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Enterprising Insights, Episode 26 – Epicor Insights and Salesforce Connections

Enterprising Insights, Episode 26 - Epicor Insights and Salesforce Connections

In this episode of Enterprising Insights, host Keith Kirkpatrick discusses two conferences he attended during the week, Epicor Insights and Salesforce Connections. Kirkpatrick talks about the key news from each event, new product announcements, and provides his take on each event. He then closes out the show with the Rant or Rave segment, focusing on a particular galling CX experience that saw a failure with applications, training, and the ability to show empathy.

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Transcript:

Keith Kirkpatrick: Hello, everybody. I’m Keith Kirkpatrick, Research Director with The Futurum Group. 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. 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. Then of course, I will close out the show with my Rant or Rave segment. Let’s get right into it, as we head into this holiday weekend.

Epicor Insights. This is the flagship event for Epicor, which is an ERP company that basically targets organizations that are very industrial focused. Think manufacturing, think distribution, those types of companies that use this ERP system. The event itself actually had about 4000 or so attendees. I got to attend 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 the tools that they need in the flow of work.

What are we talking about? That’s something we’ve heard a lot about. That basically, instead of having to utilize a bunch of different software or different applications to get a piece of data, they are designing the system and platform to pull in that data wherever the particular user is working. That is a real key element of good software design these days because really, nobody wants to be switching between applications. It’s a massive time-suck. It really does 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.

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. But Epicor actually made several announcements that were really interesting. One of the first ones, and this is the big one, was they unveiled Epicor Grow, which is a new set of AI and business intelligence functions that are really designed to enhance productivity in 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 functions. It typically has been the system of record. Well what Epicor is trying to do is transform the ERP from the system of record to the system of insights, and ultimately a system of action. What does that mean? It means that we don’t want to just use ERP as a place where people go and check to see where information is, we want to 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 supply chain, or automating tasks at the front-end with field service applications.

What Epicor is really talking about here is trying to utilize technology, both generative AI which is the big, shiny object in the room right now … That is actually through Epicor Prism, which is what they’re calling their generative AI service that’s embedding within their Epicor Industry ERP platform. That is one thing that they are announcing. This is really interesting because when we think about generative AI and how could we utilize it beyond the most basic functions like summarizing conversations that are held between a customer and a sales rep or a support rep. What Epicor is really trying to do is utilize generative AI in a much more deliberate way. One of the examples they talk about is using it as a coding system to help create automated business processes more quickly. Basically, using a low-code or no-code environment to generate a workflow or create a process without needing to actually go in and hand-code it.

Another way that they are thinking of utilizing generative AI is by using conversational to allow folks to access info held within 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 what is the lead time on a particular product given that we have a storm in the Midwest?” It will actually query the entire ERP to get that information, pulling it in from wherever the data lives. That is really technology that is on the way. It is really interesting. When we think about generative AI in a B2B context, the idea is to not just handle basic tasks, but really link systems, link different pieces of data together in order to provide more efficiency, more speed, more accuracy.

That is what Epicor Prism is being designed to do. We’re going to learn more about that as time goes on, but it is certainly interesting. I think the other thing that’s going to be interesting is of course how this functionality is rolled out within the platform, in terms of pricing, all of that kind of stuff. We’ll definitely get more clarity around that as time goes on. Another announcement that they made is focusing on Epicor Grow AI. This is utilizing AI, but not generative AI, but really predictive or more classical artificial intelligence, basically machine learning. Things like AI-driven predicted analytics, sales orders that are generated from natural language email queries, product suggestions that are based on past order histories, things like that. Predictive maintenance. All of these things are things that have been used in the past, but the idea here is that they’re wrapping this up into a product in which you’re able to really leverage the power of AI within the ERP.

Basically you’re turning all of this to work with this specific data and ultimately it’s not generic, it’s based on Epicor’s deep, deep industry expertise and experience. I’ll get a little bit more into later. That’s one of the things that I think that is really important to understand about Epicor. Now they have a few other products that they announced. Epicor Grow Inventory Forecasting, which came out of Epicor’s acquisition of Smart Software. This will let users leverage predictive analytics and they can model what-if scenarios to better manage inventory and scheduling, and all of that kind of stuff. That’s another product that was announced.

Epicor FP&A, which is financial planning and analysis. It’s another one that they announced. And of course, Epicor Grow BI. This is essentially their data visualization dashboard offering. Essentially, the idea here is nobody wants to look at spreadsheets anymore. The way to really communicate information is through a visualization. One thing that they stressed to me, that management stressed to me in my meetings with them is that certainly, while they would love for all their customers to utilize Grow BI, they have set it up in such a way that it’s possible for users to export their data to the BI tool of their choosing. Whether we’re talking about Tableau, or Power BI, what have you. The idea is that they want to meet their users where they are. I think that’s a really important thing in this day and age, where you think about the way organizations purchase technology or don’t purchase technology. There are individual purchasers or influencers, or even entire departments, that have their pet or familiar software that they like to use. In order to really make sure that there is this seamless flow of information, you need to have those integrations with all of those different tools.

Speaking of that, Epicor also announced their Grow data platform. Again, this is … If you think about a data platform, really it’s about bringing all that data into a central location to serve a single source of truth that can then feed any number of other systems or the AI models themselves. The idea here is of course is that you want to basically clean, normalize and make that data available to use throughout the organization to really make sure that all of business decisions are incorporating all the data, not just some data. If you have data locked away in silos, you can’t get a full picture of what’s going on with the business.

Those are some of the announcements that they made. I want to dive into, really quickly now, a few other takeaways from the event that were really powerful to me. When we think about Epicor, one of the things that came through is Epicor, their competitive differentiator is that they will go very, very deep into a few specific industries. Things like manufacturing, distribution. But not just manufacturing, they’ll get very granular, down into the manufacturing of let’s say fasteners, or something like that. They will actually go and meet with their customers, and that’s how they built their platform.

What their goal was, was to create a platform and applications that basically include all the functionality, the logic, the workflows, the data flows that would be needed within a very specific industry so that when they sell that product to that customer, they don’t need to then go out, hire a custom developer to create more custom implementations. That’s really a key point that they wanted to hammer home and I think it’s something that really resonates with their customers. I did speak a bunch of their customers at the show, informally at lunch, in the hallway, that sort of thing. They said that that’s really something that’s very much appreciated. Because if you think about a particular business, particularly one that has very specific processes, they want to be able to get up to speed very quickly, and know that the software that they’re using actually understands these very specific processes that go in that business and not have to bring in a custom developer to get that software to work with their business. That’s a powerful message that tends to resonate with their customers.

And honestly, when you think of the customer types that Epicor typically goes after, certainly they do have some enterprise customers, but a lot of their core customers are SMBs into the mid-market. Those companies do not have the time, budget or patience really, quite honestly, to deal with a ridiculously long implementation timeframe. Then when you layer in artificial intelligence, it really drives the need to get up to speed very quickly. Because if you aren’t able to incorporate some of these new technologies now, by the time the new stuff comes around, you’re that much further behind. I think that was a really interesting message that I heard throughout the event.

Certainly the other thing that 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. They have a thing where they have a council and you can advise. Basically, the customers can vote on different functions that could 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. A lot of times, whenever you make a suggestion, it goes into a black hole. You don’t if anything is really being considered. This, there’s a lot more transparency to this process. It doesn’t mean that all the features are going to get in, but it does certainly create … I guess it engenders a more personalized relationship between their customers and the company, which I believe is resonating with them. Certainly really interesting to see that in action.

Okay, I’m going to move on now to the next event, which is Salesforce Connections. That was held in Chicago. Of course, when we’re talking about Salesforce now, basically the conversation is about AI. But not just about AI. It’s about bringing AI into the flow of work to drive efficiency, to web different functions together. When we’re talking about Salesforce, they have unveiled a number of new things, but they all were all around the Einstein One platform and bringing that functionality, that generative AI functionality into their different clouds. Into Marketing Cloud, into Commerce Cloud.

The idea here is making sure that all of these different clouds and the folks who use that are able to get access to the data held within these other systems, and then leverage AI across all of that, to provide more trusted insights, and obviously to leverage AI throughout entire workflows. To make sure that if I’m in support, I understand or I can see what’s going on to a particular customer through marketing. It sounds logical, when you think about it. If I’m on 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 actually managed within a single source or single location. That, of course, is Salesforce’s Data Cloud.

It’s interesting to see how they are trying to unify, basically allow the creation of a unified profile, capturing all of the customer data in one place. Then making that available, whether it’s a sales function, or a marketing function, or a commerce function. That is really interesting. I’m going to be doing a more detailed research dump on each of these announcements that are coming out of Salesforce in the coming weeks. The other thing that was really interesting is we had several discussions talking about, in general, Salesforce’s vision of how the market will shake out in terms of pricing of generative AI. What they are trying to do here, like a lot of companies, they want customers to utilize AI, in the sense they seem to be operating on a freemium model.
“Let’s include a certain number of generative AI credits, which allow various AI or automation functions to be used to help customers really engage with their customers.”

So generating marketing campaigns, or marketing briefs automatically from all of the data held within Data Cloud. Things like customer information, all of the product catalog knowledge, all of that kind of stuff, to allow marketers to more quickly develop marketing campaigns. Then of course, same thing with commerce, allowing them to more quickly build out ecommerce sites that are more personalized and more engaging with customers. Again, the idea here is that 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 want to remove all that to allow marketers, merchandisers, support people focus on the higher value strategy stuff that is really required of this day-and-age to engage with customers.

What they’re doing is they are really looking at it is okay, if you buy Commerce Cloud, or Service Cloud, or Marketing Cloud, they’ll give you a certain number of credits to utilize generative AI. While they haven’t really specifically said exactly how much you’ll get, they did say it’s usually an ample supply for more businesses out there that are using it. Essentially, myself and a few other analysts went back and forth with them about it, and it’s really about figuring out the right number to spur usage. But then also, basically get companies hooked on it so as they continue to ramp up their use, they will end up having to go in and buy either more credits, or go for the full Data Cloud subscription, or what have you. The idea is to really kickstart usage. Then of course, the idea is that because this is obviously more revenue for Salesforce, it allows Salesforce to cover their costs, which are not insignificant when you talk about the cost of generative AI.

But also, it’s a way 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 customer-centric, and roll out that very granular personalization that pretty much everyone in the market believes is going to be table stakes as we move forward. It’s not going to happen for every industry, it’s not going to happen for every product. But by-and-large, if you think about B2C businesses, personalization and very smart personalization is going to become the norm. That means everything from instead of going to a website, buying something, and then getting hit with four other retargeting ads after you’re done, they’re going to know that you already bought the product.

Or making sure that, when you go to look for something, you’ll be able to type in a very, very natural language query saying, “Hey, I’m looking for an outfit to go out to a ball game. I want to make sure that it’s weather appropriate, but I want it to not stand out,” whatever the particular prompt 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 at Salesforce was that IBM and Salesforce had expanded their partnership, basically bringing together IBM’s granite series models to empower more generative use cases within the Salesforce NS1 platform. This is pretty interesting. It gives even more power to Salesforce to utilize other models, which may be tuned or structured in such a way that it works better than some of the stuff that they already are using.

The other interesting announcement that came out, which was that IBM joined the Salesforce Zero Copy Partner Network. This network is set up to enable zero copy data integration between IBM watsonx and Salesforce Data Cloud. This allows your customers to connect all of their data and utilize that data without copying it, which introduces a bunch of security issues and expense, and all of that kind of stuff. The other announcement that we didn’t really talk about a whole lot, but Salesforce actually joined the AI Alliance, which is really about bolstering its commitment 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 last year around Connections, when the company came out with their beta Einstein GPT tool, which is what they called it then, they’ve since changed names. They, out of the gate, talked about the Einstein trust layer, which was focused on making sure the data was not going to be leaked and shared with the models. Make sure that the data wasn’t going to leak out or wasn’t going to return toxic results because they went through a filter to make sure that toxicity and bias was removed, all of that kind of stuff. Then of course, implementing guardrails to make sure that hopefully, if you typed in a prompt and the information wasn’t available, that the prompt would return hallucination, it would just say, “I don’t know,” or something like that.

Salesforce has been a leader there 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, they don’t want to have those fiascos that make headlines and really impact the customer’s business. This is also important as Salesforce continues to try to expand their market down market, into the mid-market. They of course also have those same concerns. It’s great to see that Salesforce is continuing on those initiatives. I think the key takeaway from both of these events really are that, honestly, AI is here to stay. 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 get the power of generative AI without having to undertake a whole new way of working.

Because ultimately, it’s like anything, if you have to change your routine, that creates a real issue in terms of utilization. A lot of people won’t do it, it becomes a burden. If you’re not using the technology, well then it becomes something where the CIO looks at it and goes, “I don’t know if this is even worth it.” That obviously impacts the vendors. Really interesting to see how some of these products, as they start to come out of beta and start to go into production or general availability over the next several quarters, how that happens. We’ll see how customers feel about that technology. But it looks like both of these events did their jobs, in terms of exciting their user base about things to come.

With that, I’m going to quickly shift to the Rant or Rave segment. This is my segment where I take one item in the enterprise software market, CX market, EX market, or collaboration market and I will either champion it or criticize it. Now 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 Nashville on, I think it was Tuesday night, fly to Atlanta for a connection, and then fly from Atlanta to Chicago. I did do that. By the time I got to Chicago, it was about 1:00 in the morning. I had a little snafu at the airport getting transportation. That’s a whole nother issue in terms of what they’re doing in terms of making the Uber pickup area at a different terminal than where I landed, but that’s a rant for another day.

But my rant is when I got to the hotel, I was greeted 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. Had to run downstairs, went to the worker at the front and 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 in the Presidential Suite, 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 is not 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 at any rate, at that point, another associate, I don’t know if she was the manager or whatnot, jumped in, tried to get me a room. Finally succeeded, gave me a regular, standard room. By about 2:05, I was finally up in my room. Now there’s a few things here 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 two rooms I was given, they were Presidential Suites, which appear to be just meeting rooms up in the hotel that are designed to be sold with an adjoining regular hotel room to 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. That’s fail number one, from a technology standpoint. Fail 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. I’m surprised that the worker wasn’t able to ascertain that that also would have not probably worked out. Then of course, the bigger issue here, that I feel from a CX perspective, is that 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 was very polite, but all I wanted was a room. When she then asked if I needed a shower in my room, 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, 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 was Salesforce. I had been there in the year before. Generally speaking, these analyst things, they try to take care of us to make sure that we’re able to be at our best each day. It was a little surprising that I was not taken care of or that they weren’t able to read the situation. Now it’s possible that there was a block of rooms that were set up for me, but that’s where the worker lacked training 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 quickly identify an available room that met those basic requirements.

Long story short, it was a bit frustrating. Ultimately, to their credit, the second worker finally got me a room and issued me a modest credit for incidentals 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 workers have the information they need to provide the best experience, it still isn’t always happening. A lot of times, it’s a failure of not just one thing, but two things together. If you think about the old example of when you have airplane crashes, it’s a cascading of events that creates a very bad outcome. There was a similar thing here. We had 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, it will be a learning experience moving forward for that particular worker, but we shall see.

Anyway, that is my Rant of the week. Hopefully I won’t have another one like that for quite some time. All right, well that’s all the time I have today. I want to 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. Thanks, everyone, for tuning in. Be sure to subscribe or rate and review this podcast on your preferred platform. Thanks and we’ll see you next time.

Author Information

Keith has over 25 years of experience in research, marketing, and consulting-based fields.

He has authored in-depth reports and market forecast studies covering artificial intelligence, biometrics, data analytics, robotics, high performance computing, and quantum computing, with a specific focus on the use of these technologies within large enterprise organizations and SMBs. He has also established strong working relationships with the international technology vendor community and is a frequent speaker at industry conferences and events.

In his career as a financial and technology journalist he has written for national and trade publications, including BusinessWeek, CNBC.com, Investment Dealers’ Digest, The Red Herring, The Communications of the ACM, and Mobile Computing & Communications, among others.

He is a member of the Association of Independent Information Professionals (AIIP).

Keith holds dual Bachelor of Arts degrees in Magazine Journalism and Sociology from Syracuse University.

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