On this episode of the Six Five Webcast – Enterprising Insights, host Keith Kirkpatrick gives a comprehensive wrap-up of two major tech events: Smartsheet Engage and the Zendesk AI Summit. This discussion explores the latest trends, announcements, and breakthroughs presented at these leading industry conferences.
Keith covers:
- Key takeaways from Smartsheet Engage including new product announcements and strategic directions
- Highlights from the Zendesk AI Summit focusing on the future of customer experience and AI integration
- Predictions for how the announcements and trends from these events will influence enterprise technology strategy.
- Insights into the evolving landscape of work management tools and customer service technologies
- Implications for businesses striving to enhance collaboration and customer interaction through advanced technologies
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Transcript:
Keith Kirkpatrick: Hi, everyone. 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’d like to talk about two events that I attended, Smartsheet ENGAGE in Seattle and Zendesk AI Summit in New York. Now, while they’re both very different events on the surface, they’re actually a lot more similar when you really start to dig a little bit deeper in terms of the underlying themes for both events. Then once I’ve covered that, I’ll get into my rant or rave segment, which is where I pick one item in the market and I will either champion it or criticize it. So without further ado, let’s get started. So first event that I went to this week was Smartsheet’s ENGAGE, which is their user conference that takes place up in beautiful Seattle, Washington.
Now at this event, there’s really a couple things going on. First of all, the biggest news that actually happened before the conference itself was that Smartsheet actually made the announcement that it intends to go private, essentially delisting from public markets and once again being owned by private equity. Now, initially one might think that that is a sign of trouble because generally speaking, you don’t see companies deciding to go private unless there are some issues where either internally there might be something going on in terms of the overall health of the company, or perhaps there is some massive problem in terms of strategy or product issues. I don’t believe that is the case. Mark Mader, the CEO, came down and talked to us at the analyst conference and at the event itself. And for my money, I believe that the company is on the right trajectory, and really this move is just a way to make sure that the company has resources to continue investing heavily into the platform and into AI.
Let’s be honest here, AI innovations, they’re not inexpensive. It requires quite a bit of work in terms of trying to make sure that there’s enough functionality in the platform to keep all of the customers happy, given the fact that if you look at most of the companies in the space, they’re rolling out new products and features all the time. You cannot do that if you don’t have the capital to keep funding those development projects. So with that said, there are a few other things that were really kind of going on at this event that I think were pretty notable. Of course, the other main focus is that Smartsheet is rolling out some new functional updates and new features on its platform. Obviously, a lot of this revolves around generative AI. One of the main features that was really kind of pretty cool, and I actually talked to a lot of customers about it, is this feature where a user can use generative AI to go from sort of natural language to an actual formula within a cell.
And the reason that’s important is if you think about the way people work, particularly if you think about knowledge workers, project workers, which are the ones you really use, Smartsheet, they are not generally speaking experts when it comes to putting in functions or formulas within sheets. If anyone has used any kind of spreadsheet tool that’s for… For example, they know that remembering formulas the exact format. That’s a lot of work. And more importantly, sometimes it’s not about even just remembering the formula, it’s remembering what is it that you really want to do. And a lot of times people have it in their head. They clearly know they want to include these certain variables, but they don’t know how to structure it. So that AI functionality is really a great example of how generative AI can deliver a lot of benefit to really users across the spectrum, not just novice users, but even more expert users that just don’t have time to memorize every little formula or function within the platform.
That was certainly a, if you think about what’s going on with generative AI, this is an example of a really smart way to deploy it within the platform to deliver benefits right away. Now, another really interesting development that was announced is that the company is looking to migrate its users to the new platform. Well, what does that mean? Well, it means that instead of having a bunch of different disparate applications, they’re moving to a single platform where different functions will be just that. There’ll be different functions all incorporated within the platform. And as a result, they’re shifting their pricing model as well. So there, instead of let’s say saying, “I need access to six different tools and I will pay seat license fees for each,” what Smartsheet is starting to do is shift folks to more of a freemium-based pricing model where let’s say you have a group of users who need to access a certain functionality, Smartsheet will let them use it and not for a day, not for a week. I think Mader said something like, he said, customers that are evaluating a function or a tool for up to 60 days.
And why is that important? Well, if you think of an enterprise who is trying to identify what features are actually valuable and what aren’t, it’s hard to do that without spending time with that particular tool or that particular feature. What Smartsheet is doing is saying, “Look, we’re confident that users are going to get value out of the platform and we’re going to open this up. So if users want to use a certain functionality or a group of users want to use the functionality, they can without any kind of limits there, and then once they get to their renewal period, then they will actually apply the appropriate pricing to account for that usage.” Now, obviously, the devil is always in the details in terms of how that is applied. But my sense is that they have a very large loyal user base. I don’t see them nickel-and-diming their users based on specific usage patterns. I see sort of a fair assessment of usage and a fair return of value on both sides. Obviously, revenue flowing to Smartsheet for the use of that tool, but also not creating a scenario where the user feels like, “Oh, I had a team use something for a little bit,” and then they’re not going to be blown out of the water.
I believe the pricing will be based largely around usage or consumption trends, which fits with what’s going on in the larger SaaS market. Now, I think the other thing that was pretty interesting is Smartsheet announced their Amazon Q data access tool, which will allow users to access Smartsheet data really through this tool, through any other application. What does that mean? Well, if we think about the modern enterprise right now, data lives in a number of different places. There are very, very few organizations that only keep data in one place or one data lake or one application or one data platform. That just isn’t the way that most organizations operate.Because of the nature of how technology is acquired. It might acquire a system for a certain function. You generate data there and then of course, then you have all that data in an application and it’s challenging to automatically say, “We’re going to take all that and put it into a centralized location.”
Very few companies that have been around for any amount of time have taken that approach. So what this is, this tool allows an organization to access data. A lot of it that might be incorporated within or included within Smartsheet and access it throughout their organization. This is really huge because if you think about the way projects are done, you’re not always going to have data lying in a convenient spot. You’re going to need to be able to access it from all over the place. If you think of other systems that are dependent or they want to access information held within the project management platform, they’ll be able to get it through Amazon Q. Again, this is a tool that is built on being able to query for this data in natural language, which again makes it accessible to all types of users. Again, reducing the amount of friction when it comes to actually accessing the data that one might need.
Now, the other thing that I found pretty interesting about all of these new features that were announced, and there were certainly a few more specific announcements around workload heat map, workload schedule, which are designed… Basically, there are two features that are able to provide managers with a little more insight around project resource allocation, labor utilization, scheduling across the entire portfolio of projects. There’s also another feature called resource management. This allows an organization to organize all of their resourcing data within a Smartsheet report and then put all those insights onto dashboards, which really is designed to help improve enterprise-wide visibility and decision-making. And then of course, the other new announcement there is around timeline view. This allows Smartsheet customers to look at date-based work, date-based project work and allowing them to get a better sense of what’s going on in terms of deadlines, what work is being done when, looking at project milestones and where they are along that timeline in terms of completing those deliverables.
Again, this is all about visibility and accountability which is the hallmark or the pillars upon which projects are completed within organizations. Now, of course, the other thing that is going on, as I mentioned or alluded to earlier, is the use of AI within the Smartsheet platform. Again, I believe they have announced a way to analyze data just via conversational prompts. So you could say, show me a chart with all of the projects that are due within these particular dates that are being worked upon by these teams. It’s a really cool feature. I think it’s something that is ultimately, this is the way that as we move along this AI continuum, we’re going to be seeing people interact with their various applications, whether we’re talking project management, whether we’re ERP, whether we’re talking CRM, doesn’t really matter. It’s this very conversational, almost prompt-based way of interacting with data. Because in the end, it is much easier to extract data when you just have an idea of what you want to learn as opposed to going in and clicking through generating reports, that sort of thing. That can be a challenge. And particularly if you look at the way organizations are structured now, they’re tracking all of this information.
Most companies anymore, if they’re moving to a platform like Smartsheet or whatever, or Monday or whatever, they’re inputting all of this data somewhere and being able to access it quickly is really where there is value. So I think the other thing to really note here is that Smartsheet is they are definitely leaning into demonstrating their value to their customers through their pricing model. I think this is something that is going on or will be going on with a lot of other vendors moving away from a strict seat license model where it’s sort of like, “Well, let’s figure out how many people need access to the system and we’ll pay for it,” realizing that, “Well, maybe this group of users is only utilizing the software 20% of capacity versus another group that uses 80% and trying to figure that out versus more of a consumption model where essentially you pay for what you use and then you’re able to better forecast what your usage is going to look like over the coming years.
And of course you’ll be able to scale up, scale down and get a handle on where you might be moving in the future based upon that usage. So a lot of good stuff coming out of that particular conference. I would say that the other thing about Smartsheet really was… There’s a few other things about Smartsheet that were really interesting. One is that I talked to a ton of customers there. Kudos to Smartsheet for being very, very open with letting us as an analyst talk to customers without being handcuffed saying, “You can’t talk to them.” They are obviously very confident that their product is telling their story and is delivering results for their customers, otherwise they wouldn’t allow us to speak with them. Now, was every customer I talked to, were they all jumping up and down about every last feature or every last thing about Smartsheet? No, of course not. If that were the case, I’d really have to wonder whether or not they were actually customers. But I will say the one thing that stood out to me is that a couple of customers directly said to me, the one thing that Smartsheet has done particularly over the last six to 12 months is they have taken upon themselves to take a much more active role in terms of listening to their customer’s suggestions about what features they want? What features they don’t want? What integrations do they need? And then actually implementing that.
Now, again, it doesn’t mean that every suggestion has been listened to. That’s not how you run a company. That’s not how you develop a product, but taking everything from your customer’s verbatim and then popping them into the roadmap. There has to be strategy behind it. But the fact is that if you think about what these companies are really trying to deliver, it’s a great customer experience and it starts by doing that with their own customers. So with that, I would like to quickly shift to the other event I went to, which is Zendesk’s first ever AI summit, which is held in New York. Now, this is an interesting event because Zendesk has, over the past year and a half or so, like a lot of vendors, they’ve really leaned heavily into AI as a tool to not only improve efficiency of human workers, but also to do a lot of automation. And I think this event was really great sort of crystallizing the messaging that the company has, particularly around AI-powered agents. And here Zendesk is taking the approach that all of these AI features, they can be used to not only enhance self-service, but also help human-agent interactions across all channels.
So the goal is anything that can’t be done or anything that can be automated easily should obviously be done there. That’s a way to really essentially cut costs. And the sort of dirty little secret that companies don’t love to talk about. But yeah, a lot of customers, they are initially looking at AI as a way to reduce cost. And yes, that means cutting headcount in some cases. Sometimes it means cutting certain live support features because there’s a better way to do it using technology. Now of course, there’s also the other aspect of AI, which is essentially, “Okay, we’re not going to necessarily cut the overall number of humans working, but we’re going to make sure that we have the right people and the right roles to handle more complex interactions.” And with that, AI and AI agents can be used to assist them to make sure that they have the right information at the right time so they can serve customers better through live channels, whether we’re talking about live text or through voice or through app interactions, whatever it might be.
But Zendesk basically is saying that, “Look, AI, they have their AI platform, their Zendesk AI platform combined with all of the process and workflow data that they have, having worked with a lot of customers over the year, obviously in a service-based environment.” That will help make their AI agents work very well out of the box. And then of course, they did also announce something called an AI Agent Builder, which is designed to allow their customers to more easily customize and deploy agents for specific functions or specific industries. I think that’s important because as we start to get past this sort of initial wave of AI agents or chatbots or what have you, taking care of some the low hanging fruit of basic interaction like, “Oh, I need to return a shirt to this retailer, or I need to change or add a feature into my TV service.” I think where organizations will really generate a lot of value is by attacking these more complex problems. What that means is, generally speaking, if you think about people, people don’t call up or text or engage with a company through their customer support lines because they’ve got nothing better to do. They don’t want to be there doing that.
And the only reason that they’re interacting with them is because they have a real problem that they weren’t able to solve on their own. A lot of times it’s because what they’re doing is very complex, either in terms of having multiple steps or perhaps they feel they have a unique scenario that is not covered by general policy of the company, or perhaps it’s just something that just generally doesn’t come up very often and they want this situation to be handled quickly and efficiently. What Zendesk and others are saying is, “Hey, let’s figure out a way to, A, start training these agents to handle these more complex requests by learning how to understand intent better.” Or if we can’t help them through these digital self-service channels, let’s make sure that this agent is able to work in the background alongside of a human agent to make sure that agent has all of the information they need to help address these customers’ issues quickly instead of having the agent have to put someone on hold. “Go talk to a supervisor, go look through a stack of knowledge-based articles,” which is going to take time.
Perhaps it’s not even something that they’re able to know if they’re looking in the right place. So the idea here is figuring out a way for AI to really make sure that whoever is working on that customer support request, whether it’s a bot or whether it’s a human, has all the information they’re able to gather and basically surface that for them in real time so that the customer can be served more effectively. Now, all of this is great, but really obviously what it requires is that the end customer or the customer needs to have their data basically prepped and segmented properly, labeled so that AI can actually search through it and understand the semantic meaning and make sure that it grabs the right data that is pulling from the right knowledge-based article. Make sure it understands all of the interactions, and obviously that comes down to making sure data is prepared properly. And that’s true whether we’re talking Zendesk or any other company. And I think it’s important that companies like Zendesk continually make that known to their prospective customers because ultimately no matter how good an algorithm is, if the data isn’t prepared properly, it will not deliver the results that are desired.
Now, for its core, Zendesk has claimed very, very high resolution rates from some of its AI agents thus far. I would love to hear a little more detail about what exactly I think some of the resolution rates were in the 80s or even 90%. I would love to hear more about exactly what types of use cases, what types of problems were solved. But certainly that is a good start in terms of demonstrating the power of AI agents. Now, the other thing that was interesting coming out of Zendesk, it really wasn’t even from the AI summit, but actually a little earlier this year, was the fact that Zendesk is moving to a new pricing model just as Smartsheet is. Zendesk has decided that this traditional seat license pricing model isn’t necessarily going to reflect the way of the future. They’re moving to an outcome-based pricing model where essentially if you think of, let’s say an AI agent and you have an interaction where let’s say it’s a task like I’m a customer, I want to initiate a return, complete the return.
Well, if an agent is able to do that without any other sort of intervention by a human, well, that would be a completed outcome. And then of course they would be paid on that outcome taking place as opposed to more of a seat license basis or even a consumption basis around the amount of compute required to do it. This is very interesting, and I think it is eventually the way of the future because it aligns both the customer’s interest as well as the vendor’s interest in terms of actually solving customer’s problems and doing it efficiently. Now, it doesn’t mean that we’re going to turn to this model or all the customers are moving in this right away. It’s going to take time. There still needs to be more transparency in terms of how do you define an interaction? What are the parameters in terms of saying, okay, this interaction was successfully completed? Is there a callback provision if let’s say you have an interaction, you think it’s closed within the agreed upon timeframe? But then another issue comes up that’s bigger and there was actually a mistake, what happens there? Are you still charged for that? Are you not? Those are all issues that I believe are going to be worked out over time and maybe very much dependent upon the specific use case industry and even customer in terms of what an interaction actually is in terms of an outcome.
But I think it makes sense in this world where we probably are going to see more interactions move away from human interactions or human-led interactions and more toward that self-service way of doing things through a fully digital AI agents. Doesn’t mean that we’re going to have all of them move, but certainly the goal is to take more of those interactions and make them sort of self-service because realistically, most companies are already just drowning in customer service or customer support issues, or inquiries coming in. There are very few that actually have agents sitting around not taking calls or texts or whatnot. Again, I wanted to circle back to what I was talking before about how these two events seem a bit sort of like two different things, but really there’s some underlying similarities. And really one of them is obviously looking at how they are approaching pricing of AI. Both are really taking the approach that this cannot be a traditional seat license model moving forward. It just does not align what it is that AI wants to do, which is deliver more efficiency.
And you can’t just do that when you’re doing a static seat license. I think that it is also interesting that if you look at Zendesk, which has taken it to the ultimate conclusion of saying, “We’re going to hopefully charge on outcomes,” if you look what Smartsheet is saying, they’re saying, “Look, use our software, see if it actually delivers outcomes from you, and then we’ll charge you based on how much you use.” Both of those are somewhat similar in terms of saying, “Look, we’re not expecting the customer to trust us. We’re saying, look, use it, and we only get paid when you do well.” And I think that’s an interesting development in the market. I talked about this as something that was going to happen in 2024, a little less than a year ago when I did my sort of, what can we expect in 2024? And I do think this is starting to happen now, so I can’t say that… It’s not like this is going to be in the norm by the end of the year. It probably won’t be until sometime in ’25, maybe even ’26 as customers start to renew and start to look at contract terms and then start to re-up again.
But I do think that is on the way. What’s the other thing, the other point of commonality? Well, again, it’s around AI, but also around the need to make sure that customers have their data prepped in a format that can be easily used and accessed. And I feel like that has been the message that not a lot of folks have really been focusing on because it’s not as neat or it’s not talking about a feature. It’s talking about work that needs to be done by customers… Honestly, before they start really using any sort of platform if they want to actually get the results that the vendor is promising. And some of these companies certainly offer professional services to help them, but a lot of it is also just around having an understanding of what are the business goals that my organization wants to achieve? Understanding what data is required to do that, and then understanding where is that data and has it been organized in a way that if you’re using AI, it can actually extract or understand what the actual intent is within a prop, let’s say, making sure it’s able to actually grab the different elements there because it’s been properly tagged.
Those are important things to really think about as companies move forward and deploy these agents because they will never ever get the results they want if they don’t address those issues first. And then I think the third point of commonality between certainly Smartsheet and Zendesk is both companies made it a point to underscore the fact that, look, the other thing that they are very, very cognizant of is the need to highlight the fact that they are safe. They handle AI safely. They make sure that they’re in compliance with all of the different AI regulations that are out there and that they’re employing proper data security and data privacy controls, all of that kind of stuff, making sure that there’s minimal, or they minimize the possibility of data leakage, all of that. We’re starting at the point where I feel like sometimes it’s glossed over, but it’s important, particularly if you talk to CIOs, if you talk to honestly security offices, that’s still top of mind. They are not willing to undertake risk by implementing a new system unless all of those boxes are not only check, but also explained. So kudos to both companies for addressing those issues.
And then finally, I guess, the last thing of course is both companies were talking more about, again, not just features but time to value. How quickly will an organization, once they implement the solution, actually see results, see value from implementing the software? That cannot be overstated in its importance and it’s good to see both talking about it. Obviously, it’s highly dependent upon each particular implementation and each customer, but there’s certainly… That is being mentioned in their messaging, so they understand that it’s important more so than just saying, “We have XYZ feature here or AI this there.” Because in the end, at a certain point, all of that technology, all of that AI is going to become commonplace common and essentially a commodity in the market. Not tomorrow, not next week, but at some point we’re going to hit that period and it’s going to be these other basic business factors that are really going to drive business. So with that in mind, I’m going to wrap up that portion of the podcast and move to my rant or rave segment where I pick one thing in the market and I will either champion it or criticize it.
And today, I actually have a rave, and it really is around this idea that I think customer… I’m sorry that vendors are really starting to understand that they need to listen to their customers beyond just saying, “Oh, we take feedback.” Actually creating feedback mechanisms and really accounting for customers who say, “I really wish I had this feature, or I wish I had this integration.” They’re starting to realize, and I’ve had several one-on-one meetings with product leaders and strategy leaders at these companies, and they understand that customers are at a point where they understand that they have a choice in software, with the exception of a few vendors. It’s fairly easy to switch and you realize that you cannot be static, you cannot not keep moving forward in terms of delivering certain features and mainly around things like usability, data integration, those types of things, because those are how a particular package can be smoothly incorporated within an organization’s daily flow of work. That’s what actually creates efficiency. That’s what creates more productivity. That’s what helps reduce errors, increasing precision, all of that.
And the way that a lot of these companies are doing it is they’re starting to say, “Hey, customers. Keep giving us feedback and we’re not going to put everything on the road map because that’s impossible.” It’s like you have to pick and choose and see which features make the most sense for which types of customers and is that a priority? And then of course what’s reasonable in terms of a development perspective. And then of course what features or what functionalities may be best delivered outside of the platform. And certainly a lot of these organizations, a lot of these vendors realize that the most efficient way to deliver certain experiences is to partner with another organization. But I think that it is interesting to hear companies acknowledge that they do need to listen to their customer base. And again, there’s certainly still a ways to go for a lot of these organizations in terms of making sure that there is that sort of free flow of suggestions to action.
And even if not action, just acknowledging that, “Hey, thank you for your feedback. Here’s how we’re looking at addressing this issue,” and just being transparent there because ultimately everyone is using a customer experience business right now. And that certainly includes their own internal customers or their own customer base in terms of making sure that the product is delivering and meeting their experiences. So I would say that that’s certainly a rave right now. I do feel like there is this definite feeling that vendors are listening to their customers more. And part of it is that now with generative AI, they’re able to deliver a lot more features more quickly than they ever were in the past. So I think that’s a win for everybody.
All right. Well, that’s all the time I have today. So I want to thank everyone for joining me here on Enterprising Insights. I’ll be back again with another episode focused on the happenings within the market in the next week. So be sure to subscribe, rate and review this podcast on your preferred platform. Thanks again 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.
