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In a video interview with The Forecast, DataRobot CEO Debanjan Saha discusses the need for finding value and building confidence to achieve enterprise AI success.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Debanjan Seha: I was in a Morgan Stanley investment conference in San Francisco, and of course every investor quizzed me about ChatGPT and GeneAI. On the way back, the Uber driver quizzed me for half an hour about ChatGPT. And I realized that AI has arrived. But there are limitations to what some of these models can do. So what are those models doing? I mean, we have trained them on a really, really vast amount of publicly available data and sometimes on proprietary data. And once you run out of those data sources, there is a limit to how those models are going to get better and better over time. Of course, there will probably be better and better models, but I don't think it can get incrementally better for an infinite amount of time. That's not going to happen. Now, we are already pretty much fed all of these models with all the written text data that humankind has produced over the last hundreds of years.

So there is not too much text data left that you can feed these models to. What is left, by the way, is a lot of audio and video data, and that data is much easier to collect. And there are a lot of things which you don't write down. For example, when you lift a cup from a table, nobody writes it down, but there are a lot of videos which show how we go and grab the cup and pick it up from the table. So those are the kind of data sources which are now going to make these models more and more intelligent. But I do think there is a limit to which you can feed them information and make them better.

[Related: Building a GenAI App to Improve Customer Support]

I see two gaps that we are really focused on. One I call the value gap. People are spending a lot of money training models, but ultimately those models have to solve some business problems. They have to make things more productive or predict things more accurately or whatever it is. Business value has to justify the investment that's going in there. And it's not easy with AI to create that business value. And one of the things DataRobot does very well is to connect business problems with AI to solve them. So that is the first problem that we are focused on. The second thing, which I also see with any new technology, is you need to build trust. AI is kind of in that phase and there is a confidence gap. There are a lot of people who are creating, especially with GenAI, a lot of prototypes.

[Related: Enterprise IT Teams Jump Into AIOps]

It's not difficult to put together a chatbot which answers some questions. It's hard to guarantee that they're going to answer the question correctly, that it's not going to create any misleading answers or create harm in more serious cases. And there are a whole lot of things we need to do in terms of managing the risk and creating a framework where you can define and associate risk with various different types of use cases and handle them with the right level of mitigation, compliance, testing and validation, et cetera. So those are the two things which are very, very important for wide scale adoption of AI.

[Related: Role of CIO Expands with Enterprise AI]

For example, the value gap. So the most interesting and most challenging thing about the value gap is that you need people who understand the business and the business challenges that they're trying to solve. And you need people who understand AI and how those two things can be put together in order to solve a business problem. So we do ideation workshops with our customers. We sit down with customers and their people who understand the business and work with them to figure out their business challenges and what use cases they have. And then our team works with them to figure out how to address those use cases. Do they have all the ingredients…the right datasets? Do we know what business processes need to be updated if we insert AI into that? Those are the things we do first. Then people can use our platform either themselves or we can help them as co-pilots. And we sometimes have our other partners, the service provider partners, to work with them to solve those business problems and actually build those models, build those applications, and then put them in production.

Pretty much every industry you go to, there are enough problems that AI can help solve. And pretty much every business line you go to, there are enough problems that AI can help solve. But bringing AI to business, that's still not easy for everybody because you need to speak both AI and the business language. And it's very difficult to find people who speak both. So you have to have two in a box and solve those problems.

In this Tech Barometer podcast segment, technology analysts Simon Robinson, Natalya Yezhkova and Steve McDowell discuss the Broadcom acquisition of VMware and how it’s impacting CIOs and IT decision makers.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Simon Robinson: Clearly the market is in flux. It has been in a transitional phase for the past several years.

Natalya Yezhkova: Broadcom is a mature company. It has a certain way of doing business, so VMware culturally business-wise was very different from Broadcom.

Steve McDowell: They're laser focused on high revenue, high margin business, and every move that we've seen them make plays directly toward that.

Jason Lopez: Three technology analysts walked into a room and talked candidly about the Broadcom acquisition of VMware. This is the Tech Barometer podcast. I'm Jason Lopez. The analysts are Simon Robinson of Enterprise Strategy Group who covers infrastructure. Natalya Yezhkova, a research vice president with IDC who writes about enterprise workloads and emerging workloads infrastructure, and Steve McDowell, chief analyst with NAND Research. This was a press conference at .NEXT in Barcelona, Spain in May of 2024. One of the initial questions in the press conference: Why did Broadcom make this move? Natalya Yezhkova began by explaining why companies make acquisitions in the first place. One reason is to grow the new acquisition as a business unit,

Natalya Yezhkova: Or they go the Broadcom way, which I think happens rarely with companies as large as VMware small organizations. When they are acquired, they usually absorb very quickly. Broadcom wants to continue to do business the way Broadcom always did it. They wanted to go into the software business, and that's what the acquisition of VMware was for them. But they didn't see a need to adjust to the VMware culture or keep VMware as an independent company or more independent business units. So they want to absorb it quickly into the Broadcom way of doing business.

[Related: Nutanix CEO Paves Way for Those Leaving Broadcom VMware]

Jason Lopez: Simon Robinson said he's heard no indication Broadcom would integrate or encourage collaboration between VMware and other business units.

Simon Robinson: The focus here is really the opportunity as Broadcom MO (modus operani) was to take a business that has established itself at the core of most data centers globally and enabled quite an incredible change in how data centers are architected over the last 15, 20 years. But I think Broadcom saw the way VMware did that was not efficient. So the capabilities were there, but the efficiency wasn't, in their view. So that is the core focus. Well, one of the focus areas is to drive more efficiency into how they are structured, how they go to market.

Jason Lopez: Natalya Yezhkova said in terms of hyperconverged infrastructure (HCI), the overall market declined in 2023. Of those that grew, only a few vendors were not associated with vSAN.

Natalya Yezhkova: Customers now approach acquisition of HCI systems because of this uncertainty between Broadcom and VMware. There will be some additional impact on the HCI market. Overall we expect it'll continue growing, but in the short term, there is some shrink attributed to customers being cautious with purchases of vSAN.

[Related: Wondering What Broadcom Will Do Switches to Mitigating VMware Risk]

Jason Lopez: The centerpiece of the press conference wasn't necessarily the Q and A with journalists, but these exchanges between Yeshkova, Robinson, and McDowell, starting with the topic of the future lifecycle for VMware products.

Simon Robinson: The market is in flux, and that's been driven by multiple things. Primarily, I would say from on-premises IT environments to cloud-based IT environments as the major force that we've seen over the past several years, and that's had its own disruptions and impacts on the market. You've had the transition caused by the emergence of a whole new class of workloads and applications and the emergence of the technical architectural underpinnings of those things, predominantly driven by the emergence of containers and orchestration all around that. So you kind of got those two major shifts already in flight, and that was causing organizations to make some really big strategic decisions around workload, location, and optimization and building their strategies around that.

[Related: Existing IT Hardware Gets New Path to Hybrid Multicloud]

Natalya Yezhkova: And also to add to what Simon has said, we’ll see the impact across multiple years. So it's not something that we will see in one quarter and it's gone. There are customers who up for renewal with VMware. Now there are customers who up for renewal in three years from now, and they still have the old terms of licensing. For them, it's like a delayed impact and they have time to absorb. But those customers who are currently in the position where they need to make a quick decision whether they want to continue with VMware by Broadcom and adjust to new pricing, definitely in a tougher situation than those who need to make this decision in three years.

Steve McDowell: When you look at VMware technology, what's interesting about that –– but it's not true of pretty much anything else except Oracle. Equate VMware and Oracle from this perspective –– it's that they're extremely sticky and it's expensive as hell to change out. And the accountants of Broadcom [did] a really nice job of calculating what that switching cost is. The IT guys, they got to worry about this AI thing. They don't have time to re-architect how I'm doing my workloads. So they're pricing it just under the pain threshold for those customers they care about, and they never have to do another piece of innovative engineering ever because these legacy applications, it's going to be like Oracle. It's an annuity for VMware forever, mid-range of the market. Man, that's in turn. I don't think we know where they're going to land. We've seen some movement back and forth, but those guys, I'd be scared if I were a small company relying on VMware. VMware is not going anywhere.

[Related: Enterprise IT Teams Jump Into AIOps]

Natalya Yezhkova: So I agree that VMware is not going anywhere. We had a range of technologies which went away, but they [are] still here. So mainframe tape... it has been said [often that the] mainframe was dead [and] tape is dead. They're not. The market shrank significantly, but both still exist. I'm not saying that VMware is going this way, but it's transforming as a business with this change. But I would agree that it wouldn't go away. It would look very different like five years from now.

Steve McDowell: That's a good analogy. If you look at IBM with mainframe, you look at IBM with Power [System Servers]. These are technologies that have not evolved in 30 years. They're milking a captive user base, and that's what Broadcom likes to do across almost all of their products.

Jason Lopez: So as one journalist asked, who could take advantage of that position.

Natalya Yezhkova: I can say that from the HCI business perspective, pretty much everyone [can take advantage of this situation]. So with all the uncertainty which exists, I think even companies who worked with vSAN, they start looking for alternative solutions for the HCI offerings. So I think it creates great opportunities for infrastructure vendors. What companies are looking for now [is] integration of resources between dedicated environments and public cloud. They look at security and resiliency. Security and resiliency [are] always on top of the list. They look at modernization of the storage and data management strategies. VMware was in this business as well of providing the tools for managing both dedicated and public cloud resources for investing in security. So they were also in this business, but there are companies outside of VMware who do just what companies are looking for. This whole situation with Broadcom made companies start looking for what else they consider. It's a great opportunity for vendors to take advantage

Simon Robinson: Just to build on that, on the previous question, again, the dominant motion of the past 10 years has been from on-prem to cloud. In fact, one of the things that VMware talks about is about the risks to going all in on a public cloud. What generally we don't have in the industry today is portability of workloads, data applications between clouds. That's still really difficult,

Natalya Yezhkova: But customers want it.

Simon Robinson: But I think, again, this whole trend is going to reinforce [caution for] going all in on anything. [That] has its risks.

Jason Lopez: Steve McDowell followed up by reiterating. He doesn't think this is fundamentally a technology question.

Steve McDowell: They all smell blood in the water. There's money to be had, and it's not a license [but] a switching cost. It's not going to be a rush to the door because apart from a handful of small businesses, everybody's on three and five year cycles. There was a rush to renew those licenses ahead of the Broadcom acquisition. So it's going to unfold quarter over quarter for the next four years.

[Related: Focus Shifts to Migration in Wake of Broadcom’s VMware Acquisition]

Simon Robinson: Well, there's also the ways in which they're tied in, right through custom scripting and all of that, depending on the breadth of the adoption on that side. Technology vendors are nothing if not pragmatic. If they see that their customers are asking for something different, then they're going to respond.

Jason Lopez: Simon Robinson writes about infrastructure for Enterprise Strategy group. Steve McDowell is Chief Analyst at NAND Research. Natalya Yezhkova is Research Vice President with IDC, covering Enterprise and Emerging Workloads Infrastructure. This is the Tech Barometer podcast. I'm Jason Lopez. Thank you for listening. Tech Barometer is produced by The Forecast where you can find more stories on technology and the people in tech. Just go to theforecastbynutanix.com.

In this video interview, Taylor Linton of Hugging Face explains the thought process many IT decision makers go through as they select open source and proprietary AI software to run their businesses.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Taylor Linton: With open source. There's really two categories of benefits with using open source models, and it's really broken down into strategic and tactical reasons. To start off on the strategic side, it really comes down to ownership of an ip. Meta has done a great job commoditizing the models and other open source model trainers. So the fact that these companies can take their proprietary data and customize these models, that's a great opportunity to be really a competitive differentiator from their competition, having their own models that they own that are trained on their proprietary data. Another big reason on the strategic side, it really comes down to cost. And what I mean by that is sometimes your incentives aren't aligned when you're using a proprietary model because they charge based off of input data and output data. So the more value you get, the more charged. And so the fact that you can build an open source model and you're only paying for the commodity hardware, your goal is to get as much usage as possible, whether it's getting it in front of more customers or getting more AI assistance used from your employees, not only are you able to train your own model and keep it in your environment, but there's a lot of security benefits.

[Related: Role of Open Source in AI]

Customer trust is huge. Responsible AI is huge, and the fact that you can feel confident that you are sending your data to a model in your environment, it's a great opportunity to really de-risk it. Another part on the tactical side comes down to latency. If you don't own the model and you can't control the infrastructure that it's running on, there's many times when requests might take four or five seconds to get a response. So when you have ownership of the open source model, it allows you to make sure that you get that ultra low latency to support your requirements.

So that's one of the things that people really appreciate with open source models because you can download the model and run it in any environment you want to. So it could be their preferred cloud provider, it could be on-prem, it could be in their Nutanix environment too. But really it comes down to where do they want to run it? Where's their preferred environment? We often suggest our customers to go grab their own proprietary model, test out the use case, see how it performs. It offers a good benchmark to know roughly where AI is to be able to support the project from an accuracy standpoint. Now, once that POC has been built, that's where we work with them to say, okay, how should we build this with an open source approach? Now, open source AI is not a silver bullet. There's really trade-offs too. So when you start looking at cost, these models, they scale very well from a cost standpoint, open source models, and the reasoning is because you're paying for the commodity hardware that it's running on.

[Related: Enterprise IT Teams Jump Into AIOps]

The downside to that is if you don't have a large volume use case, it could require some expensive compute to host that model, and it might not make financial sense to go deploy a model for low usage. In those instances, it totally makes sense to be able to use a closed source proprietary model because you're only charged for input data and output data. So it can be really affordable in lower volume projects. But of course, as you do scale it out, that's where it gets very expensive and the scale tips pretty quickly. It's where it does make sense to deploy an open source model on your own hardware.

Related: IT Team Modifies Open Source NetBox to Help Manage Hybrid Multicloud]

So it's nearly impossible to try to automate the process of knowing which model to use and the best way to train or adjust the model for a use case. And so our engineers sit down with customers and look at what exactly are we trying to build, what type of data are we going to be sending to this model? What constraints are there, whether it's cost or latency. And so we sit down with them to understand what is the best open source model for this particular project. And then from there we can sit down and figure out how should we customize this to improve performance, whether it's speed, latency, or accuracy. These are all trade-offs that really you kind of have to pick one or the other. So we do our best to help them make the best decisions there.

In this video interview, Hugging Face’s Taylor Linton explains how enterprises can use existing open source software instead of building AI applications from scratch.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Taylor Linton: Predating me joining Hugging Face. We started in 2016 to be a chatbot company. That's where the fun hugging face name and emoji comes from, because it's supposed to be a chatbot that's your friend. So we wanted a friendly brand associated with it. But since I joined Hugging Face in 2021, we started out with 15,000 pre-trained models on the hub. And it was so interesting talking to customers then because it was, Hey, there's too many models out there, I don't even know which one to start with. And just seeing that grow from 15,000 to 650,000 in a few years, it really shows how much ai, especially open source has grown.

[Related: Enterprise IT Teams Jump Into AIOps]

It gets even more challenging. So since yesterday when you and I both looked at the models, there's already 2000 more models. It's crazy. And the tough part is you can't really automate these decisions on which model to use. It really is context dependent. You need to know what your cost requirements are, what type of input data that you're going to be sending to the model, because models are trained on different types of data and you need to try to back into the model that was exposed to your type of domain. During the training. It comes down to latency, the particular task. So these are all things that our engineers can sit down with customers and look at what are we trying to build here? What type of constraints are we working with? And we really walk through them, walk through with them the different trade-offs that you make when you're picking a different model.

[Related: Role of CIO Expands with Enterprise AI]

A lot of CEOs weren't even aware of what AI was a couple years ago, and then chat GPT came out and CEO's kids were doing their homework with it and all of a sudden they started hearing about it. So there's been a lot of interest in companies to try to take advantage of this technology. When folks talk to hugging Face, it's because we're the entire open source ecosystem around ai. And so when they want to explore and take advantage of these open source models, they might go to our hub to go grab one of the 650,000 open source models. But also, once they do grab that model and bring it into their environment, we have quite a bit of maybe 20 different libraries or tools that they use to actually go all the way from building to deploying these models. So that's really when we get involved with folks is to help them take advantage of open source AI.

[Related: Building a GenAI App to Improve Customer Support]

The community around hugging face and just open source AI is incredible. So the fact that someone might release a paper that introduces a new optimization technique, the fact that the entire community can benefit from that and be able to use it on their models, it's really exciting and it gets folks pretty motivated to be able to introduce a new novel technique and open source it to benefit a larger group of people. I think that open source AI is critical to be able to keep responsible AI across the world. The biggest reason is because the last thing that people want is to only have a few companies that have access to this technology. The fact that people can build in public and you can know certain data sets that were used to train the models. It's a great opportunity for companies to make sure they are using models that were trained on permissive data. And bias is also a really big area that folks are looking into because of course, human bias exists in these training sets, and that ends up transferring to the model's bias. So the fact that people can work together and in a community to really look into these data sets and do their best to try to identify and mitigate bias in these models, that's very important.

[Related: AI and Cloud Native Alchemize the Future of Enterprise IT]

We partner with quite a few companies, but Nutanix has really stood out to me. It really seems progressive to make sure to offer the best tools and technologies are made available to their customers. So what I've learned with GPT in a Box, the fact that they're integrating it with all of hugging faces, open source libraries, they're making it frictionless for customers to be able to deploy open source models in their environment. You don't see that often from, and they're really going a long way to make sure that the best tools are available for their customers.

The impact of cognitive impairment and dementia in the workplace is on the rise. Due in part to an aging workforce - 62% of older Americans are still employed full-time - the importance of understanding Alzheimer’s and other forms of dementia is increasingly critical to HR leaders.

Tune in as we sit down with Joanne Pike, president and CEO of the Alzheimer’s Association, to discuss the experiences of employees impacted by Alzheimer's, both those living with this disease and those who are caregivers. She also shares how employers can better support these individuals through workplace culture and benefits. Listen now.

Guest: Joanne Pike, DrPH, President and CEO of the Alzheimer's Association

Intel IT’s Information Security (InfoSec) group has adopted capability-based planning (CBP) because it provides a framework for aligning our group’s strategies with Intel’s overall business strategy. CBP helps us prioritize projects and resources, communicate more effectively with business stakeholders, reduce complexity, and become more agile and responsive to changing business needs.

CBP is a planning methodology that drives business-focused outcomes by providing a systematic, objective, and holistic view of a group’s capabilities. In our case, these capabilities center around InfoSec, such as digital identity and access management and privacy management. Using a four-phase cyclical process of alignment, assessment, planning, and management, we can:

• Determine the maturity of every capability using a precise, mathematical formula.

• Identify and prioritize gaps between the current capability maturity and the desired level of maturity.

• Create a roadmap to resolve those gaps.

This comprehensive method of capability mapping and scoring delivers a unified view of capabilities and objectives so that teams can collaborate and optimize cross-functional efficiency.

Adopting CBP has provided several benefits to the InfoSec group:

• Strengthened InfoSec strategic alignment

• Optimized resource allocation for critical security capabilities

• Proactive risk management

• Enhanced security performance tracking

• Improved collaboration for shared security

• Responsive adaptation to third-party security frameworks

As other Intel IT groups—and even business units—see the positive outcomes of CBP, we hope they also begin to adopt it, which would multiply the benefits across all of Intel. Furthermore, CBP has already fostered collaboration across departments and teams.

Our comprehensive method of capability mapping and a formulaic scorecard captures the strategic importance, business goal importance, capability maturity score, and quantified risk. It highlights what is required to execute initiatives successfully. Our solution addresses our longstanding InfoSec planning challenges but can also be applied to other IT groups and even other industries. Adopting CBP is an excellent way to align every decision with its strategic vision and capabilities and drive sustained enterprise success.

In this Tech Barometer podcast segment, Justin Mason, associate director of vendor and operations at the University of Canberra, explains how moving to Nutanix software enabled their IT operations to run demanding AI and machine learning courses and keep students engaged in their online studies.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Justin Mason: We are all AHV now. We don't have any VMware. We've been in that place now for a number of years, so we've been fortunate enough to miss this whole debacle around VMware and Broadcom at the moment. I know a lot of other organizations are kind of feeling that pressure now, so that's one less thing we have to worry about.

[Related: Birth of a Hypervisor That Unleashed the Hybrid Multicloud Era]

Jason Lopez: Justin Mason is the associate director of vendor and operations at the University of Canberra in Australia's Capital City. This is the Tech Barometer podcast. I'm Jason Lopez. What we have here is a press conference from 2024, next held in Barcelona, Spain, as in our previous reporting. These press conferences are a bit different, favoring an informal face-to-face with tech journalists. So Mason started off with some background on how the University of Canberra's IT organization adopted Nutanix. He oversees the IT strategy for the school, which numbers about 15,000 students and staff. About 10 years ago, they decided to move away from three tier architecture.

Justin Mason: We were early adopters in the sense of when we jumped on the Nutanix platform at the time, we started to look at, well, how can we do this better? How can we do things more cost effective? And that's when we looked at Nutanix and hyperconverged. Back then, we were primarily a Hyper-V shop, so we started to run Hyper-V on Nutanix. That wasn't as reliable as we'd hoped, but that was more on the Microsoft V clustering side of things. We then moved over to running VMware on Nutanix as our second POC or proof of concept. That went extremely well.

[Related: Wondering What Broadcom Will Do Switches to Mitigating VMware Risk]

Jason Lopez: Mason and his team decided to fully transition to hyperconverged in 2014,

Justin Mason: And that's when we decided, okay, we want to go all in on this platform. It was doing what we needed to do as extremely reliable, and that's when we started to look at it, move over all of our legacy architecture to Nutanix hyperconverged platform. A couple of years later, Nutanix came out with AHV because we were the kind of technology leads at the university. Were always encouraged to try new things, and at the time we were already paying additional licenses and VMware and could save some money in there. That area. It made sense. And fast forward today with Broadcom buying out VMware,

Jason Lopez: Mason explained how that early decision to move to Nutanix allowed the university's IT efforts to be free of lock-in to a VMware license. Today, the university's infrastructure hosts hundreds of VMs running on Nutanix clusters.

Justin Mason: We've probably about seven different clusters on Nutanix, 50 odd nodes, about 500 workloads, 500 odd VMs spread between the different cluster sets and some of our major corporate systems are running. So the way that things are panned out for the university, we have a cloud adoption framework that's out of date.

[Related: Existing IT Hardware Gets New Path to Hybrid Multicloud]

Jason Lopez: But Mason says, as their workloads run either on a Nutanix private cloud or in SaaS services or even run a few things on public cloud, they're revamping their cloud strategy.

Justin Mason: Getting onto public cloud is pretty easy. What we were finding is having skills to cover all the public cloud options. Like having an Azure expert, an AWS expert, a GCP expert wasn't really financially viable to have that many different skilled resources across all those technology sets. But whereas Nutanix is kind of a single platform that we've had for a long time now. So our skill sets are pretty well developed across it. And obviously with a SaaS platform being that it's a bit of a black box managed by other providers, that works well for us. So Nutanix is definitely going to play a large part in that cloud strategy moving forward.

Jason Lopez: The University of Canberra is a prime example of how antiquated the idea is that it runs anonymously in the background. Mason said the student experience plays a critical role in a university's reputation, and that experience is significantly affected by the digital platform students use to sign up for classes, submit homework, send and receive messages with teachers and administrators, and to just stay connected to what's going on on campus,

Justin Mason: Putting in systems now that can track the student experience and kind of flag when students might need additional help. They haven't been to class in a long time. We have some systems around that like, well, hey, it looks like you haven't put in your last assignment or something like that. Do you need additional help? So there's definitely a lot of, and even more of it coming out the digital strategy and putting more importance on that to make sure that the students are at the front of that experience factor.

Jason Lopez: The university's digital strategy launched earlier this year. It includes 30 initiatives aiming to enhance the student experience while ensuring staff and students develop the skills to use new digital tools effectively.

Justin Mason: It's kind of one of those things where it's a 10 year roadmap, years one to three is pretty much well bedded down and spelt out, but those other phases, while there's a high overview of what they want to achieve, they're really going to rely on what the outcomes of phase one is that's going to feed into it. A lot of it is around the student experience, the student journey, and what we're finding that comes into your question about the digital skills and training for users. I think that's a really important aspect as well. Obviously, there's a cloud strategy that's coming out of the digital transformation. There's a big piece around integration, especially universities, probably similar for other organizations, so many separate systems and getting them to talk to each other reliably.

[Related: Machine Teaching: How Schools Are Using AI in Education]

Jason Lopez: Then there's AI. Mason said there are a lot of things coming at it at breakneck speed, or as he stated in the press conference, it's when everyone's running around with their hair on fire.

Justin Mason: But from a teaching point of view, it's not new. We've had subjects in artificial intelligent and deep learning and machine learning for quite a number of years, and some of those subjects have been complimented by students using virtual desktops via Nutanix, powered by GPUs to give them the additional resources they need to do those subjects. So not exactly new in the academic world. And by leveraging Nutanix, that's allowed the students to access to those resources from anywhere at any time. They don't need to come into a specialized lab running big hulking machines. They can do it from a web browser on their normal machine, whether they're on campus or at home.

Jason Lopez: He wrapped up with this comment. Organizations considering a shift from VMware to a HV often worry about reliability and skill requirements, but those experienced with VMware find a HV easy to manage due to Nutanix's user-friendly design.

[Related: Nutanix CEO Paves Way for Those Leaving Broadcom VMware]

Justin Mason: What we found is if you're good at VMware, then you'll do AHV in your sleep, and that's Nutanix credit for making it so easy to manage. So when we move from Hyper-V to a HV, that was a bit of a learning curve for us. But then once you learn it, you kind of learn it. We found them when we moved to a HV when we were doing the migration, and part of that migration was outsourced to Wipro. There's not much pushback here, and they're doing it really quick, and it was kind of like, well, I think they were seeing the benefits of moving to it. What about getting external resources in that you might need to help with Nutanix? We've never had to do that. We've never had to go looking. There'll be times in the university where we're doing certain projects or certain initiatives. We need to bring in a resource to do something specifically like a T Net expert or a Microsoft Dynamics expert. But we've never found ourself having to bring in a Nutanix expert, so to speak, because I guess that speaks for itself. It's pretty easy to manage.

Jason Lopez: Justin Mason is the associate director of vendor and operations at the University of Canberra, located in Canberra, Australia. His press conference was recorded at 2024 dot-NEXT, which was held in Barcelona, Spain. This is the Tech Barometer podcast. I'm Jason Lopez. Tech Barometer is a production of The Forecast. It's at the forecast by nutanix.com where we've got technology articles, podcasts, and video for you to enjoy.

That's theforecastbynutanix.com. Thanks for listening.

In this Tech Barometer podcast, Northeastern University professor Dr. Norman Jacknis explains why IT leaders must concentrate on delivering business value while understanding essential and emerging innovations such as artificial intelligence and machine learning.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Dr. Norman Jacknis: Part of the reason why you need to get started is you are learning how to do this stuff well. So you have to get started. And if you don't get started, you're going to fall behind. Right now, one of the reasons why companies that are using this are ahead of their competitors is they started a mile back. They've made the mistakes. They've figured out what works and what doesn't work. And they're running. And you're in a race. If unfortunately your competitors who are ahead of you keep on running as fast as they are and you can't run faster, you're never going to catch up to them. I think also it's important just for your own jobs because in most companies, it's at least crossed the mind of the CIO to ask the CIO about how to use AI.

[Related: The CIO Mindset for Embracing AI for Enterprises]

Jason Lopez: Norm Jacknis teaches mid-career technologists how to be CIOs. He's Professor of Practice, Innovation and Entrepreneurship at Northeastern University in Boston, Massachusetts. In his career, he was an executive at Cisco and the CIO of Westchester County in New York. He was also chairman of a chapter of the Society for Information Management, a prominent organization for IT leaders and CIOs. The role of the CIO has changed dramatically. He says, it used to be the CIO just made sure all the technical parts worked. Computers stayed connected. Email wasn't lost. Software was correctly installed. As he refers to it, making the trains run on time. But then came the cloud. Here's an insight he gives his students.

Dr. Norman Jacknis: I said, you need to ask yourself, what am I doing all day? Because the stuff I used to worry about, like the data center, big data center being up all the time, I've outsourced a lot of that stuff to the cloud provider. So they're worrying about it, not me. A lot of software I'm getting is sort of off the shelf and probably most of it is in the cloud as well. And again, somebody else is worrying about it. So what do I do? Sit here all day and twiddle my thumbs? Well, everybody finds ways of filling the day. But I said, why don't you think about what you can really do in your organization? Think strategically. So now your role is not necessarily just to have the trains run on time. You still have to worry about that. But it's more, how can I, as a technology leader, make this stuff strategically useful for my company?

[Related: How Nutanix Built a GenAI App to Improve Customer Service]

Jason Lopez: Today, a CIO has to think not just about moving data around, but what that data means. And with the rise of machine learning and AI, it's become critical that CIOs take this on.

Dr. Norman Jacknis: The leader of this really should be the CIO. Unfortunately, in a lot of organizations, that's not the case. Sometimes it's been led by chief marketing officers and in other cases by a chief innovation officer. But when that happens, that's a poor reflection on the CIO. The CIO has not done his or her job properly, if that's the case.

[Related: Seeing AI’s Impact on Enterprises]

Jason Lopez: But here's the kicker in what Jackness is saying. A CIO who's the point person in an organization's development of AI isn't just doing a software migration in the background, but is actively part of the business strategy.

Dr. Norman Jacknis: I always used to joke that the IT staff always used to go around telling everybody they had to change, they had to adopt this new system, this new technology. And if you looked at their own behavior, they were always the last people to adopt change themselves. But yeah, that's part of your job. You are a change agent. You're a change leader. Particularly when you're talking about something like artificial intelligence, which is threatening to some people. Think about it. If you've got some use of artificial intelligence that identifies where there might be some new potential customers, and you also even have a way of phrasing the marketing material that would go out, and you present this to the chief marketing officer, sometimes you're going to get a reaction that's, I've been in marketing for 40 years and you think you know what? And so there's a significant change. This is worse than bringing in, when you brought in, for example, an SAP system, it was aggravating to people because they had to change procedures, but you weren't threatening their professional identity. You can do that with artificial intelligence. Now you have a really strategic technology. You have the opportunity to think about how it can be strategic for your organization. That's going to get you the invitation to sit down with a CEO and other C-suite colleagues. We go through the major algorithms, but the focus is on how you will use it.

[Related: IT Leaders Get AI Ready and Go!]

Jason Lopez: Jackness says the course materials reflect the real world. There is work focused on AI, data centers, and cybersecurity, but also that real world component. Again, students are in their mid-careers. So each semester they have to defend their ideas before a panel of industry experts, as if they were going before a capital investment committee or VCs. The goal is to empower them to be able to implement things and put together teams.

Dr. Norman Jacknis: How can I get this off the ground without asking the CEO for $15 million? And for that matter, we even go over the issues of change management. Okay, so you've done this thing. How do you figure out which groups you start with in your company? How do you get it deployed? So we're really helping them very much to sort of think about this as a leader, in addition to what the technical issues are. The later semester as well, we deal with operational issues. Okay, you've got this great idea of yours and your thesis approved. What are the day-to-day issues that you have to deal with, including, for example, ethical issues, as well as operational issues, dealing with staff, stuff like that. Think about how a CIO spends his or her day. Over the course of a year, we want to make sure we touch on all those various questions that cross that person's mind and bring the best knowledge we have, the most recent research about what works and doesn't work, and we want to bring that to bear and actually have them apply it.

[Related: AI in the C-Suite: Using Artificial Intelligence to Shape Business Strategy]

Jason Lopez: Aside from the basic tech knowledge, which is baked into the curriculum, he brings his experience into play by emphasizing what's beyond the classroom.

Dr. Norman Jacknis: I think they need to keep up with the world, you know, so they should spend at least some of their time just keeping abreast of developments in artificial intelligence, because their colleagues are looking to them as the technology leader, right? You're the CIO. You're supposed to know more about technology, including artificial intelligence, than the rest of us do, right?

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Jason Lopez: And one of the advantages of today's computer science, you don't have to spend a lot of money to get experience, just a laptop and a connection.

Dr. Norman Jacknis: I mean, there's even software now that will provide a lot of the algorithms for free, open source software. There's a lot of open data. The efficiency of the algorithms has really improved. So it's not going to use up as much of the cloud resources as you might have in the past. Some of the stuff you can even start out doing on your own laptop. And then when you go to production, you can move it to the bigger data centers in the cloud. But at least you can get a start. You can be able to show somebody, hey, I can do a good job of identifying what customers might be highly probable of churning. So we can focus in on maybe doing something extra with them.

[Related: The Amalgamation of AI and Hybrid Cloud]

Jason Lopez: The program at Northeastern awards a Master's of Science, but Jackness points out it's learning by doing. It emphasizes leadership skills in a technology environment. Perhaps think of it this way. What can you learn to bring to a company to help it succeed?

Dr. Norman Jacknis: Well, I'm not very academic, I suppose. So it's been easy for us because basically what I do is teach some of the theoretical stuff and then also have them apply it to their jobs. I teach a course on artificial intelligence and machine learning for technology leaders. I go through the major algorithms so they understand what they're supposed to do. But their assignments are not to do heavy duty data science work, but instead to figure out, OK, strategically, how is this going to apply to my company? And at the end of the semester, they're actually writing a memo to their CEO on what the AI machine learning initiative ought to be for their company. So it's very much a marriage of these two sides. And actually, it's one of the reasons why I moved to Northeastern, because Northeastern is known for what's called experiential learning, which is basically a philosophy of education that you will learn best when the concepts that you learn are actually applied to your life, to your work.

Jason Lopez: Norm Jacknis is Professor of Practice, Innovation and Entrepreneurship at Northeastern University in Boston, Massachusetts. This is the Tech Barometer Podcast. I'm Jason Lopez. Thank you for listening. We also have another podcast with Professor Jacknis, and that focused on the role of CIOs implementing AI in their organizations. The Tech Barometer Podcast is produced by The Forecast. You can find more stories on technology and people in tech at theforecastbynutanix.com. That's theforecastbynutanix.com.

In this Tech Barometer podcast, Dr. Norman Jacknis explains how CIOs can adapt to rapid change driven by AI and the value it can bring to organizations.

Find more enterprise cloud news, features stories and profiles at The Forecast.

Dr. Norman Jacknis: If I ask you to travel from New York to Boston, you pretty much know what your options are. You can fly, you can take a train, you can drive, you can get a map from Google, whatever. There's a whole bunch of things. It's pretty straightforward, and you might run into a traffic jam or something like that. It'll sell you down, but basically you know where you're going and what it's going to be like when you get there. With ai, particularly generative AI these days, it's more like the Louisiana purchase in the Lewis and Clark Expedition. You've got this whole big territory that you have to explore and you're not going to know what its value is. I mean, ultimately in the long run, it's the best $3 million investment the United States government ever made, but you don't know when it's going to start delivering. You don't know exactly why it might deliver. The whole nature of artificial intelligence is it's an iteration, it's an exploration, and so that's one of the reasons why the people who expect to see a return in the next quarter or the next two quarters are really missing. The point about what this is about.

[Related: AI and Cloud Native Innovation Spark Explosion of Business Apps]

Jason Lopez: Norm Jacknis is Professor of Practice Innovation and Entrepreneurship at Northeastern University, which is located in Boston, Massachusetts. Before coming to Northeastern to teach technology leadership, he was an executive at Cisco and the CIO of Westchester County in New York. Those are just a couple of his consequential stops throughout his career. And in this podcast, you're going to hear about the role of the CIO in establishing AI and organizations. What makes this compelling is that as a professor, he's had to gather together his deep experience in technology and organize it to teach future CIOs, and we gleaned some of his best insights. This is the Tech Barometer podcast. I'm Jason Lopez, so let's pick up from where we started this podcast with his comment about understanding what AI in the enterprise is.

[Related: IT Leaders Get AI Ready and Go!]

Dr. Norman Jacknis: There was some hype clearly, but I think there's been pushback, particularly from CEOs, they see the value in this stuff these days. What those people are talking about is generative ai, the more traditional machine learning for prediction, for improving interactions with customers so forth. That's already a given. You can see companies that have been using that and have been making money from that, but even on the generative AI stuff, I think they hyped it up too much, and I think outsiders who haven't done this successfully don't understand the nature of working with ai. It's different from a traditional software project.

Jason Lopez: Jacknis says the hype around generative AI has put so much attention on language processing. Perhaps it's easy to forget about machine learning, something he reminds students who are mid-career executives.

Dr. Norman Jacknis: There's still a lot of low hanging fruit, if you will, more traditional machine learning. That stuff can be used to help identify potential customers, help identify problems ahead of time that you can fix all that sort of stuff. So that kind of thing is still very useful and can be done, and the generative AI stuff properly used also can do some things to make it easier for your users and customers to have access to the computing resources they need instead of having to be subject to the constrictions. The restrictions that have come up about over decades of computer science history

[Related: How Nutanix Built a GenAI App to Improve Customer Service]

Jason Lopez: Those decades ago before it was AI or generative AI or machine learning, it was called computer automation where for example, you took what was on paper and put it on a screen.

Dr. Norman Jacknis: Yeah, no, I suppose it was an improvement. I'm not sure how much it was annoying, and of course, they'd ask the same questions over and over again the way you did on paper forms. Now with generative ai, one of its advantages is it can be much more conversational and it can be smarter about what to ask. So if I put down than I'm mail, it's not going to ask me how many months pregnant I am and things like that. It's a lot smarter and it makes that whole human computer interface a lot easier.

Jason Lopez: He talks about AI as a strategic tool. There are ways to deploy it that aren't so great and ways that are successful.

Dr. Norman Jacknis: One of the things about the successful uses of AI is that they help break down the silos in companies because you have to, the folks who are expert on the algorithms don't know the business. For this to work, they need to speak to the business, and the nice thing about it is AI is sexy enough. Everybody's quite willing to work together on this. So I think that's actually kind of helpful. So there's a strategic value in this as well.

[Related: AI in the C-Suite: Using Artificial Intelligence to Shape Business Strategy]

Jason Lopez: Jacknis gave us an insight in what he teaches students in his technology leadership courses, how a business can get started in ai.

Dr. Norman Jacknis: Don't go out and hire a whole bunch of data scientists and programmers to write your own AI cloud providers, as you know, offer most of the various options for AI tools. There also is open source stuff. One of the things I use with some of my students is a product called nine K-N-I-M-E, which is from Europe, open source and free. Got a lot of these algorithms there. But start off with that. So the focus is on how do I want to get the data and present it to the computer so it can come up with the best predictions and not worry about getting stuck writing all kinds of blower level code.

Jason Lopez: Once a CIO's team has become successful in AI deployment in the cloud, measuring that by an uptick in revenue, Jacknis says about 5%. Perhaps it might be time to swap out some capabilities for something more proprietary, but he emphasizes use the cloud and operate on laptops before doing anything on-prem.

Dr. Norman Jacknis: When this started out maybe a few years ago, a lot of the people in AI and machine learning wanted to use the internal data centers that their companies had because there was a big concern about data being proprietary. They didn't want a lot of people know it and all that sort of stuff. And then the CIOs who were not part of this process pushed back and said, you want to run this program, which is going to eat up gobs of computing resources while I'm trying to run a payroll or a CRM. No way. So in some respects, they were told internally, push this outside, put this in the cloud because we're not going to let you do this internally.

[Related: Seeing AI’s Impact on Enterprises]

Jason Lopez: And another piece of advice from Professor Jacknis, create the culture around AI deployment. He's seen failure around treating AI in the enterprise as a technology project, treat it as a business project

Dr. Norman Jacknis: As opposed to we have a set of tools that will help us learn more about the environment in which we're operating in, help us identify customers better, all that kind of stuff. Instead of saying, Hey, this is a joint effort of everybody who's got some experience worthwhile in our company to work together. They treat it as a technology thing and then it fails partly because typically what they'll do is they'll think, oh, I have a whole bunch of data. Lemme throw it at the computer and see what happens. Oh yeah, the computers are good, but not that good.

Jason Lopez: He goes on to say that when establishing an AI component to business operations, make sure you bring in some people who truly get the business side of it. When he spoke about this, he referred to research showing just several years ago that new AI teams were made up of mostly data scientists.

[Related: Making IT Infrastructure AI Ready]

Dr. Norman Jacknis: Ninety percent of the people in their AI projects were all data scientists. A couple of years later, it was maybe a quarter or 20% because what they found was they needed people who understood the business side at the beginning of this process, and they needed people who were better at knowing how to implement systems. Once you figured out what you have and the actual amount of data science work was small, the computer's pretty good at this. In fact, you've got some computer systems now that will sort of basically be automated data scientists. What they can't automate is the business understanding that you get from human beings, and they obviously are not going to be able to fully handle the implementation with human beings to make this stuff come to life in your company. This field of AI is moving very fast, and every problem you read about, there's a bunch of smart people working on fixing it. Sooner or later they will. In fact, there's a story that came out written by a CIO and he said Everything we thought was hard turned out to be easy. Referring to the conversational ability that you have with Gen ai, which was something the year before it came out, people would've said, oh yeah, we'll get that 2030, maybe 2035. And here we are.

Jason Lopez: Norm Jacknis is Professor of Practice Innovation and Entrepreneurship at Northeastern University. This is the Tech Barometer podcast. I'm Jason Lopez. We have another podcast in this series featuring Professor Jacknis and in that one, and check it out on forecast by nutanix.com. Check out the changing role of the CIO. Again, this is the Tech Barometer Podcast. You can find more stories on technology and the people in tech at the forecast by nutanix.com. That's all one word, the forecast by nutanix.com.

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