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AI governance ensures that AI implementations are effective, safe, and responsible. i-GENTIC AI CEO Zahra Timsah says agentic AI makes it easier to enforce guardrails for safe and responsible AI.

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Zahra Timsah: We have crossed a threshold where AI is no longer experimental at this point, right? It's kind of like an infrastructure. AI models are embedded in credit scoring, healthcare diagnostics, legal reviews. Even if you look at national defense, you have AI. They're no longer tools. They're decision makers in digital form. This is how you can think about that. When you reach that scale, governance is not optional anymore. It's kind of existential, so to speak.

Jason Lopez: Zahra Timsah, co-founder and CEO of iGentic AI, asserts that the deployment of AI in an organization requires a real-time governance layer to help see what an AI system is doing. This is the Tech Barometer podcast. I'm Jason Lopez. What you're about to hear from her is a part of our Thought Leader series on AI. And while there's the debate about AI in the news headlines, at the forecast, we're going deeper, talking to technologists who are filling us in on what they're seeing in the industry and what they're working on in artificial intelligence.

[Related: Shaping the Future of Enterprise AI with Intellectual Curiosity]

Zahra Timsah: Ungoverned AI, and this is from experience, can create harm, like real harm. You're talking about biased hiring systems, misinformation loops, intellectual property violations, and even opaque decision paths for decision makers. They are operating in a world where every single AI decision, whether you're talking about a model output, a data merge, automated recommendation, whatever, is getting two things, opportunity and liability.

Jason Lopez: She says organizations have to ensure their AI systems are transparent, accountable, and ethical. And that's why emerging regulations are rapidly shifting the conversation from optional best practices to enforceable requirements for explainability, fairness, and traceability.

Zahra Timsah: If you look at regulations that are accelerating, like look at the EU AI Act, look at the US AI executive order, look at the GCC frameworks, AI governance has really evolved. It's no longer just a compliance checkbox. It's kind of a trust infrastructure. We're seeing companies kind of form AI governance councils, I think is a very good idea. And they're including in it CEOs, CIOs, general councils, and tech leadership.

[Related: Measuring the Prime Ingredient in Enterprise AI]

Jason Lopez: Timsah says without coordination, organizations risk managing AI through fragmented tools and disconnected processes, which will struggle to keep pace with change. Bringing stakeholders together is a great step.

Zahra Timsah: You're talking about folks that specialize in GRC, governance, risk, and compliance. You're talking about legal departments, even technical experts as well. Because not only do you have people, you also have platforms. You know what they say. It's people, process, and platform. All of these are like siloed tools to manage the GRC.

Jason Lopez: This is where, she says, agentic AI can deliver. And just to highlight that agentic AI isn't so much about AI agents. Agentic AI is the operating model that sets direction, plans the work, and brings team members together to achieve a larger goal.

Zahra Timsah: With agentic AI, you have systems that are learning from human decisions and improving their governance reflexes. With an agent, they can run 24-7. They can handle a humongous amount of data points that a human cannot even imagine. You're getting consistent and fast results.

[Related: Role of CIO Expands with Enterprise AI]

Jason Lopez: In this model, AI becomes an active oversight layer rather than just a passive reporting tool. Instead of waiting for periodic reviews, leaders can monitor performance, detect risks, and respond to governance issues.

Zahra Timsah: Let's imagine a boardroom. You have executives, and they can see their entire AI landscape in front of them as a living system. This is what agentic AI can provide. Models that flag when, let's say, some sort of an AI system is drifting out of ethical or regulatory bounds. Privacy agents can mask PII, cyber agents detecting anomalies, and the list goes on and on and on. It's governance that's talking back to you. Leaders can ask it questions, and then they can give you back answers quickly, not generate reports like humans would do with the answers hidden in them.

Jason Lopez: It moves governance efforts from static oversight to real-time awareness. But every organization interprets risk differently.

Zahra Timsah: There is no such thing as one-size fits all. Every company has its own risk appetite, understanding, and interpretation of these regulations. Then you have a team that's going to review the regulations and try to understand what they mean for your company.

Jason Lopez: Timsah says internal expertise remains essential. Even with advanced automation, meaningful oversight still depends on people.

Zahra Timsah: You always have to have a human in the loop. Always. Just like you're driving a Tesla, and it can just drive itself. But you can also receive guidance so that you are the decision maker. You, as a human, is a decision maker. Agentic AI is powerful, and it can get rid of mundane tasks. But you will still generate errors, and there is nothing that can replace human experience.

Jason Lopez: And here's a part of the story that underlines what she says. Zahra Timsah did not arrive at agentic AI from a computer science trajectory. She came up through healthcare, studying cancer biology and drug discovery. She did postdoc work at places like the MD Anderson Cancer Center, and she discovered that personalized medicine was becoming too complex for manual analysis. Early on, she got involved in healthcare AI technologies, and one of her goals was to design and test patient-specific therapies using neural networks.

Zahra Timsah: What we're doing is creating a digital nervous system that's uniting ethics, risk, and intelligence into one living operating system.

[Related: Data Protection Gets Its Uber Moment]

Jason Lopez: It's about embedding governance directly into how AI systems operate, so oversight happens continuously rather than after problems arise.

Zahra Timsah: Agentic is really the world's first agentic AI operating system for governance, and it didn't take us a day or two, a month or two to build Agentic. It took us 17 years of experience to fine-tune these agents to act on our experience as founders to achieve the results. It's a platform where you have autonomous agents, but these are in reality digital chief compliance officers that can monitor in real time, which a person cannot do, enforce, and even learn, you know, like you're learning compliance, whether that relates to AI, data, privacy, and cybersecurity. So it's running 24-7, and it's instant. It's proactive. It's not reactive. So these agents think of them as intelligent layers of oversight. That's what we're doing. So everyone who's touching or benefiting from AI carries responsibility. Governance cannot be delegated to a single compliance officer, or you can't bury it inside IT.

Jason Lopez: Zahra Timsah is the co-founder and CEO of iGentic AI, a governance platform that uses AI agents to manage and enforce governance, risk, and compliance. Our story with her is part of The Forecast's reporting on thought leaders in the AI industry. You might check out some of our stories from other thought leaders, such as our profile of Greg Diamos. Go to theforecastbynutanix.com. That's all one word, theforecastbynutanix.com. This is the Tech Barometer Podcast. I'm Jason Lopez. Thanks for listening.

Intel IT is expanding and evolving our “Citizen AI” framework—named iGPT, which enables Intel employees to use Generative AI (GenAI) to streamline processes and enhance efficiency. This democratized AI platform empowers users to build chatbots and AI agents using intuitive, no-code tools. This enables them to create solutions tailored to their specific needs and quickly adapt to a rapidly changing business landscape. To improve scalability and potentially reduce cost per token, we are deploying our Citizen AI platform, both on-premises and in the cloud, using a mix of newer Intel Xeon processors and Intel Gaudi accelerators.

With the rise of agentic AI, strong governance will help organizations manage security and accuracy. ModelOp CTO Jim Olsen describes enterprise agentic AI risks and solutions.

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Jason Lopez: How do companies control, govern, and deploy AI safely at scale? When Jim Olsen, the Chief Technology officer at ModelOp, talks about AI adoption, the theme of what he says rests on the idea of restraint.

Jim Olsen: Try not to just build things for the heck of it. That's why you have to tie all this back to a use case to understand, what is my goal and what does success look like?

If you don't have a clear process in place everybody can understand and follow, then you lose that trust, and the solutions just don't happen. That's, of course, a missed opportunity.

If you have a truly resilient agentic system, it can adapt to new business needs, new things that just pop up. Given that autonomy, how do you actually control and make sure it doesn't disclose all your user passwords?

Jason Lopez: This is the Tech Barometer podcast I’m Jason Lopez. This story is part of our ongoing thought leader series with people at the cusp of AI technological development, like Jim Olsen of ModelOp, a platform that helps govern, monitor, and manage AI and machine learning models to ensure they are compliant, reliable, and aligned with regulatory and ethical standards.  Agentic AI is the operating model that sets direction, plans the work, and brings team members together to achieve a larger goal. When you talk to Jim Olsen, before he gets into the AI conversation, he’s laser focused on why you need it in your organization. What’s the use case?

Jim Olsen: If you can actually get agents to automatically do that stuff and do it reliably, it obviously increases the success of your business at its core. That's why it really comes down to what is your business value, what is your use case, and the success is going to look very different based on that. Ultimately, my business is successful, everyone's happier, and I've reduced my overall costs.

Jason Lopez: But that promise only holds if the system performs as expected. The models need to be trusted and safe.

Jim Olsen: The nature of generative AI is that it does go out and perform differently based on very minor changes or even sometimes no changes at all. If you don't have some insight into that, naturally, people are concerned and paranoid about what could happen. You need that clear, transparent process in place in order to build that trust that we can see what's going on. We do know we've put the research in behind this to make sure it's going to behave okay.

Jason Lopez: Olsen says that while a clearly defined use case is the bedrock of an organization’s deployment of AI, another critical part of the strategy has to be managing AI’s behavior.

Jim Olsen: When you use these tools or allow people to use these tools, what impact that could have? What kind of information could go out? What kind of information could come in? What kind of damage could be done? So you need an approval mechanism in place to actually do that.

You do need some automated process in order to scale this in an appropriate manner, because what you really need to know is, okay, what are the use cases out there that need to use agentic AI? Is it appropriate for them to be using agentic AI? Then what pieces are they using? What tools? What model? How many tokens are they actually using? Are you getting your value back out of your investment in these areas? You do need to track all of this information. You do need some approvals in place to make sure you have that process to do that. You can try to do it on a spreadsheet or something like that. We see that quite often, but we find that really gets lost.

Jason Lopez: One of those mechanisms is MCP, or Model Context Protocol, an open protocol designed to let AI systems securely connect to external tools, data sources, and services in a standardized and predictable way.

Jim Olsen: But now you introduce these MCP tools and true agents that can act autonomously. How do you ensure that they only have access to the tools and data that they're supposed to? That's where one piece we're providing that we saw a lot lacking that ties directly into our overall automated approval process is an MCP gateway slash proxy that only allows specific use cases to use specific tools and blocks access to tools otherwise, so that way you know what they're being used. As A2A gets in there, we're going to see even more pieces that need to go in there and monitor and understand what's going on.

Jason Lopez: Olsen says he’s seen cases of MCP tools that have access to a company’s proprietary information. What ModelOp does is essentially act as a control plane AI across an organization. It provides insight into what MCP tools are doing, where they’re enabled with agents, AI models, workflows, and governance.

Jim Olsen: What we've done at ModelOp is we've actually created that kind of a gateway or a proxy where you can deploy approved tools and actually monitor what use cases in your organization use those, and are they approved to use those tools, and block them if they aren't, and put protections in place that can do things like detect PII and say, hey, you can't send PII out of our company, these kinds of things. You can get some control around these MCP tools and understand their usage.

Jim Olsen: Reducing costs, increasing customer satisfaction, increasing accuracy of processes, et cetera, are all kind of standard business goals that a true agentic system can deliver on by being adaptable.

Jason Lopez: Jim Olsen is the CTO of ModelOp, a software platform that helps organizations govern, manage, and scale AI systems responsibly. This is the Tech Barometer podcast, I’m Jason Lopez. We’ve got some other great stories in our thought leader series on AI. Check out our profile of David Kanter, co-founder of ML Commons. That’s at theforecastbynutanix.com.

There is a focus driving your strategy, reason behind every change to your benefits program, and a story. It’s how you tell the story which determines how it lands, is accepted, and even embraced.

In this episode, we sit down with Christina Farr, author, health-tech advisor, investor and editor in Chief and CEO of Second Opinion Media, to explore why the art of storytelling is a crucial skill for all companies to learn, and how it can be particularly helpful skill for health and well-being leaders to more effectively communicate – and drive impact.

Employers will learn the importance of showing up with authenticity and empathy and how to use storytelling strategies to support big benefit changes and transformation in health care.

Guest: Christina Farr, author of The Storyteller's Advantage: How Powerful Narratives Make Businesses Thrive and editor in Chief and CEO of Second Opinion Media

In this Tech Barometer podcast, HYCU CEO Simon Taylor takes listeners into the mind of a creative, disruptive entrepreneur who’s turning data protection into an AI-powered arsenal against cybercrime and ransomware attacks.

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Simon Taylor: I was in a bar in Las Vegas and I happened to bump into an engineer that I'd known from Slovenia and I said, what are you doing now? And he said, I'm in the data protection space. I was trying to prove to him that data protection was this sort of old, ugly, fugly world that no one would be interested in because it was so old school, it was in the plumbing, who cares about it? He said, Simon, how did you get here today? So what do you mean? Did you take a taxi? No, I took an Uber. He said, aha, but there's no Uber in data protection. It struck me that if you want to truly disrupt the marketplace, you need three different things.

Jason Lopez: Simon Taylor is the founder and CEO of HYCU. This is the Tech Barometer podcast I'm Jason Lopez. HYCU is a data protection company and its name is spelled H-Y-C-U. There are ways to construe Japanese poetry with data protection, but the truth is the pronunciation, haiku, is an unintended benefit of the acronym.

Simon Taylor: Hybrid Cloud Uptime, that's where the name really comes from, H-Y-C-U, Haiku.

Jason Lopez: Taylor says HYCU was launched on the idea of making it easy to back up and recover data of disparate elements in enterprise computing.

Simon Taylor: We're called Hybrid Cloud Uptime, H-Y-C-U, Haiku, for a reason, because we believe in the power of on-prem and the power of public cloud. And we want to make sure there's a seamless transition, whether you're going one way or the other.

Jason Lopez: But before we go forward on that, let's complete Taylor's thoughts on disrupting the marketplace... you need three different things.

Simon Taylor: You need a technology that is sort of used by all and liked by none. Two, it's got to be something that is highly commoditized. And three, it's got to be something that is truly undisrupted in this space. In other words, nobody's gotten there before you.

[Related: Nutanix CEO Stokes Surge in IT Ecosystem Partnerships]

Simon Taylor: AI is here, it's here to stay, and it's only going to get better because that is the nature of things, the nature of technology.

Jason Lopez: Taylor likes to talk about AI not so much in terms of the technologists who create it, not from the economist's point of view, or from the hype in the news, but as he describes it, as a kind of energy customers have for their vision of how it can benefit their businesses and products.

Simon Taylor: I kind of break down AI into three proof points, humanity, exceptionalism, and resourcefulness. So humanity, is it good for human beings? It seems really odd, but at the end of the day, we are humans. And so the only kind of AI I care about is that which is good for human beings. The second one is exceptionalism. It's how do we leverage AI in our daily work to make sure that we're not falling behind, and in fact that we're getting ahead. And the last one is resourcefulness. How can we be resourceful in terms of driving more value to our customers?

Jason Lopez: This is where he moves from a guiding philosophy of humanity, exceptionalism, and resourcefulness into technical reality. How is it possible to better support IT services that live across on-prem and public clouds?

Simon Taylor: You've got your data in so many different places. You've got data that you're spinning up and spinning down. It's all containerized. And I think it takes modern platforms to be able to support that. HYCU is supporting 92 services across on-prem, public cloud, and SaaS. That's more than 12 times any other data protection vendor in the marketplace. HYCU's customers are able to stitch together the fabric of all of their data across 12 times more data silos than anyone else. You're going to see a lot more coming out of HYCU in the coming months where we talk about the AI fabric that's going to add tremendous value to HYCU and to Nutanix as well.

[Related: Clouds With Borders: IT Teams Design for Geopatriation]

Jason Lopez: Taylor's key message is HYCU isn’t just backing up data, it’s creating a unified view of everything.  And AI can turn that big picture into powerful insights.

Simon Taylor: I've always thought of HYCU as a giant patchwork quilt. How are we going to help our customers to process and manage all that data? That's a lot of different data silos. So we went out and we said, it'd be great if you could almost click a button, discover your environment, and then visualize where all that data is. And we call that Rgraph. So today we have an observability tool called Rgraph that will allow you to visualize every element of your data estate across on-prem, public cloud, and SaaS. So that's fabulous. I think in a pre-AI world, that's done a tremendous amount. Now I want you to imagine that that data protection toolkit can extend even further. I want you to imagine you can overlay AI across all of that. What I'd love you to just imagine is what could you do if all of the data across on-prem, public cloud, and SaaS was linked together with AI? When you combine what we do with SaaS, what we do with cloud, what we do with Nutanix, all of a sudden you can build a far more sophisticated picture of what your world actually looks like. And from that, you can leverage AI to do a whole lot of very, very interesting things.

[Related: Turnkey IT Platforms Unlock Enterprise AI Success]

Jason Lopez: Simon Taylor describes himself as a serial entrepreneur.

Simon Taylor: I would have started a fleet of ice cream trucks if the mood took me.

Jason Lopez: In his 20s he liked to write business plans in his spare time. And he says these plans weren't tied to a technology but were about almost anything.

Simon Taylor: Ultimately, I came up with an idea for an e-sourcing and e-discovery tool that would allow you to cut the time, money, and resources involved in recruiting outsourced engineers. I decided that to do that, I needed two things. I need to build an algorithm matching engine that would allow me to do that matching. And two, I had to build a database of great engineers. I felt that there was an untapped resource in Eastern Europe for incredible engineering talent. I'd read a lot about it. I'd never been there. I don't have any familial links, didn't speak the language. But I knew two things. I knew that there was a lot of engineering talent there, and that it was a heck of a lot cheaper than the United States. So I raised a little bit of cash, sold what little I had. I moved to Prague in the Czech Republic, hired an engineering team, and I got to work. Then I started traveling. I went to Estonia, Latvia, Lithuania, Romania, Bulgaria, Serbia, Slovenia. Everywhere I went, I was building out this database of engineers that then went into the product.

[Related: How Ivy League Dartmouth College Moved to a Future-Ready IT Platform]

Jason Lopez: The experience was so profound, Taylor says when his son talks about the future -- where to go to college, what to major in -- the advice was:

Simon Taylor: Just go overseas. Go international. Try different things. The more diversity you can bring into your life, the more you can open your eyes to different experiences, I think the better off you're going to be.

Simon Taylor: How expensive is it from a time, money, and resources perspective to execute an attack on an innocent victim?

Jason Lopez: He says we've moved past the era of human hackers typing commands into a terminal. We are now in the era of agentic AI, where a hacker sets a goal, such as to find a CEO's personal emails and the AI does it autonomously. In the past, you could spot a phishing email by its bad grammar or slightly off tone. AI eliminates those red flags. And the cost to hack has plummeted.

Simon Taylor: It used to be $100 to execute a white glove ransomware attack on the dark web. Today, it's $1.50. When we first started this, people said that 90% of their ransomware attacks and all of their cyber threats were occurring through on-prem data. Today, 50%, according to our study of state of SaaS survey, 50% of those who responded said that their most recent ransomware attack or cyber attack occurred through SaaS and cloud. So all of a sudden, we're seeing this massive shift, and that is happening because of AI. We're seeing better phishing attempts. We're seeing social engineering that we couldn't have imagined years ago. Right here in my office the other day, somebody got a voicemail from me that sounded like me asking for gift cards.

[Related: Mastering a Myriad of Databases in the AI Era]

Jason Lopez: But it goes both ways. AI gives security teams an advantage they’ve never had before with the ability to triage millions of data points across cloud, on-prem, and SaaS apps in seconds.

Simon Taylor: What we're starting to see now is that it's becoming cheaper for us to innovate on the side of the defenders, where we can leverage prompt engineering, we can leverage tools like Cursor and IntelliJ, we can build products faster, more swiftly, more adeptly, and we can go after these guys. We can do it aggressively, and we can do it at a fraction of the cost.

When I looked at the history of data protection, you have companies like Veritas backing up Unix years ago, and then you had Commvault for Windows, you had Veeam doing VMware. But when we really thought about the fragmentation of data protection across on-prem, public cloud, and SaaS, nobody had really looked at it from a holistic perspective.  We set out to look at how you could bring together all these different elements of the modern data estate and make it incredibly easy to backup and recover the data almost as simply as you would on an iPhone with iCloud backup.

Jason Lopez: Simon Taylor is the founder and CEO of data protection company HYCU, a data protection company based in Boston. This is the Tech Barometer Podcast produced by The Forecast, I'm Jason Lopez, thanks for listening. If you want to take a deeper dive into this story, check out David Rand's piece for The Forecast, entitled "HYCU CEO Simon Taylor Brings Order to Data Protection Chaos." You can find that and more stories on technology at theforecastbynutanux.com.

Intel IT now runs part of our database as a service (DBaaS) in an open-source containerized environment, in response to vendors’ move to per-core licensing. The new bare-metal infrastructure is driving cost efficiencies and future-proofing IT investments. We have also added important new capabilities that improve resource utilization and enhance the user experience. The new infrastructure avoids per-core virtualization license fees, lets us provision resources on demand, and eliminates several thousand cores. We expect the new infrastructure to save USD 2.5 million in hypervisor license costs annually, with an estimated return on investment period of just six months.

Agentic artificial intelligence promises to fundamentally change the way enterprises operate. Intelligent systems that automate and execute tasks on humans’ behalf, AI agents “will make work easier, faster and more effective — whether by handling tedious manual tasks, speeding up handovers between teams or accelerating market delivery,” the World Economic Forum reported in 2025.

Henry Iversen is the co-founder and chief commercial officer at Boost.ai, a company that delivers AI-powered customer experience solutions for regulated industries. He and hist company are helping organizations prepare for the agentic-AI future.

“With agentic AI, now you can see more use cases around actually executing,” Iversen told The Forecast. “It could … recommend how you actually want to do that kind of mortgage over time, and what is the recommended approach to it.”

In this Tech Barometer podcast segment, part of a series on AI industry leaders, Iversen explains how today’s standard generative AI can help with many aspects of life, like applying for a home mortgage, but agentic AI can go even further. He tells how agentic AI will reshape customer service, and what companies need to do now to get ready.

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Henry Iversen: With agentic AI now, you can see much more use cases around actually executing, not only understand a question and react to that, but also look at the sequence of things and help the customers or employees to make sure that they get an amazing experience. AI technology can open up opportunity to actually have one-to-one relationship with the customers at scale.

Jason Lopez: Henry Iverson is a co-founder of Boost.ai. The company builds conversational and agentic AI platforms that let organizations automate customer services through human-like dialogue. This is the Tech Barometer podcast produced by The Forecast. I'm Jason Lopez. As artificial intelligence continues to evolve, agentic AI is on the road to genuinely reason and do complex tasks. Iverson, who The Forecast reached out to for his AI thought leadership, says this shift could redefine how companies implementing AI get the right balance of people leadership and technology. But it's not that simple. It requires coordination between executives, technologists, and business teams to align on what AI should actually do. And one thing it should do, he says, is bring different apps and tech together.

Henry Iversen: How can we consolidate them? How can we maybe remove some of them? But I think with the new AI technology, it represents the opportunity to have a new interface. And you should get the same experience. I think the multimodality is also extremely important. I want to have an opportunity to talk to my organization, my local bank, my local insurance company, and then continue the conversation on phone, on chat, or whatever, where I need to.

[Related: How Kubernetes Catalyzes Enterprise IT]

Jason Lopez: The picture that comes to mind is the anecdote of changing a tire while the car is still moving. Iversen says the biggest challenge is things are moving very quickly.

Henry Iversen: Even on a weekly basis, there's new models coming out, new large language models performing better and stuff like that. So there's obviously a lot of things going on in this space, which again also have kind of implication in terms of like, when do we get access to the model? What is the privacy around that? How do we need to think about like actually implementing this in my own kind of call center? So there's a lot of things which is also outside the bigger ecosystems.

Jason Lopez: With new language models emerging almost every week, the technology is evolving faster than most organizations can adapt. And that constant change doesn't affect just the tools themselves. It reshapes how companies think about scale, privacy, and the entire structure of their customer operations.

Henry Iversen: AI can handle thousands or millions of interactions. One of the challenges now is just how do you put together kind of the future of your contact center or customer engagement at scale with all the moving parts?

[Related: Game Hosting Companies Unsheathe AI and Cloud Native Tools to Combat Cheaters and Cyberattacks]

Jason Lopez: Perhaps a deeper question is how the organization works with AI. It's a given. No one is under the illusion that AI just magically works. It takes effort. And that begins with getting executives, IT, and business teams on the same page to implement and use it.

Henry Iversen: Being able to really align the organization in terms of what is the needs of the future is pretty hard, I think. What I've seen so far is just make sure you can have those alignment early on with the executive leadership, but also the tech and the CIO also involved. Because everyone has different perspectives, but you really need to have that alignment. I think another key consideration you need to make is also, okay, are we going to operate and manage these systems over time? Some of these systems already out there is built for technical people. And then how do you scale up with that internally where you maybe want to have a bit more kind of a no-code interface? The business is definitely extremely important, that the business is a key part of this.

Jason Lopez: On the technology side of the equation, the company's cloud infrastructure is vital. Iversen says cloud-based systems are essential to enable AI models to function effectively.

Henry Iversen: For everything we do, it needs to be available in a cloud because a lot of these kind of generative systems obviously needs to have access to information quickly to make sure that everything works between the clouds so you can make sure that you reduce the latency. Then also you need to manage the costs. So having a vendor which can actually manage and make sure that the cost is not taking off is also extremely important for the overall business case for it.

[Related: Shaping the Future of Enterprise AI with Intellectual Curiosity]

Jason Lopez: But as critical as the IT side is, he says the business vision should drive AI partnerships and architecture. The starting point isn't speeds and feeds, but rather the organization's business goals.

Henry Iversen: How do we envision the future of customer service should look like? You can also then start reverse engineering what you need to do to get there. And we also need to realize that some things are going to change along the way, but that's okay. But it's super important to have this kind of North Star where we know where you're all headed. And that also brings a lot of kind of clarity but also alignment into the organization.

Jason Lopez: It's one thing to imagine the future, says Iversen, to map out that North Star and picture customer service. But at some point, vision meets reality. And that's where an important question comes in. Does it make sense from a financial point of view?

Henry Iversen: There's a lot of cool stuff we could do, but again, there's also a cost element to it. But I also think it's just extremely important to make sure that, okay, what level of support and experience are we going to bring out to the market? One overlooked possibility and opportunity with agentic AI is also that you can do something you haven't done before. I have one example actually from a bank where they offer new services because now they can use, say, agentic AI to handle a lot of the workload of offering that service. Some service could be made available from customers, but it's just too time-consuming. Another example could be, I could potentially, if I have one million customers, I could potentially call all of them. But again, that would probably not be very scalable if I'm the CEO of a bank to call every customer. But with AI technology, you can open up an opportunity to actually have that one-to-one relationship with the customers at scale. We can obviously automate existing processes with agentic AI, but for me, it's more like, okay, how can we open new opportunities with agentic AI?

[Related: Bridging the Gap Between AI’s Promise and Fulfillment]

Jason Lopez: And that question of how AI can unlock new opportunities rather than automate the old ones leads to a deeper point. Because innovation isn't only about efficiency, it's also about elevating the experience. As Iverson explains, the real challenge is to use these new tools not just to do things faster, but to do them better for customers and for the people who serve them. Once AI agents start acting with a degree of autonomy, the question becomes, how do you guide them responsibly without holding them back?

Henry Iversen: Yeah, that's a good question. And I think one element which is extremely important as you start scaling these systems is that you're going to have a lot of these AI agents. You want to make sure that you have the level of control, but you also need to make sure that there is a level of autonomy into it. Because you can imagine if you have hundreds of AI agents in the same way as you have humans, you're not going to be able to have control on every action they do. So you need to make sure that they can do that within a controlled space, but also being open that, okay, if there's a thing I'm unsure about, then I should maybe ask you for approval. So make sure that you have the human in loop for some of the questions coming in.

Jason Lopez: Finding that balance between control and autonomy brings the human into focus. As AI takes on mundane tasks, the role of people becomes less about managing volume and more about creating meaning. It's a shift Iversen says will be essential to how organizations design the future of their customer experience.

Henry Iversen: There's always going to be customers needing to get help from a human. But I also think that back in the days when we started kind of doing all the simple inquiries to chatbots and we gave all the complex stuff to humans, we thought that that was a good thing because all the simple stuff, humans don't want to go answering those things. But what we learned is there was some burnout in some of the people on the customer service because all the complex stuff was obviously, you need to use your brain extensively to kind of solve those. So I think just finding the right balance of things you need to kind of route through to humans is going to be pretty essential to make sure that they also support customers.

Jason Lopez: Henry Iversen is co-founder of Boost AI, a company that was founded in Norway. It builds conversational and agentic AI platforms. This is the Tech Barometer podcast produced by The Forecast. I'm Jason Lopez. You can check out more technology stories. We have them at theforecastbynutanix.com. That's The Forecast webpage. Again, that's theforceastbynutanix, all one word, dot com.

Climate change has emerged as a contributing factor to an increased risk of premature birth, low birth weight and stillbirth in the United States. In this episode, Bruce Bekkar, MD, explains this connection and outlines emerging intervention opportunities.

Employers will learn how these climate-related risks are both a business and inclusion concern and discover practical ways to educate, support and protect pregnant employees.

Guest: Dr. Bruce Bekkar, OBGYN and Climate Activist

In a Tech Barometer podcast segment, part of a series of interviews with AI company CEOs, founders and leaders, Pendo CEO Todd Olson explains how AI is reshaping how teams work and how it helps tackle time-consuming, complex tasks.

Pendo’s Software Experience Management platform has helped companies like United Airlines, Morgan Stanley and iRobot optimize software performance by identifying issues, streamlining workflows and enhancing usability. To keep it running smoothly, Olson said his engineers now spend more time reviewing AI-generated code than writing it from scratch.

With AI changing not just development techniques, but also the dynamics of software teams, Olson is adjusting to a new paradigm and encouraging others to do the same.

“Everyone needs to rethink their role and how they can do it differently,” Olson said.

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

Todd Olson: How do you know… There are certain tasks we’re hardwired not to even try because they’re long and painful. That’s where AI can do things really, really easily. AI’s not eliminating all thinking that would be a scary and dangerous place for a lot of folks. It’s just shifting how we work.

Jason Lopez: These days we can see that shift with developers who spend less time writing code and more time reviewing what AI generates, while designers and product teams are using new tools to prototype and test ideas faster than ever. This is the Tech Barometer podcast produced by The Forecast, I'm Jason Lopez. Todd Olson is the co-founder and CEO of Pendo, a company based in Raleigh, North Carolina, which helps developers and product managers make their software more effective and intuitive to use. They do this by integrating into software solutions and AI has become a growing component of that. Olson spoke with us about how AI is reshaping how teams work, especially tackling time-consuming complex tasks.

[Related: HYCU CEO Simon Taylor Brings Order to Data Protection Chaos]

Todd Olson: I was talking about this with a few developers recently, is simulating user behavior. Testing can be very, very difficult, but creating real and simulated environments can be just prohibitively expensive and very, very difficult. But it could be very easy with AI. AI can just generate fake users, fake data, whatever. You give it some level of parameters and boom, it's going to go off and go do it.

Jason Lopez: Olson says that hand coding systems for that kind of task would be much more painful. But with AI, a full simulation is far more efficient than manual testing, and opens up new ways to approach development. It expands what’s possible in the work itself and sparks new ways to collaborate for hybrid teams.

Todd Olson: We're seeing team members share very openly, wow, look what I did with this. And then another team member saying, wow, I can apply that to my own work and get all this sort of productivity benefits out of it.  I mean, a ton of our folks are recording Loom videos and posting it places and saying, here's a video of me using AI to solve and experiment with this problem. Have a look. And people are commenting and experimenting. And I just think this is a really exciting time for a lot of folks.

[Related: Is Your AI Data and Infrastructure Audit Ready?]

Jason Lopez: That spirit of experimentation isn’t just cultural — it’s changing the work itself. Developers are finding that AI doesn’t replace their jobs, but rewrites the way they do them. Olson says it’s less about writing code from scratch, and more about refining what the machine produces.

Todd Olson: I was talking with our CTO and we were talking about thinking in terms of, yeah, if I had done this without AI, I could have written this code in say four hours versus AI is going to spit out the code. And then I'm going to edit, review and adjust it for maybe one hour. So that's a, well, it's a two hour savings if I'm looking at it that way, but it's also a difference in skills, right? And you still need to have critical thinking skills because you need to understand if, if what the AI generated is correct, whether it actually meets the, the intent of what you're trying to do.

Jason Lopez: AI isn’t taking human thinking out of the equation, it’s changing what we think about. Developers spend less time writing raw code and more time improving what AI creates. And, Olson says, people are rethinking their roles.

Todd Olson: We have designers, we have product managers, we have sometimes QA, sometimes not, but someone more focused in sort of the quality testing side. We have obviously developers themselves. I think we're seeing AI sort of break down a lot of these walls and redefine what these roles actually mean. Like, like a really good example is actually in terms of designers, you know, designers historically, you know, obviously they, they would create everything from prototypes to they want to validate with customers. Now they're using it via prompt. They can essentially generate an entire working application, you know, with, with, without having to, to lean on a developer or anything to that degree, much, much, much faster. And then get that prototyped application in front of customers much faster and get sort of much better feedback and much faster than they've ever been able to do before. And then when they're collaborating with, with engineers, they can actually pass them a prototype and say, you know, Hey, this is kind of what I built. It kind of looks like this. Let's tweak this. Let's do this. Let's make sure it's scaled. Like it's a different style of working.

[Related: Get Tech Insights to Move Faster and Smarter]

Jason Lopez: He also points out that to make real progress, companies need to give teams time and space to experiment, learn, and discover where AI can truly make a difference.

Todd Olson: We're working on a project now to internationalize our products. That's taking our products and making it available in different, different languages. That's historically a very painful project. You know, you have to look for all the hard coded, essentially word string texts throughout your application, replace that with sort of an API call based on like, you know, geography.  That's actually a really good task for AI. And we've, you know, it, one thing from a change man perspective is, but if I went to my team and said, I need you to use AI for it. How long is it going to take? They've never done it before. They don't know Adam. So what we needed to do is say, okay, we'll give you a month to play around with AI and test it and prototype it for this, all this problem. Then tell us what it's going to take now. So we gave them, we carved out time, we let them experiment. And then they came back and told us they can cut the estimate in half. Well, that's a pretty good ROI, but if I hadn't given them that time to experiment, they wouldn't have actually known. So I think every company from a change, like they need to give their team space to try things out. It's not just saying you must use AI. It's give them space to like really play with the technology to learn from it.

[Related: Nutanix CEO Stokes Surge in IT Ecosystem Partnerships]

Jason Lopez: That kind of space can also depend on where the work gets done. And that’s increasingly in the cloud, where the newest AI models live and evolve.

Todd Olson: The cloud's hugely important. Most of the foundational models are delivered via cloud infrastructure. So whether, I think the interesting thing to consider is that different models need to be better at different tasks. For example, most of my engineering team, when they're doing any level of like code assistance or using anthropics models, cloud, especially a lot of the more modern, newer models. And what we're seeing is the pace of innovation and change, like you want to be using the latest models. And like the models today, in some cases are significantly better than models six months ago. So like making sure you're constantly trying to like use the same thing is sort of like top of mind. But yet for other roles, like non-engineering roles, non-code generating roles, we're seeing OpenAI or ChattVT be the dominant decision. Again, both of these are delivered via cloud solutions. So I think cloud's going to be important. Now, look, we have some customers that are very concerned around sharing their data with the cloud. So when I'm building products and I'm using these, I think one, you have to be very transparent about what models and infrastructure you're using. Two, you certainly think about building in flexibility of potentially calling self-hosted models, which would be potentially open source models, think Metaslama or even things like DeepSeek. I think we're seeing more people like think about leverage of those, which would be, again, you're using a third party, but it's open source and it's hosted by yourself versus a third party hosted model. But I also think there's gonna be opportunities to like, at least for us, use our customers' models.

[Related: Turnkey IT Platforms Unlock Enterprise AI Success]

Jason Lopez: Many of Pendo's customers have invested heavily in developing their own AI models, trained with their own data and context. The question Olson raises now is how they can design applications that tap into those customer built models instead of hosting everything themselves. But if there's a guiding principle that emerged in our conversation with Olson, it goes back to his strong belief that we're still in a wide open learning phase, especially human learning.

[Related: How Kubernetes Catalyzes Enterprise IT]

Todd Olson: We're in the middle of our hackathon for our engineering team. Hackathon's a week where they can basically work on whatever they want. But to stimulate AI activity, we altered how we do prizes. So we're doing $1,000 cash prizes to teams who either build AI capabilities in some unique or interesting way or use AI on their projects in some unique or interesting way. And guess what? People are experimenting. People are doing things. We're seeing interesting work being done. And yeah, look, money's a nice little carrot, nice little motivator. But to me, it's all about getting people to be uncomfortable and try something new. And I think you've got to find a way to be a catalyst for change at your company. And you've got to prioritize it. You've got to give people space to experiment. So give people space. Let them experiment. Even if the first few things they do may not quite yield the efficiency gains you were hoping for or may not be as high quality as you wanted, they are learning every step of the way. Encourage that learning and sharing. But yeah, I think that's what you need to do.

[Related: Solving the Public Sector’s AI Agent Safety Puzzle]

Jason Lopez: Todd Olson is co-founder and CEO of Pendo, a company which provides software helping companies understand how their software is being used to help improve the product experience. This is The Tech Barometer podcast produced by The Forecast, I’m Jason Lopez. The Forecast published tech news and features both in text, audio and video. Check out more stories at theforecastbynutanix.com.

Help decrease commercial database licensing costs using Intel® Xeon® processors with high performance per core.

By upgrading to servers with a higher performance per core, Intel IT consolidated commercial database cores by 82%—significantly reducing licensing costs—with a six?month return on investment.

Intel’s business is driven by data and informed by data-driven decisions. Therefore, commercial databases are a critical component of Intel’s day-to-day operations, forming the backbone for hundreds of online transaction processing (OLTP) and online analytical processing (OLAP) workloads, such as enterprise data warehouses, data analytics, and smart factory applications. It is crucial that Intel IT minimize database costs while providing business units with the database performance they require. That’s a constant balancing act.

Challenge:

Recent changes in commercial database and virtualization solution pricing structures have resulted in escalating software licensing costs. Projections from one database vendor indicated that these costs might triple in the coming months. These looming expenses motivated Intel IT to investigate how to reduce the number of cores used to run the commercial databases in Intel’s database-as-a-service (DBaaS) environment, without negatively affecting database query performance. Two options that we considered were rewriting applications to use non-commercial databases or transitioning to a software-as-a-service (SaaS) model. Neither of these options seemed feasible because rewriting applications is time-consuming, expensive, complex, and risky; likewise, SaaS solutions don’t include licensing fees but instead come with their own high costs and often require employee retraining. We sought a solution that would create a cost-effective core density, deliver equal or better performance, and support quick and seamless migration to the new infrastructure—all while reducing total cost of ownership (TCO).