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The Agile Brand with Greg Kihlström®: Expert Mode Marketing Technology, AI, & CX
#821: From eTail: CommerceIQ's Himanshu Jain and Bill Schneider on delaying the gap between strategy and execution
00:24:48
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- [00:01]
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- [00:57]
Himanshu Jain
Foreign.
- [01:06]
Greg Kilstrom
Welcome to Season eight of the Agile Brand Podcast. This season we're going all in on Expert Mode, MarTech, AI and Customer Experience, talking with the people and platforms behind the brands you know and love. I'm Greg Kilstrom, your host and I help Fortune 1000 companies make sense of martech, AI and marketing ops. Hit subscribe or follow to make sure you always get the latest episodes and leave us a rating so others can find us as well. And make sure you check out our sponsor Tech Systems, an industry leader in full stack technology services, talent services and real world application. For more information go to teksystems.com now let's dive in.
- [01:49]
Podcast Host (Interviewer)
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 action 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 ITEL 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 Himatra and Bill, welcome to the show.
- [02:48]
Himanshu Jain
Great to be here. Thanks.
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Podcast Host (Interviewer)
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? Sure.
- [02:57]
Himanshu Jain
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 I've been with Commerce IQ really to help this guy kind of bring all the great innovations that he's creating to market and agent commerce is our latest innovation. So really excited about that.
- [03:24]
Bill Schneider
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 trompole. Yeah, love it.
- [03:44]
Podcast Host (Interviewer)
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 some of the points in that as well and we'll include a link to the research as well in the show notes. So that research notes that 80% of commerce leaders are feeling overwhelmed by data. 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?
- [04:19]
Himanshu Jain
Yeah, really interesting. So we, as you pointed out, we just did a survey of 250 cpg e commerce leaders trying to get an understanding of what were their biggest performance challenges heading into 2026 early on, 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, 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 supergubidas are voicing that they're overwhelmed with the amount of data and having data that's not shown actionable. So when you think about an 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 their awareness, in their understanding to make actual decisions and that's a root out.
- [05:23]
Bill Schneider
I'll give you an example. Mocha the Valentine's Day just passed or super bowl event was there. There's so many of these moments 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.
- [05:46]
Himanshu Jain
Right, right.
- [05:47]
Bill Schneider
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 were 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. 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 site to a real site. 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 pushed for.
- [06:54]
Podcast Host (Interviewer)
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 manner of speaking, is this primarily cost saving? Is it? Or is it, you know, a different fundamental strategic move that, you know, maybe to gain speed, gain scale, you know, what's the story here?
- [07:22]
Himanshu Jain
Yeah, it's a more foundational shift that's taken place because ultimately, when you think about retail today, it's all algorithm. 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 stay off.
- [07:43]
Bill Schneider
Right. They need.
- [07:44]
Himanshu Jain
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.
- [08:13]
Podcast Host (Interviewer)
So in, in practice, let's talk a little, a little tactical here. And so you know, enabling agenta commerce certainly implies more than just automation, which you know, automation's been done to some degree for, for many, many years at this point. Can you, can you maybe walk us through a tangible example? How does an AI agent handle a task like responding to a competitive pricing change from identifying the event to actually responding to it?
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Bill Schneider
Yeah, I'll take a couple of examples. Automation is basically following a 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 comparator, price change happened, price match, low 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. 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 font from 1250-1220 dollars. Now a dumb system will say they reduce the price. Let me also price match them versus agentic system will first say is that, does that even matter? The 37 cent price, is that even the right comparator 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 here? What happened when last time? They also have memory. So what happened when last time this comparator rubbed 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 systems are very silent. 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. But what if I reduce my price point from 1250 to only 1240, what will happen if I reduce from 1250 to 1230, what would happen? And taking all that into account, they will 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 recommendation that the AJOT system is playing. So 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 her. The same story which we talked about as 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 Marty or Perplexity or chatgpt? 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 to a certain extent.
- [11:54]
Podcast Host (Interviewer)
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 is, what is agentic change here? And you know, what does that unlock for that? The, the rest, the 80%.
- [12:24]
Himanshu Jain
So ultimately it helps you get to the long tail because ultimately now you've got agents that are not just there. There's no scale limit in that case right there. There's not a 9 to 5 that they have to worry about.
- [12:36]
Podcast Host (Interviewer)
Right.
- [12:36]
Himanshu Jain
They're running all the time in the background and you can apply that to your whole SKU catalog and make updates and adjustments. So Maartu just talked about a content agent as an example that is analyzing your pim, analyzing your PDP across your retailers, analyzing your analytics data, analyzing retailer guidelines. 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 SUP catalog for a human team to make that adjustment. And we've seen in the field where typically to make a P2P 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.
- [13:22]
Bill Schneider
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 the teams are focused just on top two retailers. What about retailer number 3, 4 5, 6, 7. The 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 bucks per click versus on a hy vee 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 buying.
- [14:18]
Podcast Host (Interviewer)
And so from a, from a measurement perspective, I mean you had mentioned, you know, kind of bringing down the, the timescale from you know, let's say 30 minutes to 30 seconds just to round round numbers there. Which is amazing in and of itself beyond speed. Even though what are some of the key business KPIs that leaders should be looking at to measure the true ROI of this agentix shift?
- [14:44]
Bill Schneider
Yeah, I think ultimately for any brand what matters is sales share and margins, right? Yeah, 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 ChatGPT 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 a inventory agent, it could be reduction in out of stock race. 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 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 deals or better contracts. Now a merchant or a account manager at a Nestle or a PNG can spend more of time in creative work, negotiation work and lead to better outcomes. Yeah, yeah.
- [16:17]
Podcast Host (Interviewer)
And so you know exactly to what you were saying. I mean this AI agents are now supporting humans. It's, you know, I know there's talk of lots of things but you know that that idea of augmenting humans and supporting humans is really powerful. What does this mean? For you talked about some examples of where time can be spent. What about the skill sets? Like what kind of skill sets should human see? You know, should managers and leaders be trying to encourage in their team members
- [16:47]
Bill Schneider
is 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 take an analogy. Agents today are, and generalizing it as a. 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 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 that intern for a few months on in one specific process. 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 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 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 bogue 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 or of giving their feedback, checking their work and becoming 10x more productive than what they measured. Yeah. Yes.
- [19:07]
Podcast Host (Interviewer)
And so, you know, we're here at Utel Palm Springs certainly surrounded by a lot of, a lot of talk about, you know, potential opportunity 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.
- [19:27]
Himanshu Jain
Well, I think what's interesting right now is if you look back a year, everybody was in a pilot ball. 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. So I think what's really exciting now and also hearing the discussion that's going on here at ETEL is that people have made that shift. They've gotten comfortable generally with AI, they've recognized that AI 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 give companies more scale.
- [20:15]
Bill Schneider
I think change management is very, very important. I think like we saw statistics skills months back that 80% or 90% of AI pilots are failing. And one of the critical reasons was or 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 we have deployed that as well is we are calling as forward deployed engineers. So instead of giving a software to our 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 I described like what in one year from now they'll be doing, they don't know how to do it with it. So they onboard the agent, they understand what are the unique processes of a particular company and tweak the agent and customize it for there or integrate with multiple systems that they have. Like I talked about, they have tens of different systems and data is lying in very in silos. So that is very pocket. 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 councilors 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.
- [21:54]
Podcast Host (Interviewer)
Yeah, so we're still early, early on at Ital Palm Springs here. But what's something you're looking forward to most here?
- [22:05]
Himanshu Jain
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, how are you?
- [22:23]
Bill Schneider
I think I'm looking forward to 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 many different brands, understand their pain points and offer some guidance if there is a need. Yeah. Love it.
- [22:45]
Podcast Host (Interviewer)
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?
- [22:53]
Himanshu Jain
I mean the thing that I do, right, I mean one agility is a mindset. So one is constantly looking at my mindsets, 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 taking place right now. I mean, I think if I look back at my career, 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 taking place right now. There's a lot of opportunity that's taking place. If I look at my role over the last, you know, six 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.
- [23:45]
Bill Schneider
Yeah, I would say like moving beyond the chat bot. That's like a lot of us have been using ChatGPT or Gemini or cloud for or they summarize an email or create a doc on some or understand a particular area. I think start to build things, not be afraid of, let's say like crowdboard terminal or cursor or go into these agent decoding platform and start to tinker with it and start to automate just one or two processes. Let's look at all your days. Where do you spend? You do the same thing again and again and again every day. Can you automate just one or three of them? That would be the best learning experience that you can get in this world. And it's actually very easy to work. Yeah, Love it.
- [24:33]
Podcast Host (Interviewer)
Again, I'd like to thank Himanshu Jain, co founder and head of product and Bill Schneider, VP of Product Marketing at Commerce iq for joining the show. You can Learn more about Himanshu Bill and Commerce IQ by following the links in the show Notes.
- [24:48]
Greg Kilstrom
This episode is brought to you by TechSoup Systems. They're leaders in full stack, tech services, talent solutions and helping companies put it all in action. You can learn more@teksystems.com and thanks again for listening to the Agile Brand podcast. If you like the episode, hit subscribe and drop a rating so others can find the show too. And if you're interested in consulting, advisory work, or if you need a speaker for your next event, feel free to reach out. Just visit GregKillstrom.com that's G R E G K I H L S T r o m.com the Agile brand is produced by Missing Link, a Latina owned, strategy driven, creatively fueled production co op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, stay curious and stay agile.
- [25:44]
Strayer University Announcer
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Bill Schneider
It's done.
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Hey everyone. Check out the at this guy and his bird. What is this, your first date?
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Bill Schneider
Oh, no.
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Podcast Host (Interviewer)
Yeah, the bird looks out of your league.
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- [26:48]
Himanshu Jain
Liberty. Liberty. Liberty. Liberty.