 From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE and ETR. This is Breaking Analysis with Dave Vellante. The viral awareness and adoption of foundation models like ChatGPT have created both an opportunity and a threat to automation platforms generally, and RPA point tools specifically. You know, on the one hand, large language models can reduce complexity and accelerate the adoption of enterprise automation platforms. Now the flip side is software robots are designed to improve human productivity through intelligent automation and GPT models could cannibalize some, if not many, use cases initially targeted by RPA vendors. This reality is causing customers to rethink their automation strategies and vendors to rapidly evolve their messaging to position foundation models as an accelerant to their platforms. Hello and welcome to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, we provide you with a perspective on how foundation models could impact automation platforms. We'll review ETR data that quantifies the ascendancy of open AI. We'll also show survey data that measures the overlap between ML AI systems and automation platforms. And then we'll review the recent quarterly performance of UI path and share how we think the company must position itself with respect to the onslaught of noise and potential disruption from GPT models. Let's start with a look at how open AI has literally burst into the mind share of enterprise buyers. Here's a graph from ETR's emerging technology survey, ETS is the acronym, which shows net sentiment on the vertical axis. That's a measure of intent to engage with a platform. And on the horizontal axis is mind share. In just a few short months, open AI has blown past all emerging tech companies that ETR attracts, including the well-established Databricks. Now, of course, we're talking about a predominantly free tool versus companies that are transacting business, major business in the example of Databricks in the enterprise. But there's no denying that chat GPT has created a Netscape moment in the technology business. And here's how buyers are looking at this emerging mega trend. These are direct quotes from CIOs at a recent ETR round table, and they summarize the opinions of a number of users that we've talked to. Quote, when you look at RPA, and you look at ML and AI, essentially it's trying to solve the same business use case, automation, or removal of excess resources, whether they be human or otherwise. The other quote, what open AI has shown with chat GPT is that you can get rid of a lot of overhead, complicated artifact building around typical RPA. I see this as a very interesting value proposition to be able to supplant some of these workbenches in a classical RPA that take quite a while to master and quite a while to get any value past the regular use cases. Now, according to ETR's Eric Bradley, there's a very high overlap between people citing evaluation plans for open AI and the RPA customers in the latest technology spending intention survey, TCIS, especially for UiPath. This data could mean, he says, that the two technologies are simply aligned together or the more ominous view that RPA clients are evaluating open AI as a potential supplement or replacement. Microsoft Power Automate also has a very high overlap but Microsoft's massive investments in open AI and integration into and across its platform will clearly be a tailwind in this regard for Microsoft. Now, later in this program, we'll project how we see this impacting UiPath specifically and RPA automation vendors generally and what they are doing about it, what we think they should do about it as well. Well, we do that. Let's step back a bit and take a broader look at the market in general. Here's a chart from the Q1 ETR survey of 1500 IT decision makers and we're isolating on the big four areas, the sectors with the greatest relative spending velocity that's MLAI, RPA, containers and cloud. The vertical axis is net score or spending velocity and the horizontal axis measures the pervasiveness in the data set. Now, we only show outsourced IT in there because it's the very lowest on the momentum scale and it makes it easier to sort of isolate the big four. That red line at 40% indicates highly elevated spending momentum. And as you can see, despite the tech slowdown, it's a little hard to see, but MLAI and RPA, they rebounded in the Q1 survey. You can see they tick up a little bit. This is perhaps the dual combination of interest in AI from the buzz around chat GPT and a rejuvenated UI path, which is showing up with the data as shown in its recent quarterly performance. So I want to look at that very briefly here. UI path significantly beat and raised this past quarter. Here's a table that shows key metrics from the company's Q4 actuals relative to consensus. Not shown here is net new ARR, which came in above $90 million, a very strong number and much larger than was expected. A very important indicator, probably the most important. But you can see the company beat on virtually every metric and guided hire for fiscal year 24. A particular note is the operating profit, which shows the co-CEO structure seems to be working. Rob Enslin came into UI path from Google. We reported on this. He's partnered up with Daniel Deniz. Enslin is the go-to-market pro and Deniz is the engineer. And now Deniz is spending more time in R&D while Enslin shores up the operations along with CFO Ashim Gupta, who appears to be doing a great job of communicating to the street. You can see the uptick in the stock chart on the right side of this graphic, which shows the stock reacted very positively to the earnings news at $9.4 billion valuation. However, this is still well below and well off. It's $40 billion market cap high, $40 billion plus market cap high. So the company still has a lot of work to do. And look, while one quarter doesn't make a trend, the financial discipline, the focus, the product investments and a much improved communications are very encouraging. That said, foundation models like chatGPT loom large. And that's one reason UI path co-CEO Daniel Deniz had this to say on the earnings call, quote, I would like to emphasize the use of GPT-3 in large language models in our upcoming clipboard AI. And this is going to be a tool that caters to all business users. It will basically allow everyone to transfer information from any source, every document to any application. He continued, we are using a huge combination of our own AI models, GPT-3, Google, Amazon, everything that is combined there. Again, I'm extremely bullish on the prospects of UI path with adopting generative AI technologies. So Deniz is underscoring that UI path has been on the AI curve for quite some time and sees these developments as a positive. Even the street certainly reacted accordingly to that narrative. So let's test this a bit and dig deeper. This chart takes the ETR survey data and isolates it on those customers that are spending on ML AI and RPA. So this cut takes the 1500 and down to 477 and we filter the data only on those companies with more than 50 responses within this cut. So net score or spend momentum on the vertical axis and overlap or penetration into the dataset on the horizontal axis. The takeaway is that these are the companies at the intersection of ML AI and automation and we use RPA as a proxy of course for automation. Microsoft, AWS and Google as you know are the leaders in AI as is Databricks. IBM, Watson and Oracle are the legacy whales and they're maintaining their relevance by driving business value for their customers and integrating AI into their largest states. What's most striking is Power Automate from Microsoft and Microsoft's AI offerings both up and to the right. And this points to the future of how Microsoft is infusing AI into its entire portfolio and that includes RPA offering Power Automate. That's no exception and it's well positioned to capture wallet share in our view. That said, the number one pure play UI path is also very well positioned on this chart. Significantly above the red 40% elevated dotted line. Automation anywhere, another pure play is more than respectable but it doesn't have nearly the market presence or the spending momentum of UI path. The bottom line is the two pure plays are up against some giants and must out innovate them. Well, at the same time partnering with AWS, Google because they're the AI leaders and potentially Microsoft. Dinesh didn't mention Microsoft specifically in his narrative because potentially it's such a competitor. So we'll see if that was intentional or just an omission. Each of these companies is going to have to build on their current momentum to continue to extend their platforms and move faster than the big competitors. I'm talking about the pure place here. And this is not a task for the faint of heart. It's going to take a lot of effort, a lot of innovation, strong balance sheets and great leadership. Let's take another cut the data and look at how customers are spending on ML AI and how those customers are adopting RPA. So same dimensions on this X, Y, net score and the vertical and presence in the data set in the horizontal. This chart again, it shows the spending on RPA platforms within those customers that are spending on AI. So the end gets reduced to 318. We're slicing the salami a bit more here, but it's clear that power automate and UI path are separating from the pack. That said, a number of the others appear to be well positioned. AA, PEGA, Blue Prism, et cetera. And it's imperative that all software companies and especially automation firms get ahead of the curve when it comes to AI generally and GPT models specifically. Okay, so now that we've taken a look at the overlap and intersection of AI and RPA, let's dig into how customers think they may use foundation models and how that might impact RPA in automation platforms. This chart shows results of ETR asking customers if they are evaluating GPT models and if so, for which use cases. 56% of customers said they're not evaluating which is a bit surprising. I say they better start evaluating. The heads are in the sand, but the ones evaluating, you can see customer chat is the main use case than text and data summarization, generating code and documentation, writing sales and marketing copy. Chat GPT is awesome for that. And finally, image editing and design as the major use cases. So at the surface, one could conclude that RPA and automation platforms will absolutely benefit from GPT models that these use cases are largely complimentary. You know, for example, a foundation model could write or accelerate the development of automations that could direct software bots. Sometimes that's really complicated, but at the same time, there's a looming overlap between the capabilities of large language models and some of the early RPA use cases. And this overlap is likely going to increase over time. So how are automation platforms responding? We'll take a look at this graphic from UI Pass Investor Day last year. In the blue is UI Passive Value Play, up the ROI, you know, there's complexity in the enterprise. The orange is where Power Automate is traditionally focused. And by the way, UI Pass initial entry into the market was there as well. That's sort of personal automation. But through acquisition and organic development, UI Path has expanded into process mining, task mining, API integration, and conversational intelligence and other areas. As well, the company has moved from an exclusively on-prem model into a cloud native platform. Cloud now comprises roughly a third of UI Pass ARR. The point is UI Path is repositioning itself. It launched a new branding this month with a new tagline, quote, the foundation for innovation. And their go-to-market focus is targeting the C-suite where they can sell the value and become more strategic. And to achieve this, the platform of UI Path specifically, but virtually all end-to-end automation platforms has to evolve. As an example, early UI Path implementations were about identifying mundane user tasks, building bots, or, you know, a single bot sometimes, and then running those automations and then managing them. It was pretty straightforward. This chart shows what UI Path's platform looks like today. As you can see, it's evolved quite dramatically in a large part through acquisitions and integrations, discovery through process mining, task mining, communications mining, ideations, and so forth are supported by a spectrum of automation capabilities with a foundation of enterprise class capabilities that the company hopes will differentiate it from not only automation anywhere, but Microsoft, which you can never ignore. And so where do GPT models fit in this picture? And the answer is throughout. As Dinez said, they have a lot of experience with AI and generative models, but there is overlap. And this is going to continue to be a fast-moving market. It's likely that UI Path is going to keep moving up market and bring its large cohorts along the journey. And this is again a two-edge sword because UI Path has numerous customers that are smaller with lower average contract values. The opportunity is to bring them deeper into the automation, bring them into the automation nirvana, while at the same time differentiating from the good enough crowd, which increasingly is going to include GPT models. So some quick final thoughts here. This market remains a tale of two cities. Tough times mean companies look for ways to cut costs and automation is a way to do that, but it does require companies to spend money to make money. GPT models are catalyzing new thought and both buyers and sellers are pivoting to turn foundation models into opportunities. Initial use cases for GPT models are interesting, but not a direct replacement by any means for enterprise automation platforms. But low-end automations are at risk and there's no question that there is a van here. The bottom line is pure plays like UI Path and automation anywhere must continue to rapidly advance their platforms to differentiate themselves. As we said early on in our RPA coverage years ago, the number one player is likely going to do really well. Number two, they'll do okay and the rest of the pack is going to struggle somewhat. Nonetheless, all vendors in our view have to leverage GPT models to simplify and accelerate adoption and buyers have to step back and do some experimentation to see how they can deploy these new innovations and add value to their businesses. We suspect at the end of the day that most companies will buy AI, not build it themselves meaning they'll consume it as part of their existing SaaS platforms. And we think that bodes well for the UI Paths of the world because they can through automation they can connect fragmented SaaS platforms they can provide solutions that automate, translate, optimize, accelerate and comply with all that data that's floating around the organization. All right, that's it for today. I want to thank Eric Bradley for his input to today's episode. Alex Meyerson, thank you. He's on production and manages the podcast. Ken Schiffman as well out of our East Coast studios. Kristen Martin and Cheryl Knight they helped get the word out on social media and in our newsletters and Rob Hoef is our editor in chief over at siliconangle.com. Does some great editing for us, thank you. Remember all these episodes are available as podcasts wherever you listen just search, breaking analysis podcast. I publish each week on wikibon.com and siliconangle.com. You can go to thecube.net for all the video action and you can email me at davidotvalante at siliconangle.com or feel free to DM me at dvalante or comment on our LinkedIn posts. Please do check out etr.ai for the best survey data in the enterprise tech business. This is Dave Vellante for theCUBE insights powered by ETR. Thanks for watching and we'll see you next time on breaking analysis.