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Transcript:

Patrick Moorhead: The Six Five is live here at OpenText World 2023 in Las Vegas. Daniel, it’s great to see you. And gosh, here we are at the event, talking about probably two of our favorite topics. First one of them is AI, and then some things that we’re always talking about here is how do you get the data in the right position to be activated by generative AI?

Daniel Newman: Can I be honest with you?

Patrick Moorhead: Yes, please.

Daniel Newman: I did not know what you were going to say as number two. You said AI, and I’m like, “Is there anything else?”

Patrick Moorhead: I don’t know. I don’t know. We’ve only done 900 of these. You can’t read my mind yet?

Daniel Newman: Well, there was a couple of different ways you could go. We’re both writers, written seven books. You might’ve said content. Content’s a pretty big thing here at OpenText.

Patrick Moorhead: It is a big deal here, for sure.

Daniel Newman: So it’s been a really good couple of days and you know that every enterprise on the planet is facing this challenge to try to figure out how to manage their data estate. Are you bringing your data to the AI? Are you bringing your AI to the data? Are you going to build it yourself? Are you going to partner? And then as you have this massive sprawl of enterprise applications, where do you go? Who do you trust? What tools do you use to really get the value out of your AI, out of your data and do it in a way that’s secure, a way that takes care of people’s data and privacy and trust. Pat, this is a big challenge and that’s what’s been talked about here at OpenText World.

Patrick Moorhead: It is. I think it’s a great entree and I think we should introduce somebody who’s quite qualified to answer many of these questions. Mark, how are you doing?

Mark J. Barrenechea: I’m doing great. Great to see you both here at OpenText World, live from Las Vegas.

Patrick Moorhead: Absolutely. First time guest and hopefully not the last, so hopefully you can help us decrypt for our watchers and listeners out, what’s going on here at the conference.

Mark J. Barrenechea: Do my best.

Patrick Moorhead: Thank you.

Daniel Newman: Mark, it’s interesting, because OpenText is really on a growth spurt. The Micro Focus acquisition. You’ve doubled the size of the company in a very short period of time and you’re such an important presence across so many enterprises’ day-to-day business. But I also think sometimes the company can be a little bit mysterious. And so this is where I want to start, because Pat, we’ve been to what, a hundred events this year. It’s been pretty ridiculous.

Patrick Moorhead: I lost count.

Daniel Newman: And AI has been the center point of all of them, and so I feel like it would be the most appropriate place to start and say AI was at the center of today’s keynote that you delivered. But everyone wants to know why is OpenText different than company A, company B, company C, who are all getting up on stage and telling a story about AI? So I’d love for you to just give the; what’s the OpenText thesis? What’s the vision that you have about AI and how the company’s going to do it differently?

Mark J. Barrenechea: Absolutely. So look, we posed a question this morning; what makes great AI? And we think it’s great information management. And we’ve been focused, for three decades, on automating information management, automating content, automating experiences, automating the business network. It’s a big year for us, as you noted, we acquired Micro Focus and expanded our information management mission into IT assets, application assets, into migrating workloads from the mainframe into the cloud, doubled down on security.

So, we’re a $6 billion company focused on information management automation. And through that automation, we’ve helped our customers create some of the world’s largest data sets. We have customers who have 30 trillion records, processing a billion pages a day. So, you need the information management automation, large data sets, and with the rise of models and algorithms, we’re now going to apply that to the information automation. So we are in a very unique position, because we’re managing some of the world’s largest data sets, private data sets, to help unlock the next generation of value through artificial intelligence.

And the destination isn’t gen AI, it’s a whole set of features that get us to artificial general intelligence, which is much wider. It’s IOT, it’s robotics, complex decision making, and ultimately where workflow becomes a decision engine to actually make the choice for you, which may make some of our humans a little uncomfortable.So we think we’re very differentiated, that we want the data to stay right with the automation, your operational and experience data. Now layer on top of that learning data, the learning data generated from learning models. I know there’s a lot there.

Patrick Moorhead: No, no, no, this was good. And by the way, I’m a visual learner and therefore the pictures I take at these events are ones that I want to keep and put in my back pocket. And the one where you laid out the types of data that you store and the amount of data, that was one of the pictures I took, because I thought it was impressive. It’s funny, a lot of people say, “Oh, in the age of generative AI,” okay, my company says this too. “You really have to have your information estate and your data estate,” you have to have your act together to be able to do that.

And for what it’s worth, we said that in the ’90s with analytics, we said it in the 2000s with machine learning. We said it with deep learning and we’re saying it again with generative AI, but there are some characteristics that do make generative AI and machine learning bigger challenges about information management. What are the challenges that your customers have with machine learning and the solutions you have for information management? And the same for generative AI, what is your solution you’re providing here?

Mark J. Barrenechea: Yeah, it’s a great question. I think there’s some real similarities with BI, but yet some real differences. You can have okay data and get goodBI results. Not so true with learning models and machine learning. Look, I got some problems I’d like to automate inside of OpenText. Sorry, there’s some problem areas I like to apply Aviator to, our AI product, but the automation isn’t good enough yet. So I first got to focus on the automation and when the automation is really good, you get really good data.

And I got another problem set where the automation’s great, but the data’s not in great quality. So, we’re certainly evangelizing that prerequisites to get to gen AI, other forms of AI, is get the automation right, get the data right, and then you can really make a substantial investment. Because it’s an investment to vectorize your information, to be able to get the encodings right, to then be able to do prompt engineering around it. So it’s a real capital investment to get the benefits out versus just some experimentation. So you got to get the automation right, get the data right. BI, you could have okay data and get reasonable results. It’s harder with gen AI.

Patrick Moorhead: Yeah, when I’m talking to CIOs and even CEOs, one of the things that petrifies them is this notion of, “Okay, it was one thing to act upon the data that was in an ERP, alone, or a CRM, alone.” And now this notion of connecting all these pools of data to HCM, ERP, SCM, to legal, to product types of data, they’re still trying to figure this out. And at least from what I’ve researched so far, you have a pretty good track record in knowing how to do that and also not needing to change the security model to get there. Am I accurate in that assessment?

Mark J. Barrenechea: I’d say there’s two things in there. I think data, like a cabbage, rots. And so it’s really important for customers to keep their… You got to automate. Automate, automate, or keep your data fresh, keep your data relevant. And we’re not a fan of extracting the data out of automation and moving it to some far away planet and applying language models to it. So we think the automation and the AI need to be integrated. And that’s a really important point.

The other piece is permissions and security. Not every employee should see every piece of data and once you extract the data out of the automation, you lose relevancy, you lose currency, you’re going to lose identity and trust and permission levels. So one of the things we’re going to differentiate on is what our acquisition of Micro Focus, NetIQ is an incredible tool. So we’re going to have permissions and hierarchies built into our language models to help you preserve permission access for the data.

Patrick Moorhead: Cool.

Daniel Newman: I’ve been thinking about an analyst Aviator. I just-

Mark J. Barrenechea: No, you were-

Patrick Moorhead: Is this the Dan bot?

Daniel Newman: Look, the new vector for Dan Bot would be; I take all the photos of you on stage talking and it can actually pull all that data in and then it can synthesize it. Tell me which images are important and maybe write a few tweets for me, crank out a Forbes piece and then prepare my remarks for a CNBC hit. Can you do this? Kidding. Don’t answer that. I’m not going to put you on the spot.

Mark J. Barrenechea: It’s all about the capital investment.

Patrick Moorhead: I don’t know, Dan, I saw his trial program out there for $350,000 you can check out.

Daniel Newman: A million, what was it? I can get a million pages or a million…

Mark J. Barrenechea: Yeah.

Daniel Newman: Yeah. I did write that down. Pat may be reaching out to get on that list right away.

Mark J. Barrenechea: Yeah. We introduced today what we call the Get Your Wings program, inspired by the 90-day wonders that came out of World War II actually, where you have a lieutenant go into a flight school and in 90 days they’d come out with their wings. So, we called it the Get Your Wings program and we’re offering to customers a guaranteed program, a million documents that will get you up and running in two weeks. And you raised this point a little earlier, how do customers start? Where do they want to experiment? So we offered a program today, Get Your Wings, two weeks guaranteed. Give us a million documents and we will return you a platform that’s metadata, objects, indexes, vectorized, prompt ready, POM-2 or T5 and open source. So, move a million documents in, move a million contracts in.

Daniel Newman: I like it.

Mark J. Barrenechea: A million claims, you’ll be up and running in two weeks.

Daniel Newman: I like your decisiveness and I like the fact that you’ve shown how you’re monetizing. I’ve said for a while, especially the street is really trying to understand what companies are finding a way to make money on AI and who’s just talking about AI. And with that in mind, I thought you had some pretty thoughtful, provocative comments today about the evolution of software, of AI, and I’m going to give you a choose your own adventure question here, because software and AI are evolving at a really quick-

Mark J. Barrenechea: Do I get to choose my adventure partner?

Daniel Newman: Yes. You’ve already seen what kind of questions I’m asking. I know who you’re going to choose.

Mark J. Barrenechea: Okay.

Daniel Newman: But software’s going through this really rapid revolution and you commented on everything from responsibility, trust, ethics, you looked at… I still liked your comments about Alexander Graham Bell and some of the… Talk a little bit about how AI is changing the software industry as a whole
from someone who’s not only delivering, but also partnering with so many companies that are building software solutions.

Mark J. Barrenechea: Yeah, we talked about today a couple things and thanks for giving me an opportunity to pick my own adventure.

Daniel Newman: There’s a lot of ways you can take this one.

Mark J. Barrenechea: Yeah, I talked at the keynote this morning. When you think of a car, you don’t think of a safe car or a car, you just think of a car. And with AI, this debate of AI versus ethical AI, there’s only ethical AI. There can’t be an unethical AI.

Patrick Moorhead: It wouldn’t do very well, would it?

Mark J. Barrenechea: Yeah. So, ethics has to be built in. And we’ve thought very deeply around, as soon as you include something, by that act you’re excluding something. So how do we deliver against tech for good, ethical AI, be inclusive to bring the best skills into our ecosystem? And we think, for us, it’s starting with value-based design. Go all the way back to the design. What are the values and principles that we want to put into the output?

And in a lot of ways, the individuals who lead us through that design process have to be a bit agnostic and not biased by any means, and they have to really start with a set of principles and values, because the end result, with any technology, is a dual use. I went through a history of dual uses, all the way back to Socrates being criticized that the written word would confuse our thinking, that when the telephone came out, those criticized Alexander Graham Bell that the phone would turn us into mindless jellyfish. Where Edward Tufte, who talked about power crops and PowerPoint crops absolutely. And we have Dr. Joy Buolamwini speaking tomorrow about design justice and value-based design. So we want to build the values early into how we design and build and choose the optimistic view of the world and the optimistic use of technology.

Patrick Moorhead: So I’d like to pivot to products and a big announcement that you made and that you really gave a lot more details on was Aviator, so big that we have Ice in the background. We have a brand new mascot. Anytime a software or SaaS company comes out with a new mascot, it’s got to be big, okay. So can you talk about, Aviator was everywhere. It was on every one of your services and you could access it in multiple ways, which is great. I know you love all your children the same, but are there some of the Aviator family that you think will bring the most value to your customers?

Mark J. Barrenechea: Yeah, we announced a very comprehensive set of products today in the AI space and our brand is Aviator. So, built the company 30 years, 6 billion in revenue on automation. We’re now want to keep going on automation. There’s a long way to go, but we’ve opened up a new flight path in AI and our brand is Aviator. I think some of things top of mind; Aviator Search. It’s a whole new way to connect across multiple data sources and not have a graphical user interface in that data, but have a conversational interface. And just reimagine of how you can extract all the value from millions and millions and millions of documents, not have this flat search thing that comes back, but gets deep insights really rapidly. So I think that’s top of the list.

A couple more, Content Aviator. We demoed today OpenText Content Aviator, which we showed a claims processing, good old contract claims process, that in the traditional graphical user interface would take maybe two weeks to resolve. We got it down to five minutes and we really mean it. We think you can get to that business result from weeks down to minutes. We also took our Business Network Aviator and showed how the supply chain can make a real time connection to start to trade. Imagine some of the world’s largest CPG companies. They have to connect ERP to ERP, bank to bank, ACH to ACH, do credit checks, you’ll flow funds. That’s a 60-day process to start to set up a new trader. We think we get that down to days and thus the change for companies to be able to experiment at scale in the supply chain. So, we love all our children, but the three odd call out are Search, Content and really the supply chain revolution that we think we can enable.

Patrick Moorhead: For what it’s worth, the Search, I take pictures of things I want to put in my pocket and remember, I took a picture of that. It really reinforces how this new AI revolution is different. It crosses, I think you may have had 25 different data sources and not just is it giving you a search result that’s vanilla for every result, it’s actually combining the data for those results and coming up with something that’s uniquely intelligent. And you also had public data sources in there as well-

Mark J. Barrenechea: And TSB incident reports.

Patrick Moorhead: Yeah, and I also saw inside of Aviator Search, even YouTube results coming back that… Anyways, it was a really good example. Again, Dan and I were on the road at a hundred AI events, I think, looking at this and I thought that was very unique in the way that it should… Don’t get me wrong, the invoicing and all of the back office stuff was cool too. Dan and I have businesses and AR and contract negotiations and stuff like that is super important for us. And I took another note to look into that, but I like those.

Mark J. Barrenechea: Yeah, this is a really good example. I didn’t mean to interrupt. We want to take an example and it was Log4J. We all lived through Log4J. It was really an existential moment that stopped the industry for three days. When Log4J came out, behind the scenes, software stopped for three days, it just stopped. The world stopped for three days. What is it? How pervasive? Were things planted? And the world’s infrastructure, the world stopped for three days. The sun rose every morning and set. But for software companies, the world stopped for three days.

And we wanted to show what the world could be like with AI and an Aviator in a very immersive experience to try to solve for Log4J, because the next one will come. And it was three days of panic for the world. Could we get that down to an hour or two in a very immersive experience? So I’m glad you picked that up, because it was the genesis behind of how we wanted to bring Search to life and use an example that everyone experienced, which was Log4J.

Daniel Newman: I think to some extent where you really have the opportunity to add substantial value though is when you can identify these industry specific problems that are well understood, at scale, and have not yet been solved. And when you can apply that, it becomes almost a foundational model of automation. And then of course you can layer a generative AI right on top of that.

Mark, I’d like to take us home and home is not always in the data center. Home is not always in the cloud. Sometimes it is at the edge. Sometimes IOT. You showed some really interesting examples today. Pharmaceutical labeling of, and this extends the supply chain. You clearly see the IOT, the Edge as a massive opportunity and I think we know the exponential data that sits outside the data center. Talk a little bit about why you’re leaning in there and how you see that proliferating, and then the role of course OpenText has to play there.

Mark J. Barrenechea: Yeah, it sounds great. We’ve spent the last 30 years automating for humans. And humans have been fun. Thank you.

Daniel Newman: Are you not entertained?

Mark J. Barrenechea: Yeah. Humans have been a lot of fun. Some of my best friends are humans.

Patrick Moorhead: Some of them.

Mark J. Barrenechea: Yeah, some. But it’s clear, the other big trend is mineralization, things getting really small and around everywhere. And this idea of, just like we had this breakthrough moment, the child speaks, it was 50 years of model development in which ChatGPT-3.5, the child, finally speaks. It’s been 20 years of things getting smaller or about to hit a threshold. If RFID tags and antennas can get down to a penny, they will be everywhere.

Patrick Moorhead: It’ll be printed.

Mark J. Barrenechea: So, we think there is another explosion about to happen, which is literally everything connected. And so we’ve opened up our services, Thrust Aviator Services. We’re taking the lens for the internet of things, people, places and things. We used insulin as an example, going from manufacturing through transport delivery, hospital, a lost pallet through claims management. But the premise is, just as we’ve hit a breakthrough moment for language models and the child speaks, we’re going to hit an inflection point for things getting really small. And, we think we have a role to play, because we’re a data cloud, and we’d like that machine data, that IOT data, the trillions of bits to be managed in our data cloud, so we can offer you all the automation and AI capabilities.

Daniel Newman: Seems like a tremendous opportunity for Aviator and of course for OpenText. Mark Barrenechea, thank you so much for joining us here on The Six Five.

Mark J. Barrenechea: Pleasure. Thank you for being here at the event. We really appreciate it.

Patrick Moorhead: Thank you.

Daniel Newman: Good luck. Everyone out there, hit that subscribe button, and join us for all of our coverage here at OpenText 2023 in Las Vegas. The Six Five is on the road and we are enjoying these conversations and we hope you are too. We got to say goodbye for this episode, but we’ll see you all really soon.