About the Guest
Ranjith Maniyedath is the co-founder and managing partner of Perfaware, a digital commerce agency specializing in enterprise transformation across e-commerce, order management, inventory, and customer service. With over 25 years in the industry, he brings a practitioner’s lens to AI adoption, helping clients identify high-value use cases and build the data foundations required to make them work.
Show Highlights
- The AI Workforce Shift: AI is reshaping white-collar roles, not by reducing hours, but by expanding what’s expected of individuals. Workers who adapt by moving beyond strict specialization will have the advantage; those who don’t risk being left behind.
- From Monoliths to Composable Commerce: Cost pressures and operational complexity are accelerating the move from legacy platforms to modular, SaaS-based architecture, creating the conditions where AI can actually take hold.
- Identifying High-Value AI Use Cases: The fastest wins are in automating repetitive, data-rich tasks like product descriptions, review analysis, and customer service, but Ranjith cautions that use case selection has to start with your data foundations.
- Smarter Order Routing Beyond Proximity Rules: Machine learning can outperform rule-based fulfillment engines by weighing processing time, shipping cost, and markdown risk together, not just defaulting to the nearest warehouse.
- Why Every AI Project Is a Data Project: Siloed systems, inconsistent records, and disconnected channels aren’t just operational headaches. They’re blockers to any meaningful AI initiative. Fixing the data layer isn’t optional; it’s the prerequisite.
- The MCP Ecosystem and Agentic Commerce: The emergence of the Multi-Context Protocol points toward a future where AI agents operate across platforms without being locked into any single vendor, and KIBO’s open architecture is designed with exactly that in mind.
Transcript
00:00.93
Natalija Pavic
Hello. Welcome to the show. Thank you so much for joining me. I am so excited you’re here. don’t you tell me a little bit about who you are and what you do?
00:08.86
Ranjith
Ranjit Khoshanagamanan, Nat. It’s great to be on your show. ah Folks out there, my name is Ranjit. I am the co-founder and managing partner of Perfware. Perfware is a digital commerce agency that helps enterprises with their digital transformation initiatives, especially around commerce. And when I say commerce, it is e-commerce, order management, inventory, and customer service primarily, and then extending into some adjacent areas as well.
00:35.41
Natalija Pavic
That’s amazing. um and And so i think, you know, i’d love to know something from you, which is what is an unpopular opinion that you have?
00:46.12
Ranjith
popular opinion. Okay, some folks may not like it. But I for one thing that AI is gonna make a lot of jobs less required or change it so dramatically that people in the marketplace today for a job may not be the right ones unless they change their, you know, attitude or approach very dramatically very, you know, soon. and So and I really feel maybe there’s just gonna be fewer need for at least the some of the white collar jobs, right, then people have to get than the society.
01:18.54
Natalija Pavic
Well, I’m so happy that you decided to talk about that because it feels like it’s been a bit of an unspoken rule to kind of like stay away from that topic. And since you are ah partner here, you have kind of a little bit more freedom to to say what you want, right? So my point is you said it, not me, right?
01:35.93
Natalija Pavic
Now… now It’s interesting because it’s definitely going to change jobs um tremendously. Tell me little bit. of Let’s dive into your unpopular opinion a little bit here before we get going.
01:48.58
Natalija Pavic
Tell me a little bit about how you think it’s going to change.
01:52.25
Ranjith
okay So, you know, some people, I’ll just take it back a little, right? And there are some people say that when, you know, computers and calculators came out, know, people had the same fear.
01:57.03
Natalija Pavic
Mm hmm.
02:02.02
Ranjith
And I wasn’t there around then, right, to nurse those views. And of course, you know, when computers came and calculators came, the world changed, but they created a lot of new jobs. And when AI is coming around now, I’ve been in in the industry for 25 plus years, and I’m just seeing, you know, what it took for an enterprise to do a software development or software consulting.
02:21.68
Ranjith
the the amount of resources you need to add value to your clients, you know, was quite a bit up until three, four years ago. And today, you know, after the eval evolution of Gen AI and the tools around it, we as a company and our our clients and other people we speak to, we can see them doing a lot more with fewer resources, right? and And it’s not just doing getting by, but delivering great value and, you know, improving, you know, their output, whether it’s in the the consulting organizations like us or at the client side, right?
02:51.69
Ranjith
So I’m seeing that and I, you know, we ourselves as a small company, we are, you know, leveraging tools and, you know, whether it’s free tools or, you know, some or sometimes paid tools, and we are able to do a lot more things that we could with the same amount of people, right? And I don’t see that changing. I just see that getting better over time.
03:10.47
Natalija Pavic
Yeah, I think it’s definitely going to be huge shift and it kind of feels like um like when the all of like the computer science you know revolution happened and the internet happened, it felt like um education never really caught up.
03:24.07
Natalija Pavic
yeah It was like they’re still teaching you know a lot of courses that are not, you know they needed to have more courses like social you know ethics and social media, like all this stuff that they never really developed. And now it’s like we’ve moved beyond that again. And so it feels like education never caught up to the old world. Now it has to catch up to a new world.
03:40.19
Natalija Pavic
So um I heard, i don’t know if this is true, but I heard that, um you know, computer science enrollment’s down because people are saying ah development is going to be the one thing that’s going to be automated quite quickly. um And so people are not as keen, um like young people are not as keen to learn development.
03:59.34
Natalija Pavic
um But what’s interesting about that is, ah you know, to your point, there may be a transference of jobs, not so much a reduction. When we think about we are able to do more, it’s kind of like the fallacy of um you know, we thought that all this automation, all these improvements, all these SaaS services, et cetera, would actually make it so that we as a society don’t have to work.
04:21.56
Natalija Pavic
more, but it seems that we’re working more than ever because there’s like, it’s not that we’ve had few, we like, okay, now we’re done. We’re done with the job. We caught up. It was like, oh, since you can do this, now do these 10 to 100 other things that you never got to before. So I think people are getting so scared that there won’t be enough work to go around. Me personally, I’m scared about the amount of work we’ll be expected to do now.
04:46.65
Ranjith
I think where what might happen is that there may be you know people like, it’s like a joke, right?
04:46.97
Natalija Pavic
It’s a little bit different.
04:52.41
Ranjith
like If you have some work to do, who do you give it to? Do you give it to person who is idle person who super busy? You usually as a boss want to give the person who is super busy because they just know how to get stuff done. right But I think what’s happened, and if you’ve read there’ some stat statistics around it, like if you look at ah at a home, and know like a homemaker or somebody at your domestic you know doing chores, I think after the invention of the microwave and the vacuum cleaner, like
05:02.63
Natalija Pavic
Yeah. Yeah.
05:08.73
Natalija Pavic
yeah
05:13.61
Ranjith
The work is not changed much. right All the other stuff that’s come out is not made like you know help you with the dishes or help you with the laundry and whatnot. right But even that might change if robots become more calm mainstream. right but So that’s it i think an unsolved problem. But coming on the work front,
05:32.11
Ranjith
I feel like a lot of the work ah growth in the you know services industry or just professionals has been in like desk jobs. And that’s where I think the AI has made the greatest strides in the last few years.
05:43.42
Ranjith
And that’s why I feel that people, you know they may be you know they really rethink their value to the companies. And it’s not just, you know if they’re just developers, they need to get better at other aspects of it right and and and do more if they’re to be productive.
05:55.37
Natalija Pavic
Yeah, there’s going to be. We’ve definitely gone through like a phase of like specialization was really in vogue. And now being a generalist will be more important, like like being dangerous in multiple areas and being able to use the tools to execute is going to be more important than specializing in any one area.
06:11.20
Natalija Pavic
um ah Besides AI, which is so topical, and I’m sure you’re thinking about it, I’m thinking about it every day. ah What else is on your mind? Because you you, you know, you obviously run Perfware, and you talk to a lot of clients. What are you seeing out there that’s coming up more and more?
06:27.42
Ranjith
Sure. ah So for PerfAware, we work with enterprises, lot of them are in the retail sector, right? Some maybe like in manufacturing and consumer goods. But as we focus on the commerce area, there too, you know there’s been a lot of pressures because of the macroeconomic conditions, the the whole thing with the tariffs and and whatnot, right?
06:46.28
Ranjith
There’s always pressure to cost pressures and productivity sort of you know agenda that they’re driving. And there too, you know AI comes in handy, right? And so customers are looking to use you know software. So we went from sort of the monolithic software to more agile you know software development approach from waterfall to agile and and a more frequent releases.
07:07.13
Ranjith
They went to some composable architecture and flexible architecture. So and we’ve seen our retailers you know ah ride that journey. right And now you know they’ve done the SaaS, you know a lot of the modern, they’ve moved modern you know from mainframes to modern solutions and know in many cases SaaS solutions.
07:24.29
Ranjith
And now the next stage is to kind of continue on that journey of you know transformation and you know taking the power of AI and you know consuming it not just as AI for their chart task, but you know going off late as the agentic AI. There’s a lot of hype and excitement around ah the possibility of agentic commerce.
07:44.93
Ranjith
And I think that’s where a lot of cons conversations im I’m seeing when I go to trade shows and I talk to clients and prospects. right so just leveraging AI both for their internal activities, whether it’s for engineering and and store associates enablement, but also to do agentic commerce and start thinking about that so that because consumers eventually may be groomed by the Amazons and the Googles to you know explore agentic commerce and agentic shopping, right?
07:56.90
Natalija Pavic
Thank you.
08:12.66
Natalija Pavic
Yeah, um I think, you you know, people are already using, right, tool openly available tools like ChatJPT to do product research. And so to your point, they’re learning to use Agentech. And so as as um brands get into the game, of obviously, it’s better to be early and it’s better to be set those to set those use cases early.
08:32.19
Natalija Pavic
um Now, I know that there may also be hesitancy. I hear that there’s a lot of hesitancy to using those tools. I think on the one hand, to your point, people are going to have to look for cost efficiencies in this new climate.
08:44.84
Natalija Pavic
And one way to do that is to get AI to unlock sort of the next level of productivity that they need to maintain costs low, right? which is kind of funny because you kind of have to spend on ai in order to save ah through AI, right?
08:59.24
Natalija Pavic
um But I know that some brands and retailers are still out there thinking, how do i use it What’s the best use case? What’s a worthwhile investment? How are you guiding customers through deciding what is a good use case for AI and what projects they should be focusing on?
09:17.37
Ranjith
That’s a great question and I think everybody should start with that sort of question right because the answer could be different depending on where they are in terms of their software stack and maturity and you know their operational sort of efficiencies and whatnot right.
09:33.39
Ranjith
So I, you know as as a founder and as sort of as being responsible for that strategy, I i spend a lot of time you know following you know thought leaders on LinkedIn or going to these conferences and you know trade shows where they talk about what has worked for their use case. right so i see a lot of examples are a lot of value from what other retailers are seeing.
09:55.81
Ranjith
And I know you know people are seeing value and ah in um the engineering side for you in the development and also sort of improving the software development cycles and on the operation side, whether it is for you know product copy on the you know on the e-commerce side, generating product copy, you know reviewing you know summarizing and reviewing the customer feedback and customer reviews.
10:17.71
Ranjith
So there are definitely, we see some common you know use cases at different retailers.
10:22.49
Natalija Pavic
Yeah.
10:22.89
Ranjith
So what retailer or brand is sort of, as you look at it from themselves, right they should look at, okay, what is their sort of landscape looking like?
10:23.24
Natalija Pavic
Yeah.
10:30.41
Ranjith
right Are they collecting customer reviews? And you know if if so, then maybe that summarizing customer reviews could be a challenge, right?
10:36.79
Natalija Pavic
right
10:37.10
Ranjith
where if they’re generating lot of new products, seasonal merchandise, and you know there’s a lot of effort and cost going into generating the product copy, then that’s maybe an area that they can start off. right So they can map their activity and their business and and with their understanding of their costs and their data to see, okay, this is what other retailers have already seen value and then map you know between within among their list of sort of areas prioritize them and then pick one which is work for somebody in the similar space maybe, right?
11:05.05
Ranjith
Because, you know, that you can see at whether it’s the hyperscaler like Google’s conference or AWS conference, are a lot of retailers and brands talking about what they’ve already achieved, right?
11:09.48
Natalija Pavic
Mm-hmm.
11:13.80
Ranjith
So, you know, they can easily tap into that information or they can work with partners like us and other strategic partners to talk about, you know what are we saying to come up with you know some possible ideas and then the pick one that is ah will would add value based on their and know challenges and their ah and problems in their landscape.
11:33.40
Natalija Pavic
That’s interesting. So there’s no, there’s no like, ah to your point, there’s no broad broad strokes. Hey, everybody should be doing X, Y, Z. It really kind of depends on the challenges that the customer is having, right? So you mentioned the two examples that you gave, you know, if you have a lot of reviews, but not a lot of maybe ah understanding of how to read those reviews, um then you could do a review summarization. If you have a ton of product, because not all, not all companies have complex products, right? if you have like 10 products, do you really need…
12:00.97
Natalija Pavic
to use a a gentic for that. Right. Um, and yes, you’re right. There’s already brands innovating out there. So we’re at this stage where i I think those that are sitting on the sidelines may be, uh, waiting too long.
12:12.81
Natalija Pavic
Right. So we are, are, yeah, we’re getting there basically.
12:14.10
Ranjith
You don’t
12:17.14
Natalija Pavic
Right.
12:17.75
Ranjith
want… I think some people, and it’s always, right, there’s always going to be the laggards and the front runners, right?
12:18.09
Natalija Pavic
yeah. Yeah.
12:24.45
Ranjith
But I feel with AI, things are moving so fast.
12:24.97
Natalija Pavic
Yeah.
12:27.53
Ranjith
And I think that although there’s a price, as you mentioned, to try it out, right, and the cost to try it out is is dropping. And my fear is if they sit on the sidelines too long, it may be too late because, you know, unless you build a culture of experimenting and sort out some to you some of the prerequisites, right?
12:44.04
Ranjith
Then you never get on that wagon and and and and know of innovating and and moving where the AI is going and where the customer would want you to be.
12:51.42
Natalija Pavic
Yeah, that’s fascinating. Yeah, I had an interesting call with another guest on the pod and they said, you know, some companies are now incentivizing instead of giving their direct targets, employees targets, they’re giving them um um experiments. So they’re measuring by the number of experiments that they take in year, the number of experiments that they that they do with new technology.
13:16.58
Natalija Pavic
And that’s sort of like a faster way to innovation because you’re right. You know, innovation is now becoming faster. um and we are if we don’t kind of keep up with that, then we may see clear, you know, we may see clear winners and losers in five years.
13:30.47
Natalija Pavic
Right. It may be a totally different world out there. um What are some use cases that you’re like, man, i I wish somebody was working on this or I wish more customers were adopting? Because I’m sure, you know, you get into conversation and you’re like, huh, I mean, i have kind of have to agree with you because, um you know, you’re hiring me, but i i i think we should be doing so much more than we are. So what what are those use cases that you’re seeing?
13:57.90
Ranjith
Sure. So because we do a lot of work in the order management space, I will talk a little bit about in that area itself, right, in order management.
14:04.00
Natalija Pavic
Yeah.
14:06.80
Ranjith
So, you know, one of the things that with an order management where AI can come in handy is in the ah areas of order routing and order optimization, you know. So especially if you’re the retailer, there’s a big retailer, they’ve got multiple DCs or fulfillment centers and they also do a fulfillment from store, there is you know a lot of times you know where your inventory is spread out and it could be sometimes seasonal merchandise that could drive reasons to ship. It’s not always shipping shipping from the nearest location is the answer, right?
14:39.14
Ranjith
So there are other factors that can drive, you know, your optimization decisions so from a sourcing perspective. So that is, you know, a good use case or a good example where, you know, especially for retailers with a broad, you know, ah you know distribution network and with items that could be, you know either seasonal or it could you know be candidates for a ah markdown and and whatnot where you could definitely look at using you know the ai and ml models to help you know allocate or fulfill from the sort of the best location right not necessarily the nearest location because you know the shortest distance of the nearest is not the answer when you consider you know your markdown costs your shipping costs your processing costs, right? So, and it becomes, so some of these problems are better solved with a, you know, non-traditional rule-based engine, right? So I think that’s definitely one use case where I’m not seeing enough adoption. i know some of them, some retailers have, but, you know, there’s definitely, I think there’s more that can be done.
15:34.96
Ranjith
The other thing is also, you know, for any retailer of any size, right, making customer service more efficient. You know, we’ve been talking for many years about, you know, people, a lot of times the calls come into the call center asking, where is my order, right?
15:49.01
Ranjith
you think You think that by now, you know, over these years, that’s been nailed. there’s still a lot of, you know, the customers don’t have that answer on their fingertips, you know, like they’re not proactively pushing it or making that available through a chat interface or through the, you know, their web portals, right?
16:04.51
Ranjith
And that can all be done with, I think it’s little bit of automation and the right OMS in place. You don’t even need a lot of AI, but AI would help in the, if it’s a conversational context and if there’s, you know, more deeper questions around returns and and a lot of those things can be with with the conversational, you know, chatbots becoming a lot better.
16:22.46
Ranjith
You can couple that with a good, and know a strong understanding of your inventory picture and your order positioning and fulfillment. So you can you know take away a lot of the calls that come into your call center by you know with a bunch you with little a bit of automation and a good use of AI.
16:38.25
Natalija Pavic
That’s really interesting. and And, you know, having come from the almost pure e-commerce world, I mean, I did a little bit of OMS, but now I’m doing a lot of OMS at Kibo. And I realized how much sort of more commoditized and mature the e-commerce space is in OMS.
16:55.26
Natalija Pavic
And I’m looking at the OMS landscape. I’m like, oh, wow, like there’s so much more work to be done here.
16:57.86
Ranjith
Yeah.
16:59.71
Natalija Pavic
Because I think everybody for a a long time focused on really that front end, that pane of glass interacting with the customer and making that really cool and appealing and effective. But then to your point, there’s so much more on the supply chain side.
17:12.47
Natalija Pavic
know, like, you know, people still expect free and fast and cheap delivery. And yet we’re running short. Like to your point, we don’t have the best routes. We don’t have the best ah shipping rules. We have stockout issues.
17:25.28
Natalija Pavic
um You know, we’re still kind of living this world where we haven’t quite figured out how to merge online and offline channels. um And it’s interesting because that’s actually to your point where the race will be won in the future as people start to buy you know the same merchandise from multiple different sources, maybe not necessarily your site, maybe marketplaces, agents, right? So as those sort of different channels begin to proliferate, the game can really be won or lost by strong your operation right?
17:54.09
Natalija Pavic
Yeah.
17:57.28
Natalija Pavic
um And so that’s going to be, I think, to anybody listening, if you’re not doing that, this is your chance to outrun your competition, right?
18:04.90
Ranjith
That’s great point, right? it’s I think that comes from the top, right? Like, no, you know, in the past, organizations were very siloed.
18:08.90
Natalija Pavic
Yeah.
18:10.64
Ranjith
Even when e-commerce came out, like the person who responsible for e-commerce was different from the stores and, you know, different from the call center, know?
18:18.35
Natalija Pavic
Yeah.
18:18.48
Ranjith
So every every department had their own sort of tools and the data, like, you know, they were not talking to each other, right? I think now, you know, the retailers were trying to create these omni-channel responsibilities or digital officer roles which kind of overlap or, you know, expand and across multiple of these, you know, old world silos, I think they have a better chance of success because they’re sharing data, they’re willing to look at the customer, you know
18:33.20
Natalija Pavic
Yeah.
18:42.32
Ranjith
respect of which channel they started their journey because today, and I think nobody’s a single channel shopper anymore, right? Even your grandparents are now are shopping or they’re used to using the phone and the mobile solutions and whatnot.
18:49.29
Natalija Pavic
Right.
18:54.36
Ranjith
So it’s going to be a very very, you will be left in the dust if you really continue to operate in a siloed manner and don’t make your you know your tools talk to each other and be of provide that one face to the customer, right?
18:56.25
Natalija Pavic
Yeah.
19:06.84
Ranjith
In respect of where they shop, where they return, where they do their research.
19:11.18
Natalija Pavic
Yeah, and that’s going to be like the way that people select brands in the future is based on the strength of their customer service, to your point. Not so much the strength of the checkout because that’s to a great extent been sort of um solved and optimized for a lot of retailers.
19:26.71
Natalija Pavic
If it’s easy to get the buy button, then it’s hard to get the stuff. And if returns are complicated, then that kind of puts you off of returning, right? Yeah. um Now, a lot of people will say, hey, that’s great, Ranjit, but um my data is the worst. Okay. So like I can’t do anything because my data is like the worst.
19:44.82
Natalija Pavic
What do you say to to those people?
19:49.06
Ranjith
That’s a tough one, you know, because data, like
19:51.11
Natalija Pavic
putting you on the spot.
19:54.01
Ranjith
I… So this is, and I’ll bring up a funny, this thing, I i was at a talk by a Google leader for retail, right? Paul Teppinford, artist’s name is it ah this in Dallas. So he was talking about every project. So he’s responsible for Google retail and all the yeah initiatives they’re doing there.
20:06.51
Natalija Pavic
Yeah. Yeah.
20:07.21
Ranjith
And one of his comments was… you know ah every AI project starts off as a data project. right So I think you know it’s that’s the pain they have to solve. They need to look at their data, look at making sure they get you know the data in one sort of reliable system.
20:17.58
Natalija Pavic
yeah
20:22.45
Ranjith
you know i think So one of the things that they have to make a conscious effort is even before they embark on the the AI path is to make sure that data is housed in solid, scalable, and consistent systems, whether it’s their order data, whether it’s their item data, inventory data, and customer data.
20:37.72
Natalija Pavic
Yeah. Yeah.
20:38.16
Ranjith
like So each system should have like each ah sort of entity should have its own system of record. I think what we have seen is you know a lot of organizations, either because of their their strategy of acquisitions or the way they’ve grown over time, they have like multiple systems doing the same thing, or they’ve got, you know, multiple, you know, systems of record and those systems are not in sync.
20:59.53
Ranjith
So I think it has to be, again, ah from the upper management, that sort of vision has to come out as to how they’re going to, you know, ah lay out their data, what is going to be the systems of record, card and then work towards of those systems of record, because that’s going to be you know imperative for your omni-channel initiatives, as well as any AI initiatives that you’re doing down the road.
20:59.85
Natalija Pavic
yeah
21:23.24
Natalija Pavic
that’s That’s interesting. And you know what? That’s a good point is your AI project is a data project. And so, you you know, it doesn’t make sense for you to say, well, I can’t do AI because my data, if you want to the AI project, you have to fix your data. That has to be part of it. It has to be scoped.
21:38.88
Natalija Pavic
And, you know, Google’s amazing. Obviously, we partner with them very closely. They’ve got a ton of data tools. You’d be surprised like they can help you generate data. They can help you clean data. They have AI tools. So when you start to go into that, you’re not on your own. There are tools out there to help you on your AI journey.
21:55.41
Natalija Pavic
um Now, where do you think we’re going? Like, what do you think is the eventual evolution of this agentic stuff?
22:02.37
Ranjith
So I think with the analytics of that, something that I’m not thinking about all the time. Right. And I’m watching, you know what ah ah partners like Kibo are doing, what, you know, other industry leaders like whether it’s AWS or Google are offering out there. And I think it was just even not too long ago, I think Google sort of came with their the bot announcement, right, for the shopping, assistant shopping, you know, around that.
22:24.01
Ranjith
So I think it’s it’s a matter of time before that becomes more common. Someone had put out a framework which talked about the AI. We are still early on, right, in the agentic evolution. Maybe we’re at like level two or level three, where we still have like a a couple of more levels to go to the where we get fully autonomous agents buying on your behalf, right?
22:38.07
Natalija Pavic
Yeah.
22:43.37
Ranjith
So I think eventually we’ll get there. But I think right now we are in a very interesting time because each of the software vendors and we see vendors that are specializing in e-commerce or auto management, customer service.
22:55.49
Ranjith
So there’s all different vendors that have products in one or more of these areas. And lot of them have been coming out with agentic tools. Now it’s it’s also important. I think in the last few months, we have seen the emergence of the MCP, of the multi-context protocol where agents can talk to each other.
23:12.84
Ranjith
I think that’s where it’s getting really interesting because, you know, a lot of enterprises and and retailers are no exception. They don’t have all software from one vendor, right? So they may have the best of breed e-commerce.
23:21.83
Natalija Pavic
Yeah.
23:23.15
Ranjith
They may have ah a solid OMS. They may be using something else in the store and so on and so forth, right? So it’s important that these agents and and that the software vendors are working on, you know, they solve problems within that their software, you know, ah sort of box, whether like the auto management box or the e-commerce box.
23:40.77
Ranjith
But it’s also important that they are able to talk to the or agents or infrastructure in the adjacent layers. right So an OMS agent may need to tap into the e-commerce, a customer service sort of a solution may need to top into the talk to the OMS because that’s what’s really going to add value to the associates within that enterprise as well as to the the customers. right So I feel that now you know the the enterprise vendors were embracing that sort of an agentic approach and not just building agents that can work within their sort of four walls, but would embrace these sort of MCP type approaches and can you know ah work with the adjacent solutions, I think they they will really stand to gain and and and partners like us will also sort of benefit in this process because we can also ah you know do it in a couple of ways, right? One is we can innovate and and create you know new solutions and new you know ah ah innovative offerings that can help
24:39.51
Ranjith
you know specific brands and retailers ah embrace these technologies faster, and then we can add value both to the you know the software vendor we’re partnering with as well as to the customer. right So I think it opens up lot of interesting possibilities. So that’s a space we are specifically watching out for.
24:56.90
Ranjith
And with Kibo as an example, Kibo, a couple of months back at the Connect, they announced their agentic commerce bots for shopper agent and and and others that are coming out.
25:09.65
Ranjith
And so they have announced and they’ve been flexible to let partners also build. And I was very excited to see that they’ve approached it with the MCP in mind, where but they’re saying that, hey, you know we are going to use some agents as a starter block. We are building some stuff, but doesn’t mean that partners or customers can’t, you know, sort of build on top of it and and create their own agents and, you know, make our agents talk to, say, your e-commerce agent and whatnot, right? So I think that was very exciting to me. And, you know, we’re trying to work ah with Kibo Engineering and specifically to see if we can, you know, push that innovation. And we’re talking to our clients to see if, you know, there are things that they would like, right, which may not be on the product roadmap, but maybe we can help build it for them or help them sort of, you know, take this agentic commerce, you know, of,
25:54.96
Ranjith
but further and faster.
25:57.26
Natalija Pavic
um That’s an excellent point. i mean I think that there’s a lot of ah garden like to your point agents that only operate within walled gardens right now. and The challenge with that is the future like would the amount of tools required to be successful in commerce have proliferated.
26:14.29
Natalija Pavic
And they’ve grown in complexity such that despite, you know, we we know some of the big software vendors have gone through a lot of acquisitions, but despite having made those acquisitions, there have been like 10 to 15 new categories that have opened up of things that people need.
26:29.38
Natalija Pavic
um So there’s a ah finite amount of acquisitions that you can go through you know without integrating that will make you effective. you know um And so what’s interesting is your point is multi-agents to future, distributors to future, and age we know Agents that have access to more data, AI agents will win, right? And so if they can connect to more things, that will be the differentiator. And I wouldn’t be saying this if that’s not, that was not our approach to the market, right?
26:55.62
Natalija Pavic
Oh my gosh. But yeah, this is exactly what Kibo’s focusing on is freeing the agents from the platform and making them platform agnostic, which is very exciting.
27:06.16
Natalija Pavic
All right. Well, all good stuff. I’m loving the conversation, but let me ask you one last question. I’m sure we’ll have you back on in six months to check your assertions. um What are some of your favorite AI tools that you’re using personal or for work?
27:22.94
Ranjith
Personal or for work? So definitely I have been a big user of ChatGPD and Gemini, right? You know, lot of using it for personal, you know, when it first came out.
27:28.69
Natalija Pavic
Yeah.
27:32.57
Ranjith
But now we are a Google shop ourselves, like for workspace, you know, for our emails and and and and files. So we use now Gemini has been available. So that’s more available to our team as well. So we’re using Gemini you know for some of the stuff.
27:45.27
Ranjith
And then we’re also using other tools where you know ah for development like Copilot and for some of the you know development use cases, we’re trying start to use that.
27:45.37
Natalija Pavic
Mm-hmm.
27:54.79
Ranjith
And we’re, of course, working with the partner no tools and understanding how that can help our customers. and And a lot of Gemini and Cloud ah chat GPT’s for research, we have been started using it for you know like ah marketing and for and information. We’ve got you know ah tools that have already come in with AI, right? Like even Canva, for example, which is our go-to tool for marketing, they’ve started using AI, right? So it’s all already baked in, and we don’t even see it.
28:22.06
Natalija Pavic
Yeah, i mean, you should see the stuff my designer does with Figma. I can’t even believe so. It’s ah like, I think that’s how kind of how we started the conversation. ah Before recording, I was like, what’s going on? It feels like our both of our jobs have changed this week.
28:34.89
Ranjith
Yeah, definitely.
28:35.02
Natalija Pavic
So, well, thank you ah so much for coming on. um You know, if people want to get in touch with you, where can they find you?
28:43.45
Ranjith
Sure. So I’m always on LinkedIn, probably a little too much you know to my family’s liking. So that’s the best place to find me, Ranjit Maniadat, Perfware. Our website is perfware.com. with us through that forum as well.
28:58.41
Ranjith
I’m trying to be more active on LinkedIn, posting wise, but I definitely so respond to messages and and and requests. right So we’ll catch you guys there.
29:06.72
Natalija Pavic
Amazing. Thank you, Ranjit. And to everyone else.
29:09.90
Ranjith
Thank you. Thank you, Nat. Glad to have talk to you again. I appreciate it.
29:17.53
Natalija Pavic
And to everyone else, until next time.