January 22, 2022

The World Stock Markets Tips & Targets, News, Views & Updates

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C3.ai, Inc. (AI) Management Presents at 24th Annual Needham Virtual Growth Conference (Transcript)

C3.ai, Inc. (NYSE:AI) 24th Annual Needham Virtual Growth Conference Transcript January 11, 2022 4:15 PM ET

Executives

Tom Siebel – Chairman and CEO

Analysts

Unidentified Analyst

Great. Thank you for joining us here. We have with us, Mr. Tom Siebel, and the management team from C3.ai. Again, thank you for joining us at the Annual Needham Growth Conference and joining us for this fireside chat here later in the day. Tom, thank you very much for joining us and giving us this opportunity.

Tom Siebel

Thank you, Chris [ph]. Nice to see you.

Question-and-Answer Session

Q – Unidentified Analyst

So, I guess, one of the things that I just want to jump right into and I know we’re a little short on time here, but in my discussions with investors, people are still trying to figure out what C3.ai actually does? How you fit into a broad, very fragmented analytics landscape. On the most recent earnings call, I know that you guys said that you are, quote, selling vehicles when everyone else is selling ball bearings. I thought that was a good analogy and a good place to start. But can you talk to the core value prop for C3? Why is it different than others in the marketplace?

Tom Siebel

Great question and thanks for asking. Obviously, if you think of the C3.ai suite, there’s always spent a decade and almost a $1 billion building, okay. And this provides all of the services necessary and sufficient to design, develop, provision, operate an enterprise AI application.

So you can think of this as the union of all of the other AI capabilities that you know of in the market, so all of those capabilities are features. So let’s take Palantir, Palantir does data aggregation takes, it allows you to aggregate structured data, non-structured data, telemetry, what have you into a unified federated image, okay, and represent those relationships visually, okay, that’s our data integration capability.

Let’s take Data Robot, Data Robot and H2O are companies to do auto ML. We have — we — auto ML is within — is a capability that we provide. Let’s take platform independent persistence technologies, like Snowflake would be a platform independent persistence technology and we — basically a database and so we have that.

What would be other examples. Alteryx is for citizen data scientists and we provide that capability and function that we call Ex Machina, so we have took all of these bright shiny things, Databricks, Data Robot, Alteryx, Lambda, TensorFlow, all of those capabilities and put them in a cohesive package that all work together out of the box, so you don’t have to spend hundreds of millions of dollars trying to cobble those pieces together. That’s what we offer to the market, okay. That’s the C3.ai suite.

On top of that, we build 42 — I think, 40 or 42, enterprise AI applications that are turnkey applications built with that platform that address the value chains of banking, financial services, manufacturing, telecommunications, defense and intelligence, oil and gas, utilities, what have you. And about half of our revenue accrues today from these applications and half of the revenue accrues from the platform itself.

Unidentified Analyst

Great. Thank you for that backdrop. And perhaps we can discuss a real world example, right? So you’re typically dealing with very large enterprises, who have already purchased a number of these data analytics solutions and have built processes around. How is it C3’s architecture fits within this landscape and are you partnering, are you integrating with some of these vendors, are you placing others? How does all this hold together?

Tom Siebel

Okay. And I would think, historically, it’s true that our customer base was almost exclusively very, very large organizations. Today, I would say, that’s changed over the last couple of years and today we have relationships with larger organizations, with medium sized organizations. We have small relationships with larger organizations and we have organ — we have relationships where we’re selling to product for $400 a month to very small organizations, let’s take, San Mateo County, okay, would be a relatively small customer.

Now, a — one of the larger use cases we have out there would be Shell. Shell is the, I think, fifth largest company in the world. Largest — one of the largest oil companies in the world, maybe second or third. And there we’re using enterprise AI across the entire value chain to build applications upstream, downstream, midstream, integration of renewable.

With whom do we partner? We partner with Microsoft Azure, okay, that provides the cloud platform. We partner with Nvidia, that provides kind of — that provides kind of very powerful GPU technology. We partner with Baker Hughes, that has deep expertise in oil and gas. We partner with Databricks, that provides virtualization technology. We partner with a Alteryx that they’re using for their citizen data scientists. We partner with Microsoft, that’s using Power BI for reporting. And what we are — and this is — there’s a, I think, they’re using — they might be using Data Robot for auto ML. So that would be one of the partners.

So, by the way, when I said we provide all of the capabilities necessary and sufficient for these applications, some companies will want to use their technology, their favorite virtualization technology, say, Databricks, instead of ours. We use the open source version of Databricks, Spark, or they might want to use, these guys are using Data Robot for auto ML instead of what we use out of the box is the open version — open source version of H2O. So that’s fine. The companies can mix and match whatever the solutions they want.

But we’re building their applications for upstream, downstream, midstream, everything from AI-based predictive maintenance for devices on oil — offshore oil rigs, predictive and descriptive analytics for, I think, a 0.5 million valves or we have 2 million machine learning models in production, a hydrocarbon loss accounting, process optimization, and refining and cracking, production optimization in oil production facilities, fuel station analytics, this they have 5,000 fuel stations, they sell more coffee in the gas stations than Starbucks sells in any given day, integration of renewables, distributed energy resource management.

So these are the classes of problems that we’re solving at Shell, as Shell reinvents itself into a net zero carbon footprint company by 2050, we’re very much part of that. So it’s us, Microsoft, Baker Hughes and these other companies working together to get Shell where they want them to — where they want to go.

Unidentified Analyst

Understood and thank you for that example. If I’m thinking about the core of your platform, right? Can you talk to the changes from customer to customer across and I know you’re working in a number of different verticals, we highlighted telecommunications, financial services, just the process that you guys have built in place as far as connecting to these different data sources and the user interface involved with that? Can you help us think about that as well?

Tom Siebel

Yes. One of the requirements of any application of this nature, be it in financial services or utilities, or precision health or whatever it might be, is you need to aggregate — if you want to buy enterprise AI to optimize any process, supply chain, demand chain, production optimization, demand forecasting, customer churn, whatever it might be, we need to aggregate very large and a large set of disparate data sources from within the enterprise and from outside the enterprise and from sensor network.

A particularly large example would be Enel. Enel is the largest utility in the free world that based in Rome. They have about 16 million meters in 40 countries, putting that in perspective there are about 100 million meters in the United States. So there’s a pretty big grid. And now we’ve aggregated 150 trillion rows of data from, I believe, 18 instances of SAP, 12 instances of Salesforce, some instances of Siebel systems that are still there, two different SCADA systems, Maximo Atlas.

We go out to the extranet for weather trends, social media, that gets updated 62 billion times a day. And then we are connected to a constellation of, I think, 47 million sensors, 42 million smart meters, some of these are emitting signals at 90 hertz cycles, that would be 90 times a second, okay, and we aggregated this data into a unified federated virtual image, where we process these data at the rate they arrive.

So our data integration and visualizations technology is one of the really keys to the success of this company. And whether we’re dealing with the missile defense agency, whether we’re dealing with the intelligence community, whether we’re dealing with readiness for aircraft, manufacturing production optimization at places like Philips, process optimization at LyondellBasell. This is the aggregating data in a unified federated image is kind of a level zero requirement. It’s something we’re very, very good at and one of the reasons why we’re being achieving the success that we are achieving.

The other aspect that I will point out, that is counterintuitive. That — as a result of this model driven architecture, whether we’re doing anti-money laundering at a bank, whether we’re doing process optimization for a polyethylene cracking unit or whether we’re doing predictive maintenance for devices on an offshore oil rig, 97% of the code in all those applications is the same. It’s the same code that’s running in every one of these customer solutions. All that changes are the data sources, the machine learning models and the user experience expression. So this is why we’re able to scale this so rapidly across verticals and from vertical-to-vertical.

Unidentified Analyst

That’s great. That is a great background. And I did want to touch on it, because I know you had made mention, but thinking about the machine learning models, how long does it take to train these new models and develop new use cases and absorber in just new data types? How is it you guys are managing to all these different problems and increasingly complex world we find ourselves in?

Tom Siebel

Let’s tease that apart a little bit, because there’s a…

Unidentified Analyst

Yeah.

Tom Siebel

There’s a number of questions there. I think that, the one you’re really getting at is training the machine learning model, okay. Now we’re — we can — when we were doing visitation, the first application we did at Shell was AI-based predictive maintenance for low pressure compressors and offshore oil rigs, okay, where we had to add great massive amounts of data, SAP, PI tags, other things.

We aggregated those data in a unified federated image, built the user experience, built the machine learning models, that would predict failure with I think about 85% precision, 80% recall, giving them 18 hours notice, which doesn’t sound like a lot, but it’s enough on an offshore oil rig to prevent a problem and these guys get real twitchy about problems failures on offshore oil rigs, as you can imagine. It took us four weeks to do that from beginning that. So that would be an example.

I think that the first management application that we built for cash management at Bank of America, it took us six — and Bank of America is a pretty complex organization, the data is particularly complex, because it is the — as you know, the aggregation of a very large number of banks and you get into get into Charlotte, where the data centers are, I mean, these are data centers for many, many organizations entirely incompatible formats.

And we aggregated that, those data into a unified federated image, built the machine learning models, train the machine learning models, instruct the user interface in 16 weeks, at the scale of Bank of America. So, that will give you a feel for the speed at which companies are able to realize the benefit from what we do.

Unidentified Analyst

That’s great. And again pausing or splitting up the different questions, I can’t move them all into one. But if I’m thinking about the new use cases that you guys continue to address, I’m curious how many of these are customers coming back to you saying, hey, we have a need for XYZ as a use case, versus you guys actually developing these use cases and then bringing them out to market…

Tom Siebel

100%…

Unidentified Analyst

…that make sense?

Tom Siebel

Yeah.

Unidentified Analyst

Okay.

Tom Siebel

100% of the cases, the customer is coming to us, presenting us with a problem, presenting us with the roadmap and if the CEO is, somebody who’s an aerospace business and they have a roadmap. The person who runs the business is in the room, they have the budget and the will to do it, where they are space business and we’re building it and then we and then — but we do it, Mike [ph], in a way that it is of utility, not only to that aerospace organization, say, the United States Air Force, but we’ll have the same utility for a Boeing or an Airbus. So then we’re in, so but it’s where the — in the long we will address for all verticals. The sequence in which we address specific verticals is very much coin operated at demand driven.

Unidentified Analyst

Understood. Understood. Okay. And another thing that’s come up on earnings close recently is, disadvantage you guys talked to with respect to human capital, right? The recruiting process, the hiring that you’ve done, can you talk about what goes into that and the culture that you guys are building at C3? How sustainably can you continue to ramp at these levels?

Tom Siebel

I think that is a human capital at C3 is a very distinctive and unique competitive advantage. To give you a feel, if I’m not mistaken, last quarter, we had 18,600 job applicants from around the world at C3, come on we are 700 people, 18,600 out fast math, I think that analyzes to something like 72,000, okay, a year of people, okay. In banking, people from MIT, okay, data scientists from Princeton, computer scientists from name the companies, sales people, finance people, what have you who are applying and saying, hey, I want to work at C3.

I — yeah, I think, of those 18,000, I believe that we interviewed 6,000 of them and hired 200. So this would be an order of magnitude more selective than Princeton. I — if I’m not mistaken, almost 70% of our people have advanced degrees. I believe 9% have PhDs. The people who work here, I’ve tried, tested and experienced professionals, extraordinarily well educated, who are on the top of their game.

People we tend to self select for people who like to work, for people who like to work in teams, who people like to work with customers, who are people who are challenged by very difficult problems. And I think if you go look at glassdoor.com and anybody who’s interested, and you’ll see the — you’ll see the comments that our employee — that employee — anonymous comments that employee post, some of them are they’re very damning, okay. There are — people who didn’t like it here, I mean, they throw us under the bus.

But the great majority of them you will see are, you will consistently ranked amongst the top companies in the world to work for and it is a very high performance culture and I believe that that is, it is absolutely sustainable. It is probably the promulgation and communication of corporate culture is my important job, my most important job, it’s what I do in every communication that I do, whether it’s to the markets or to customers or to investors, really, I’m talking to my employees. And so I believe it’s sustainable and I believe to the extent that we have succeeded in the market, that is the reason why and I believe the extent that we will continue to succeed, that will be one.

Unidentified Analyst

Great. And one of the things that we hear from people in the industry talking to maybe the best way to gauge differentiation is looking at data scientists talking, right, which obviously can’t scale, it’s a different way to look at the industry. What in your view is the best way to gauge talent, differentiation of the workforce from an outside perspective like ours?

Tom Siebel

You meant, would that be our work force or is this issue that, I think the question you’re asking is, do you can you gauge the competitive advantage of a company by the number of data scientists that work there? I think in the long-term, that’s true. I think it’s true for Bank of America. It’s true for Needham. It’s true for Goldman Sachs. It’s true for General Motors. It’s probably less true for us.

I think we have about 90 people in our data science organization. I suspect we’d have probably 180 data scientists in the company. But it’s less true for us. What we’re really not doing. We’re less about doing data science, than we are about writing a set of utilities that allow data scientists and all these other companies to be enormously productive at applying data science to their financial services company or their automotive company or their agricultural company or whatever it might be to achieve their digital transformation objectives.

Unidentified Analyst

Got it. Got it. And I know one of the main reasons for the IPO was build out this sales motion with multiple different channels, right? How is it you’re thinking about those sales channels today? How are they scaling versus your expectations?

Tom Siebel

I think this is going great. This is where I spend the bulk of my effort on developing a large, powerful, global ecosystem. We have tens of thousands of professionals selling with us around the world today. I have 12,000 people selling with us from — in oil and gas, from Baker Hughes. I have tens of thousands of people selling with us around the market today at and might — with the partisan Microsoft Azure team. I have 4,000 professionals selling with us at Google Cloud and Thomas Kurian. I have thousands of people selling with us in financial services at FIS, the thousands of people selling with this in the Defense Intelligence Community at Raytheon.

So you can expect to see us continue to invest in this partner ecosystem to take the success that we have achieved and I believe today, okay? I believe we are the world’s leading provider of enterprise AI applications. If I am able to leverage this through a large and powerful partner ecosystem, including the hyper scalars, okay, and then specialists is the oil gas would be Baker Hughes, the utility market and ESG and sustainability would be MG, financial services would be FIS, defense intelligence would be Raytheon, more to follow, think telecommunications, think precision health, they ad, whatever it might be. That is going to be one of the — we will prove when the book is, when the story is written, will have proven to be one of the critical success factors that contribute to the success of the company.

Unidentified Analyst

And maybe just taking a step back, when you’re — how is it you are first going about identifying these partners and then working with them to announce like a some more formal partnership, like I think about the Google Cloud partnership that you recently announced. I’m obviously top of mind lord’s name in the industry. But how did you decide to go with them versus maybe another guy or I think about Raytheon and/or FIS? What’s the process you guys go through on your site internally there?

Tom Siebel

The truth is that Baker Hughes selected us, okay. The truth is that Google Cloud selected us. So Thomas Kurian, decided that he was going to compete in the hyper scalar market on the applications layer, rather than compete based upon selling against Azure and Oracle or whoever it might be, AWS, based upon CPU seconds and storage hours with an architecture that is may well be arguably better. So rather than compete on that basis, he decided, he wanted to compete to the applications layer.

And so he approached us, because we have 42 turnkey applications that addressed the needs of the entire value chain of oil and gas, of utilities, of financial services, telecommunications, manufacturing, what — we have. So if you want to not be — if you want a family of 40 turnkey enterprise AI applications in multiple verticals that are tried, tested, proven in the market, that you want to immediately be selling worldwide, there’s exactly one door you could knock on in the world and it’s that this is it, okay. We’re the only company in that space.

So they approached us and Thomas said, hey, we want to sell your applications worldwide through my global sales organization. And as the CEO of Siebel Systems doing the best I can to represent the interests of our shareholders is kind of a IQ test, right?

As it relates to another example would be Baker Hughes. Baker Hughes, as you might recall, was formerly General Electric. General Electric was formerly this thing called Predix, okay. Predix was bout a, as I recall, $6 billion catastrophe, okay, to try to build something equivalent to the C3.ia AI platform, okay. After years of failing at Predix, Baker, who’s the CEO of Baker Hughes showed up in my boardroom and said, Tom, I want to partner with you in oil and gas, and in turn 12,000 people are those selling these products globally into Ramco, Rosneft, Gazprom, Shell, LyondellBasell, Flint Hills Koch, what have you and that was an offer that was pretty difficult to refuse.

So I’d say in many cases these guys are approaching us and they are approaching us and I think we can agree that, companies like Raytheon, Microsoft, Google, Baker Hughes, these are — actually these are very, very high quality go-to-market partners.

Unidentified Analyst

Right. And I know other companies have tried to do this right, unsuccessfully, they’ll try their hand and then they’ll eventually turn to you or what is it that you think that the market still has players out there who are trying to go it alone and develop it themselves? What is the realization that, hey, instead of continuing to go down this route, why don’t I just go with the C3?

Tom Siebel

Virtually at, first of all, yes, okay. Just like every other market, we have seen development information, software and information — software information technology in the last 40 years, relational database, CRM, ERP, whatever it might be. I mean, companies tried to build all these things themselves. I mean, how many companies succeeded at building their own relational database system? That would be none, okay. How many succeeded — they use — this is what IT used to do.

IT wants to build, wanted to build their own CRM systems? How many people succeeded at that? Not very many. So a virtually every one of our customers, Baker, Hughes, Shell, NG, Enel, Bank of America will have tried one time, two times, three times, four times to try to build this platform themselves before they throw in the towel and decide that, maybe that isn’t their core competence is building extraordinarily complex enterprise application software and that they should focus, rely on a professional organization to provide it and focus on areas that are their areas of core competence.

Unidentified Analyst

Great. Great. Really just, yeah, focus on your core competencies, outsource this piece to you guys, if you’re developing those 42 turnkey applications that leverage so well with your partners?

Tom Siebel

And make no mistake, our competitor today, okay, is companies that want — is basically build it yourself that is the competitor, okay. And if I had to look at our revenue, rough numbers, I would estimate, and this is this is just a guess, okay, so again, hold me to this, okay. But I would say that 80% of our revenue last quarter accrued from companies that at one point in time turned us down, because they were going to build them, they were building themselves and came back to us one year, two years, three years or four years later and standardize on our technology. So this is how we do business is letting companies go build themselves for a while and fail at it, then we come in and help them out.

Unidentified Analyst

Great. And one of the things too, I know, I think was the last earnings call, but you guys also spoke to the entry point for these contract deals. I think it was $16 million a couple of years ago, the most recent quarter, I want to say, it was somewhere around maybe $4 million. And I know that there are pieces that go into that from your platform, whether or not we’re talking about X mark as an example. Can you talk about how that’s making C3 maybe more approachable, even in the mid-market or making it easier for enterprises to buy the bullet rather than spending billions of dollars trying to build this themselves before they finally do turn around and come to you?

Tom Siebel

Our average transaction value has continued to decline over in recent years and this is from memory. But I think it went from average from $16 million to $12 million to $7 million to $5 million to about $3 million, okay, in terms of average transaction costs. This is creating — getting greater diversity in our revenue and should be taking the lumpiness out of our bookings, okay, last quarter is being an exception, okay.

And the — but today, I mean, we sell products, if you go on our website, download X mark, which is a common application for citizen data scientists is for free for 30 days. Now, if you decide to keep it, I think, it’s maybe $400 a month. And so our transactions vary from very, very large to really quite small and whereas, if we look at 2014, 2015, I’m sorry, yeah, 2016, 2017, we’re really focused on only the world’s largest corporations. Today, we sell to large corporations, midsized corporations, small businesses, local governments, San Mateo County Government, Stanford Hospital, and so they go from some of the world’s largest organizations to small businesses.

Unidentified Analyst

And with something like an X mark, right, if you have this 30 day free trial, people are aware of like, you’re almost building up your cheerleader base, right? People get familiar with technology. They fall in love with it. Is that the approach thing that you’re seeing from this almost bottoms up while you guys are still attacking from the top down or is that a way to think about the go-to-market there?

Tom Siebel

Middle and the bottom up, I mean, we’re going after the, I mean, our goal is to establish my global leadership position, okay, in enterprise AI and so you can expect to see us to continue on major markets, enterprise accounts, middle market accounts, okay, and citizen data scientists.

Unidentified Analyst

Terrific. I’m looking at the vertical partnerships. I know we were hitting on that earlier. But with those different verticals and the different partners, because obviously everyone’s going to look at this differently, which verticals, which partners, are you most optimistic on today or do you have the highest expectations for as far as gaining traction out there in the market?

Tom Siebel

Microsoft is huge, Google is huge, Baker Hughes is — Baker Hughes, I mean, those numbers speak for themselves? Those are, I think, our relationship with Raytheon is really productive. So those the ones that — those are the partners who are, I know, we are hitting a long ball and this is where I spend the bulk of my time on the creation and development and nurturing of those partnerships and I’m very pleased with the way it’s going.

Unidentified Analyst

Are there a certain verticals, like, if I think about FIS feeding into the financial sector, would you say the financial sector is further ahead or behind other verticals out there that you’re currently feeding into?

Tom Siebel

Financial services is a relatively new vertical for us, okay. I think it’s going to be huge. I mean, we started in utilities, then we went into oil and gas, then we went into manufacturing, now we’re in financial services. I mean in the long run, all enterprise applications will be predictive, okay. All enterprise applications will require the kinds of capabilities that we offer. All enterprise applications will require different forms of machine learning. So, I believe the largest market for what we do in the long run will be unquestionably precision health, okay, but it’s a — it’s not an early adopter.

Unidentified Analyst

Terrific. And I think we have to leave, I’m sorry, I thought we had more time here, but we are at the top of our time. So thank you very much, Tom, and to our listeners. I really do appreciate your participation in the conference.

Tom Siebel

Thank you, Mike. Enjoyed the conversation.

Unidentified Analyst

Absolutely. Be well.

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