Enterprising Insights, – Low Code Development, Skills Development, AI Safety, and AI Optimization, Episode 25

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 change, so I'm gonna be focusing in on some interesting in industry news from the past week. Then, of course, I'm gonna close out the show with my ran to 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 kind of 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, uh, you know, take hold, uh, right now. And, and really what I'm talking about here is this continuing demand for low-code, no-code development tools. Uh, what's sort of going on here?

Well, there are a number, a number of vendors that are offering these types of tools in the market. Uh, Pegasystems, creatio, uh, there, there are many others as well, and, and actually other large platforms that are offering no-code tools. Uh, and really what what they do is they allow, uh, folks who are not dedicated developers to really kind of jumpstart the application or workflow, uh, development process, uh, essentially creating small applications, uh, that are really kind of purpose built.

Uh, the idea here is that if you think about the way the nature of work today, particularly as we start to incorporate gender of ai, you have certain processes really that, you know, really require ma multiple steps, and you really wanna be able to do things like connect different applications or different departments or different pieces of data. And, you know, in the past, you would need to actually contact your IT department, probably get in a queue and have them and, and then try to work through with them saying, I need this particular application and I needed to grab this data, and I would like the interface to look like this. I want it to show up here in this application.

Well, there's, there were 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, uh, you know, department head and make sure everything was coordinated, because that's a massive resource, uh, and massive resource intensive job. What these low-code no-code, uh, platforms do is it allows folks who maybe are not sort of full-time developers to really start developing applications, uh, that, you know, conform to all of the enterprises.

Uh, particular, uh, governance is rules, uh, make sure that, uh, you get all the right permissions for grabbing certain data, but it allows, uh, 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 having to kind of, if we think back 30 something years ago in the paper world, if you had to get a piece of information or, uh, pull together, uh, a particular report, you might have to go to individual, you know, different systems or departments to pull paper documents. You know, as we sort of 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, you know, 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, this, this, and this. But really, you know, there aren't enough developers in any organizations available to handle those types of tasks. Um, and that's gonna continue.

Uh, now when you have, you know, the infusion of generative ai, which is modernizing the way that people can interact with code and interact with applications, uh, 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, you know, uh, you know, create applications without letting others know whether it's IT 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, um, you know, the things that really kind of, you know, was the catalyst for, 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. It makes, and it's also something that is really sort of lit the fire under everyone saying, I want information, and I want to be able to interact with systems in a very, very, sort of non-technical way, because it's a lot easier to say to a machine, you know, 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, uh, you know, writing code.

So, uh, we're definitely seeing more activity there, not just from the platforms like the Pega, like the create ratios, but also from other larger platforms that are integrating, you know, these no covid development type tools that will allow you to connect different systems, uh, in, in, in a very visual way, you know, essentially as a workflow. So I expect to see that continue. Uh, I expect to see more announcements of that from a number of different platform providers, just simply due to the fact that, uh, 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 or available, uh, for that role. Now, another interesting thing that I've, I've been kind of looking into as my coverage area kind of 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, you know, obviously there, there was a, you know, big study that came out last week, uh, from Microsoft and LinkedIn. It was the work trends index.

And it said one of the, one of the key points there was that, you know, 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, with respect to training, uh, that need to be addressed. If we think about individual lines of business, there are certain, uh, skills that are required to do certain jobs.

Uh, there are certain, you know, 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 problem, of course, is that human resources understands human resources.

They don't necessarily understand the nuances of what it takes to be, uh, you know, an accountant in keeping, keeping the skills up on the accounting side, or, you know, someone who works in finance or in healthcare, whatever the specific domain is. They're not gonna have as much granular insight as, uh, a line manager, Mike. And what we've seen here is sort of this, uh, 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 sort of in the dark, and they're not even sure what training is being used. They're probably say thinking, well, you know, we're probably double, you know, 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, you know, what skills are being, uh, trained within the organization and which are not. So it really interesting, um, I saw some news that came across my desk last week about, uh, it's from Oracle. It's their Oracle Grower Business Leaders, uh, skills development program.

And basically, uh, what this is, it's a way for managers, uh, and HR professionals to, to, to really work together or a lot more closely. Uh, you know, the service basically has, you know, a technology component in terms of sort of a centralized dashboard that offers real time visibility into the various development progress and skills, you know, across the organization. So it's not just, uh, looking at it from an HR centric perspective, is 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, the key thing is that, uh, by sort of, you know, implementing a, a platform that allows that visibility, uh, it really provides sort of a check and balance, you know, to make sure that, you know, if you know the business leads, you know, don't, 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, you know, where our skills programs, uh, you know, where is the money going for these skills development programs, uh, and are there other options there that might be, you know, more cost effective or more efficient. So, uh, really interesting, um, uh, service there, um, from Oracle.

And I think it's something we're gonna continue to see, particularly as organizations make skills development a priority, uh, as we sort of 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 re-skilled for certain jobs. And of course, there's always the pressure of, you know, if you have an organization that is downsizing or rightsizing or whatever term you wanna call it, uh, people may need to be retrained to take on additional responsibility. So, very interesting, uh, topic there.

Sure, I'm gonna revisit Leader Now, I cannot take, uh, I could not, you know, have an Enterprising Insights podcast without talking about artificial intelligence and safety. Of course, we've talked about this really, uh, quite a bit, particularly around the idea of AI models, generative AI models that are hallucinating. Um, a lot of the discussion has been focused on, you know, what happens with these text to image generation models and are they, you know, spitting out content that is biased, that is, you know, inaccurate, you know, and, and certainly still a big issue.

Uh, I will just quickly note that I know Adobe just came out saying that they are implementing, uh, I believe it's a review panel to, you know, to address some of those issues. And I, I, I, I think that's a really great thing, but I wanna 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, you know, particular, uh, use cases. One of them is, um, looking at how is generative AI being deployed in really purpose-built applications?

Uh, and for example, in A CRM, uh, if you think about it there, it's about, you know, handling sort of relatively basic use cases like summarization of interactions or allowing, um, the generation of, you know, suggested responses, uh, 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, uh, you know, that is grounding these models is in fact, you know, vetted, properly, trusted is being cleaned, it's being labeled properly, all of that kind of stuff. Um, and, you know, I actually had a conversation with, uh, sugar CRM, which is a CRM vendor that primarily, you know, deals with mid-market companies.

Some, some at larger small businesses. They do have some enterprise clients, but obviously they're not going after, you know, for the most part, the same customers that a very large vendor like a Salesforce might go after. But, you know, it was interesting to talk to them about their approach to trusted ai.

And, you know, they, like other vendors out there do have a very sort of specific process, uh, you know, to make sure that the LLMs that they use, uh, are only acting on trusted data. And the interesting thing here is, in speaking with them, is that, uh, the approach is really about, you know, worrying about things like masking sensitive data, and what do I mean by, you know, sensitive data? Well, it could be things like personally identifiable information, you know, 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. Um, sugar takes the approach of masking all of that information before it goes out to, to the LLM, uh, to, to make sure that, you know, that information never leaves, uh, the customer's, uh, domain.

And the idea here is that, you know, that is among the most important things that they are concerned about. Uh, you know, they're not doing image generation, they're not worrying about some of the issues that, you know, Google was worried about with Gemini or, or whatnot or, or Adobe or anything like that. But there, it's really about making sure that you know, the information, the very, very valuable customer information is protected, uh, in a, in a very sort of strategic and process built way.

And they're doing it. Um, you know, they're, they're building their platform to do that. Uh, they are, they have not in the past talked as much about this, but I think it's important, you know, for all of these vendors, regardless of, you know, whether they're a CRM vendor or an ERP vendor, you know, when we talk about safety, it's a very, you know, similar conversation in terms of the level of importance, but we're probably talking about, you know, a, a different focus area in terms of, you know, how do you protect personal data or, you know, IP as opposed to worrying about things like hallucination around, you know, crazy images.

So, uh, I just wanted to really address that because a lot of the talk, 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. Uh, so interesting stuff going on there as well as, uh, you know, and other companies, uh, that are clearly focused on, you know, AI safety. And I, 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, you know, faux pa, and you know, it, it, it'll be very rare. It's, it's difficult to, uh, kind of sweep that under the rug these days with social media. And we've all seen the issue with a certain airline that, that had a chat bot kind of go, uh, go rogue.

So, alright. Now, um, again, continuing with ai, I wanted to talk a little bit about, uh, the use of AI and how, and its impact on customer experience. Now, we've all heard about, you know, using AI to, you know, suggest potential responses for, uh, customer service agents or, you know, summarizing, you know, conversations to make sure that, you know, everything is captured accurately, uh, all of that kind of stuff.

But one thing that I think has been, um, 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, uh, uh, you know, 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, you know, 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, uh, a lot of, you know, a good number of companies are actually implementing AI to go through and really look at, uh, not just ai, but just analytics to look at, at the very granular elements that impact customer experience, uh, you know, due to friction.

And that could be, if I'm on a site and I, 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, uh, you know, not happening because, you know, I needed to scroll down to find something. Well, uh, a number of companies have actually introduced AI to actually help improve these experiences.

Blast Box is one of them. Uh, certainly, uh, you know, digital adoption platforms like, walk Me Do this as well. And the idea is that, uh, by using data to go through and actually measure what's going on, and then even suggest fixes, really helps, you know, analyze what the cus customer is going through without sort of a tedious manual process.

Um, I think one of the interesting things also is that instead of doing like AB testing, which is, you know, uh, a, a challenge, you're able to actually go through and identify, you know, what's going on almost in a real time way based on the behavior. So as you make changes, you could sort of see, okay, if I move this, this text box over here, you know, maybe it's reducing the dwell time or you're seeing, you know, higher clickthrough 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, you know, goes a long way to making sure that their experience is better.

Uh, I think that, um, the, the other interesting thing, and I know Glass Box does this, is it will also integrate other data, existing data held within a cdp, uh, you know, to, you know, help optimize that process. If you think of for certain users, they may, uh, you know, be gravitating toward, uh, you know, certain content or certain structure based on, you know, previous behavior or also just whatever they are. Uh, you know, typically whatever a typical user might do.

And I'm trying to think of a great example of that. But, um, if you think of someone who is a, uh, you know, 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, you know, just be clicking through some pictures and you can use all that data to optimize that, that site and that experience based on the type of user that they are. So I think that's all really interesting, uh, ways to utilize AI to improve experience, you know, without really, you know, jumping up and down and just calling attention to it, uh, in the way that, you know, generative AI might be a little more visible in something like a chat bot.

So I think, um, one thing, uh, you know, finally, I think the last thing I really want to talk about here is, you know, as we're moving into, or I'm moving into sort of the final, you know, phase of, of spring travel with analyst meetings and things like that, I do want to kind of call out something, um, that I found really interesting is, you know, when, obviously I wasn't able to go to any of these things without talking about ai, but I think the, the, the interesting thing that I'm hearing more and more is ai, there's a real acknowledgement that, as, you know, novel as AI really is right now, we're starting to get to that point where vendors are, are really starting to, to acknowledge that at some point we're not gonna be talking about AI as sort of 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, uh, 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, uh, I'll get into pricing in a later date in terms of what I think is gonna happen there, but I think it's a good thing, particularly for end users, because when things are just sort of expected and are sort of just become table stakes, it really starts to move the conversation, particularly when it comes to purchasing into other things, uh, other aspects which in the end tend to be more important, uh, in the long term. And that kind of 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, uh, one thing that keeps coming up is they are acknowledging that time to value is really becoming sort of a key, key decision criteria for, for their customers. And they're acknowledging that, and they're adjusting some messaging. So it's not just about, you know, 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? Uh, you know, it means you're going to be able to, um, you know, accomplish something you know, much more quickly with this feature. And then of course, the biggest thing, of course, is looking at, you know, how are these vendors able to quickly implement their solution so that from the time the contract is signed to the time, uh, a company is actually able to generate revenue using their, uh, product, um, they're really focusing on, on minimizing that.

And, you know, that's part of their pitch. Now, if they're really kinda leaning into that more heavily, uh, and of course leaning back on, you know, their technology that allows them, you know, whether it's their, their architecture or, you know, other partnership models of, of, you know, 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, you know, look over here, it's ai, look over here, we have this new tool, uh, and really getting into a more, you know, focused, uh, you know, conversation on what does the vendor bring to the table in terms of actually delivering value very, very quickly.

And a lot of that is around, you know, the folks they have on the staff and their expertise. A lot of it's based on, you know, look, we work with X number of clients before and this is how we've been able to been able to learn from those, uh, experiences. So that is a, uh, certainly a big rave, uh, that goes out today to all of the vendors in the space.

So with that, I am out of time for this week, but, uh, certainly I wanna thank everyone for joining me here on Enterprising Insights. I will be back again, uh, 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.