In this episode of Enterprising Insights, host Keith Kirkpatrick discusses news from the world of enterprise applications, HR, and AI. He focuses on the growing use of no-code/low-code development platforms, the focus on enterprise skills development, the use of AI guardrails within a CRM, and discusses how AI can be used to optimize digital customer experiences. He then provides a Rave during his Rant or Rave segment, praising vendors’ increasing willingness to focus on time to value as a key messaging point, eschewing the endless focus on feature sets.
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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’m actually not on the road for a change, so I’m going to be focusing in on some interesting industry news from the past week. Then, of course, I’m going to close out the show with my rant or rave segment where I pick one item in the enterprise applications, CX, EX, or collaboration space, and I will either champion it or criticize it. So let’s get right into it. What I wanted to talk about to kick off this week’s show is a trend that I’ve been seeing really I guess it’s been over the last couple of years, but we’re really starting to see it take hold right now. And really what I’m talking about here is this continuing demand for low-code, no-code development tools. What’s going on here?
Well, there are a number of vendors that are offering these types of tools in the market, Pegasystems, Creatio, there are many others as well, and actually other large platforms that are offering no-code tools. And really what they do is they allow folks who are not dedicated developers to really kind of jumpstart the application or workflow development process, essentially creating small applications that are really kind of purpose-built. The idea here is that if you think about the way the nature of work today, particularly as we start to incorporate generative AI, you have certain processes really that really require multiple steps, and you really want to be able to do things like connect different applications or different departments or different pieces of data. And in the past, you would need to actually contact your IT department, probably get in a queue, and then try to work through with them saying, “I need this particular application and I need it to grab this data, and I would like the interface to look like this, and I want it to show up here in this application.”
Well, there are really a couple issues with that. One, trying to get on the list to actually have that done meant you probably had to go through not only IT, but actually go to your department head and make sure everything was coordinated, because that’s a massive resource intensive job. What these low-code, no-code platforms do is it allows folks who maybe are not full-time developers to really start developing applications that conform to all of the enterprise’s particular governance rules, make sure that you get all the right permissions for grabbing certain data, but it allows this development task to be democratized across a greater number of folks. And why is this important? Well, if we think about the nature of the work today, it is all about creating seamless experiences, not just for customers, but also for employees as well, because the days of, if we think back 30 something years ago in the paper world, if you had to get a piece of information or pull together a particular report, you might have to go to individual different systems or departments to pull paper documents.
As we migrated to a more digital way of doing things, it became something where you would manually have to go to different applications or different data stores to get all that information. Now we’re at the point where the idea is to really create a way for people, for productivity workers to get all of the information they need basically pulled into them in a form that works for them. Now, it would be great if everyone had their own personal developer where you could sit there and say, “I’d like this, this, and this,” but really there aren’t enough developers in any organizations available to handle those types of tasks, and that’s going to continue. Now, when you have the infusion of generative AI, which is modernizing the way that people can interact with code and interact with applications, it is creating an environment where people can actually start to build their own applications. Now, that doesn’t mean that people should go off and create applications without letting others know, whether it’s IT or department manager or anything like that, but the goal really is making sure that these folks have the capability to do it as long as they go through the proper channels.
So I think it’s really the things that really was the catalyst for these types of tools to really gain more traction now, of course, it’s generative AI, because of the fact that it does make it so easy, and it’s also something that has really lit the fire and everyone’s saying, “I want information and I want to be able to interact with systems in a very, very non-technical way,” because it’s a lot easier to say to a machine through a prompt or something like that, “I’d like this data and I’d like it like this,” as opposed to learning how to actually pull that together writing code. So we’re definitely seeing more activity there not just from the platforms like the Pega, like the Creatios, but also from other larger platforms that are integrating these no-code development type tools that will allow you to connect different systems in a very visual way essentially as a workflow. So I expect to see that continue, I expect to see more announcements of that from a number of different platform providers just simply due to the fact that we are in a developer shortage that will continue yet just because there’s an increasing demand for application development and, honestly, there are just fewer people going into that who are available for that role.
Now, another interesting thing that I’ve been looking into as my coverage area spans a wide range of different topics is, again, this issue of looking at workers. Obviously we talk a lot about customer experience, but, on the other hand, there is the employee experience, and one of the things that is really coming into focus now is the need to invest in skills development and training. If you think about, obviously there was a big study that came out last week from Microsoft and LinkedIn, it was the Work Trend Index, and one of the key points there was that generative AI is making its way into enterprises, but there is not a lot going on in terms of training. Now, it’s not just generative AI, it’s a number of different issues with respect to training that need to be addressed. If we think about individual lines of business, there are certain skills that are required to do certain jobs, whether it’s regulatory issues or operational issues, all of that is really important. The challenge has been that typically training was seen as a human resources function and domain. The problem, of course, is that human resources understands human resources. They don’t necessarily understand the nuances of what it takes to be an accountant, in keeping the skills up on the accounting side, or someone who works in finance, or in healthcare, whatever the specific domain is.
They’re not going to have as much granular insight as a line manager might. And what we’ve seen here is this challenge where you have individual managers that don’t feel that HR can really understand what’s going on there, so they go off and they do their own training, and that leaves HR in the dark, and they’re not even sure what training is being used, they’re probably thinking, well, we’re probably spending too much, covering the same topics in some ways there’s some overlap there and we’re not being efficient with the way that we’re training, and also they just don’t have visibility into what skills are being trained within the organization and which are not. So really interesting, I saw some news that came across my desk last week, it’s from Oracle, it’s their Oracle Grow for Business Leaders skills development program. And basically what this is, it’s a way for managers and HR professionals to really work together or a lot more closely. The service basically has a technology component in terms of a centralized dashboard that offers real-time visibility into the various development progress and skills across the organization. So it’s not just looking at it from an HR-centric perspective, it’s really looking at it from what’s going on in department A, B, C, D, E, F, and G, so there is more visibility, there can be more coordination, and really it will help the overall organization maximize all of its resources to training and skills development.
And I think the key thing is that by implementing a platform that allows that visibility, it really provides a check and balance to make sure that if the business leads aren’t getting what they need either in terms of resourcing, HR knows what’s going on and can see that deficiency, and vice versa, HR can actually see where are skills programs, where is the money going for these skills development programs, and are there other options there that might be more cost-effective or more efficient. So really interesting service there from Oracle. And I think it’s something we’re going to continue to see, particularly as organizations make skills development a priority as we morph into this AI-centric world that we’re heading into where people are going to need to upskill, they’re going to need to be reskilled for certain jobs, and of course there’s always the pressure of if you have an organization that is downsizing or right-sizing, or whatever term you want to call it, people may need to be retrained to take on additional responsibility. So very interesting topic there I’m sure I’m going to revisit later.
Now, I could not have an Enterprising Insights podcast without talking about artificial intelligence and safety. Of course, we’ve talked about this really quite a bit, particularly around the idea of AI models, generative AI models that are hallucinating. A lot of the discussion has been focused on what happens with these text-to-image generation models, and are they spitting out content that is biased, that is inaccurate, and certainly still a big issue. I will just quickly note that I know Adobe just came out saying that they are implementing I believe it’s a review panel to address some of those issues, and I think that’s a really great thing. But I want to take a look at this issue from not an image generation angle, but taking a look at the use of generative AI in other constructs or in other particular use cases. One of them is looking at how is generative AI being deployed in really purpose-built applications, for an example, in a CRM. If you think about it, there it’s about handling relatively basic use cases like summarization of interactions or allowing the generation of suggested responses that can be then passed on to a customer service representative.
Now, there are still concerns there in terms of making sure that the data that is being used that is grounding these models is in fact vetted properly, trusted, is being cleaned, it’s being labeled properly, all of that kind of stuff. And I actually had a conversation with SugarCRM, which is a CRM vendor that primarily deals with mid-market companies, some at larger, small businesses. They do have some enterprise clients, but obviously they’re not going out there for the most part the same customers that a very large vendor like a Salesforce might go after. But it was interesting to talk to them about their approach to trusted AI. And they, like other vendors out there, do have a very specific process to make sure that the LLMs that they use are only acting on trusted data. And the interesting thing here in speaking with them is that the approach is really about worrying about things like masking sensitive data. And what do I mean by sensitive data?
Well, it could be things like personally identifiable information, names, addresses, account numbers, things like that. That type of stuff is obviously very, very valuable to organizations and of course the vendors that serve them. Sugar takes the approach of masking all of that information before it goes out to the LLM to make sure that that information never leaves the customer’s domain. And the idea here is that that is among the most important things that they’re concerned about. They’re not doing image generation, they’re not worrying about some of the issues that Google was worried about with Gemini or whatnot or Adobe or anything like that, but it’s really about making sure that the information, the very, very valuable customer information is protected in a very strategic and process built way, and they’re building their platform to do that.
They have not in the past talked as much about this, but I think it’s important for all of these vendors, regardless of whether they’re a CRM vendor or an ERP vendor, when we talk about safety, it’s a very similar conversation in terms of the level of importance, but we’re probably talking about a different focus area in terms of how do you protect personal data or IP as opposed to worrying about things like hallucination around crazy images. So I just wanted to really address that because a lot of the talking points I’ve had over the past couple of weeks here on the podcast have been really focused in on hallucination as it revolves around image generation and creating images that might not look the way it’s supposed to. So interesting stuff going on there, as well as at other companies that are clearly focused on AI safety. And I think at this point, I don’t see any vendor that actually services mid-market or enterprise customers that are taking it lightly.
They do realize it’s a big deal because they realize that all they need is a faux pas and it’s difficult to sweep that under the rug these days with social media. And we’ve all seen the issue with a certain airline that a chatbot go rogue. All right. Now, again, continuing with AI, I wanted to talk a little bit about the use of AI and its impact on customer experience. Now, we’ve all heard about using AI to suggest potential responses for customer service agents or summarizing conversations to make sure that everything is captured accurately, all of that kind of stuff. But one thing that I think has been I wouldn’t say overlooked, but perhaps we haven’t focused on enough, is the ability to use AI to actually look at a customer’s experience within a particular interaction. And I’m talking specifically about digital interactions. So if you think about a typical customer going to a website to make a purchase, the idea is to hopefully have it where all of the information is available to them, anything they want to do, whether it’s find more information or click deeper into a product description or go through a checkout process should be pretty seamless challenges. It doesn’t always work that way. And manually going through and trying to identify where the points of friction or roadblocks are can be really challenging. That’s where a good number of companies are actually implementing AI to go through and really look at not just AI, but just analytics to look at the very granular elements that impact customer experience due to friction.
And that could be if I’m on a site and something isn’t clear, it can track my dwell time to see am I spending too much time looking at this page because I can’t find where to click, or is a process not happening because I needed to scroll down to find something. Well, a number of companies have actually introduced AI to actually help improve these experiences. Glassbox is one of them, certainly digital adoption platforms like WalkMe can do this as well. And the idea is that by using data to go through and actually measure what’s going on and then even suggest fixes really helps analyze what the customer is going through without a tedious manual process. I think one of the interesting things also is that instead of doing A/B testing, which is a challenge, you’re able to actually go through and identify what’s going on almost in a real time way based on the behavior. So as you make changes, you could see, okay, if I move this text box over here, maybe it’s reducing the dwell time, or you’re seeing higher click-through rates, that sort of stuff.
So it’s an interesting way to apply AI that isn’t necessarily going to be apparent to the end user, but it goes a long way to making sure that their experience is better. I think that the other interesting thing, and I know Glassbox does this, is it will also integrate other data, existing data held within a CDP to help optimize that process. If you think of for certain users, they may be gravitating toward certain content or certain structure based on previous behavior or also just whatever a typical user might do, and I’m trying to think of a great example of that. But if you think of someone who is a power user of a particular product, they may be more interested in certain product information that is heavy duty on specifications as opposed to a more casual user who might just be clicking through some pictures. And you can use all that data to optimize that site and that experience based on the type of user that they are. So I think that’s all really interesting ways to utilize AI to improve experience without really jumping up and down and calling attention to it in the way that generative AI might be a little more visible in something like a chatbot.
Finally, I think the last thing I really want to talk about here is as I’m moving into the final phase of spring travel with analyst meetings and things like that, I do want to call out something that I found really interesting is… Obviously, I wasn’t able to go to any of these things without talking about AI, but I think the interesting thing that I’m hearing more and more is AI, there’s a real acknowledgement that as novel as AI really is right now, we’re starting to get to that point where vendors are really starting to acknowledge that at some point we’re not going to be talking about AI as this shiny new object in the corner. It will just become something that is incorporated into the product, into that platform, into that application as a key part of their functionality. And I think that’s a really important point to make, because that is going to really influence the way that platforms are evaluated, it’s going to impact pricing, and it’s going to eventually impact usage. Now, I’ll get into pricing in a later date in terms of what I think is going to happen there, but I think it’s a good thing, particularly for end users, because when things are just expected and just become table stakes, it really starts to move the conversation, particularly when it comes to purchasing, into other things, other aspects, which in the end tend to be more important in the long term. And that brings me to my rant or rave section today.
And I want to rave about the fact that in speaking with all of these vendors over the past several months, one thing that keeps coming up is they’re acknowledging that time to value is really becoming a key decision criteria for their customers, and they’re acknowledging that, and they’re adjusting some messaging so it’s not just about here’s my shiny new function, here’s my shiny new feature. They’re really talking about, okay, here’s this feature and what does that mean? It means you’re going to be able to accomplish something much more quickly with this feature. And then the biggest thing, of course, is looking at how are these vendors able to quickly implement their solution so that from the time the contract is signed to the time a company is actually able to generate revenue using their product, they’re really focusing on minimizing that, and that’s part of their pitch now. They’re really leaning into that more heavily, and of course leaning back on their technology that allows them, whether it’s their architecture or other partnership models of working with specific partners who have a lot of domain expertise, all of these things are really starting to come together into a more cohesive message.
I think we’re starting to get away from just look over here, it’s AI, look over here, we have this new tool, and really getting into a more focused conversation on what does the bring to the table in terms of actually delivering value very, very quickly. And a lot of that is around the folks they have on the staff and their expertise, a lot of it’s based on, look, we’ve worked with X number of clients before and this is how we’ve been able to learn from those experiences. So that is certainly a big rave that goes out today to all of the vendors in the space. So with that, I am out of time for this week, but certainly I want to thank everyone for joining me here on Enterprising Insights. I will be back again in the very new future with another episode focused in on the happenings within the enterprise application market. So thanks again for tuning in, and be sure to subscribe, rate, 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.
