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Enterprising Insights, Episode 18: New AI Enhancements from Five9, Salesforce, ServiceNow, and SAP Concur

Enterprising Insights, Episode 18: New AI Enhancements from Five9, Salesforce, ServiceNow, and SAP Concur

In this episode of Enterprising Insights, The Futurum Group’s Enterprise Applications Research Director Keith Kirkpatrick discusses several new generative AI-focused product announcements and enhancements focused on contact centers and service applications, including those from Five9, Salesforce, ServiceNow, and SAP Concur. He then closes out the show with the Rant or Rave segment, where he picks one item in the enterprise software market, and either champions or criticizes it.

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Transcript:

Keith Kirkpatrick: Hello, everyone, I’m Keith Kirkpatrick, Research Director with The Futurum 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. This week, I’d like to go through some announcements in the enterprise application market, specifically around some new product launches and enhancements to existing products. Then, as always, I’m going to close out the show with the rant or rave segment where I’ll pick one item in the enterprise software market and I will either champion it or I will criticize it.

So, let’s get right into it. So, this past week, Five9, the contact center company, announced that they have released a new product called GenAI Studio, which it says is the first enterprise ready, user friendly generative AI model and prompt management hub for the contact center. Now, they say that this will power all of Five9’s AI applications, and it will allow its customers to customize, deploy, and test generative AI prompts based on business specific contextual data with just a few clicks. Now, this is really interesting because if you know anything about contact centers today as well as just the overall focus around customer experience, personalization is key. Customers will not accept a sort of generic approach to, you know, marketing or sales or even service outreach or even handling inbound calls.

They expect that these companies know who they are, what they like, what they don’t like, when was the last time that you interacted with them. They expect that all of the relevant information about a customer will be available to a company basically every time they connect with them. So what this tool is doing is it allows the integration of many different forms of contextual data. And then GenAI Studio will be able to take all this data to help answer questions, summarize calls, and even guide agents with information that’s actually unique, to that particular business and to the customer. This is really sort of the, this is almost becoming table stakes now in terms of what customers and customers expect, as well as businesses that are interacting with these customers.

Now, the other thing that this is doing is it actually allows the use of generative AI without being locked into a single large language model or generative AI engine. This really ensures that an organization can choose the right model for their particular business needs or company, whether it’s a third party or proprietary LLM or foundation model. This is particularly important if we look at the different types of use cases for generative AI. Some require large language models. Others require smaller models. Some requires models that are tuned to very, very specific tasks or specific industries. This flexibility is really important for organizations to make sure that they are able to use the right LLM or right foundation model to meet their specific needs.

Now, of course, one of the other things that we can’t overlook, of course, is the importance of responsible AI. This GenAI studio is going to do is allow companies to have the precise control and monitoring of all the generative AI outputs using their own trusted generative AI models that it will allow the customers to test their models using real world call transcripts and utterances to actually measure the performance and make the improvements. This is, you know, something that we’ve unfortunately seen sort of, the bad experiences out there where these digital chatbots have gone off the rails because they weren’t tested properly, or there weren’t the right guardrails that were put in place. I think the goal of these, of this particular tool, the GenAI Studio, is to really enable a greater amount of unassisted, you know, work to be done, you know, through bots, as well as assisting existing agents it’s serving up the information they need to provide personalized service in a way that is responsible and can be trusted and really the only way that we’ve seen this time and time again, the only way you’re actually able to deploy sort of trusted AI solution is really to, you know, make sure that this is tested, you know, a lot right, you know, before it’s actually released and then on an ongoing basis, because if you don’t do that, there’s always going to be some actors out there who are going to try to break it.

And they’re going to try to put in, put in information that will cause the AI to act in strange ways or to provide outputs that aren’t really desired by the company. So, it’ll be interesting to see you know, with Five9, if they’re able to really gain traction with this particular product. There’s clearly demand for using generative AI in contact centers because agents are overworked. They’re being tasked to do very huge amount of of different things. Of course, the only way that they’re able to do that successfully is to make sure that they have all of the information about a customer at their fingertips and available to them at all times. Now some other news coming out this week. Salesforce, they actually announced some service cloud contact center innovations.

This is an interesting announcement because obviously we think about Salesforce. They’ve been sort of a leader and pioneer in terms of utilizing generative AI. So if we look at this announcement it is powered by Salesforce’s Einstein One platform. Of course, what we’re talking about there is Salesforce has built their AI platform around a theme of responsibility, making sure that that model or those models are only acting upon data that is deemed vetted and safe. And this is extremely important, as we know, because you know, if you think about organizations using generative AI, they want to make sure that it’s only acting on their data, not sort of the world at large, you know, which is what a lot of the public models are doing.

And, and if you, the other thing that’s really interesting about this particular announcement is all these features are designed, they’re for Service Cloud and they’re designed to provide agents and supervisors with AI powered insights as well as, you know, the ability to generate content, the ability to automate different capabilities to really help agents, you know, improve satisfaction, improve loyalty, And really transform contact centers from sort of a cost center mentality to one that can actually generate revenue. Now this is particularly important, you know, one thing that Salesforce mentioned in their release is that there’s this growing trend of, you know, people who are interacting with companies, they’re not usually having, you know, an issue resolved just by speaking with one person. I think the survey results from Salesforce’s state of the service found that 66 percent of people often have to explain or re-explain information to different representatives.

And that is a massive cause of friction. It’s a massive cause of frustration and really it makes serving that customer and honestly delighting that customer much more difficult when you’re already starting from a point of frustration or, you know, you know, really just aggravation for customers who are obviously having a problem to start with. And it just adds to that frustration if they have to repeat information over and over again. So, what this is doing, what Salesforce’s enhancements are doing is trying to make sure that these agents, whether they’re digital or human, have more context and more information to serve people much more efficiently and quickly. So, they are announcing several new AI and data innovations within Service Cloud, and these include Einstein Conversation Mining, where all of the conversation data that comes into an organization can be mined and analyzed. And this is across voice, messaging, third party applications, and really taking all that and turning those insights into action.

And what are we talking about here? Really? Well, it’s obviously using analytics to see what are the most common issues that come up. And also, what are the edge cases? What are the issues that might not come up very frequently, but are challenging to answer that are challenging to fulfill in terms of certain requests. By using all of this data, you can actually train an Einstein bot how to handle a specific task that may not be something that is done frequently, but is a point of aggravation for customers, or it would require agents to go and look something up or get approval from a manager or or find out, you know, I’ve never had this case before. By by using conversation mining, it’s a way of really going out and making sure that you’re capturing all of the different types of interactions, including those use cases that might be, you know, very, very you know, rare when they occur.

You know, kind of along the same lines Salesforce also mentioned that generative AI surveys, some summarization will be available. So this is really interesting because, when, you have a large base of customers and they are providing feedback, like customer satisfaction scores, the challenge is identifying what are the real root causes here. You know, someone can say, well, I’m not satisfied. Well, you really need to dig down into that, into a close examination of what is their point of dissatisfaction? Is it something with the product? Is it something with the way the product is not has not been properly explained, you know, in terms of, you know, when the customer bought it. Is there something going on with you know, a maintenance issue, anything else like that?

The idea here is again, to go in and deep dive dive deeply into this survey data and these feedback responses to really make sure that they understand, the organization understands what kind of trends are going on, what kind of edge cases are out there, and then really activating that data and having agents be able to drill down and then proactively follow up with customers. To make sure that, you know, if, let’s say, you know of a customer that has bought this particular product and you know that there’s, there could be an issue with, you know, maybe installation or, you know, refilling it or something like that, anything with the product, by proactively reaching out, you’re doing a couple of things.

One, you’re attacking the problem before it happens so you’re cutting down hopefully on, you know, those angry service calls, but you’re also building goodwill with those customers because you’re saying, I acknowledge there’s an issue, but I’m going to reach out and tell you about it before you actually run into that problem. Now, and the third thing, which is really interesting is this knowledge powered AI. So apparently they’ve set this up where Einstein can actually scan live customer conversations across a number of different channels, phone, I think WhatsApp they have, Facebook. And then it will suggest the best knowledge articles to help the agent solve these cases more quickly and improve the experience. So this is really, really interesting. This is where we’re starting to get into this next level of experience.

And what I mean by that is if you think about how in the past you would address issues, you would go through call transcripts after the interaction has happened and go, “Well, we probably should have the agent should probably have pointed this particular customer to this knowledge base article, or they should have done this or that.” By doing this, on a real time basis while the interaction is going on you’re going to resolve cases more quickly And you’re going to empower that agent to feel better and more confident in his or her job because they’re not going to think, “Oh I’m not going to be able to answer this question and the customer is going to get angry.” Here they’re actually being given tools that are not only, you know, at their fingertips, but it’s actually being delivered to them in a very relevant way. It’s they’re getting the information that’s directly relevant to the interaction they’re having at that very moment.

So that is all really interesting. You know, really interesting enhancements to Service Cloud. And, you know, again, I think that, you know, when it comes to sort of the big issues in terms of serving customers and making them happy, it’s you know, essentially creating essentially super agents who have all the information that they need right at their fingertips, no matter what’s thrown at them. You know, by doing that, you’re leveraging generative AI in such a way that you’re combining the best of human agents, which hopefully is creating empathy, creating a real connection, but also giving them the power of technology to provide them with all this information that they couldn’t possibly have, you know, in the back of their heads, particularly when they’re trying to manage, you know, two or three or four or however many simultaneous interactions. So, really interesting news from Salesforce on that front.

Now let’s switch gears and talk about ServiceNow. They actually released a new platform called Washington DC. It’s the, it’s the first new platform release of 2024. This includes several new features that are designed to improve intelligent automation and deliver faster time to value. Now there are a few, you know, interesting tools that were included in this sales and order management. This is designed to help organizations grow revenue by connecting the sales and order life cycles across the front middle and back office teams on the ServiceNow platform. What does this mean? Well instead of having you know, a bunch of different departments that don’t know what’s going on at any particular point in time you would actually have a fully connected ecosystem. So if something happens in the sales cycle, everyone who is, you know, supposed to know about it will know about it right away.

It is just so critical not only from a consumer standpoint, but as we start to look at B2B use cases where you may have, you know, multiple stakeholders involved in the process, multiple points of contact you know, approvers, influencers you know, buyers themselves, all of that, who may be contacting this organization and want to make sure that, you know, the process is seamless. They want to make sure that everyone knows what’s going on at all times. So that’s really, really interesting. There are a couple of other little parts that I thought were interesting. New features have been released that are designed to enhance the now assist generative AI experiences. So what are we talking about here? You know, certainly some enhancements to the virtual agent experience, enhancements to the now assist for IT operations management platform. Really, the point with all of this is that you’re going to have better generative AI experiences that will allow better workflow, across the entire spectrum of tasks.

Again, this really is designed to make sure that when generative AI is, is basically embedded into the platform, it works throughout the entire process. You know, that makes sure that everybody knows what’s going on, and that, you know, flows are intelligent, and they’re powered, you know, from one kind of step to the next that increases efficiency, it can decrease cost, and most importantly, it can also reduce errors when it comes to things like, again, you know, one system or one department not knowing what’s going on with a particular flow in another part of the company. So that’s pretty interesting. I think the other thing to really discuss about ServiceNow here is that they are releasing some new enhancements around their virtual agent designer. This is designed to you know, help create virtual agents or bots that are much, much more localized and personalized.

You know, again, if we talk about, we go back to the top where I was talking about personalization, you know, it’s one thing to do sort of a mass personalized approach. Okay. I know I’m talking to this particular type of customer, but it is really, really impactful when an organization knows exactly who I am as that particular customer and can service me in that way. You know, through a digital bot as opposed to having to, you know, deploy the human. Now, I’m not saying that, you know, that humans are not necessary. In fact, they are. But in terms of handling certain tasks at this point, you know, being able to do that In a totally digital environment, in a personalized way, that’s going to, again, you know, speed up the time to resolution, hopefully improve satisfaction, because you don’t have to wait around to connect with a real person, and again, hopefully improve accuracy, because you don’t have that potential for any kind of human error being incorporated there.

Now, obviously, ServiceNow and others are implementing technology to make sure that when you do bring in, a human, they’re brought up to speed immediately with all of the relevant information. So, finally, I just want to talk a little bit about you know, ServiceNow and, and kind of, you know, where they are now and kind of where they’re going in terms of, you know, some of the things they’ve been doing. Obviously, they’ve been acquiring companies over the past couple of years really focusing in on companies that are providing technology that will help them focus on specific vertical markets. I believe if we think back in telecom Atronet for industry for the manufacturing space and G2K for retail. Those are just a couple of examples. Why is that important? Well, because ultimately when we’re thinking about workflows, when we’re thinking about generative AI, really, you know, the key is making sure that these models are being trained on the processes that are very, very specific to, you know, certain industries, because manufacturing is very difficult, or I’m sorry, is very different than retail.

It has very little in common with, you know, let’s say telecommunications it may not have anything anything in common with travel, it may not have anything in common with say automotive, you know, particularly you know, retail selling of cars. So the idea is that you want to make sure that the platform, you know, or versions of the platform can be very, very tuned to a specific industry or vertical because that is in the way that you can make sure that the model is totally focused on those types of processes, you know, and you don’t have that confusion with if you use a term that is, you know, generic in one industry, but is very specific in another that there’s confusion there. So I think that’s a really great strategy. And, you know, again, if we’re if we’re talking about an overall overarching sort of trend or theme these days, it’s really about which vendors are able to deliver generative AI in a way that will deliver our ROI very quickly because ultimately the technology changing so fast, you don’t want to have something where, you know, it’s going to take you know, 4, 6, 8, 12, however many months to actually see a return on the investment because by that point, you’ve already moved on to the next thing.

So, certainly really interesting stuff going on there at ServiceNow. And then finally, I want to talk a little bit about SAP. SAP just announced some new generative AI features for SAP Concur users. This is really interesting. They actually had their SAP Concur Fusion event. It’s their conference for Concur users and experts. What they’ve announced here are, you know, the integration of generative AI capabilities within a number of SAP Concur solutions. They’ve upgraded Concur Travel and they’ve kind of redesigned or reimagined Concur Expense and its user experience. They’re implementing generative AI and they’re optimizing it for mobile devices. And then I should include some additional capabilities that were delivered through a partnership with MasterCard. So I wrote a research note on this and the interesting thing here is really all of these announcements are essentially trying to take that consumer grade user experience that a lot of that most people are familiar with when they’re trying to, let’s say, book their travel, you know, for their family vacations, and they’re kind of moving that over into that business travel environment.

They’re trying to make the look and feel and the usability very similar to your Expedia’s or your Travelocities or whatever platform you prefer. And, how are they doing that? Well, they’re certainly trying to provide more direct integrations to various travel services and platforms. This provides more clarity, more transparency to the booking process, and, you know, it, you know, It’s really, you know, just makes things much more easy than with sort of these legacy approaches. They’ve also improved the integration with Microsoft Teams. Why is that interesting? Well, in particular, if you think of how folks are working these days, a lot of times, Teams, they used to be in a single office, that won’t necessarily be the case anymore, perhaps you might have someone in Denver and someone in Florida and someone in Europe and whatever, but they all need to, you know, coordinate on their travel, particularly if they’re doing like a multi-city trip or tour where teams need to kind of coordinate on where they’re going.

Apparently what’s going on here is because Concur is now integrating with Microsoft Teams, these business travelers can actually share their reservations directly from Concur Travel through Teams, so the co workers can easily book the exact same trip. And that, again, is particularly important. Obviously, some people may have different origins, but along the way, they’re going to want to make sure they book the same flights, or they’re staying in the same hotels, or they’re you know, whatever the case may be, it’s really valuable because you don’t always have that sort of centralized approach to booking corporate travel the way you might’ve had years ago. The other interesting thing here is that they’ve optimized concur expense for mobile devices. This is really interesting because if you think about it, I don’t know of any business travelers who don’t carry their smartphone. And if you think about things like, you know, capturing receipts.

Well, what’s easier than to take a photo and have an automatically upload with all of the expense categories correctly mapped, you know, to a back end system. I mean, that’s a huge time saver, number one. It also helps cut down on errors. I mean, what’s the biggest problem, you know, when it comes to reconciling business trips and expenses is things are miscategorized, or maybe there’s a missing receipt, or, you know, there isn’t enough information entered. By making the interface very, very intuitive, it really helps with compliance when it comes to, you know, getting folks to actually input their information, you know, number one, getting them to input it, but also getting them to do it, you know, right after their trip. So they’re not waiting around for the close in the month and everyone puts in their expenses late.

Now, part of this, this is also interesting they partnered, SAP Concur partnered with MasterCard and apparently this Beaker allows expense reports to be automatically, you know, created. So if you go and, you know, basically, you know, scan like a hotel bill or something like that, it will automatically, you know, itemize the various elements of that into, into expense categories. So you might have your room charge, you might have your taxes and fees, and then you might have other expenses that are not necessarily, you know, reimbursable. Maybe, you know, you hit the minibar or something like that, but at least it segments that out, so it’s much easier to identify which expenses need to be reimbursed, which need to go into, you know, specific categories, and which ones can be ignored. So, all of this, again, is very much about making that user experience seamless, not just for the sake of doing it, but to achieve larger business goals.

Compliance. Accuracy. Timeliness. You know, all of those are, you know, really important and that’s where I really think organizations will see value for using, you know, new technology like artificial intelligence and particularly generative artificial intelligence. So that’s all the news that I have for today. We’re gonna, you know, do a little bit of a shorter version of the podcast this week as I’m about to head off to an event this week, but, of course, I could not leave without doing my rant or rave segment, and this week I have a rant, and we’re gonna get around to why this actually relates to the enterprise application market. There’s a lot of people, you know, have figured out when they go on LinkedIn, they get absolutely inundated with requests, requests to connect, requests to follow all of that kind of stuff. And I have no problem with that. It’s all about networking and obviously creating that larger network of influence as well as just, you know, connections where you can help someone else out.

But the one thing I’m going to rant about is when I connect with someone and then automatically they send a marketing or sales email directly to me that has absolutely zero relevance to me, to my job title and function or to my company. And it’s really shocking in this day and age because there is the ability for folks to easily, you know, go out, scrape all of this information using software to make sure, yes, you’re connecting with me that I’m the right type of target. And it is, you know, quite honestly you know, this isn’t even a technology issue. This is just a hey, you know, folks need to do a better job of identifying, you know, what is a real business target? You know, what are the titles that might be appropriate there? What are the companies that might be appropriate? What geographies? All of that kind of stuff. And making sure that they have a system in place to capture all the information, analyze it, and then you know, suppress anyone who doesn’t fit that.

I think that it, particularly in this day and age, we are way beyond this idea of, hey, let’s just play a numbers game, you know, and just hit everybody and hope that somebody bites. Because everybody knows that all of this information is available, that there is software out there, even for small independent vendors out there, to quickly grab that information, parse it, and you know, then essentially segment out the ones that are not relevant. I’m, I’m honestly surprised that organizations or, you know, I guess independents are going to do whatever they’re going to do, but I’m surprised that they would take such a haphazard approach or such a carpet bombing approach to you know, lead gen and sales when there’s so much information out there that can help. This isn’t 35 years ago where you had a name and a company and maybe a job title. It is just really shocking.

And I think that, you know, there’s an opportunity for, you know, application vendors, whether, whether we’re talking, you know, large scale enterprise vendors to SMBs, you know, to, to really reach out to folks and say, “Hey, this is an opportunity for you to improve your outbound sales and marketing and marketing activity through the use of a platform that can offer analytics.” Can you offer AI just in terms of looking at going through scanning all this data and going, ‘Hmm, is this a good fit for what I’m trying to, to offer?” And if it’s not, then, you know, stripping it out, because it does not do anyone any favors to just take that sort of, you know, antiquated approach to sales and marketing. Anyway, that’s my brief rant for today. So I want to thank everyone for joining me here on Enterprising Insights. Of course, I will be back again next week with another episode focusing on the happenings within the enterprise application space. Thanks again for tuning in and be sure to subscribe rate and review this podcast on your preferred platform, 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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