On this special episode of the Futurum Tech Webcast – Interview Series I am joined by Ed Abbo, President and Chief Technology Officer at C3 AI. Our conversation centers around AI applications, how these programs can help enterprise customers find value from their data, and much more you won’t want to miss.
In our conversation we discussed the following:
- A broad overview of enterprise applications and enterprise AI-based applications
- A deep dive into the expansion of C3 AI’s partnership with Google
- A look into C3 AI’s broad portfolio of solutions
- An exploration of the current economic situation and the economic value AI applications can bring to customers
During the conversation, Ed also shared a customer success story about the US Air Force’s Rapid Sustainment Office. You can check out the full video that Ed referred to here.
If you’d like to learn more about C3 AI, check out their website here.
Watch my interview with Ed here:
Or grab the audio by way of your favorite streaming platform:
Don’t Miss An Episode – Subscribe Below:
Disclaimer: The Futurum Tech Webcast is for information and entertainment purposes only. Over the course of this webcast, we may talk about companies that are publicly traded and we may even reference that fact and their equity share price, but please do not take anything that we say as a recommendation about what you should do with your investment dollars. We are not investment advisors and we do not ask that you treat us as such.
Transcript:
Daniel Newman: Hey, everyone. Welcome back to another episode of the Futurum Tech Podcast. I’m your host today, Daniel Newman, principal analyst, founding partner at Futurum Research. Love these shows, I’m very excited for this particular one where I’m going to have Ed Abbo, he is the CTO, Chief Technology Officer, at C3 AI. C3 AI is a company I have had a number of different conversations with across their leadership team, including a recent conversation on my Making Markets podcast. And I couldn’t be more excited to have Ed join his esteemed CEO colleague, Mr. Tom. Siebel here on this show today. So without further ado, Ed, welcome to this show. Ed, how are you doing?
Ed Abbo: I’m great, Daniel. Thanks for having me on this show and I look forward to the conversation.
Daniel Newman: Yeah, this is a really big topic. It doesn’t matter if it’s been… Last week, I was on CNBC Squawk Box talking about AI, and everybody seems to think that the world and technology and growth are over because we have this sensational personality that when things are going up, they’ll never stop and when things are going down, they’ll never stop. And the truth is, as I told them, if you believe that there’s a future of AI, then you’ve got to be long on this stuff.
And while I know, this show is not about the market and the stocks, what I really do mean though is that, these technologies are going to have ebbs and flows because economics will change. But the fact that these technologies are going to change the world really isn’t going to change at all, it’s just that old story, Ed, that growth doesn’t actually look like this, it tends to be a little bit more wobbly, the lines go like this. But in the end, where I’m doing it wrong way, it ends up like this. Anyways, how about a quick introduction? I told everybody you are the CTO. It sounds like you’ve been with the company a long time, so how about just a quick intro and background and talk about your time here at C3?
Ed Abbo: Sure thing, Daniel. And I like your line analogy, because in hindsight it looks linear, but while you’re in it, it’s nothing like linear. But yeah, Daniel, I’ve been in the enterprise space for many decades, since the mid ’80s, ’86, ’87 to be precise with a company at the time, a little known company at the time by the name of Oracle Corporation that I worked with…
Daniel Newman: Never heard of them.
Ed Abbo: … never heard of them. Yeah. And then I work with an entrepreneur by the name of Tom Siebel whom you have interviewed recently at his company Siebel Systems. And then we started C3 AI back in the 2009 and we are reinventing enterprise applications with C3 AI.
Daniel Newman: All right. There’s a question right there. C3 AI versus traditional enterprise applications, what’s the difference from enterprise apps and enterprise AI based apps?
Ed Abbo: Great, great question. The traditional enterprise applications that we built were great systems of records. So they track things and could tell a company and a corporation, for example, how much inventory they had at any point in time and where that inventory was across their manufacturing plants. They could tell in their cash balances, they could tell them which customers were on their contract, but it couldn’t basically tell them anything predictive. And to operate companies today, you really need to learn from the past to project the future. And so what enterprise AI applications do is they basically say how much inventory do I need in order to meet the demand function?
That’s a very subtle difference but instead of just telling you, “This is how much inventory I have,” it tells you how much more of a specific part or how much less of a specific part or product that you need in order to not run into trouble, delivering for your customers in the future. And that’s just inventory and customer, it’s basically not just which customer churned, but basically which customer is likely to churn in the next 90 days. And what can you do to basically save them? You get the sense that these are future looking applications that basically provide business people with insights. And so what’s likely to happen so they can take action and improve their business.
Daniel Newman: And this is sort of the grail for just about every company right now that has enterprise apps is, we’ve heard Oracle, you’ve heard from Salesforce, you hear from Microsoft, they’re all doing something. And again, I’m not saying it’s all right or all wrong, but I’m saying, everyone’s sort saying we’re adding an AI layer to what we are doing. And so I fundamentally agree with everything you just said, except the fact is I really don’t think there’s much of a market left for enterprise apps without AI. I think that the market is going to be entirely expecting that every enterprise app has an AI or a set of AI capabilities that are going to be inherently built in and/or using partnerships, connectors, APIs, to be able to access an AI that can then somehow utilize the data like you said, to better understand intent, to better understand churn, to better understand demand.
And so I think we’re getting to a point where soon that won’t be a discussion, but right now companies, there’s haves and have nots. Speaking of partners though, I did listen to you guys recently talk about expanding a partnership with another small company that few people have heard of called Google. Sorry. I just wanted to build on your pun there. That’s the last time I’ll do that. I won’t do that again. But I’ve been following that partnership for a while. Ed, your C3 AI announced talked about this. It seems that it’s really growing. Talk about what that expansion looks like and why is Google teaming up with you? It feels like, a pretty big company, lots of resources. You must have some secrets source.
Ed Abbo: And I’m happy to talk about it. Daniel, I do want to just go back to your last point about-
Daniel Newman: Yeah. Okay. I should have given you a chance, but I want to get all the questions in before we run out of time.
Ed Abbo: Thank you, because I want to comment on that. You’re absolutely right that the traditional software companies are basically tacking on AI to what they’re doing, but I do want to distinguish that that’s not sufficient and the reason it’s not sufficient is because the way they’re thinking about it is, we’ll just apply AI to the data that is in the Oracle system or is in the Salesforce system. Making businesses predictive actually requires you to have a completely different architecture. And the easiest way to understand this is, the data that you need to make predictions goes well beyond what’s in your current CRM system, what’s in your current PRP system, to basically taking exogenous data that’s data from outside the company, data from entirely across the company to make that prediction about what is the demand going to be for your product? Yes, if you’re predicting, if you’re forecasting demand, as an example, if we park on that for a moment, you are looking at what sales orders are coming in, but you’re also looking at markets signals.
And so this might be weather, it might be inflation, it might be pricing in the market, competitive information. All of this data is also needed in order to make an informed demand forecast. All this data is needed in order to make sure that you characterize properly your supplier delivery. All this data’s needed to basically look at your production schedule. This is a completely different architecture. It’s not the architecture that we built the traditional applications on. So it’s a fundamental re-architecture of all of enterprise applications to make them enterprise AI, to make them predictive. And I’ll pause-
Daniel Newman: Yeah, I’m going to actually just re-ask you the second question, but I just want to say one thing because a little bit like you’ve got systems of record, you’ve got systems of engagement, you’ve got systems of intent and you’re basically it’s the problem that most of the database companies are having today is that structured data alone isn’t enough. And this is where the advent of the data lake and the cloud data warehouse and the data warehouse and the data lake and the data lake warehouse. And I’m just saying, you’re seeing it now because you’ve got too many data sources of too many different archetypes. The data’s not usable. So you’re kind of trying to solve that problem as kind of the, it’s the grail, that’s solved all that data being coming usable.
Ed Abbo: That’s exactly right. The data of most medium and large size corporations is literally scattered across tens or hundreds, sometimes thousands of systems. And the challenge is kind of aggregating, unifying that data across these systems is non-trivial and that’s what we’ve done at C3 AI is made that, simplified that so that you actually have context across your business in real time, near real time. And then we’re basically applying algorithms on top of that to learn from the past, to make predictions about the future.
Completely different architecture and it’s been a 13 year journey and it’s a fantastic market because we can now apply this architecture to manufacturing, aerospace, defense intelligence, you name it, any industry, healthcare and basically deliver an application platform and deliver applications of which we have 40 that can be quickly deployed. And that’s really the key is, getting these things deployed quickly so they can return value quickly for corporations.
Daniel Newman: All right. We went over on the first question, so I’m going to have to ask you be a little quicker on this to get me back on pace Ed, but the Google partnership really excited about that. Give me that, what’s the secret sauce? Why is Google doubling down on C3 AI?
Ed Abbo: Google has taken a differentiated path in the market and they’re laser focused on business outcomes for the enterprise. And so rather than selling CPU hours and gigabytes of storage, they’re basically elevating the conversation to the business. And just like we’ve been speaking about how do we make supply chains more resilient? How do we understand demand better? How do we engage customers that are likely to churn? That’s what they’re focused on.
And they recognize that by partnering with a company like C3 AI that has pre-built applications, they can accelerate business outcomes for their customers. And a byproduct of that is there’ll be consumption. There’ll be use of all their technologies, Vertex AI, there’ll be use of big query and big table and cortex. And all of the technologies will come as a byproduct of having business discussions and helping companies meet business objectives.
Daniel Newman: I like how you said that. I used to say, people don’t buy technology to solve technology problems, they buy technology to solve business problems. I guess as consumers, maybe you call those life problems, but they’re not… It’s never for sake of, and I do agree with you and we advise AWS, we advise Oracle, we advise Microsoft and we advise Google and I’ve always admired their AI focus. And obviously Thomas Kerryn has doubled down on that. It’s been something very much in their differentiation strategy.
And of course I think it’s a very nice validation and endorsement of what you’re doing because, I do think Google certainly has shown the propensity in the past to build on its own. It’s got the resources and it’s got the financials and the capabilities and the fact that they looked at what you’re doing and said, “This is a partnership that we want to have in our portfolio and we want to get behind,” says that they looked at it and said, “We probably can’t do better. And if we can, it’s going to take a long time and a lot of money and this gets us in market and we’ll collaborate.” So you got to love that.
Ed Abbo: That’s right. That’s right. Thomas Kerryn really gets it and is gone long on the strategy of delivering business outcomes. I think you hit the nail on the head, it’s no value in necessarily just cloud. Okay. The transition to the cloud was basically initially for IT cost savings as economies a scale, then there was the data lake and then there was data exploration, but none of that has delivered any value for the business. Where you deliver value for the business is if you basically reduce their inventory that they’re carrying, carry the right parts, have a great demand forecast that can drive the rest of your organization, make your supply chain more resilient, that delivers value for the business. And that is what C3 AI in partnership with Google is doing.
Daniel Newman: Absolutely. Let’s kind of chat a little bit about, when I initially got to know the company, it was primarily under the onus of prebuilt AI applications kind of off the shelf take it. And of course the market needs that, wants that because a lot of companies just can’t get it right. They don’t have the resource, don’t have the developers, if there’s a talent issue, the talent issue’s not going away. That prebuilt thing was compelling, but of course just like every application on the planet, you get it inside of a large enterprise and they go, “God, only if it could do this and if it could do that.” And that’s why even the most pure SAS companies in the world hit a point in their scale where they have to start to do enterprise customization or they just never reach the potential of adoption.
And it seems that that’s kind of become your strategy too. You’ve got the prebuilt, which of course addresses all these different industry needs and you can kind of take it off the shelf and use it with certain economic geo and other datas that are going to help these industries go to work. But you’ve also seeming they’re spending time on that developer platform, intentional is what I said. Is that what you’re seeing? Is there another reason you’ve really gotten down that path? What’s the reason you’ve been so focused on both?
Ed Abbo: Great question, Daniel and you’re right. We started by basically pre-building applications and we built them where there was high repeatability. A lot of customers in the manufacturing industry needed these applications for inventory or supply network, et cetera. We just built them once. And this is a similar strategy to say, SAP, like people don’t need to build their HR systems. They don’t need to build a general ledger for price sakes. They just go and get it from SAP. But while we were doing that, and this has been a very deliberate strategy over the past four or five years, we’ve basically made available in the market, an application platform as a service, that’s the C3 AI platform. And the reason to do that is because yes, the pre-built apps can be configured using the same tooling that we use to build these apps.
But they’re also hundreds and thousands of applications across industries that are needed to basically operate a business more effectively. C3 AI is delivering today 40 applications pre built that are available across different industries, manufacturing, defense, aerospace, oil, and gas, energy, et cetera. We’re going to rapidly expand that. You’ll see 400 different applications, but that’s still not enough. And customers to your point are going to want to build their own and leverage the same technologies that we use for low code, deep code, citizen data scientists to basically build their own and do their own AI applications.
The strategy is deliberate. We’ve been working on it since the beginning of the company, frankly, to make sure that we have a very robust platform. With version eight of C3 AI, we’ve made onboarding new developers, onboarding data scientists, onboarding citizen data scientists, much easier in order to scale up the customer base and accelerate what customers can do internally. That’s the strategy and we’ve been executing it for many, many years.
Daniel Newman: Yeah. I think that’s probably not only the right strategy, almost a requirement. I love the idea of kind of saying, “Let’s do it all off the shelf.” I liken it to track home builders. Then you got semi customs. Then you have the customs. And I think the enterprise world, they want the flexibility of that sort of quick to adopt, swipe the credit card, get started, but once a tech or an app or software grabs hold in an organization, it just has, it’s like an amoeba, it just kind of has this life form and it shifts and changes and grows and it can’t be too rigid. I think you made the right move in a standpoint, how do you get adoption interest saturation and get people to try? But then once they buy, they need to have that developer capability.
Platforms are everything right now, the word platform, it’s so pervasive, it’s almost lost all meaning, but I think that you would agree that this is going to be the key to that net revenue expansion growth and becoming a even bigger and more critical part of every enterprise. Speaking of becoming even bigger and more critical, I want to end here, because I’ll give you a chance to talk about all this and is… The AI, the economic value, the opportunity, I kind of talked in the beginning about the market. Everybody would agree, I think, that it’s going to be large. How big do you think this opportunity is?
Ed Abbo: Well, if you look at IDC as an example, their recent report, which was August, just this last month, they’re projecting an over 700 billion enterprise AI market by 2026 or 2027, so pretty close. It’s going to be very significant in and the way I would kind of frame it as enterprise AI sits on top of the existing transaction systems from SAP and Oracle, et cetera and is going to be critical to how companies operate. And so this is very pivotal in ensuring that you can run your company efficiently and frankly, competitively going forward. The key that I would come back to Daniel is, what C3 AI is doing with these pre-built apps and with the C3 AI platform is accelerating the pace at which deployments occur. And so we start with a three to six month deployment into production of an application.
We make the customer self-sufficient and the customer systems integration partner self-sufficient, so they can then basically execute on a roadmap of tens, in some cases, over a hundred AI applications across their entire value chain. And companies are really looking at this as a competitive differentiator. If you take Shell, for example, they have deployed broadly across their upstream, midstream, downstream infrastructure.
In one area, which is reducing non-productive time, they’re basically deployed across 25 different assets. They’re streaming data from about 1.2 million sensors and monitoring 13,000 pieces of equipment across there. And the value that companies get out of this is measured in hundreds of millions and billions. And so this is actually crucial to their survival frankly, and their competitive advantage. And I’ll leave it at that.
Daniel Newman: No, absolutely. And I’ve talked in the past to some of your execs, and so just citing some of your peers, citing that obviously you guys are always looking for those big use cases where companies can get. You’re not talking about, “Hey, it’s not a dollar 20 return on a dollar.” You’re looking at, “Hey, this investment could return you five to one, 10 to one, 100 to one on savings or revenue expansion, or a combination of excellence. One thing, maybe just as a last item, if you don’t mind about a minute or two left is, an example of how kind of this whole off the shelf development and then ROI chasing can come together for one of these large companies. You have anything in mind that you could point to?
Ed Abbo: Well, I’d love to show you Daniel, a video that the US Air Force produced out of their rapid sustainment office. And we’ll figure out a way to plug it in here.
Daniel Newman: I’ll drop it at the link in the show notes so everybody out there… You send me the link, I’ll make sure it gets into the show notes. This kind of runs through all those numbers?
Ed Abbo: Yeah, it’s a great case of a deployment that’s deployed AI Predictive Maintenance across 16 aircraft platforms. This is roughly about 5,000 aircraft and what AI Predictive Maintenance does for them is it takes all the information available. It could be telemetry off components from a, sensor telemetry off the components, maintenance records, whether environmental information, mission information.
So these are flights that they take, when they took them, where they took them, et cetera, and aggregates all of that to predict 50 to 100 flight hours in advance, whether a component or a subsystem is likely to experience issues.
And that way they can basically deal with it on a scheduled basis. And that’s just the beginning of it. Obviously then you can position parts and maintenance capacity in the right places that they’re available to address those issues. So it really starts with improving readiness or reliability of the aircraft and progresses through what they call logistics and supply chains for them. The economic value as attributed by the Defense Innovation Unit is over five billion a year, when these are fully enabled and deployed. It’s a great case study and we’ll roll the video for you.
Daniel Newman: All right. Well, thanks for giving a little bit of the background. For those of you that are tuned in and are on a device where you can click into the show notes, check out that example. I’m sure that video is actually probably a little bit more interactive than even we are here, but it’s been great having your face here on video, Ed. I do look forward to getting out to the office one of these days and doing some shaking hands. I know everybody’s back at work at C3, and that’s a great thing to see and to hear. I believe the AI market’s got a lot of growth potential, and everybody that was listening, you heard it here, not just from me, but from someone that’s been doing this based upon the hair color, at least as long as I have. He still got a lot more than me to a little credit to you there, but I want to thank you so much Ed for tuning in, joining the show. And let’s do this again sometime.
Ed Abbo: Thank you, Daniel. And look forward to housing you here. Take care.
Daniel Newman: All right everybody, hit that subscribe button. We appreciate you tuning in to the Futurum Tech Podcast. We have many great interviews here with some of the biggest thinkers, coolest technology companies and all kinds of other things you would want to know about. Tune in, stick with us, follow us on social, all that. Got to go now. I’ll see you later. Bye-bye.
Author Information
Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.
From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.
A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.
An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.
