In this episode of Enterprising Insights, The Futurum Group’s Enterprise Applications Research Director Keith Kirkpatrick discusses the news coming out of the Zendesk and Avaya Analyst Days, focusing on new product enhancements around AI, corporate strategy, and automation. He then closes out the show with the Rant or Rave segment, where he picks one item in the market, and either champions or criticizes it.
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Transcript:
Keith Kirkpatrick: Hello, everybody. 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 actually like to recap two analyst days that I attended, which started this week in Las Vegas at Zendesk and concluded in Torrey Pines just north of San Diego at Avaya. Then as always, I’m going to close out this week’s show with the rant or rave segment where I pick one item in the enterprise software market and I either champion it or criticize it.
So let’s get right into it. So as I mentioned, I spent the first part of the week at Zendesk in Las Vegas. Now they actually had a very interesting analyst day right before their Relate conference, and they made several announcements at this conference, and obviously when they met with the analyst, the information hadn’t gone public. So we got a little more commentary, some of which I can share, some of which I cannot. But really there’s a couple of different things that they announced that were really interesting.
One is that they have announced some innovations to its customer service and workforce management solutions that incorporate these autonomous AI agents that really the underlying technology comes from an acquisition they made earlier in the year, from Ultimate. This is designed to really enhance the level of autonomy and automations that can be implemented within a customer support or service operation, really allowing essentially a complete self-service or a completely automated experience.
So that means that you don’t need to have agents involved with any kind of low level interactions. So that’s one of the big announcements there. I’ll go into it a little bit more, but one of the things I wanted to call out is Zendesk CEO Tom Eggemeier has taken the view that within the next five years or so, autonomous agents are actually going to be able to handle about 80% of all support requests. This is a pretty lofty goal because if you think about an actual autonomous experience, the system really needs to be able to quickly measure intent to find out what the end customer wants to do, and it needs to be able to figure that out very quickly, and then of course have all of the engagement tools and systems around to really fulfill that request so that the call doesn’t get kicked back to a human agent.
Now, let’s talk a little bit about some of these autonomous agents. They are essentially AI driven, highly sophisticated agents that can be integrated with any kind of knowledge base within the organization and allows these organizations to fully customize them to handle more intricate use cases. So essentially if you think about an autonomous agent, you want it to almost have this deeper level of understanding, again, of what is it the customer wants to do, what are the resources that are needed to get the answer or to address the solution, and then essentially they need to be powerful enough to follow workflows to get that task achieved.
So if you think about it, the goal here is really making sure that the organization or actually the provider has done all the work at the beginning, essentially making sure that these agents are trained on the types of functions and experiences that are likely to occur in a support role. So that means if you look at what Zendesk has done with its own agents and then of course incorporating the agents from Ultimate, they’ve actually gone through and had millions of interactions where it really can help understand, so the agents understand when someone wants to do something, how to quickly respond, and of course the most important thing is being able to ground those agents through any number of techniques, but they were talking specifically about retrieval augmented generation, which makes sure that the algorithm is only going to a certain pre-vetted source of information to provide an answer.
So for example, if you’re thinking about a customer who comes in has a question about say their checking account in a banking scenario. They would make sure that the only information used to answer that would be vetted information internal to the company where they would have access to the customer’s own data. But of course, all of the rules and internal knowledge that is required to handle a transaction, but it would not let that bot act upon any kind of public information because then you wind up having that solution or that bot hallucinate and perhaps return information that’s not correct.
Now, another thing that they mentioned here was their agent co-pilot, which is really an AI-based tool that’s targeted at the agent persona. Essentially, this is for customer service agents or customer support agents really to let them have information that is required served up to them by an autonomous agent or by a co-pilot tool. So again, if you’re working with a customer and the system detects that a customer is looking for a specific or is talking about a specific topic, it will understand what’s going on and then serve up potential responses or actions that the agent can either choose to enact or can choose to modify or just ignore entirely. The goal here again, is to provide agents tools to help them do their jobs more quickly, give them the relevant information within context, and then of course hopefully serve those customers very quickly, but also serving them well.
Zendesk also made an announcement around its workforce engagement management offering. These tools are really around helping to improve the performance of human agents as well as bots. So they have things like predictive workforce tools which integrate these forecasting algorithms to really help match staffing with demand, and that can be particularly important for businesses that are seasonal in nature or if there’s a business where external events would affect demand for their services or products. I can think of if you are a home improvement retailer, you might see a surge in demand in advance of a predicted storm or perhaps right after the storm as well.
Now along with this is a new service called Voice QA. This technology is designed to analyze call transcripts, and basically it’ll analyze the call transcripts, go through and score the interactions and identify when there’s anomalies or outliers that really require some sort of coaching of the agent and review. Now, this is available for both human agents but also for AI agents. What this does is it enables sort of a continuous learning scenario for these agents so they get better over time, particularly as this review process starts going in and identifying, “Okay, here’s where the agent or the AI agent didn’t do exactly what we wanted it to do”, and you’re able to tune their performance over time.
Again, this is a real improvement over the previous way that a lot of organizations did QA, where I think it was something like maybe less than 2% of all interactions are ever really reviewed through manual processes because there’s just not enough worker power and time to do it, and it can be very expensive. But by using AI to do that, to examine transcripts, that’s a way to really improve customer experience and really help out both live agents, human agents, as well as the bots that are increasingly being used to provide frontline customer support and service.
I want to also call out something else that was interesting is Zendesk did a good job of really talking about how it’s trying to implement guardrails around its AI offering. If you think about all of the news around public facing generative AI bots being used in customer support scenarios, and we’ve heard about obviously the Air Canada thing where these things, they haven’t done what they’re supposed to do. It’s really encouraging to hear Zendesk talk about how it is really taking that seriously and making sure that there are robust controls so that these customer-facing bots can be relied upon not to hallucinate, not to essentially put out false information that can be very detrimental to an organization and its customers.
So finally, before I wrap up with Zendesk, I want to just talk a little bit about what are they trying to do? Well, they’re really trying to focus in on a specific market segment, and that would be SMBs and mid-market companies who are really looking to incorporate more automation, more AI in their processes, but honestly, they don’t have a gigantic IT step or a large budget to really handle these complex integrations incorporating different point solutions. Their job, what they were trying to do is obviously get folks onto the Zendesk platform where they hopefully are using that to handle a number of customer experience and customer service functions.
And in the case that a company wants to bring a different tool to handle a specific function, perhaps they like their CRM platform or something like that, they’ve been working to really build out integrations with a number of different tools and a number of different other platforms to make it easy and allow Zendesk’s technology to work across these different parts of the technology stack. The big thing though that I would say is they did talk a lot about these new features, these new enhancements, and while that’s great, I still think that Zendesk like other vendors out there, they really need to lean into specific messaging where it comes down to things like time to value.
Being able to quickly deliver a solution to customers, because ultimately feature sets are really one week, one company may have a bot that outperforms another. In the end, it’s all going to become table stakes, and really what we’re going to be looking for are organizations that are able to quickly stand up a solution and do it in a friction-free way so that when you make that investment, you’re not waiting for 2, 3, 4, or however many quarters, to see a return.
So I want to now switch gears to talk about Avaya. They had an analyst event. In fact, it’s the first analyst event they’ve had in quite some time, certainly since they’ve emerged from the bankruptcy event that they had a couple of years ago. Really what I want to talk about here is they were really focused on kind of refocusing and resetting around their messaging and the way that they are delivering their services to the market. Clearly, they want to focus not on new feature sets. Of course they have a platform which incorporates AI and all of that kind of stuff, but they’re really focusing on I think this promise of innovation without disruption. What does this mean?
Well, if you think about their core customer base, which is obviously they have a lot of different customers, many sizes, but they’re really trying to focus in on that enterprise customer who has really made a lot of legacy investments into technology that sits, quite honestly, on premises. Their belief is that even though everyone’s talked about, “Hey, we should move to the cloud, move to the cloud, you can do all some sorts of neat stuff with AI and everything else.” Really, it is unrealistic to expect that companies of a certain size and scale who have made these large investments into on-premise contact center technology are going to simply rip it all out and move to another solution.
So their focus is on basically looking at a way to provide this new functionality on top of or adjacent to in a hybrid environment, an on-prem solution with the option of eventually moving more functions and more technology to the cloud over time if that’s what their customer wants. This is pretty interesting because if you think of the messaging that’s been going around about the cloud, there are vendors that are heavily, heavily pushing organizations to the cloud saying that the only way you can really incorporate new technology is to have an entire lift and shift strategy. Avaya says, “No, that is not what they’re going to need to do.” So they have actually announced an enhanced and redefined platform called the Avaya Experience Platform.
So basically the design here is to let customers choose their own solution deployment path, whether it’s on-prem, in a private cloud, public cloud, or most likely in some sort of a hybrid deployment combination. This is interesting because they’re also talking about being sort of the glue or the orchestration partner that will allow organizations to pull other solutions that they might prefer for say collaboration or for messaging or what have you, and really saying, “Look, we want to be the organization or the vendor that really lets you focus on your business goals and then we’ll do everything we can to support that, your choice in terms of what other solutions you want to use.”
So it’s a really interesting message. I wish I could talk a little more about them and I will be able to over time, but I know that a lot of the information presented was under NDA and I don’t want to break any of that, but I’ll say though it was interesting to hear their strategy about how they were looking at the market, how they were looking at what they’ve done in the past hadn’t worked, and what their plan is for the future. I think the key takeaway for me is obviously they need to lean in and really have the customers that have stuck with them also talking about why they’re staying with Avaya, why they still have the ability to deliver on the promises that they’re making in terms of functionality, in terms of delivering value. So it’ll be interesting to see over the next coming months and years how they do that, but it was certainly an interesting event and one where I think they’re really, hopefully, are setting themselves up for success moving forward.
So with that, I’m going to wrap up the main part of the show and move on to our rant or rave segment here. Today, I have a rant, and this is not an enterprise applications rant, but it is a customer experience rant, and of course we’re going to go back to our favorite punching bag, and that of course is the airline industry. Now, I think there’s no secret that there are disruptions that are beyond the airlines’ controls, weather events in particular or demand issues, that sort of thing. But I want to call out something. I was on a flight, it was actually about a week ago where there was a problem. They had started to board us and then at a certain point they stopped boarding and made an announcement over the PA saying they’re having a problem with a system in the cockpit, and they made everyone get off and they were not able to for the next hour be able to provide us with any information.
Finally, I guess they must have solved the problem, but the announcement came over the PA where the gate agent basically said, “Now we’re ready to board, but please everybody, we need the board very, very quickly because if we don’t move soon, the crew is going to time out. They’re going to go past their mandatory number of hours that they can work within a given period.” I think I understand why they may want to be transparent about that, but I think the problem of course is there is a… It’s almost like it’s putting their issue on the burden of the customer saying, “You need to hurry up, rush, get on board, or of course you’re going to wind up in an even bigger problem.”
What I think needs to happen, and I know it’s a challenge for the industry, and this is why hopefully many airliners out there, I know they’re trying to do things like incorporate more advanced workforce management solutions to better balance demand, manage contingencies, all of that kind of stuff with crews, because when you have a solution like that, if they weren’t able to board the airplane on time and we weren’t able to get off the ground, it would’ve created yet another customer experience headache, having to rebook people on different flights and then you have to bring in a whole nother crew.
I think that there’s a real opportunity for the airline industry to look at how can they invest in software that really helps them account for not only typical demand, but things like this. It’s inevitable, as we’ve seen, unfortunately, things go wrong with planes, and thankfully most of the time they want to take the very sort of conservative approach and not just say, “Oh, we’ll worry about it later.” They want to actually address the problem and make sure it’s fixed. I just think that there has to be, when we think about customer experience, there needs to be something done on the back end to cover it through those contingencies without publicly putting it on to their customers saying, “You need to hurry up and quickly board and get your stuff up into the overhead bins really quickly, and if you don’t do it, then we’re going to be delayed.”
And I think that’s my rant for the week. Again, it’s more of a messaging issue on one hand, but it’s also sort of a back end software issue on the other in terms of figuring out, understanding you’re going to have disruptions. How do you do that? How do you utilize technology to plan for it and then quickly set up a contingency plan that can be enacted without much of a hassle? All right, that’s all the time I have today, so I want to thank you all for joining me here on this relatively short episode of Enterprising Insights, but I will be back again next week with another episode focused on the happenings within the enterprise application space. So thanks everyone for tuning in and be sure to subscribe, rate and review this podcast on your preferred platform. Thank you very much, 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.
