What if the biggest bottleneck in your commerce strategy isn’t the strategy itself, but the time it takes your team to actually perform the actions to execute it?
Agility requires not just having the right insights, but also the operational capacity to act on them at the speed the market demands.
Today, we’re going to talk about a critical bottleneck many brands face: the delay between data-driven insight and real-world execution. Commerce teams are often drowning in data but struggle with the manual, time-consuming work of implementing changes, whether it’s updating product pages or optimizing media spend. This has led to a major shift, where brands are looking beyond traditional agency models and toward a new paradigm of agentic AI—using automated agents to handle execution, freeing up human experts to focus on what they do best: strategy.
We are here at eTail Palm Springs, and to help me discuss this topic, I’d like to welcome, Himanshu Jain, Co-Founder and Head of Product, and Bill Schneider, VP Product Marketing at CommerceIQ.
About Himanshu Jain
Himanshu Jain is the Cofounder and Head of Product at CommerceIQ, a Series D agentic AI company based in the Bay Area. CommerceIQ is a leader in retail technology, having raised $200M from SoftBank and Insights Partners, and serving 10 of the top 12 CPG brands globally. He builds vertical AI and autonomous agent platforms that help the world’s largest consumer brands win across ecommerce and omnichannel retail. Over the past decade, he has repeatedly taken AI products from zero to product–market fit, scaling them into multi-million-dollar businesses across retail media, pricing, supply chain, and digital shelf. With deep roots in machine learning, SaaS and enterprise strategy, he operates at the intersection of advanced AI systems and measurable commercial impact.
Himanshu Jain on LinkedIn: https://www.linkedin.com/in/bill-schneider-b32a6a/
About Bill Schneider
Bill has 20+ years of experience in product marketing and communication roles building and leading product marketing and external communications . In addition to his deep knowledge of the product marketing role, Bill has has a wealth of experience working for SaaS growth companies in analytics, mobile engagement, shopper marketing, and identity verification.
Bill Schneider on LinkedIn: https://www.linkedin.com/in/bill-schneider-b32a6a/
Resources
CommerceIQ: http://www.commerceiq.ai
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Transcript
[0:00:49] Greg Kihlstrom: What if the biggest bottleneck in your commerce strategy isn’t the strategy itself, but the time that it takes your team to actually perform the actions to execute it? Agility requires not just having the right insights, but also the operational capacity to act on them at the speed the market demands. Today, we’re going to talk about a critical bottleneck many brands face: the delay between data-driven insight and real-world execution. Commerce teams are often drowning in data, but struggle with the manual, time-consuming work of implementing changes, whether it’s updating product pages or optimizing media spend. This has led to a major shift where brands are looking beyond traditional agency models and toward a new paradigm of agentic AI, using automated agents to handle execution, freeing up human experts to focus on what they do best: strategy.
[0:01:36] Greg Kihlstrom: We are here at eTail Palm Springs and to help me discuss this topic, I’d like to welcome Himanshu Jain, co-founder and head of product, and Bill Schneider, VP Product Marketing at Commerce IQ. Himanshu and Bill, welcome to the show.
[0:01:48] Bill Schneider: Great to be here. Thanks.
[0:01:49] Greg Kihlstrom: Yeah, looking forward to talking about this. Before we dive in though, why don’t you each give a little on your backgrounds and your role at Commerce IQ?
[0:01:56] Bill Schneider: Sure, I’ll start. So, you know, I’ve been in Martech and e-commerce tech for the last 20 years, essentially helping brands get closer to the customer, either in web analytics or shopper marketing and customer engagement. And now, as of the last year, been with Commerce IQ, really to help this guy kind of bring all the great innovations that he’s creating to market. And agentic commerce is our latest innovation, so really excited about that.
[0:02:24] Himanshu Jain: We empower commercial teams at brands and retailers with AI agents and have them achieve the business outcomes, which is higher sales, share, and profitability. I’ve been in this industry for more than 15 years now, and prior to that, I was in consulting and product management.
[0:02:43] Greg Kihlstrom: Yeah, love it. Well, yeah, let’s let’s dive in here and we’re going to talk about a few things and I know Commerce IQ recently did some research, so we’ll be touching on on some of the points in that as well. And we’ll include a link to the research as well in the show notes. So the that research notes that 80% of commerce leaders are feeling overwhelmed by data. I I think I know some of those leaders. but the core issue isn’t insight, it’s actionability. So from a strategic standpoint, where does this breakdown between insight and execution most often happen for large brands, and why has it been so difficult to solve?
[0:03:19] Bill Schneider: Yeah. really interesting. So we, as you pointed out, we just did a a survey of 250 CPG e-commerce leaders trying to get an understanding of what were their biggest performance challenges heading into 2023 and beyond, right? And what was interesting about that is that it wasn’t company culture, it wasn’t strategic alignment, it wasn’t strategic direction or process, it was data. And we’ve gotten to this point that when you think about SaaS overall, right? You’ve gotten to this point now where we have access to the customer journey in so many different tools, so many different processes and custom or CPG leaders are of working that were overwhelmed with the amount of data and having data that’s actual actionable. So, when you think about a, e-commerce leader that is managing hundreds of SKUs, thousands of SKUs across multiple retailers, there’s just so many different data points that they have to keep in in their awareness, in their understanding to make actionable decisions. And that’s that’s a real challenge.
[0:04:23] Himanshu Jain: I I’ll give you an example. The Valentine’s Day just passed or Super Bowl event was there. There are so many of these moments that are that are available for brands to capture. But let me ask you, like if you were shopping last week for a gift online, how many brands did you notice curate experiences to capture those moments? Very few, right, right? And and the challenge is that it’s not that they don’t know that inside, there’s Valentine’s Day coming. It’s the operationalizing of changing the PDPs or changing their experiences to curate for these moments, it’s very, very hard. For example, like one of our customers that we are working with, in order to change the content or change the experience on a retailer site, you need to first tap into your agency to give you the right content. And that agency may say, look, I’m busy till Black Friday because I’m planning nine nine months ahead. Right. So there’s nothing there, right? Then there is their content lies in their PIM system. Then their digital asset lies in another system. And then there’s another syndication tool that actually moves the content from their side to your retailer side. So there are like tens of different systems that they need to log into, extract information, and update, and it’s manually impossible to do it. And that’s where I think agents can play a huge role because they can speed up that process, they can scale this process, and they can optimize towards a goal, and that’s what we are going to do.
[0:05:54] Greg Kihlstrom: Well, and some of the conversation at least is around shifting budget from what was traditionally outsourced to agencies or other things to AI now. So, you know, insourcing in a in a manner of speaking, is this primarily cost-saving? Is it or is it, you know, a a different fundamental strategic move that, you know, maybe to gain speed, gain scale, you know, what what’s the story here?
[0:06:22] Bill Schneider: Yeah, it’s it’s a more foundational shift that’s taken place because ultimately, when you think about retail today, it’s all algorithms. Yeah, you know, search, inventory, managing the buy box, managing your media. It’s all being driven by algorithms and traditionally, agency teams are not able to keep pace with that amount of scale, right? They need brands need a 24/7 agent to help them manage all those processes effect. And in the survey, what we found is that teams voiced that ultimately their agencies were not able to scale and keep pace and 80% of them were willing to look at agents as a way to solve that problem, providing that there was a level of transparency and also decision-making from from the human expert at the end to actually make the final decision.
[0:07:13] Greg Kihlstrom: So, in in practice, let’s talk a little a little tactical here. And so, you know, enabling agentic commerce certainly implies more than just automation, which, you know, automation has been done to some degree for for many, many years at this point. Can you can you maybe walk us through a tangible example, you know, how does an AI agent handle a task like responding to a competitive pricing change from identifying the event to actually responding to it?
[0:07:43] Himanshu Jain: Yeah. I’ll take a couple of examples. Like automation is basically following the series of steps. If X happens, do Y, right? That is what automation has been or software has been in the past. Like you said, competitor price change happened, price match the competitors, right? but what agents bring to table is planning and intelligence. So they can actually optimize towards a goal, rather than saying if X happens do Y, it based on what I know today and what is happening in the market and your goals, can I optimize towards a goal? Can I plan and optimize towards a goal? So let’s take two examples. Let’s take this price change example. Let’s say a competitor reduced their price of a detergent pod from $12.50 to $12.20, right? Now, a a dumb system will say they reduced the price, let me also price match them. Versus a agentic system will first say, is that does that even matter? The 30 cent price. Is that even the right competitor for me? And then so they first evaluate, is that a real event, or should I or is it something that I need to respond to? What happened when last time, they also have memory, so what happened when last time this competitor dropped price? Did it actually affect my sales or not? Then they will actually look at 10 different systems, and that is a very important problem in CPG. The the systems are very siloed. So they will check the inventory system to say, if I do a price cut, do I have enough inventory? Do I have enough margin to support a price cut? Do I have trade funding to run another promotion? And then they will look at your business strategy. Am I here to increase my cash flow? Am I here to gain market share? And so on and so forth. And based on that, they will create a plan. And then they will simulate that plan. What if I reduce my price point from $12.50 to only $12.40, what will happen? If I reduce from $12.50 to $12.30, what would happen? And taking all that into account, they will give give a recommendation, and then a human can come in and look at all of these different analysis, then apply their own human judgment, and accept one of the recommendations that the agentic system is providing. See, imagine this whole process could be very dumb, just match the competitor, which can lead to margin erosion and sales erosion, or you would not ignore it because you can’t run all of that analysis. And now an agent can do 90% of the work and a human is applying 10% judgment on top. The same story which we talked about is in content, right? Like how do I blog into 10 different systems? How do I understand what is these answer agents, like can I reverse engineer Rufus or or Marty or or perplexity or chat GPT? What is working there? Understand all that information and then curate the content versus just match whatever is there on the PIM to to the retailer side. So intelligence and planning is the new is the most fundamental shift that is happening in the automation world, and that is why agents can mimic human judgment at the seventh.
[0:10:54] Greg Kihlstrom: Well, and of course, a lot of the legacy automations that have been done have also been done based on, you know, humans only, there’s only so many hours in the day. And so, you know, you can use the 80/20, you know, kind of principle of, okay, we’re going to focus on the top 20% of our SKUs, 80% acceptable loss, or just there isn’t enough, you know, hours in the day. What does what does agentic change here and you know, what does that unlock for that the the rest, the 80%?
[0:11:24] Bill Schneider: So, ultimately, it helps you get to the long tail. Because ultimately, now you’ve got agents that are not just, there’s there’s no scale limit in that case, right? There there’s not a nine to five that they have to worry about. They’re running all the time in the background, and you can apply that to your whole SKU catalog and make updates and adjustments. Himanshu just talked about a content agent as an example that is analyzing your PIM, analyzing your PDP, constantly, across your retailers, analyzing your analytics data, analyzing retailer timelines, and it can take a look at where the gaps are, where the optimizations need to be, and then apply that, at scale across your SKU catalog for a human team to make that adjustment. And we’ve seen in the field where typically to make a PTP adjustment, it would take, you know, half an hour to an hour to do that. now with the agentic model, that’s down to less than a minute.
[0:12:22] Himanshu Jain: Yeah, yeah. It’s also looking at it’s not just the long tail, but it’s also long tail SKUs, long tail retailers. Like if the teams are focused just on top two retailers, what about retailer number three, four, five, six, seven that combined generate about 50% of the revenue? And the opportunities that those retailers provide, for example, if winning a bid on snack keyword on an Amazon or Walmart might cost you $10 per click. Versus on a Hi-V or a or Ahold, it might only cost you two bucks. So when you’re spending that incremental dollar, where should you spend? Is a decision most brands and agencies are not making because they can’t go outside the top two retailers. But there’s a massive amount of opportunities that are available on these long tail retailers and long tail SKUs that you can start.
[0:13:14] (Music)
[0:13:19] Greg Kihlstrom: And so from a from a measurement perspective, I mean, you you had mentioned, you know, kind of bringing down the the time scale from, you know, let’s say 30 minutes to 30 seconds, just to to to round round numbers there, which is amazing in and of itself. beyond speed, even though, what are some of the key, you know, business KPIs that leaders should be looking at to measure the true ROI of this agentic shift?
[0:13:45] Himanshu Jain: Yeah, I think, like ultimately for any brand, what matters is, sales, share, and margins, right? Yeah. So, but those are output metrics. But in order to get higher sales or higher share, you need to optimize for inputs, which is, for example, in the content agent, if you have beautiful content that is relevant, that is optimized for chat GPT or Rufus or Marty, you will start to see that your conversion will improve. Or you will start to see you will rank higher in search ranking. And if that happens, your traffic increases, your conversion increases, that ultimately translate to sales. Similarly, for an inventory agent, it could be reduction in out-of-stock rates when people are trying to buy your products online or in-store, they will find their products more often than not. So you optimize for these inputs, and then they translates into sales, share and and margins. It’s also unlocking the second order effect of that is most employees are now spending with agents. They can now spend 80% of their time on strategic activities that agents are not good at. For example, negotiating with a retailer. An agent won’t negotiate with a Walmart or Target and better better deals or better contracts. Now a a merchant or a or a account manager at at a Nestle or a P&G can spend more of time in creative work, negotiation work, and lead to better outcomes.
[0:15:16] Greg Kihlstrom: Yeah, yeah. And so, you know, to exactly to what you were saying, I mean, this AI agents are now supporting humans. It’s, you know, I I know there’s talk of of lots of things, but, you know, that that idea of augmenting humans and and supporting humans is is really powerful. What does this mean for you talked about some examples of of where time can be spent. What about the skill sets? Like what what kind of skill sets should human team, you know, should managers and leaders be trying to encourage in their team members?
[0:15:47] Himanshu Jain: That’s a very, very interesting question. It’s a very, some people are creating doomsday scenario, right? around it as well. But I think, let’s let’s take an analogy. Agents today are I’m generalizing it a little bit. In some areas agents are better, in some areas they are worse. But agents are at a level of an intern to a junior analyst. So, when you hire an intern or a junior analyst, what do you do first? You say, look, let me onboard this person in one specific task or one specific process. Now, what does onboarding mean? You provide the business context, you tell the the the junior analyst, here are the people that you should talk to, here are the systems and processes that are available in our company. Here’s how we make decision in this area. Here are the gotchas in this area that you should be aware of. So you are teaching the the that intern for few months on in in one specific process.
[0:16:44] Himanshu Jain: Now, the same way you onboard an agent. You teach that agent on your business context, you tell him this is how I make decisions and so on and so forth. Now, once an analyst is onboarded, then for the first few weeks or months, you check its work. Same way in the agent side as well, you check the work end to end when they have completed the work. And then you say, okay, 80% of that by within this threshold, you can make your own decision and act independently, 20% you do all the work, but give me the final output, so I will apply my own judgment on top of that and and and then we can execute. So judgment becomes very important. Expertise becomes very important. Five years back, a generalist was considered a great, like Jack of all trades used to be like this the the Vogue in the town. Now it is a depth in a particular area, expertise in a particular area, matters a lot more because you are teaching an agent to do that. And finally, you are providing continuous feedback. In not so distant future, maybe in a year from now or a year and a half from now, I expect every white collar employee to onboard a bunch of these agents and go through that loop of training them, of of giving them feedback, checking their work, and and and becoming 10X more productive than what they were doing.
[0:18:06] Greg Kihlstrom: Yeah, yeah. And so, you know, we’re here at eTail Palm Springs, certainly surrounded by a lot of a lot of talk about, you know, potential opportunity and and new technology. What do you see as either the next maybe hurdle or the next big opportunity for brands in this kind of agentic model?
[0:18:27] Bill Schneider: Well, I think what’s interesting right now is if you look back a year, everybody was in a pilot wall. They were starting to experiment with conversational AI, chatbots and using it particularly for a lot of different content use cases. Now that’s shifting to moving into initial projects, you know, expanding different use cases. And so I think what’s really exciting now, and also hearing the discussion that’s going on here at eTail is that people have made that shift. They’ve gotten comfortable generally with AI, they’ve recognized that AI is is a level-up capability that’s going to give them additional scale and power in their role. And so I think now, you know, this year is really going to be about execution and rolling out these types of agents and agentic services to I’ll give companies more scale.
[0:19:16] Himanshu Jain: I think change management is is very, very important. I think like we we saw statistics few months back that 80% or 90% of AI pilots are failing. And one of the critical reasons was two reasons the MIT story published. One was change management and second was business context. So we talked about training the agents on context and so on and so forth. I think change management, the way I think most successful companies are doing and they are deployed that as well is what we are calling as forward deployed engineers. So instead of giving a software to a or services to our end customers, we actually put an engineer within the four walls of our customers. And what they do is, they onboard an agent because it’s hard for people what what I described, like what in one year from now they will be doing, they don’t know how to do it today. So they onboard the agent, they understand what are the unique processes of a particular company and tweak the agent and customize it for them or integrate with multiple systems that they have. Like I talked about they have tens of different systems and data is like in very in silos. So that is very important. And I think most most successful brands and retailers would be the one who would embrace this change and are agile enough rather than getting caught in the red tape of lots of AI like the boards and counsels and things that are internally. I think it’s important to have the right governance, but it’s also important to move really, really fast, because the pace of change is massively.
[0:20:53] Greg Kihlstrom: Yeah, yeah. So, we’re still early, early on in at eTail Palm Springs here, but what’s what’s something you’re looking forward to most here?
[0:21:04] Bill Schneider: Oh, you know, just seeing what the conversation is. I mean, that’s one of the great things about being at an event like this is you kind of you get a chance to plug in, see, what types of conversations people are having, where they are currently in their journey with AI in particular.
[0:21:22] Greg Kihlstrom: How about you?
[0:21:23] Himanshu Jain: I think I’m looking forward to let’s some interesting use cases, like most of the successful AI use cases are boring use cases, so, but they work, right? And I’m looking forward to talking to many different brands, understand their pain points and offer some guidance if there is there’s a need.
[0:21:44] Greg Kihlstrom: Yeah, love it. Well, thanks to you both for joining today. Last question for each of you. what do you do to stay agile in your role and how do you find a way to do it consistently?
[0:21:54] Bill Schneider: I mean the the thing that I do right, I one, agility is a mindset, right? So one is constantly looking at my mindset, trying to stay kind of in that in a beginner’s mind mindset, looking for where there’s opportunities. And we’re in a fundamental shift that’s taken place right now. I mean, I think if I look back at my my career, you know, the browser, that was a huge shift and then mobile was a huge shift, and AI is another huge shift. And so there’s a huge amount of change that’s taken place right now. There’s a lot of opportunity that’s taking place. If I look at my role over the last, you know, 6 to 12 months, it’s changed dramatically. I mean things that used to take me days or maybe a week to complete are now done in less than a day. And that’s that’s really invigorating and exciting. And so I, you know, just being willing and open and leaning in.
[0:22:46] Himanshu Jain: Yeah, I would say, like moving beyond the the chatbot, that’s like a lot of a lot of us have been using ChatGPT or Gemini or Claude for to summarize an email or create a doc on or understand a particular area. I think start to build things, not be afraid of, let’s say, like Crotport terminal or cursor or go into these agentic coding platforms and start to tinker with it and start to automate just one or two processes. But look at all your your days, where do you spend you do the same thing again and again and again every day? Can you automate just one or two of them? That would be the big the best learning experience that you can get in this this world. And it’s actually very easy to do it.
