterns, et cetera, for different geographies

00:22:23.679 –> 00:22:27.349
and where they’re going to, you know, both supply inventory and then plan for

00:22:27.549 –> 00:22:29.259
on-season and off-season purchasing.

00:22:29.581 –> 00:22:33.720
So on one hand, allowing these teams to do those workflows much quicker,

00:22:33.720 –> 00:22:37.289
taking very large skill teams into smaller ones and having much more accurate,

00:22:37.359 –> 00:22:39.379
you know, determinations for where they should be purchasing.

00:22:39.769 –> 00:22:43.569
And then actually, also, we talked a lot about the consumer experience for ejecting

00:22:43.569 –> 00:22:47.079
shopping, But procurement for businesses is crazy.

00:22:47.389 –> 00:22:52.269
And I will tell you, you know, anecdotally, one business is working with,

00:22:52.269 –> 00:22:56.229
think of one of the top like five largest furniture businesses.

00:22:56.579 –> 00:23:00.839
They have, you know, non-strategic procurement. So purchasing of things that

00:23:00.839 –> 00:23:02.499
may be within their warehouses like pallets.

00:23:03.160 –> 00:23:07.110
There was a company that took just pallets as a category and said,

00:23:07.260 –> 00:23:09.960
let’s go on to market and understand like how you’re purchasing across all these

00:23:09.960 –> 00:23:13.540
different warehouses and, you know, such is really mapped out.

00:23:14.030 –> 00:23:19.350
They identified $4 million worth of overspend, went through to all their suppliers

00:23:19.350 –> 00:23:22.560
and said, hey, we got these better quotes, meet them or we’re going to drop you.

00:23:22.970 –> 00:23:26.640
And then past that, once they actually secured new contracts,

00:23:27.010 –> 00:23:32.010
identified that like two different warehouses that are maybe six miles apart

00:23:32.180 –> 00:23:35.550
were buying from the same supplier at completely different SKU prices.

00:23:36.370 –> 00:23:42.490
And one of the renegotiated contracts the year following, they tried invoicing

00:23:42.490 –> 00:23:44.410
them for 500K more than it was agreed upon.

00:23:44.700 –> 00:23:47.050
And because they just don’t have the bandwidth to really, you know,

00:23:47.050 –> 00:23:49.290
check the details and granularity across all these contracts,

00:23:49.290 –> 00:23:52.940
all this purchasing are just burning those dollars away. So it’s really good

00:23:52.940 –> 00:23:56.240
to see like intelligence on the spending side, being able to structure all this

00:23:56.240 –> 00:23:59.070
data in a way that wasn’t possible before that I find super fascinating.

00:23:59.530 –> 00:24:04.110
Yeah. Yeah. It’s funny. People don’t talk about it, but like worldwide consumer

00:24:04.110 –> 00:24:08.020
spending is about seven B2C is about seven trillion dollars a year.

00:24:08.450 –> 00:24:13.430
B2B is 14 trillion dollars a year and way more automatable. Like there’s it’s

00:24:13.430 –> 00:24:17.080
lower like there’s all these EDI transactions. And per your point,

00:24:17.080 –> 00:24:18.770
like the amount of friction in them is shocking.

00:24:19.220 –> 00:24:23.650
Yeah, and the lack of tying it all back together because it’s been so frictiony.

00:24:24.360 –> 00:24:29.200
You have the unique visibility of having been at this show both years. So I was there as well.

00:24:30.869 –> 00:24:33.879
How would you kind of describe it to folks that haven’t seen the progression

00:24:33.879 –> 00:24:36.409
over a year with this show of how it’s changed?

00:24:37.342 –> 00:24:39.919
You’ve probably attended a bunch of the content. What are some of the more interesting

00:24:39.919 –> 00:24:41.269
things you’ve seen on stage?

00:24:41.769 –> 00:24:46.930
Yeah. So maybe to compare year over year, I was really impressed to come back

00:24:47.180 –> 00:24:48.850
and see the progression.

00:24:48.850 –> 00:24:52.870
It seems like it’s been five years in terms of everyone’s like mental model

00:24:52.870 –> 00:24:57.040
for using AI in this industry where it actually has a measurable return.

00:24:57.395 –> 00:25:00.969
I think last year it was much more like showboaty surface level of,

00:25:01.259 –> 00:25:05.419
oh, we’ve experimented with this and deployed that across whatever it might

00:25:05.419 –> 00:25:07.349
be, like automated emails and such.

00:25:07.689 –> 00:25:11.340
And now people are coming in with much more crisp questions and expectations

00:25:11.340 –> 00:25:14.730
for answers. And how are you actually measuring your AI traffic?

00:25:14.940 –> 00:25:18.570
Are these numbers that you’re throwing up on the board like actual AI traffic

00:25:18.570 –> 00:25:24.250
or is it 20% consumers and 80% bad actors from Chinese bots?

00:25:24.589 –> 00:25:29.459
So the one skeptical side of things and people knowing how to dissect a lot

00:25:29.459 –> 00:25:32.630
of the data and things that are thrown up on the board is pretty great because

00:25:32.630 –> 00:25:36.320
that is ultimately to adopt AI in a better form factor.

00:25:36.890 –> 00:25:42.339
I also say from the content that I’m seeing, really like how people are breaking

00:25:42.339 –> 00:25:47.459
down different layers that need to be implemented to open up some of these opportunities.

00:25:47.829 –> 00:25:53.139
So, for example, Stripe, you know, walking through one, their agentic checkout,

00:25:53.139 –> 00:25:56.419
you know, saying how most of these agents are failing in the checkout experience.

00:25:56.419 –> 00:25:58.809
And that’s why you’re really not seeing a lot of shift into revenues.

00:25:59.129 –> 00:26:02.610
So they’ve actually just come out with AI native agentic experience.

00:26:02.610 –> 00:26:05.660
It’s embedded in anyone that has Stripe checkout, which is really impressive.

00:26:06.031 –> 00:26:09.200
As well as like underlying policy and intent. People talking about,

00:26:09.200 –> 00:26:13.040
hey, like, where is all of the, we’ll call it, you know.

00:26:13.720 –> 00:26:16.940
If the lawyers get involved and people start doing returns and complaining and

00:26:16.940 –> 00:26:19.860
pointing fingers at agents, who’s going to take, you know, the foot of the bill?

00:26:20.060 –> 00:26:24.500
So not just high level, is AI going to work? Is it not? Like in this actual

00:26:24.500 –> 00:26:26.920
experience, like how do you solve for some of these different layers,

00:26:26.951 –> 00:26:27.949
which I think is super interesting.

00:26:28.169 –> 00:26:31.469
Yeah, we went from theoretical to super tactical in one year. We’re crazy.

00:26:31.699 –> 00:26:33.650
Yeah. Very exciting. Yeah.

00:26:34.349 –> 00:26:37.179
Well, we really appreciate you taking time. I know you’re busy.

00:26:37.179 –> 00:26:38.739
You got a lot more to go see.

00:26:39.580 –> 00:26:42.110
You want to take us out, Jason? Yeah, yeah, yeah. So to sum it up,

00:26:42.470 –> 00:26:45.630
the main reason this year is way better is because I’m here this year.

00:26:46.230 –> 00:26:49.059
Got it. Of course. And I’m invited here. Yeah. And, you know,

00:26:49.059 –> 00:26:52.090
sadly, that’s all the time we have for today. So we’re going to have to wrap

00:26:52.090 –> 00:26:55.360
up. But thanks very much for stopping by and sharing some insights with us.

00:26:55.590 –> 00:26:58.020
And until next time, happy commercing.

00:26:59.560 –> 00:27:03.930
You’ve been listening to The Jason and Scot Show. For all the latest news and

00:27:03.930 –> 00:27:10.990
trends on e-commerce and shopper marketing, subscribe to us on iTunes or visit JasonAndScott.com.