C3.ai, Inc. (NYSE:AI) Q4 2023 Earnings Call Transcript May 31, 2023

C3.ai, Inc. beats earnings expectations. Reported EPS is $-0.13, expectations were $-0.17.

Operator: Good day and welcome to the C3 AI’s Earnings Call for the Fourth Quarter Fiscal Year 2023, which ended on April 30 2023. My name is Amit Berry and I lead Investor Relations at C3.ai. With me on the call today is Tom Siebel, Chairman and Chief Executive Officer and Juho Parkkinen, Chief Financial Officer. After market close today, we issued a press release with details regarding our fourth quarter results, as well as the supplemental of our results, both of which can be accessed through the Investor Relations section of our website at ir.c3.ai. This call is being webcast, and a replay will be available on our IR website following the conclusion of the call. During today’s call, we will make statements related to our business that may be considered forward-looking under federal securities laws.

These statements reflect our views only as of today and should not be considered representative of our views as of any subsequent date. We disclaim any obligation to update any forward-looking statements or outlook. These statements are subject to a variety of risks and uncertainties that could cause actual results to differ materially from expectations. For a further discussion of the material risks and other important factors that could affect our actual results, please refer to our filings with the SEC. All figures will be discussed on a non-GAAP basis unless otherwise noted. And during the course of today’s call, we will refer to certain non-GAAP financial measures, a reconciliation of GAAP to non-GAAP measures is included in our press release.

Finally, at times in our prepared remarks in response to your questions, we may discuss the metrics that are incremental to our usual presentation to give greater insight into the dynamics of our business or our quarterly results. Please be advised that we may or may not continue to provide this additional detail in the future. And with that, let me turn the call over to Tom.

Tom Siebel: Thank you, Amit. Good afternoon, everyone, and thank you for joining our call today. We finished the fourth quarter strong and the coming year looks stronger. I believe that is generally agreed that the overall market for enterprise AI now appears substantially larger and is growing at a much greater rate than most analysts and expert predicted. We have been working since 2009 to develop product leadership and establish thought leadership in enterprise AI, assisting private and public sector enterprises to apply AI to improve operational processes. C3.ai has been at the vanguard of enterprise AI of the enterprise AI market for over a decade. As the market has developed from its routine IoT to supervised learning, unsupervised learning, NLP, deep learning, reinforcement learning, and now generative AI.

In the past 14-years, we have developed and enhanced the C3.ai platform and now offer over 40 enterprise AI applications developed with that platform that allow our customers to rapidly take advantage of AI to improve their business processes. We have been communicating for over a decade that we believe that the market for enterprise AI solutions would be quite large. And now as we enter the summer of 2023 has become a dominant theme in technology discussions, government — or AI has become a dominant theme in technology discussions, government discussions, media reports, defense and intelligence imperatives and government and business imperatives. I did not believe that it’s an overstatement to say that there is no technology leader, no business leader and no government leader, who is not thinking about AI daily.

AI chipmakers like NVIDIA are accelerating production to try to keep up with the very real demand that’s out there. And all of this is being accelerated by the advent of generative AI. The interest in AI and in applying AI to business and government processes has never been greater. Business inquiries are increasing, the opportunity pipeline is growing, demand is increasing. And C3.ai is well positioned to serve that increasing demand with our tried, tested and proven AI platform, our applications, our global footprint, and our large global ecosystem. The world is, in many ways, now coming to us. The interest in applying AI to business processes is substantially greater than we have ever seen. In the fourth quarter, we increased our customer base, expanded our work with existing clients and saw especially strong growth in our federal business.

In the fourth quarter, our total revenue was $72.4 million, our free cash flow was $16.3 million and we ended the quarter with over $812 million in cash and cash equivalents. Importantly, we have a well-defined plan to be sustainably cash positive and non-GAAP profitable by the end of this fiscal year. For fiscal year 2024, I’m sorry, for the fiscal year 2023, okay, total revenue was $266.8 million, an increase of 5.6% over fiscal year ’22, okay. Subscription revenue was $230.4 million, representing an 11.4% increase over the prior year. Let’s talk a little bit about the AI applications market. Now as the enterprise AI market has developed. It appears that the bulk of the demand is increasingly for turnkey enterprise AI applications rather than for development tools.

This thesis is supported by an evaluation of our bookings for the past fiscal year that indicates that 83% of our bookings were driven by application sales, 17% of our bookings were driven by the sales of the C3.ai platform. Importantly, we are seeing increasing diversity in the industries we serve. For fiscal year ‘23, an analysis of our bookings includes: oil and gas was 34%; federal, defense, aerospace was 29%, high-tech was 13%; energy and utilities 11%; manufacturing 4%; food processing 2%; chemicals 2%; life sciences 1.5% and other industries made up the remaining 3%. An important leading indicator of our increasing industry diversity is evidenced by the trial and pilot agreements closed in Q4. Federal, defense and aerospace made up almost 37%; manufacturing comprised approximately 16%; and high-tech made up more than 10%.

Oil and gas also made up more than 10%. When we look at ag, state, local, chemicals, energy, and financial services each made up approximately 5% of our bookings. As a result of the increased demand for enterprise AI, helped by our transition to consumption based pricing, we are seeing a substantial increase in opportunities and shorter sales cycles. In Q4, we closed 43 agreements, including 19 pilots that were initiated in the quarter. The number of qualified enterprise opportunities targeted for closure within 12-months in our sales pipeline has increased by more than 100% in the past year. During fiscal year ‘23, we closed 126 agreements, up from 83 in the prior year. The average sales cycle for new and expansion deals was 3.7 months down from five months in Q4 of the previous year.

An examination of the composition of our pilot account profile suggests there is significant opportunity for growth as these accounts convert to consumption pricing. Of the 19 pilot accounts signed in Q4, seven were accounts greater than $100 billion in revenue, seven were accounts between $10 billion and $100 billion in revenue, four were accounts between $1 billion and $10 billion and one was an account less than $100 million in annual revenue. In fiscal year ‘23, we expanded our application footprint at a number of our customers, including Shell, Koch Industries, the United States Air Force Rapid Sustainment Office, PwC, Ball, ExxonMobil, Con Edison, The Defense Counterintelligence and Security Agency, Baker Hughes, the New York Power Authority, Duke Energy, ATB in Canada, Defense Innovation Unit, Roche, Cargill, and Engie.

We also established many new relationships during the year, including the Department of Defense Common DoD; AI office; Daly City,; California; DOW; ExxonMobil; Flex; Hexagon; Nucor, Owens-Illinois; Pantaleon; Riverside County, California; Stark County, Ohio; Telus; Department of Defense SOCOM; Department of Defense, TRANSCOM; and ESAB. Many of these also expanded their AI engagements with us in the course of the year. Let’s address the C3.ai partner network. The C3.ai partner ecosystem is increasingly effective at opening new doors with our partners, who are able to provide prospects, the assurance of success with — and the highest quality service. In fiscal year ‘23, we closed 71 agreements with and through our partner network, including Google Cloud, AWS, Microsoft, Baker Hughes, and Booz Allen.

C3.ai increased its qualified pipeline with AWS by over 24% in the fourth quarter, with particular focus on state and local government. With Google Cloud, our joint qualified 12-month opportunity pipeline grew from 25 opportunities at the end of fiscal year ‘22 to 140 opportunities at the end of fiscal year ‘23, a 460% increase. And importantly, we closed 10 new oil and gas accounts in the year with our strategic partner Baker Hughes, with accounts including ExxonMobil, ADNOC, ENI, and others. In Q4 we released the C3 Generative AI solution to the market. Our generative AI solution leverages the capabilities of the C3.ai platform and is distinguished from other GPT/LLM solutions in the market in several ways. Number one, it allows enterprises to access all their enterprise data and open source data ERP, CRM, SCADA, text, PDFs, Excel, PowerPoint, sensor data, .

Secondly, importantly, it provides traceable deterministic consistent answers. Thirdly, it enforces the corporate information access controls and security protocols that are currently in place. Fourthly., it has no risk of IP or data exfiltration caused by the large language model. And importantly, it is hallucination free. So if the system doesn’t know an answer, it doesn’t fabricate it, which is clearly unacceptable for any commercial or serious government application. After releasing the product in March, we rapidly closed three generative AI applications in the quarter with large enterprises, including Georgia Pacific, Flint Hills Resources and the U.S. Department of Defense Missile Defense Agency. We expect these applications to be live during this current quarter.

We are currently working up quite substantial pipeline as additional C3Candida of AI Opportunities With Large Corporations. The C3 generative AI opportunities with large corporations. The C3.ai generative application is now available — today available on both the AWS marketplace and the Google Cloud marketplace. It is difficult to estimate size of the addressable market for these generative AI solutions, but it appears to be extraordinarily large. We saw a lot of momentum last year and in fourth quarter with the our U.S. federal business. The U.S. federal sector represented 29% of our bookings in fiscal year ’23 and it continues to show significant strength. Our predictive maintenance solution, predictive analytics and decision assistant, also known as PANDA, has been in production used for several years at the United States Air Force Rapid Sustainment Office.

And last quarter, it was selected as the system of record for all pretty good maintenance for virtually all United States Air Force assets, okay? This important designation expands our opportunity really substantially in the U.S. Air Force and other services. Let’s talk about guidance. C3.ai has a consistent and solid track record of meeting or exceeding guidance as we have done in every quarter since we’ve been public, okay? And we are at this time, we are not inclined to pod on the table regarding guidance. In general, we feel comfortable with the expectations that the sell side analysts have set for the coming year and we are not inclined to change those expectations at this time. For fiscal for Q1 fiscal year ‘24, we see revenue in the range of $70 million to $72.5 million.

For the full-year of fiscal year 2024, we expect revenue to be in the range between $295 million and $320 million. As it relates to non-GAAP loss from operations, we expect to fall between $25 million, $30 million in Q1 and $50 million to $70 million for the year. As we begin fiscal year ‘24, C3.ai has never been better positioned. The addressable market is large and expanding, the overall business environment for enterprise AI is strong, and C3.ai is front and center in the minds of CEOs and government leaders. Our balance sheet is strong, and with over $812 million in cash and cash equivalents, we are in a great position to expand market share. As the dynamics of the enterprise AI market are developing so rapidly, we thought it appropriate to host a mid-quarter Investor Day in New York City on June 27th.

We will provide, at that time, we will provide C3.ai investors a company update, additional information about our product roadmap, product demonstrations, direct access to the C3.ai executive team, updates on our partner ecosystem, C3.ai, C3 generative AI demonstrations, and additional company developing news, okay. We hope you can attend either in-person or online, and that Investor Day event would be available to view online live, okay, for all investors via webcast. I will now turn this call over to my colleague, Juho Parkkinen, Chief Financial Officer for additional details regarding our financial results. Juho?

Juho Parkkinen: Thank you, Tom. I will now provide a recap of our financial results, add some color to the drivers of our financials, provide more detail on our first quarter and full-year fiscal 2024 guidance, and I will conclude with some additional color related to the consumption based revenue model we introduced three quarters ago. All figures will be discussed on a non-GAAP basis on this otherwise noted. Overall, the business activity is higher than we have ever seen. Our sales reps are more engaged there are more opportunities they’re working on and they’re more interest from our prospects. During Q4, our ability to close agreements was more consistent throughout the quarter, compared to prior quarters this fiscal year.

We ended the fourth quarter with a total revenue of $72.4 million of which subscription revenue was 78.5%. As we discussed last quarter, we expected professional services would be within our historical range 10% to 20% with our actual professional services coming in at 21.5% of the mix. Gross profit for the fourth quarter was $53.9 million and our gross margin was 74.4%. We generated $27.1 million in positive operating cash flow and $16.3 million in free cash flow for the quarter. As mentioned during the prior updates, we have a short-term pressure on our gross margins, due to a higher mix of pilots, which carry a higher cost of revenue than production deployment. Operating loss of $23.5 million was improved due to more rigorous expense management.

As a reminder though, the fourth quarter is when we host our C3.ai transform customer events, as such, our marketing expenses ramped up to support the successful execution of that event. Operating loss margin was 32.5% in Q4, where the sequential increase was driven by our annual conference. With the full year fiscal 2023 our revenue was $266.8 million, an increase of 5.6% from fiscal 2022. Non-GAAP loss from operations was $68.1 million and free cash flow was negative $187 million. Our gross margin for the year was 77%, our subscription revenue was 86% of total revenue, compared to 82% in fiscal ‘22. We ended fiscal ‘23 with $812.4 million in cash and investments. At the end of Q4, our accounts receivable including unbilled receivables was $134.6 million.

Unbilled receivables at quarter end was $77.6 million, inclusive of $70.7 million related to Baker Hughes. During the quarter, we collected from Baker Hughes, nearly $35 million. The general health of our accounts receivable is excellent, 76% of our receivables were current or less than 30-days past due. For the entirety of our FY ‘23, our bad debt expense was approximately $300,000. Now turning to RPO and bookings. As consumption based go-to-market model continues to pick up, RPO is less important indicator of future performance. We reported GAAP RPO of $381.4 million, down 20% from last year, which is expected as a result of the transition to consumption based pricing. Trade GAAP RPO of $186.3 million, is up 9.8% from last year end and up 5.7% on a sequential basis.

We continue to see positive trends in pilot bookings diversity as we have sold pilots to a broad range of nine different industries during the quarter. Regarding our outlook for fiscal ‘24, regarding Q1 revenue to range between $70 million to $72.5 million. For the full-year 2024, we expect revenue to range between $295 million and $320 million. As it relates to the full-year, we finished the third quarter of our transition under the consumption pricing model, as a model, we expect flatness and somewhat of a decline in revenue during the transition with an acceleration as consumption starts to have meaningful portion of our in-quarter revenue. As such, we expect the second-half of FY ’24 to have higher growth rates on a sequential basis than the first-half.

We expect our non-GAAP loss from operations to range between $25 million and $30 million for Q1 and for full fiscal ‘24 we expect non-GAAP loss from operations between $50 million and $75 million. As a reminder, we expect to be non-GAAP profitable for Q4 ’24 and beyond, and as it relates to full fiscal ‘24, we are guiding to a range in operating loss due to the potential investments we made through — for C3 generative AI applications. We expect our cash and investments to be at its lowest at around $700 million during fiscal ‘24. Turning to customer metrics. Historically, we have provided a quarterly customer count estimate as a proxy for the adoption of our products and solutions. However, due to the complexity of our contractual and pricing structures and the involvement of resellers, we believe comparing customer counts from quarter-to-quarter based on our current methodology, we — apologies.

Customer comps from quarter-to-quarter based on our current methodology does not fully convey the acceptance and adoption of our products and solutions. To help address this, we retained an external Big Four consulting firm to update our current customer count methodology consistent with best practices to be consistent, systematic and auditable. As a result of that review, and adoption of those recommendations, we believe a metric that demonstrates contracted use cases that our customers are using our solutions to solve would provide a more meaningful understanding of the product reduction. This is defined as customer engagement. The customer engagement increased from 247 to 287, comparing Q3 ‘23 to Q4 ‘23. Our traditional customer count metric went from 236 to 244 for the same period.

There will be additional detail included in the supplement, which is available on our website. We are on track with our planned for profitability for Q4 ‘24 and expect to have cash positive this quarter starting Q4 ‘24 on a consistent go forward basis. The entire executive team is managing the business to have detailed budgets on our plan for profitability. We are expecting to invest aggressively to generative AI initiatives during the first-half of the year, which is reflected in the operating income guidance. As it relates to the model assumptions that we provided three quarters ago for our consumption based pricing, our preliminary analysis of the actual results suggest we are on that model. Overall, we’re very excited about the business momentum as we start FY ‘24.

As a go forward KPI for the investing community to assess our performance, we believe good KPIs to focus are the number of pilots started during the quarter the conversion of those pilots to production and finally, the actual vCPU consumption fees generated. With that, I would like to open this up for questions. Operator?

Q&A Session

Operator: Thank you. Thank you. Our first question comes from the line of Kingsley Crane of Canaccord. Your line is open.

Kingsley Crane: Hi, thanks for taking the question. So Tom, you said that sales cycles were down to 3.7 months from five months last year. Why do you think that is? Is this entirely due to the consumption model? How much of this is due to general excitement around the potential in the space? And even potentially increased sales force productivity?

Tom Siebel: No, I think — thanks Kingsley. I think it’s all of that. I mean, clearly, AI is on everybody’s mind, the consumption based pricing model that we have makes it much easier to adopt our technology. In the old days, one and two years ago to do business with us was $5 million, $10 million, $20 million, $50 million to open the door. And now the transaction is pretty much, you know, we’ll bring the application live in six months or $0.5 million. If you like it, keep it and pay $0.55 per CPU hours to be CPU hour, so we’re pretty easy to do business with. And so we’re seeing the number of transactions increase dramatically as we’d expect. The ease of contracting with us. As you know, we have largely reconstituted the sales organization in the last 1.5 years to a sales team that is candidly much more productive and effective than our other sales organizations.

So I think all of those are contributing to increased pipeline, increased business, increased business activity by which we’re quite optimistic.

Kingsley Crane: Thanks, Tom. That’s really helpful. And then one for Juho. When I think about the timing of the transition. So — if it is the case that the vast majority of existing customers are not necessarily migrating to the consumption model, how should we think about the contribution of consumption over time and particularly in the back half? Because I think that you said revenue could accelerate as consumption increases in mix?

Juho Parkkinen: Yes, Kingsley, thanks for that question. So that’s exactly as we sign and initiate more pilots within the quarter. The pilots are generally two quarters long, and then you start to see the consumption revenue kick in. As we finished the quarter with 19 pilots last quarter, we had a good increase in pilots with 17 pilots as well. You can start seeing those layer on to the revenue by Q3 and Q4 of this fiscal year. Now to your point about renewals, we do expect our existing customers with the large enterprise agreements to continue to remain on those types of agreement structures, but you will see the RPO trickle down as these contracts enter into renewal phase, and then we would expect to see a pickup as they renew.

Kingsley Crane: Okay. Very helpful. Thank you.

Operator: Thank you. Our next question comes from the line of Pat Walravens of JMP Securities. Your line is open.

Pat Walravens: Oh, great. Thank you. Tom, can you talk some more about the opportunity with National Security and the Department of Defense? And then also, you said something I thought was interesting about a version of generative AI that doesn’t hallucinate, if you could maybe comment a little more on what hallucinating is? And how you prevent it from doing that, I think that would be really interesting? Thank you.

Tom Siebel: DoD, well, Pat, you asked kind of many times about the — we have two basically authorities to operate contract vehicles once were $100 million and once were $1.5 billion in DoD that are associated. It could be applicable to what we’re doing at RSO, that was the rapid sustainment office and the predictive maintenance application that we’re doing for the United States Air Force for F-15, F-16, F-18, F-35, KC-135, et cetera. And what we made a proposal to the Secretary of the Air Force to take that into full production for all the aircraft in the Air Force, which is 5,000. I think the proposal would have increased aircraft availability for the Air Force by 25%. And I think decrease their cost of maintenance and readiness by about $6 billion.

So he considered that as did his Chief of Staff, General Brown, and they went off on their own for a few months, while you were asking the questions, and we didn’t have the answers and these guys go into their start chamber the way they do. What they came out with was not — was a selection of C3 as the standard — as the system of record, not only for aircraft in the United States Air Force, but for all AI-based all predicted maintenance, okay, in the United States Air Force for all assets. So this is genuinely a big deal, okay. Now we have the opportunity to make this a line item in the budget. So this is — it’s hard to over describe the impact of the – overestimate the impact of this. And then not only do we have it in Air Force, we can talk now to other services like the Army and the Navy and the Marines and the National Guard, what have you.

So this is a big one. The second one has to do with generative AI. So one of the problems with generative AI is the — is you’re limited to the number of data sources that you can use with these large language models typically is text, HTML and sometimes code. And the large language model will interact directly with the data. But one of the problems is you get, kind of, random answers. Every time you ask the question, you get a different answer. If two people ask the same question they get a different answer. And the — there’s no traceability. It doesn’t tell you where the answer came from, okay, and finally, if it doesn’t know the answer, it makes one up. This is what they call hallucination. So it doesn’t know so it just kind of wings it, makes up an answer.

So we’ve leveraged — we’re using the entire C3 platform. And the way that we do that is we incorporate — as you, I think, all know, we’re very good at aggregating enterprise data, extraprise data, code, images, text, sensor data, what have you, into a unified federated image. When we do that, those data are read by a deep learning model and they happen to be stored in a vector database that we have a kind of a firewall between that and the large language model. Now our customer uses any language model they want, be it ChatGPT, be it Palm, be it Bard, be it FLAN T5, whatever it may — whatever comes along next. Now, but we built a firewall between the large language model and the data. So it will — every time — I mean what’s really — every time you ask the question, it will give you the same answer.

Okay, if two people ask the same question and they have the authority, they will both get the same answer every time. Associated with the answer, it provides you traceability to see if they click on it, you can see exactly where the data can come from, okay? And very importantly, there’s no risk of LLM caused data exfiltration, see Samsung for details where they find out that all of their proprietary information is not published on the Internet, okay? And finally, there’s no risk of LLM caused hallucination. If it doesn’t know the answer, it tells I don’t know the answer rather than making one up. So for these, you think would be kind of table stakes, and they are table stakes for any large commercial or government installation, and this is something that really distinguishes the C3 generative AI offering.

And one of the reasons that we’re seeing very high levels of interest.

Pat Walravens: Great. Thank you.

Operator: Thank you. Our next question comes from the line of Sanjit Singh of Morgan Stanley. Again, our next question comes from the line of Sanjit Singh of Morgan Stanley. Your line is open.

Sanjit Singh: Appreciate you guys squeezing me in for the question. Tom, earlier this week, you guys announced had a press release about the C3 generative AI suite being available in the Amazon Marketplace. And it got me thinking about what the sales motion going forward is going to look like? As you sort of mentioned, generative AI is permeating the boardroom, the C-suite in a pretty substantial way. And when we look at sort of converting this interest into deals and ultimately revenue. How much of this is going to be like flywheel kind of self-service consumption-based marketplace type deals versus you working with partners to get more consultants as and helping these large enterprise customers, sort of, navigate the world of generative AI actually deliver value?

Tom Siebel: Great question, Sanjit. So our first three engagements that are evolved in now will be — our organization is the order of $100 billion or greater in revenue, okay, and have — we’ll bring the application live in 12 weeks. We’re not doing it — and we have like three people on the project, so it’s pretty straightforward. Now the issue of going from, say, six customers to 60 customers to 100 customers, it’s pretty straightforward. We know how to do that, okay? The real key is, okay, in terms of blowing the doors off this thing, can we go from six customers to 60 customers to 6,000. So for 6,000, now we have to leverage these channels like the AWS marketplace where the product is available today, the Google marketplace where they’re available today.

But in terms of usability, it needs to be with the Apple iPhone. You open the box, you take the cellphone off, you turn it on and it works. And so now we’re — the next generation — the next — the really serious development work that we’re doing now on that product, kind of, relates to really product design, okay, and making it like an Apple product, you open it up, you turn it on and it works. And so that’s the challenge that’s before us. I think we’re up to it, okay? And if we’re able to hit that note, hold on to your stock.

Sanjit Singh: I appreciate the color, Tom. And then maybe one follow-up. Maybe this is for Juho and Tom as well. And it sort of relates to the guidance for the full-year. I’m trying to contextualize like what’s really driving the guidance for next year? Because we’re coming off a year, fiscal year ’21, I think you guys grew north of 30%, 33%, 34%. And this past year, you guys grew sort of mid-single-digits. The initial guidance calls for growth sort of mid-teens at the midpoint sort of 20% at the high-end. And I want to understand, like is the acceleration you’re seeing a function that you’re coming off a tougher year where you had a spending environment is more difficult, sales reorg, those types of things versus generative AI really coming online in fiscal year ‘24. And so is there any way you can, sort of, attribute those two things between sort of coming off of a tougher year versus the demand that you’re seeing in pilots out in the field?

Tom Siebel: Let me address the premise, okay? Before we all bring our hands about tougher year, tougher year, tougher year. I think we got that in 4 times in the call for all the audience. Okay, let’s remember, okay? When we are now the transition to consumption-based pricing, okay, we made it very clear that this was going to have a short and mid-term, okay, negative effect on revenue growth. It’s actually a mess. Anybody who knows how to use a spreadsheet can figure this out. If we’re closing $0.5 million deals instead of $10 million, $20 million, $30 million, $40 million, $50 million deals, the short-term impact on revenue is to dampen revenue growth. So I’m not certain that’s so tough. Okay, that is basically we’re actually getting exactly the plan that we set.

So this thing is exactly on track. Now when you run this three year a little extra spreadsheet model and you hit the carriage return, okay, and you run it out a few years — a few quarters out there, you can do the math and you know what happens. But I’m not — so I think we’re exactly on plan with what we did. We made the investment. I think it was a great decision. It was a good investment. And now in fiscal year ‘24 and ‘25, we’re going to yield the returns from that investment.

Sanjit Singh: Great. Appreciate for that.

Operator: Thank you. Our next question comes from the line of John Katsingris of Wedbush. Your line is open.

John Katsingris: Hi, thanks for taking my questions. John on for Dan Ives, so given the increased diversity seen, I guess, across industries served, how have you seen these use cases develop? And how do you see them playing out in the future? Thank you.

Tom Siebel: Great question, John. Right now, I mean, in terms of applying AI to enterprise we’re in first-half of the first inning and the first guys that, okay. So this is an embryotic market. I mean, where we’re seeing the biggest uptake media. First, it was in the SmartGrid, okay. Why the SmartGrid because they had invested $2 trillion, okay, upgraded infrastructure globally to make all the devices in the SmartGrid, remotely machine addressable, a huge IoT constellation. So that’s where we saw it first. The next large — what we’re seeing in the past year, the largest market is in AI reliability, basically predictive maintenance. So they can move the military, they called readiness, okay, or in the Par average sector, they call reliability.

So AI-based predictive maintenance is the largest segment today. How will this evolve? I mean, it’s clear we will be applying AI to all business processes, production optimization, demand forecasting, supply chain risk okay, stochastic optimization, the supply chain CRM, either. I think there is no aspect of business operations that will not be or — and medicine, okay, and research and the science and literature, entertainment that will not be accelerated by the use of AI. So we’re just going to have to — we’re along for the ride and we’re going to see where this goes in the next few years and stay on the balls of our feet as it develops, but it is a rocket ship.

John Katsingris: Thank you, Tom.

Operator: Thank you. Our next question comes from the line of Mike Cikos of Needham & Company. Your line is open.

Mike Cikos: Hey, guys. Thanks for taking the questions here. Maybe the first would be going to Juho. So I know that you guys have cited the 43 deals you closed this quarter, 19 of those are pilots. Can you further refine that for us? And maybe it’s just the classifications or names we’re using for this. But like how many of those pilots are purely consumption-based versus maybe pilots that are still coming in under the old pricing model?

Juho Parkkinen: Mike, these are all — these would all follow the new consumption-based approach. These are not under the old model at all.

Mike Cikos: Okay. And so I guess the follow-up that I have on that is with the 19 deals that are consumption based, and I know that you guys have pulled — I’m sorry, 19 pilots that you guys closed or initiated that are a consumption base this quarter and the other pilots that we’ve decided in previous quarters. Do we have a feel for how many of these pilots have now converted to production? And do we have a gauge for what the vCPU is for those consumption deals once they move into production?

Juho Parkkinen: That’s a great question. So Michael, on the first quarter, we announced this would have been Q2, which obviously, you take six months from that, we get towards the end of Q4. So we are very early in that in the conversions. We are standing by with the model assumptions, i.e., whatever we provided three quarters ago where it is, each pilot is expected at 70% likelihood to convert into a follow-on consumption deal. But I would say that the first quarter before consumptions, we really will start seeing more of that this quarter since it was late in Q2 as we entered into those original pilot arrangements.

Mike Cikos: Understood. Understood on that. Thanks for that. And then just one quick follow-up, if I could. But I wanted to add just on the professional services revenue. I know it’s you just a tick higher versus the typical 10% to 20% range we’ve been talking about. And I just wanted to see, is 10% to 20% still the appropriate range we should be thinking through? Or is there maybe more handholding for these pilots as you guys engage in them? Or is it maybe handholding of potentially federal sector customers? Like how do we think about the higher pro-serve revenue generation in Q4 versus what you guys are thinking about over the next year?

Juho Parkkinen: I think on a go-forward basis, we expect to be in the 10% to 20% range. There’s always going to be these types of projects that our customers want, and it’s difficult to forecast a specific in a go-forward revenue, but we believe 10% to 20% is appropriate on a go-forward basis.

Mike Cikos: Thank you for that. I’ll turn it back to my colleagues. Appreciate the time.

Operator: Thank you. Our next question comes from the line of Brad Sills of Bank of America. Your line is open.

Brad Sills: Hey, great. Thank you. This is Adam Bergere on for Brad Sills. So you’re pretty well positioned in the current market, just given AI use cases are coming into focus. So kind of curious if it’s changed your cadence for R&D investments at all?

Tom Siebel: Well, this is Tom. I mean, clearly, the investments we’ve made in the last 14 years are paying off, okay, in that we have over 40 applications and people want applications. And I think we’re the only company in the world, but that’s for the applications. I think that — I think the only recent change that we’ve made is we’re a little bit shocked by the response that we had to C3 generative AI. I mean that is — we’re a little bit overwhelmed by that. But that’s a big opportunity. And so now we just came off a plane mean and we decided to really invest in that product category in a big way, because it’s just difficult to estimate that market, but it’s extraordinarily large.

Adam Bergere: Yes. Fair enough. And then for kind of the generative AI use cases and solutions thus far, I guess, the first is your first take on it. But do you see any outsized uptake or expect any outsized uptick within certain verticals over others in your view? Thank you.

Tom Siebel: It’s a good question. It kind of seems like everybody is interested in this. They want — at the level of the CEO or the person who operates manufacturing and the person to operate sales they want basically a Google-like interface where they got to a web browser like interface where they can ask any question about their business, okay? Where the problems in our supply chain? If I’m the Chair of the Joint Chiefs, what am I ready to less levels, they have 35-ish quadrants and okay, in Central Europe. I mean that’s what we call Google for DoD, but their open initiatives can provide the Secretary of Defense or the Chair of the Joint Chiefs of Staff answers to that question in seconds. Right now, it actually takes a week for him or most people to get those answers. So it’s I don’t know any industry that will not be taking use of this technology. It’s really quite amazing.

Adam Bergere: Alright. I appreciate the perspective, Tom. Thank you.

Operator: Thank you. Our next question comes from the line of Mike Cikos of Needham Company. Your line is open.

Mike Cikos: Hey, guys. Thanks for getting back and I did just have one quick follow-up. And maybe building on the question that Sanjit had asked earlier, but taking a different look rather than looking at the revenue, let’s talk profitability for a second. But obviously, you guys are issuing guidance now, which is below street and below what you guys had initially flagged if we go back a quarter ago, maybe for Juho. Can you help us think about the additional levers you have to pull on to ensure that C3 is achieving its target of exit fiscal ‘24 with non-GAAP profitability?

Tom Siebel: Before we answer it, Mike. I do want to poke at the premise a little bit, okay. I think our guidance is pretty much in line with what the Street expectations are once you take out like one outlier or two outliers. So our current guidance is in line with what the Street has. I’m pretty confident in that. Now Juho the other question related to how are you sure you’re going to get to profitability?

Juho Parkkinen: Right. So Mike, one of the things that I had on the prepared remarks was our planned investments into generative which combine that and vendor expenses, I think we can control spending towards the end of the year, if for whatever reason, the expected revenue would not occur from those. But we’re pretty bullish about the generative AI opportunity.

Mike Cikos: Got it. Thank you, very much.

Tom Siebel: Let me. Said, we don’t need the generative AI for purity to be generally, generative AI could be zero, okay? And we’re still going to run a cash profitable business in Q4. Well, you have a very, very detailed plan that’s been to all the members and the management team. They all have big budgets. They know that’s going to operate and you can expect it to be in cash positive profitable business non-GAAP pro-business in Q4, hard stuff.

Mike Cikos: That’s right.

Operator: Thank you. One moment, please.

Tom Siebel: One more question?

Operator: Thank you. Our next question comes from the line of Noah Harman of JMP Securities. Your line is open.

Noah Harman: It is great to see the average sales cycles for agreements ticked down, I think, by about one, three months year-over-year. What’s really driving that? And where do you think a sustainable sales cycle basically concludes that maybe thinking about the rest of this year?

Tom Siebel: Well, I think the consumption-based pricing model is driving it where it’s pretty easy to do. We’re not going to have a large-scale enterprise AI application live in production in six months for $0.5 million. I mean that’s nothing guys in terms of what it costs to bring in an Accenture or an IBM or somebody to try to bring one of these things, that’s going to be scores of millions of dollars in the years. So it’s a pretty easy sale. It’s a shorter sales cycle. And so I’m not sure where it ends up, but as we move more into more of our products, onto the AWS marketplace, the Google marketplace and other leverage channels like this. So we’d expect to see it get shorter.

Noah Harman: Great. Thank you.

Tom Siebel: Thank you.

Operator: Thank you. At this time I’m going to turn the call back over to Mr. Siebel for any closing remarks.

Operator: Thank you. Ladies and gentlemen, this does conclude today’s conference. Thank you all for participating. You may now disconnect. Have a great day.