Enterprising Insights, – Epicor Insights and Salesforce Connections, Episode 26

Hello, everybody. I'm Keith Kirkpatrick, research director with The Future Room Group, and I'd like to welcome you to Enterprising Insights. It's our weekly podcast that explores the latest developments in the enterprise software market and the technologies that underpin these platforms, applications, and tools.

So this week I was back out on the road again at two events, Epicor Insights in Nashville and Salesforce connections in Chicago. This week, I'm going to share my thoughts and impressions about both events as well as cover some of the announcements that were made. And then of course, I will close out the show with my ran or rave segment.

So let's get right into it as we head into this holiday weekend. So, Epicor Insights, this is the flagship event for Epicor, which is an ERP company that, that basically targets, uh, organizations that, that, that are very sort of industrial focused. So things that, um, think manufacturing, think distribution, uh, those types of companies that use this ERP system.

So the event itself, uh, actually had about 4,000 or so attendees. Um, I got to attend, uh, some of the major keynotes, which took place on the first day of the conference. Really interesting because they were really delving into the company's strategy, which is really about making sure that users have, uh, the tools that they need in the flow of work.

So, what are we talking about there? That's something we've heard a lot about. But basically, instead of having to utilize, you know, a bunch of different software, uh, or different applications to get a piece of data, they are designing the system and the platform to pull in that data wherever the, uh, particular user is working.

And that is a, a real key element of, you know, good software design these days, because really, you know, nobody wants to be switching between applications. You can have, uh, it, it's a mess of time suck. Uh, it really does, you know, create problems when you're trying to handle more complex tasks and pulling data from other systems, not only within the organization, but of course outside the organization as well.

Uh, the other thing that they were really talking about, of course, is artificial intelligence. That is something we cannot get away from, no matter how hard we try. Uh, but Epicor actually made several announcements that were really interesting.

Uh, one of the first ones of course, is, and this is sort of the big one, was, uh, they unveiled Epicor Grow, which is a new set of AI and business intelligence functions that are really designed to enhance productivity and manufacturing organizations, distribution companies, and retail companies. Now, if you think about what is ERP, really it's the tech that sits at the core of the organization, and it's designed to manage mission critical processes, information, all of that kind of stuff across a variety of business motions. It typically has been sort of the system of record, but what Epicor is trying to do is transform the ERP from the system of record to the system of insights and ultimately system of action.

What does that mean? It means that, uh, we don't wanna just use ERP as a place where people go and check to see where information is. We wanna actually be able to utilize that information in such a way that it allows predictions to happen based on the data that is held within the company.

It's about making sure that people can actually conduct workflows more quickly and more efficiently by leveraging all of that data and actually using that data across different types of functions, whether we're talking about things like, uh, supply chain or, uh, automating tasks, uh, at the front end with field service applications and workers. So, what, uh, Epicor is really talking about here is trying to, uh, utilize technology, both generative ai, which is sort of the, uh, big shiny object in the room right now. And that is actually through Epicor Prism, which is what they're calling their generative AI service that's embedded within their Epicor industry ERP cloud.

Uh, that is one thing that they're, um, that they're announcing. So, uh, this is really interesting because when we think about, uh, generative ai, how it can be utilized beyond, uh, you know, sort of the most basic functions like, you know, summarizing, you know, conversations that are held between a customer and a sales rep or support rep. Uh, what what Epicor is really trying to do is utilize generative AI in, you know, a much more deliberate way.

Uh, one of the examples they talk about is using it as a code assistant to help create automated business processes more quickly, basically using a low code or no code environment to, uh, generate a workflow or, or create a process without need to actually go in and hand code it. Uh, another way that they, you know, are thinking of utilizing generative AI is by using conversational ERPs to allow folks, uh, to access information held within the ERP in a more conversational way. Instead of having to know which file to go into or which system, you could actually just enter a prompt saying, I'd like to know, um, you know, what is the lead time on a particular product given that, uh, we have a storm in the Midwest?

And, you know, it will actually query the entire ERP to get that information, pulling it in from wherever it needs or wherever the data lives. Uh, so that is really, um, you know, sort of technology that is on the way. It is really interesting when we think about, uh, generative ai, uh, you know, in a B2B context, the idea is to not just handle sort of basic tests, but really, you know, link systems, uh, link different pieces of data together in order to provide more efficiency, more speed, more accuracy.

And that is what Epicor Prism is, uh, being designed to do. Uh, we're gonna learn more about that as time goes on, uh, but it is certainly interesting. Uh, I think the other thing that's going to be, uh, interesting is, uh, you know, of course how this functionality is rolled out within the platform in terms of pricing, all of that kinda stuff.

Uh, we'll definitely get more, um, more clarity around that as time goes on. Uh, the other thing that, um, you know, or another sort of announcement that they made is, uh, focusing on Epicor Grow ai. Now, this is, um, utilizing ai, but not generative ai, but really sort of predictive or more classical artificial intelligence.

Basically machine learning. So things like AI driven predictive analytics, uh, you know, sales orders that are, that are generated from natural language, uh, email queries, uh, product suggestions that are based on past order histories, things like that. Uh, predictive maintenance.

All of these things are, uh, you know, things that have been used in the past. But the idea here is that they're wrapping this up into a product in which, uh, you're able to really leverage the power of ai, uh, within the a RP that has been, you know, basically you're tuning all of this, uh, to work with this specific data. And ultimately, it's not sort of generic.

It's based on Epicor sort of deep, deep industry expertise, uh, and experience. And I'll get a little bit more into that later. Uh, that's one of the things that I think is really, um, really important to understand about Epicor.

Uh, now they, they have a few other products that they announced, uh, Epicor Grow Inventory forecasting, which came outta Epicor acquisition of Smart Software. Uh, this will, you know, let users leverage predictive analytics and, uh, they can model what if scenarios to sort of better manage inventory and scheduling and all of that kind of stuff. Um, so that's another product that was announced, uh, epic Epicor, uh, fp a, which is Financial Planning and Analysis.

Uh, it's another one that they announced. And of course, Epicor Grow bi. And this is essentially their, uh, data visualization, uh, dashboard offering.

Essentially, the idea here is, you know, nobody wants to look at spreadsheets anymore. Uh, the way to really communicate information is through a visualization. And, uh, you know, one thing that they stress to me that, that management stress to me in my meetings with them is that, you know, certainly while they would love for all their customers to utilize Grow bi, uh, they have set it up in such a way that it's possible for users to export their data to the, uh, BI tool of their choosing.

You know, whether we're talking about Tableau or, you know, uh, power bi, what have you, uh, the idea is that they want to meet their users where they are. And I think that's a really important thing in this day and age where you think about, uh, the way organizations purchase technology or don't purchase technology. There are, you know, individual, uh, purchasers or influencers or even entire departments that have their sort of pet or, you know, familiar software that they like to use and order to really make sure that, uh, there is this sort of seamless flow of information.

You need to have those integrations with all of those different tools. Uh, and speaking of that, uh, Epicor also announced their Grow Data platform. Again, this is, uh, you know, if you think about a, a, a data platform, really it's about bringing all that data into a central location to serve as a single source of truth that can then feed, you know, any number of other systems or the AI models themselves.

The idea here is, of course, is that you wanna basically, uh, clean normalize and, you know, make that data available to be used throughout the organization, to really, you know, make sure that all of the, all of the business decisions are incorporating all the data, not just the data that is, uh, not just some data. You, you update a locked away in silos. Uh, you can't get a full picture of what's going on with the business.

So, uh, those are some of the announcements that they made. Uh, I wanna dive into real really quickly now. Uh, a few other takeaways from the event that, that were really powerful to me.

Uh, when we think about Epicor, one of the things that came through is Epicor is really their, their sort of competitive differentiator is that they will go very, very deep into, you know, sort of a few specific industries. Uh, things like manufacturing distribution, but not just manufacturing. They'll get very granular, uh, down into the manufacturing of, let's say fasteners or something like that.

And they will actually go and meet with their customers. And that's how they kind of built their platform. 'cause what their goal was, was to create a platform and applications that basically include all the functionality, the logic, the workflows or data flows that would be needed within a very specific industry, so that when they sell that product, uh, to that customer, they don't need to then go out, hire a, you know, custom developer to create more custom implementations.

That's really a, a, a key point that they wanted to kind of hammer home. And I think it's something that really resonates with our customers. I did speak with a, a bunch of their customers, you know, at the show, you know, informally at lunch, in the hallway, that sort of thing.

And they said that that's really something that's very much appreciated, because if you think about a particular business, particularly one that is not, that, that is very, has very specific processes, they wanna be able to get up to speed very quickly and know that the software that they're using actually understands these, you know, very kind of specific processes that go on in that business and not have to, you know, uh, bring in a custom developer to get that software to work with their business. So that is really, uh, it's a powerful message that tends to resonate with their customers. And, you know, as honestly, when you think of the customer types that Epicor tend typically goes after, uh, certainly they do have some, uh, enterprise customers, but, uh, a lot of their, their sort of, you know, core customer are SMBs into the mid-market.

You know, those companies do not have the time budget, uh, where patients really, quite honestly, uh, you know, to deal with a ridiculously long implementation timeframe. Uh, and then when you kind of layer in artificial intelligence, you know, it really drives the need to get up to speed very quickly, because if you aren't able to incorporate, uh, you know, some of these new technologies now by the time the new stuff comes around, you're that much further behind. So I think that was a really interesting message that I heard throughout the event.

Um, you know, certainly the other thing that, um, you know, that, that kind of resonated with me there is again, the fact that customers, when I talked to them, they felt really great about the fact that Epicor seemed to be right sized to them in that they still would talk with their customers. You know, they have a thing where they, uh, have a council and you can advise, or basically the customers can vote on different functions that could be incorporated into the next rev of the product. That's really interesting, because when you think about any particular software application or SaaS platform, there's always some feature that you want.

And a lot of times, whenever you make a suggestion, it kind of goes into a black hole. You don't know if anything is really being considered this. There's a lot more transparency to this process.

Doesn't mean that all the features are gonna get in, but it does certainly create a, you know, I guess it engenders a, a more personalized relationship between their customers and the company, which I believe is resonating with them. So, uh, certainly really interesting to see that in action. Okay, so, uh, I'm gonna move on now to the next event, which is Salesforce connections that was held in Chicago.

And of course, when we're talking about Salesforce now, you basically, the conversation is about ai, but not just about ai. It's about bringing AI into the flow of work to drive efficiency, you know, to make, to, to link different functions together. So, uh, when we're talking about Salesforce, they have unveiled, uh, you know, a number of new things, but they all kind of revolve around their Einstein One platform and bringing that functionality, that generative AI functionality into their different clouds, into Marketing Cloud, into Commerce Cloud.

And the idea here is making sure that all of these, uh, you know, different clouds and the folks who use that are able to get access to the data held within, uh, these other systems, and then leverage AI across all of that to provide more trusted insights, and obviously to leverage AI throughout entire workflows, you know, to make sure that, okay, if I am in support, I understand, or I can see what's going on to a particular customer, you know, through marketing. You know, it sounds logical when you think about it. If I'm a support, if I'm one with support, that hopefully that support person knows exactly what I've seen.

But it's not always easy to integrate all of that information together because you need to make sure that all of that data is harmonized and captured or, or actually managed within a single source or a single location. And that, of course, is Salesforce's data cloud. So, uh, it's interesting that to see how they are trying to unify, uh, basically allow the creation of a unified profile, capturing all of the customer data in one place, and then making that available, you know, whether, uh, it's a sales function or a marketing function or a commerce function.

So that is really interesting. Uh, I'm gonna be doing a more detailed research note on each of these announcements that are coming outta Salesforce, uh, in the coming weeks. Uh, the other thing that was really interesting is, you know, we had several discussions talking about in general, uh, Salesforce's, I guess, vision of how the market will shape out, shake out in terms of pricing of generative ai.

Uh, what they are trying to do here is like a lot of companies they want customers to utilize, utilize ai, and in a sense, it's sort of, they seem to be operating on a freemium model. Let's include, uh, a certain number of generative AI credits which allow, you know, various AI or automation functions to be used to help customers, you know, really engage with their customers. So, you know, generating marketing campaigns or marketing briefs automatically from all of the data held within Data Cloud.

So things like customer information, you know, all of the product catalog knowledge, all of that kind of stuff, you know, to allow marketers to more quickly develop marketing campaigns. That, of course, same thing with, uh, commerce, uh, allowing them to more quickly build out e-commerce sites that are more personalized and more engaging with customers. And again, the idea here is that by take, by using AI to take out the challenging, repetitive, manual processes of inputting certain information, or trying to manually build out all the different iterations of marketing campaigns, the idea is that you wanna remove all that to allow marketers, uh, merchandisers, uh, support people focus on the higher value strategy stuff that is really required in this day and age to engage with customers.

So what they're doing is they are, uh, really looking at it as, okay, if you buy a Commerce Cloud or Service Cloud or, uh, you know, what have you, marketing Cloud, they'll give you a certain number of credits to utilize generative ai. And while they haven't really, uh, specifically said exactly how much you'll get, uh, they did say is usually ample and ample supply for most businesses out there that are using it. And that's really, you know, essentially, you know, we, we, myself and a few other analysts kind of went back and forth with them about it, and it's really about, you know, figuring out the right number to spur usage, but then also get, basically, get companies hooked on it.

So as they continue to ramp up their use, they wind up having to go in and buy either more credits or go for a full, uh, data cloud subscription or what have you. But the idea is to really sort of, uh, you know, kickstart usage. And then of course, the idea is that because, you know, this is obviously more revenue for Salesforce, it allows Salesforce to cover their costs, which are not insignificant when you talk about the cost generative ai, but also it's a way, uh, you know, for these customer organizations to massively improve their productivity and hopefully improve their bottom lines by being much more efficient.

And by being able to be much more, you know, uh, customer centric and, and roll out that, that very granular personalization that pretty much everyone in, in the market believes is going to be table stakes as we move forward. It's not gonna happen for every industry. It's not gonna happen for every product, but by and large, if you think about B2C businesses, um, personalization and, and very smart personalization is going to become the norm.

And that means, you know, everything from, instead of getting, you know, going to a website, buying something, and then getting hit with, you know, four other retargeting ads after you're done, they're gonna know that you already bought the product, you know, or making sure that, you know, when you go to look and, you know, to look for something, you'll be able to type in, uh, a very, very, you know, natural language player saying, Hey, I'm looking for an outfit to go out to a ball game, you know, and I wanna make sure that it is weather appropriate, but, uh, I want it to not, you know, stand up whatever that particular, uh, you know, problem might be. But the idea is that they want to give this power to their customers because ultimately customers are really starting to demand it. Now, the other announcement that came out, uh, Salesforce was that IBM and Salesforce had expanded their partnership, uh, basically allowing, uh, uh, IBM or basically bringing together IBM's Granite series models, uh, to power more generative AI use cases within the Salesforce Einstein one platform.

Uh, this is pretty interesting. It gives, uh, you know, even more power to Salesforce to utilize other models which may be tuned or, or structured in such a way that it works better than some of the stuff that they already are using. Um, the other interesting announcement that came out, which was that IBM joined the Salesforce zero copy partner network, and this was really, this, this network is set up to enable zero copy data integration between IBM Watson exit, IBM Watson Exit, and Salesforce Data Cloud.

This allows your customers to connect all of their data and, and, and utilize that data without copying it, which introduces a bunch of security issues and expense and all of that kind of stuff. Um, the other thing, the other announcement that we didn't really talk about a whole lot, but, uh, Salesforce actually joined the AI Alliance, uh, which is really about bolstering its commitment to, to deploy responsible AI and providing customers with trusted and reliable AI tools. Now, I believe Salesforce is one of the leaders in the market.

When, when last year around connections, when the company came out with their, uh, you know, kind of beta, uh, Einstein, uh, GPT tool, which is what they called it, then they since changed names. Uh, they, you know, out of the gate talked about the Einstein Trust player, which was focused on, you know, making sure that data was, uh, not gonna be leaked and shared with the, uh, you know, the open, the, the models, uh, make sure that the data wasn't gonna leak out or, or wasn't gonna return toxic results because they went through a filter to make sure that toxicity bias was removed, all of that kinda stuff. And then, of course, implementing guardrails to make sure that hopefully, you know, if you typed in a prompt and the information wasn't available, that, you know, the prompt wouldn't return hallucination, it would just say, I don't know, or something like that.

So Salesforce has been sort of a leader there, leader there, and, and I expect they will continue to be, because when you think about the types of customers that Salesforce has on the enterprise side, which is their bread and butter, uh, they don't wanna have those fiascos, uh, that make headlines and really impact the customer's business. And this is also important as Salesforce continues to try to expand their market down, you know, down market into the, uh, mid-market. You know, they of course also have those same concerns.

So it, it's great to see that Salesforce is continuing on these initiatives. So I think the key takeaway from both of these events really are that, you know, obviously AI is here to stay. Uh, organizations are looking at ways to deploy AI safely and also within the flow of work to make it easy for organizations and their users to, you know, get the power of generative AI without having to undertake a whole new way of working.

Because ultimately, you know, it's like anything, if you have to change your routine, that creates a real issue in terms of utilization. A lot of people will do it. It becomes, you know, a burden.

And if you're not using the technology well, you know, then it becomes something where the CIO looks at it and goes, Hmm, I don't know if this is even worth it. That obviously impacts the vendors. So, uh, really interesting to see how, uh, you know, some of these, uh, you know, products as they start to come out of beta and start to go into production or, or general availability over the next several quarters, how that happens, um, see how, uh, customers feel about that technology.

But it looks like, uh, both of these events were, were, you know, did their job in terms of exciting their user base about things to come. So with that, I'm going to quickly shift to the ran or rave segment. This is my segment where I take one item in the enterprise software market, uh, CX market, ex market or collaboration market.

And I will either champion it or criticize it. Now, uh, as I mentioned earlier, I was at both of these events this week, and I had to travel between the two. I had to leave, uh, Nashville on, I think it was Tuesday night, fly to Atlanta for a connection, and then fly from Atlanta to Chicago.

So I did do that, and by the time I got to Chicago, it was about one in the morning, I had a little snappy at the airport getting transportation. That's a whole nother issue with what they're doing in terms of, uh, making, uh, the Uber pickup area at a different terminal than when I landed. But that's a rent for another day.

But my rent is, when I got to the hotel, I was greeted, uh, by a somewhat surly front desk worker, asked her for a room, took a little while to get a room, finally got my room, went up to the 30 whatever floor went, opened up the door, looked around, wow, this is a beautiful suite. There was no bed, and there was no bathroom with a shower. So had to run downstairs.

Went to the, uh, worker at the front, said, this is the problem, no bed, no shower. Asked for another room. After about 10 minutes of checking, gave me another room, went upstairs, and the same thing, it was a mirror image of that first room where it was basically the presidential suite, uh, but no bathroom and no bed came down again.

And I asked the lady, can I please get a room? It doesn't need to be anything special. It's one, it's now about 1 45.

I'm tired, I need to get a room. She says to me, do you really need a shower? Which I don't even know how to take that.

But any rate, at that point, another associate, I don't know if she was the manager or whatnot, jumped in, you know, tried to get me a room and finally succeeded, gave me a regular standard room. And by about 2 0 5, I was finally up in my room. Now, there's a few things here that, that I was a little bit shocked about from a CX as well as an enterprise applications frontline worker perspective.

First of all, the first room, the first two rooms I were given, they were presidential suites, which appear to be, you know, sort of just meeting rooms up in the hotel that are designed to be sold with an adjoining regular hotel room, hotel room to, you know, serve as the bedroom and bathroom area. I was shocked that the front desk worker was not able to ascertain that when she gave me a room. In this day and age, it's very surprising that they would not have that visibility.

So that's kind of fail, number one, from a technology standpoint. Now, number two is that when the worker decided to give me another room, it was the same room just on the other side of the hall, same scenario. So, uh, I'm surprised that, um, that the worker wasn't able to ascertain that, that also would've not probably worked out.

And then of course, you know, the, the bigger issue here that I feel from an CX perspective is that the, the worker didn't have any empathy. She could see I was coming in late and I was tired. I was just trying to get a room.

I wasn't trying to, I was very polite, but all I wanted was a room. And when she then asked if I needed a shower in my room, uh, I thought that was pretty offensive and certainly lacking in empathy, which is one of those things that contributes highly to customer satisfaction and loyalty and all of that. Now, the other thing is that she apparently wasn't able to use data, not able to see, I was a rewards member that I was there as a guest of the host, which is Salesforce.

I've been there in the year before, generally speaking, you know, these analyst things. They, they try to take care of us to, to make sure that we're able to be on our, uh, you know, uh, at our best each day. So it's a little surprising that, uh, I was not taking care of, or, or that they weren't able to read the situation.

Now, it's possible there was a block of rooms that were set up for me, but that's where the worker lack trained to realize the situation. I was tired. I said, I just need a room with a bed and a shower.

They should have been able to, I quickly identify an available room that met those basic requirements. So, long story short, uh, it was a bit frustrating. Uh, ultimately to their credit, the second worker, uh, finally got me a room and issued me, uh, you know, a, a modest credit for incidentals, you know, for my trouble.

But I do think it is interesting that in this day and age where we go to conferences and we hear about all of this, about personalization, making sure that that workers have the information they need to provide the best experience, it still isn't always happening. And a lot of times it is a failure of not just one thing, but two things together. If you think about, you know, the old, um, example of, of, you know, when you have, uh, airplane crashes, it's a cascading of events that creates a, a very bad outcome.

It was a similar thing here. Uh, we had, you know, a lack of training. We had a lack of data and a lack of empathy all coming together, which conspired to provide me with a not great experience, but hopefully, uh, you know, it will be a learning experience, uh, moving forward for, for that particular worker.

Um, but, uh, we, we shall see. So anyway, that is my rant of the week, and hopefully I won't have another one like that for, for quite some time. Alright, well that's all the time I have today.

So I wanna thank everyone for joining me here on Enterprising Insights. I'll be back again next week with another episode focused on the happenings within the enterprise application market. So thanks everyone for tuning in and be sure to subscribe, rate and review this podcast on your preferred platform.

Thanks, and we'll see you next time.