Let’s Solve IT! Podcast

The hypervisor landscape is changing. Licensing costs are rising. AI is reshaping enterprise IT. For many organizations, it feels like two major waves colliding at once. 
In this episode of Let’s Solve IT!, Matt Brown sits down with Spencer Sells, Vice President of Global Technology Alliances at NetApp, to discuss why today’s virtualization disruption shouldn’t simply be viewed as a cost crisis—it should be viewed as an opportunity. 
Drawing inspiration from The Odyssey, they explore how IT leaders can navigate uncertainty, make more intentional technology decisions, invest in their teams, and build platforms that will support the next decade of innovation. 
The conversation also examines why successful IT transformations depend as much on people as technology and why organizations that neglect training, experimentation, and culture risk falling behind. 
In this episode you’ll learn: 
Why the hypervisor disruption is about more than licensing costs 
How leading organizations are approaching VMware and alternative platforms 
Why AI and virtualization are creating simultaneous transformation challenges 
The importance of investing in people, not just technology 
How to encourage experimentation while maintaining governance 
Why “not making a decision” is still making a decision 
Practical leadership advice for IT managers navigating change 
Whether you’re evaluating your virtualization strategy, preparing for AI adoption, or leading an IT organization through change, this episode offers practical guidance for turning disruption into opportunity. 
 
You are not alone. Let’s Solve IT. 
 
Learn More: 
IT case studies | NetApp 
 
Connect with us!
https://www.linkedin.com/in/cmattbrown 
https://www.linkedin.com/in/spencersells 
 
The hypervisor odyssey: Spencer Sells of NetApp on turning IT crisis into strategy 
Episode overview 
In this episode of "Let's Solve IT!," host Matt Brown talks with Spencer Sells, Vice President of Global Technology Alliances at NetApp, about two crises hitting IT organizations at once: the disruption in the hypervisor market following Broadcom's acquisition of VMware, and the rapid rise of AI. Playing off the movie blockbuster, "The Odyssey," Matt and Spencer frame the hypervisor shift as an odyssey, with changes forced by a licensing and pricing shock rather than a genuine technology gap. Spencer argues IT leaders should not let this crisis go to waste and should instead use it to build their next platform deliberately. 
Throughout the conversation, Spencer Sells returns to one argument: IT platforms are enabling tools, but the people running them, described repeatedly through the metaphor of "rowing the boat," determine whether an organization succeeds through the transition. Spencer Sells advises IT leaders to budget time for staff training, address the psychological effects of change directly, and get hands-on with new technology themselves to build credibility and empathy with their teams.  
Spencer shares data from his customer conversations: about 20 to 25% of customers plan to stay on VMware by Broadcom and move to VCF, while about 60% are actively evaluating or blending in new hypervisor or Kubernetes platforms. Spencer cites a specific operational example: a NetApp account team planned to migrate a customer off VMware by Broadcom in a single month, a timeline Spencer says grossly underestimates the work required for staff training, networking changes, data protection, monitoring, support-level planning, and CMDB integration. 
Key takeaways 
The following points summarize the operational guidance Spencer gave for IT leaders navigating the VMware by Broadcom transition and the concurrent adoption of AI. 
IT organizations face two simultaneous disruptions: the hypervisor market shift caused by Broadcom's VMware acquisition and pricing changes, and the emergence of AI as a business-critical technology. 
About 20 to 25% of customers Spencer Sells has spoken with are staying on VMware by Broadcom and moving to VCF, judging the risk of switching too high. 
About 60% of customers Spencer Sells has spoken with are actively evaluating or blending in new hypervisor or Kubernetes platforms. 
IT leaders should treat the hypervisor disruption as an opportunity to build a platform intended to last ten to fifteen years, not just a reaction to cost increases. 
Migrating a critical hypervisor platform requires more than swapping licenses; it requires new skill sets, new tooling, and new integrations across networking, data protection, monitoring, and CMDB systems. 
One NetApp account team planned to complete a VMware by Broadcom migration in one month; Spencer Sells says this timeline underestimates the training and integration work required. 
Rewarding only reactive "firefighting" work while ignoring steady operational work creates a culture that produces more crises over time. 
Investing in staff training during a platform transition boosts morale by signaling that the organization is investing in each employee's future, not leaving them behind. 
Senior IT leaders should personally use new tools, such as building an MCP server, to gain credibility with their teams and better understand the challenges of the transition. 
Individual contributors facing pressure to learn new platforms or AI tools should treat the moment as a career opportunity rather than an obstacle. 
Topics and themes 
This episode addresses the operational, financial, and human dimensions of the current hypervisor market disruption and its overlap with AI adoption. 
The VMware by Broadcom acquisition: changes to product strategy, licensing, and pricing that triggered what Spencer Sells calls a "hypervisor crisis." 
Hypervisor and Kubernetes platform migration: the technical complexity of moving critical workloads, including differences in networking, multi-pathing, and offloads between platforms. 
IT staffing and skills investment: training current staff on new hypervisor, Kubernetes, or AI tools rather than treating platform changes as purely a licensing decision. 
IT budget constraints: flat IT budgets, reduced staffing levels, and the effect of unexpected license cost increases on planning. 
Organizational culture and crisis management: the risk of rewarding only reactive problem-solving over steady, proactive operations. 
Change management for IT teams: addressing employee anxiety about learning new platforms, including hypervisors, Kubernetes, and AI tools. 
Leadership behavior during technology transitions: the value of senior IT leaders gaining hands-on experience with new tools such as MCP servers. 
AI adoption alongside infrastructure change: the compounding effect of AI's emergence occurring at the same time as the hypervisor market disruption. 
Notable quotes 
"I understand the stress and anxiety on what may feel for many like a thankless job of keeping the lights on." (1:21) 
"Don't just run from something, run to something or row to something." (4:38) 
"It is the people themselves and how motivated they are, how supported they are, and their skillsets that allow your IT operations to do what you need it to do." (5:31) 
"You will not do it. You will not accomplish it. You are grossly underestimating the time required for the people aspects, the skills, the migration time, as well as the research you need for all of the other tooling." (8:05) 
"If you are only rewarding those people who run into the fire and you don't think about and reward the people who are just rowing regularly and keeping things going, then you are subtly creating a culture where I suspect you're going to have many more crises than you would need." (8:46) 
"We're going to back you. We're going to give you the time, give you the training, but you got to get your rear in gear and get moving on." (10:15) 
 
Hypervisor, VMware, Broadcom, virtualization, enterprise IT, AI, artificial intelligence, IT leadership, cloud strategy, Kubernetes, infrastructure modernization, NetApp, Spencer Sells, Matt Brown, enterprise technology, digital transformation, IT operations, technology leadership, Let’s Solve IT, platform modernization, IT strategy 

What happens when cyber vulnerabilities can be discovered and exploited faster than your organization can safely deploy a patch? 
 
In this episode of Let’s Solve IT!, Matt Brown welcomes back Gavin Guttersen, NetApp CISO, to discuss how AI-powered cyber models are transforming the threat landscape. They explore why traditional patching timelines may no longer be fast enough, how years of accumulated security debt are increasing organizational risk, and why the long-standing expectation of zero downtime may need to be reconsidered. 
 
You’ll hear: 
 
How AI is dramatically lowering the barrier to entry for cyber attackers
Why vulnerabilities that once took weeks to exploit can now be weaponized in minutes
How security debt and aging infrastructure increase business risk
Why faster patching can create operational risk and unexpected downtime
How to identify the systems, applications, and data your business cannot afford to lose
Why limiting the potential blast radius is critical when preventing every breach is unrealistic
How IT, security, system owners, and business leaders should share responsibility for cybersecurity decisions
What organizations can do in the short, mid, and long term to prepare for AI-accelerated threats
 
The question is no longer whether an attacker will get in. It’s whether your organization knows what matters most and is prepared to minimize the impact when a breach occurs. 
 
You are not alone. Let’s Solve IT. 
 
Learn More 
IT case studies | NetApp 
 
Connect with us! 
https://www.linkedin.com/in/cmattbrown 
https://www.linkedin.com/in/gaving  
 
Gavin Gutterson on AI cyber models: why zero downtime is becoming a security risk 
Episode overview 
In this episode of "Let's Solve IT!," host Matt Brown talks with Gavin Gutterson, NetApp's CISO, about how AI-powered cyber models have changed the calculus around uptime and patching. Gavin Gutterson explains that a model called Mythos, along with competing AI cyber models racing to outpace each other, can now identify critical vulnerabilities in decades-old platforms and generate working exploits "in a matter of minutes." Gavin Gutterson states plainly: "This is just the beginning. This is the very, very tip of the iceberg." 
The main argument from Gavin Gutterson centers on a shift in IT's core assumption. IT organizations have always prioritized always-on availability, patching systems on a 30-day cycle after testing in subproduction environments. Gavin Gutterson argues that this timeline no longer works when AI can create exploits in hours, and he raises a direct question for IT leaders: how do you patch in 30 minutes when your process was built for 30 days? 
Gavin Gutterson also introduces "security debt" as the security equivalent of technical debt: outdated, unpatched systems (Gavin Gutterson cites Windows 98 as a real example still running inside NetApp for regression testing) that accumulate risk over time. Gavin Gutterson's recommended response involves three levers: cleaning up old systems, adding compensating controls around systems that cannot be patched, and limiting the "blast radius" when, not if, a bad actor gains access. 
Key takeaways 
The following takeaways summarize the guidance Gavin Gutterson shared for IT and security leaders managing AI-accelerated cyber risk. 
AI cyber models, including Mythos, can find critical vulnerabilities and build exploits for them in minutes, a process that previously took bad actors months. 
Mythos is not the only AI cyber model driving this shift; Gavin Gutterson describes multiple models "playing leapfrog with each other," each improving over the last. 
Traditional patching cycles (30 days, with testing in subproduction before deployment to production) cannot keep pace with AI-generated exploits that emerge in hours. 
Security debt describes the accumulation of outdated, unpatched systems in an environment, and Gavin Gutterson equates it directly with technical debt. 
Organizations should decommission any system not required for business operations, then clean up what remains, according to Gavin Gutterson's recommended approach. 
Three risk-management levers exist for every organization: clean up existing systems, add controls around systems that cannot be patched, and limit the blast radius when a breach occurs. 
Not every system requires the same protection level; Gavin Gutterson distinguishes between systems tied to an "extinction level event" for the business and lower-priority systems. 
Security ownership belongs to whoever owns the system or environment (marketing, customer support, or any other business unit), not solely to the security or IT team. 
Leadership, not IT alone, bears responsibility for balancing uptime against security risk, because Gavin Gutterson frames this as a business decision. 
Gavin Gutterson's top three pieces of advice for fellow CISOs: don't over-rotate, inventory the problem before acting, and communicate with leadership in business terms. 
Topics and themes 
This episode covers the operational and organizational impact of AI-accelerated cyberattacks, framed around patching speed, security debt, and risk ownership. 
AI cyber models and exploit generation: how models like Mythos identify vulnerabilities and build exploits in minutes rather than months. 
Patching velocity versus operational risk: the conflict between rapid-response patching and the standard subproduction-to-production testing process. 
Security debt versus technical debt: outdated, unpatched systems as a growing liability, illustrated by legacy platforms like Windows 98. 
Zero-downtime paradigm under pressure: whether always-on availability remains realistic given the current threat landscape. 
Risk tolerance and extinction-level events: identifying which systems absolutely cannot go down versus which systems carry lower priority. 
Blast radius limitation: reducing the scope of damage a bad actor can cause after gaining access, rather than assuming breaches can be fully prevented. 
Security ownership across business units: the distinction between who sets security policy and who executes it within their own environment. 
Leadership accountability: framing uptime-versus-security tradeoffs as business decisions rather than purely technical ones. 
Notable quotes 
"Had you asked me that three months ago, my answer would've been 100% different than it would be today." (01:22) 
"Something that might've taken a bad act for months in the past was now being accomplished in literally a matter of minutes." (01:41) 
"Oh, this is just the beginning. This is the very, very tip of the iceberg. Everybody is going to keep leapfrogging." (03:28) 
"If I've got something that's end of life, close to end of life, considering coming up on end of life, I need to be getting rid of it as rapidly as possible." (03:28) 
"It is not good enough now to patch things once a month, once a quarter, once a year, not when the bad actors can create an exploit in a matter of hours. Those days are gone." (08:12) 
"There are two or three things that I would consider an extinction level event. Things that absolutely cannot ever happen to this company. So I would start by focusing on those things and making sure that you absolutely protect that, limit that blast radius." (09:01) 
 
AI cybersecurity, zero downtime, security debt, technical debt, AI cyber models, vulnerability management, patch management, cyber risk, operational risk, cyber resilience, legacy systems, security hygiene, blast radius, ransomware, data security, risk management, CISO, business continuity, enterprise security, responsible AI, NetApp, Gavin Guttersen, Let’s Solve IT
 
 

Is your AI delivering business value, or are you just paying to generate expensive garbage? 
In this episode of Let’s Solve IT!, Matt Brown talks with Cecile Kellam, NetApp Senior Solutions Architect, about why successful AI initiatives depend on more than good prompts. They discuss the importance of clean, relevant, secure data; why data ownership and custodianship must involve the business; and how organizations can avoid wasting tokens, budget, and trust as AI projects move from pilot to production.  
You’ll hear: 
Why many organizations are wasting AI tokens on poor-quality data 
Why great prompts alone cannot overcome bad or outdated data 
How to identify the right data sources for different AI use cases 
Who should own data quality, governance, and AI success 
Why business leaders—not just IT—must be involved in AI initiatives 
How data integrity directly impacts AI accuracy, trust, and adoption 
How to build AI initiatives that deliver measurable business value instead of unnecessary costs 
If your AI can’t trust your data, why should your employees trust your AI? 
You are not alone. Let’s Solve IT. 
 
Learn More: 
IT case studies | NetApp 
 
Connect with us! 
https://www.linkedin.com/in/cmattbrown 
Cecile Kellam | LinkedIn 
 
Cecile Kellam on AI tokenomics: why clean data beats clever prompts 
Episode overview 
In this episode of "Let's Solve IT!," host Matt Brown of NetApp talks with Cecile Kellam, Senior Solutions Architect at NetApp, about a problem she sees across enterprise AI projects: teams "spending precious tokens on trash." Cecile Kellam defines tokens as the cost of every AI query, and Cecile Kellam explains that this cost covers more than money. Tokens also consume energy and people-management resources, so every prompt an organization sends counts as a real investment. 
The central argument from Cecile Kellam is direct: clean, secure, well-governed data matters more than a perfectly written prompt. Cecile Kellam points out that a strong prompt still fails when the underlying system pulls from outdated engineering code, incomplete records, or poorly governed HR data. To make the point concrete, Cecile Kellam contrasts AI needs across engineering, finance, and HR departments, since each department queries very different tools and data. 
Cecile Kellam also warns about the cost of scaling AI from a small pilot to full production. Cecile Kellam describes customers who successfully built digital twins in the cloud, then watched costs turn "astronomical" when they tried to run those digital twins for more than a couple of hours a day. Cecile Kellam ties this risk back to a familiar pattern: companies that went "all in" on the cloud without clear goals and later faced surprise bills. 
Key takeaways 
The following takeaways summarize the practical guidance Cecile Kellam shared for IT leaders planning or scaling enterprise AI. 
Tokens represent the full cost of AI queries, including financial spend, energy use, and people-management costs, so wasted prompts waste real budget. 
A well-written prompt fails to deliver good results when the AI system lacks access to clean, current, and relevant data. 
Data accuracy is an ongoing discipline, not a one-time task, because information that was correct years ago can produce wrong answers today. 
Data ownership and governance often cause friction at the start of AI projects, so organizations should settle who owns, manages, and uses the data early. 
Engineering, finance, and HR departments each require different tools and data access, so AI systems must be built around each department's specific use case. 
Line-of-business owners hold the strongest stake in AI success, since they see the return when their teams save time and automate processes. 
End-user buy-in improves when leaders position AI as a tool that removes tedious work rather than a replacement for jobs. 
Scaling a successful AI pilot to full production can create "disastrous" bills, so teams should define production costs and success metrics before scaling. 
Topics and themes 
This episode covers the operational and financial realities of enterprise AI, framed around cost control, data quality, and readiness to scale. 
AI tokenomics: the total cost of AI queries, measured in tokens, and the waste created by unclear goals. 
Data as the foundation: the "garbage in, garbage out" principle applied to AI, with emphasis on clean, secure, compliant data. 
Data governance and ownership: resolving who owns, manages, and uses data before an AI project begins. 
Department-specific AI use cases: distinct tool and data requirements for engineering, finance, and HR. 
Data security and access control: protecting sensitive HR records while keeping needed data accessible to the right bot or agent. 
Goal-oriented AI strategy: defining a clear use case and success metrics as the first step of any initiative. 
Stakeholder alignment and end-user buy-in: framing AI as a productivity tool tied to measurable business return. 
Production scaling risk: the jump from a 10-user pilot to a 10,000-user deployment, with cloud digital twins as a cost cautionary example. 
Notable quotes 
"Unfortunately, it does seem like in a lot of cases we are absolutely spending our tokens on trash." (1:17) 
"If you're putting those tokens in and you're not using them in a meaningful way, you're essentially just sending money into a vending machine and getting trash out." (1:39) 
"Your prompt can be absolute fire and hit all the points that you're supposed to, but you're still not going to get the results that you're looking for." (2:46) 
"The end users need to understand that this isn't a project that is being brought to you because we want to take away your job. This is a tool that we have brought to you because we want you to be able to do the things in your job that you enjoy more and spend more time there." (8:39) 
"I've had customers successfully build digital twins in the cloud, but then when they looked to run it for more than a couple hours a day, the cost was so astronomical they would burn through their entire year budget in less than a month." (10:55) 
"You've got to get your data house in order, ensuring that you have safe, secure, compliant, clean data." (12:01) 
 
Episode keywords:  AI data readiness, tokenomics, data integrity, AI governance, data custodianship, data ownership, enterprise AI, AI scalability, clean data, responsible AI, AI ROI, data governance, unstructured data, AI adoption, production AI, AI infrastructure, business alignment, NetApp, Let’s Solve IT 

Technology isn’t the biggest obstacle to AI adoption. People are. 
In this episode of Let’s Solve IT!, Matt Brown talks with Hem Nerkar, NetApp Senior Vice President and CIO, about why AI adoption is fundamentally a cultural transformation, not just another technology rollout.  
They discuss how CIOs can overcome fear, build trust, align leadership, and create an environment where employees feel empowered to experiment, learn, and embrace AI as part of their daily work.  You’ll hear: 
Why AI represents a cultural shift far bigger than a technology upgrade 
Why fear, not technology, is often the biggest barrier to AI adoption 
How executive alignment creates confidence across the organization 
Why communication, empathy, and transparency are critical to successful AI initiatives 
How measurable AI goals encourage experimentation instead of perfection 
How NetApp IT is building an AI-first culture through leadership, training, and Centers of Excellence 
Why organizations should focus on progress over perfection as AI continues to evolve 
If AI changes technology overnight, how do you prepare people to change with it? 
You are not alone. Let’s Solve IT.
 
Learn More 
IT case studies | NetApp 
 
Connect with us! 
https://www.linkedin.com/in/cmattbrown 
https://www.linkedin.com/in/hem-nerkar-445b731 
 
Episode overview 
In this episode of Let's Solve IT!, host Matt Brown interviews Hem Nerkar, NetApp's Senior Vice President and Chief Information Officer, about how large organizations adopt AI. Hem Nerkar argues that AI is not another technical wave like the personal computer or the internet. Hem Nerkar describes AI as a cultural tsunami, a disruption larger than anything organizations have faced before, and frames that scale as an opportunity rather than a threat. 
Hem Nerkar explains that the role of IT is shifting from a support function to a strategic enabler of business transformation and shareholder value. That expanded role now includes managing organizational psychology, which covers employee fear, governance, and adoption. Hem Nerkar's central claim is that AI will augment jobs rather than replace them. AI moves teams from a traditional pyramid structure to a diamond structure, where more employees focus on higher-value work while AI handles routine tasks. 
NetApp uses specific mechanisms to drive AI adoption. NetApp established Centers of Excellence (COEs) to share best practices across the company, created secure innovation labs for experimentation inside the firewall, and embedded AI objectives into annual employee reviews. NetApp reports measurable results in quality assurance, where AI reduced testing time, and in IT infrastructure, where AI accelerated the vulnerability patching lifecycle. Throughout the conversation, Hem Nerkar identifies empathetic, continuous communication as the factor that makes AI transformation stick. 
Key takeaways 
The episode gives IT leaders a practical framework for moving an organization past fear and into measurable AI adoption. Each takeaway below stands on its own. 
AI adoption is a cultural challenge first and a technical challenge second. 
NetApp's organizational model is shifting from a pyramid structure to a diamond structure, where AI handles routine tasks and employees focus on higher-value work. 
Centers of Excellence (COEs) capture and share AI learnings across NetApp so teams don't repeat the same work. 
Innovation labs give NetApp employees a secure, low-risk environment to test AI use cases inside the firewall. 
AI objectives embedded in annual employee reviews turn adoption into a tangible, measurable goal. 
Anonymous employee surveys give NetApp leadership an honest read on how comfortable teams feel with AI. 
Empathy combined with constant communication is the primary driver of successful AI adoption, and Hem Nerkar states that no amount of communication is enough. 
A "fail fast" mindset gives teams permission to experiment and values the attempts, not only the end result. 
Topics and themes 
The conversation covers both the philosophy and the mechanics of leading AI change inside a large organization. Each theme below appears in the episode. 
AI as a cultural tsunami rather than a purely technical shift. 
The evolving CIO role, from technology evaluator to manager of organizational psychology. 
Employee fear of job loss, reframed by Hem Nerkar as AI augmentation rather than replacement. 
The shift from a pyramid organizational model to a diamond organizational model. 
Centers of Excellence as a method for scaling and sharing AI knowledge across NetApp. 
Innovation labs and "scaffolding" for secure, standardized AI use case development. 
Performance accountability through AI objectives written into annual employee reviews. 
Measuring cultural adoption with anonymous surveys and qualitative feedback. 
Measurable AI results in NetApp's quality assurance and IT infrastructure functions. 
Notable quotes 
The quotes below capture Hem Nerkar's main arguments, with timestamps for reference. 
[1:07] "AI is much, much bigger than IT or organization. No one has seen this before. This is huge cultural disruption and I would say opportunity as well to do the bigger and better things." 
[2:28] "I truly believe that AI is here not to replace the jobs. ... AI is here to make our jobs much better and faster." 
[8:53] "It's okay not to be perfect. And especially technology teams, we want to be black and white, but the key is to start getting comfortable, start using it." 
[9:15] "Fail fast. Absolutely. I think we should. That's what I'm doing in my organizations, avoiding the tries, not necessarily just the end game." 
[9:33] "I think the approach is empathy. As IT leaders, it's really important for us to understand what are the main reasons why the adoption is not happening and then work with the team to address some of that. I think it's communication, communication and communication." 
[10:42] "This is something that human race has not seen before. And this is something that we all will be adopting. Let's get on it as fast as possible and get more comfortable so we can write it." 
Listen to this episode of Let's Solve IT! on Spotify, Apple Podcasts, and Podbean. For real-world use cases from NetApp IT practitioners, visit netappit.com. 

AI was supposed to make work easier. So why are companies racing to cut people before the results even exist? 
 
In this episode of Let’s Solve IT!, NetApp’s Matt Brown sits down with Dave Blodgett, VP, Global Head of Infrastructure, to unpack the uncomfortable truth behind AI hype, skyrocketing infrastructure costs, and the growing fear that “productivity” is becoming corporate code for layoffs. 
 
You’ll hear: 
 
Why companies are investing billions into AI before provingreal businessvalue 
How “productivity gains” are becoming justification for workforce cuts
The hidden infrastructure and cloud costs powering enterprise AI
Why AI hype is colliding with operational reality inside IT organizations
What the future of work couldlooklike as automation accelerates 
Why the biggest AI challenge may not be technology but trust
 
Because the future of work may not be what the AI evangelists promised. 
You are not alone. Let’s Solve IT! 
Episode keywords:  AI infrastructure, enterprise AI, AI costs, future of work, workforce automation, cloud infrastructure, AI productivity, generative AI, AI strategy, IT operations, cloud operations, artificial intelligence, AI adoption, enterprise technology, AI investment, digital transformation, automation, infrastructure scaling, tech layoffs, AI and jobs, operational efficiency, CIO strategy, infrastructure management, NetApp, cloud computing, AI hype, business transformation, IT leadership, AI governance, productivity gains 
 
Learn More 
IT case studies | NetApp 
 
  
Connect with us!  
https://www.linkedin.com/in/cmattbrown 
Dave Blodgett | LinkedIn 
 
Transcript Episode overview: 
Is AI being built to replace people—or to help IT teams move faster, work smarter, and focus on the problems that actually differentiate the business? 
In this episode of Let’s Solve IT!, host Matt Brown sits down with Dave Blodgett, NetApp’s VP of Cloud Infrastructure and Operations, for a direct conversation about one of the biggest questions facing CIOs, CTOs, and IT leaders today: how do you harness AI without losing the trust, judgment, and innovation that only people bring? 
If your organization is under pressure to deliver AI-driven productivity gains, this conversation reframes the issue. The real opportunity is not replacing people. It is using AI to unlock the work IT teams have been too constrained to do—work that improves operations, accelerates delivery, and helps the business compete. 
At the center of the discussion is a practical leadership challenge: AI can increase human velocity, but only if teams understand the strategy, trust the intent, and have real access to the tools. Dave argues that AI is already delivering meaningful gains in areas such as software development, code quality, operational triage, and NOC services. But he is equally clear that complex engineering work still depends on human judgment, context, and innovation. 
If your team is still asking whether AI is coming for their jobs, this conversation offers a better question: how can AI help people move faster, solve harder problems, and focus on work that humans are uniquely equipped to do? 
Topics covered: 
Why AI should be treated as a force multiplier, not simply a workforce reduction tool 
How AI can help IT teams shift attention from keeping the lights on to strategic, differentiating work 
The limits of “vibe coding” and why engineering judgment, nuance, and expertise still matter 
What makes this AI wave different from previous automation and cloud transformations 
How autonomous NOC workflows, AI agents, event correlation, and root cause analysis can materially reduce time to resolution 
Why AI adoption requires transparency, hands-on exposure, business-value metrics, and team trust 
How leaders can help employees move from fear to fluency by making AI part of the engineering reflex 
Episode themes 
AI as Augmentation, Not Replacement: The idea that AI will enhance and assist human workers, particularly skilled ones like engineers, rather than replace them, was a consistent theme throughout the interview [1:57] [12:55] [13:07] (1:21, 11:58).  
Efficiency Driving Differentiation: Blodgett repeatedly connected the operational efficiencies gained from AI to the opportunity for teams to focus on higher-value, "differentiating work" that improves a company's competitive edge [2:28] [3:04] (1:21).  
Transparency and Trust: The importance of leaders being transparent with their teams about AI initiatives to manage fear and foster trust was emphasized at both the beginning and end of the conversation [8:18] [11:58] (8:18, 11:58).  
Adoption Through Exposure: The belief that practical, hands-on experience with AI tools is more critical for adoption and assimilation than formal training was a key theme [11:04] [11:12] (11:04, 11:12).  
Key takeaways  
AI's primary purpose is to act as a "force multiplier" to increase efficiency, not to facilitate mass layoffs [1:57] (1:21). Blodgett argued that tech company layoffs were a correction for overhiring, with AI being used as a convenient narrative [1:21] (1:21).  
Increased efficiency from AI will allow IT organizations to shift their focus from essential but non-differentiating work like maintenance and patching to strategic initiatives that make the company more competitive [2:48] [3:04] (1:21).  
While some lower-skilled, repetitive roles may be reduced, engineering jobs are safe from wholesale replacement due to the complexity and need for nuance in their work [3:38] [4:14] (1:21).  
Successful adoption of AI requires moving beyond abstract concepts to hands-on exposure, which helps build fluency and makes its use an "engineering reflex" [10:06] [11:28] (9:41, 11:12).  
Leadership must operate with high disclosure and transparency regarding AI strategies to build team trust and mitigate fears of job displacement [8:18] [11:58] (8:18, 11:58).  
Context and background  
Contextual Information  The interview was framed by the current climate of public and employee anxiety surrounding AI-driven job displacement [8:05]. This context was explicitly established by the interviewer's reference to recent layoffs at the "magnificent seven" tech companies, who are also making massive investments in AI [0:42]. The conversation also acknowledged that while the concept of AI is old, dating back to 1953, the recent advancements have renewed these concerns [0:19].  
Related Events  The primary related events referenced were the widespread layoffs in the tech industry, which some companies have linked to their AI investments [1:21]. Blodgett also mentioned an internal company hackathon as a specific event that spurred the creation of a valuable AI tool, the "autonomous Knock" [8:33].  
Potential Impact  Blodgett's statements could have a reassuring effect on engineers and other IT professionals, reframing AI as a tool for empowerment and career enhancement rather than a threat [12:55]. His focus on using AI for competitive differentiation could influence business leaders to adopt a value-creation mindset for their AI strategies, rather than one purely focused on cost reduction [3:04]. Furthermore, his practical advice on fostering adoption through transparency and hands-on experimentation offers a tangible model for other managers and executives navigating the same challenges [11:58] [11:12].  
 
Interview flow  
The interview began with a direct, challenging question about whether AI is being built to fire people [1:16]. Dave Blodgett addressed this head-on, establishing a pragmatic and reassuring tone that he maintained throughout the conversation [1:21]. The discussion flowed logically from this central fear to the practical applications of AI in IT [6:36], leadership strategies for encouraging innovation and managing employee concerns [8:05], and finally to a broader philosophical view on AI's role in augmenting human ingenuity [12:45]. There were no significant shifts in Blodgett's calm and authoritative tone.  
Episode description 
How do leading IT organizations get real value from AI? 
Start by putting AI where the work is measurable, repetitive, and operationally constrained: 
Development acceleration through tools like GitHub Copilot, Cursor, and Claude Code, especially for repetitive coding patterns, code generation, and code quality checks 
Low-variability operational workflows, such as NOC services, where incidents can be detected, triaged, correlated, and enriched before human intervention 
Observability and event correlation that help teams move faster from incident detection to root cause understanding 
Measurable business outcomes, including reduced time to resolution, faster time to market, improved code quality, and better operational efficiency 
Dave gives a concrete example from his team: an autonomous NOC model where the observability fabric detects an incident, routes a ticket, and allows an AI agent to perform triage, correlate indicators, identify likely root cause, and recommend next steps. By the time the human engineer receives the ticket, the work has already been enriched with context. That is the difference between AI as a vague productivity promise and AI as an operational capability that can be measured. 
But Dave is careful not to overstate what AI can do. He draws a clear line between automation that supports engineering work and the idea that AI can replace engineers outright. His own experimentation with vibe coding tools reinforced that technical complexity still requires engineering expertise. A non-engineer can generate a basic utility, but complex systems quickly demand architecture, reasoning, validation, and judgment. 
That distinction matters for leaders. If AI is framed only as a cost-cutting mechanism, teams will resist it. If it is framed as a way to remove operational drag, accelerate learning, and create space for more meaningful work, teams are more likely to engage. 
A major theme throughout the episode is trust. 
Moving from AI fear to AI fluency requires leaders to: 
Operate with high disclosure so employees understand the intent behind AI investments 
Share concrete examples of what teams are building and where AI is producing value 
Give people access to tools so they can experiment, tinker, and discover relevant use cases 
Connect AI to real workflows, not abstract hype or generic training 
Track business outcomes so investment can be tied to measurable improvements 
Dave compares today’s AI adoption curve to the early days of the PC. The technology may be available, but broad adoption depends on fluency, tools, supporting frameworks, and a culture that knows how to use it. The difference is speed: what took years with the PC will happen much faster with AI. 
Practical advice for IT leaders: 
Be transparent about AI strategy and acknowledge employee concerns directly 
Focus first on use cases where AI can safely reduce operational friction and produce measurable outcomes 
Give teams hands-on access to AI tools and examples so adoption becomes practical, not theoretical 
Use AI to free engineers from repetitive work and redirect capacity toward competitive differentiation 
Build guardrails for non-deterministic AI systems, especially where agents are making recommendations or taking action 
Measure AI value through outcomes such as time to resolution, code quality, operational efficiency, and faster time to market 
Ultimately, this episode reframes AI as a leadership and trust challenge as much as a technology challenge. The organizations that succeed will not be the ones that simply deploy the most AI. They will be the ones that help their teams understand it, use it, measure it, and apply it to the work that matters most. 
Supporting evidence  
To support the continued relevance of engineers, Blodgett cited his personal experimentation with "three or four different vibe coding platforms," where he observed that users without an engineering background "very quickly get in trouble" [3:58] (1:21).  
He provided a concrete example of AI augmenting work by describing a hackathon project that produced an "autonomous Knock," where an AI agent triages incidents, performs root cause analysis, and enriches tickets before a human engineer intervenes, materially decreasing resolution times [8:33] [9:02] (8:18).  
He pointed to existing tools like GitHub Copilot, Cursor, and ClaudeCode as real-world examples that "absolutely three, five, 10X engineers" by handling basic, repetitive coding tasks [6:36] [7:02] (6:36).  
Blodgett used the historical analogy of the PC's introduction in the 1980s, noting it took 15 years for broad adoption because the surrounding ecosystem didn't exist [9:41]. He suggested AI faces a similar, though "dramatically compressed," adoption curve [9:57] [10:06] (9:41).  
 
Question Analysis  The interviewer's questions were effective and well-structured. They started with the broad, fear-based premise common in public discourse about AI and progressively narrowed the focus to specific business expectations, leadership tactics, and real-world applications [1:16] [6:24] [8:05]. Questions like "How does that shape the way you lead your team today?" prompted Blodgett to draw insightful comparisons between past and present technology waves [4:31] [4:43]. Blodgett's responses were direct and substantive, often supported by specific examples from his own experience or his team's work, such as the "autonomous Knock" project, which added credibility and depth to his arguments [8:33].  
Notable quotes: 
“Ultimately AI is a force multiplier and we hear these terms 3X, 5X, 10X, 100X, so forth.” (1:57) — Dave uses this phrase to explain that AI can increase the output of engineers, but he cautions against the simplistic conclusion that more productivity automatically means fewer people. In IT, demand already exceeds supply, and AI can help teams finally get to the backlog of valuable work that has been pushed aside. 
“The stuff that gets sacrificed is the differentiating work, the stuff that makes you as a company more competitive.” (3:04) — Said while describing how basic operational hygiene often consumes IT capacity. AI can create room for the strategic work that moves the business forward: better products, faster delivery, and more competitive capabilities. 
“We have this whole universe of non-deterministic artificial intelligence where you input a bunch of things and you’re really not sure what you’re going to get.” (5:08) — Dave contrasts today’s AI with earlier rules-based automation. This new wave opens up enormous opportunity, but also requires clear guardrails so AI agents do not overstep, overdeliver, or create unintended consequences. 
“First of all, we’ve been very transparent with the team.” (8:18) — Dave explains that leaders cannot “lurk in the shadows” when it comes to AI. Transparency, open communication, and active contribution from engineering teams are essential to reducing fear and building trust. 
“So, we’re seeing our time to resolution metrics decrease materially.” (9:08) — This was said while describing an autonomous NOC workflow where an AI agent triages incidents, correlates events, performs root cause analysis, and enriches tickets before a human engineer takes action. The point is clear: AI value must be measurable. 
“AI solution patterns have to become part of the engineering reflex.” (11:28) — Dave argues that adoption cannot remain an abstract concept or occasional experiment. Teams need exposure, access, examples, and practice until AI becomes a natural part of how they approach technical problems. 
“I have not seen any indication that AI will out-innovate people. I can’t imagine that ever happening.” (12:45) — Dave closes the conversation by reinforcing that AI interpolates human-produced data. It can accelerate work, but it does not replace human nuance, judgment, creativity, or innovation. 
“I think it is to increase human velocity, not supplant human innovation, human contributions.” (13:07) — This becomes the core takeaway of the episode: the best AI strategy is not about removing people from the equation. It is about helping people move faster, make better decisions, and focus on higher-value work. 
Follow-Up Questions:  
You mentioned the need for "guardrails" to ensure AI agents don't become "rogue actors" [6:02]. What specific types of technical or ethical guardrails are you implementing for your AI systems?  
You gave the example of the "autonomous Knock" reducing resolution times [8:33] [9:02]. Can you share another specific project where AI has been implemented and what the measurable business outcomes were?  
You contrasted the slow adoption of the PC with the "dramatically compressed" timeline for AI [9:57] [10:06]. What are the biggest cultural or technical obstacles you see to this compressed adoption, and how is your organization addressing them?  
You described the application of AI for creative work and ideation as a "big gray zone" [7:36]. In what ways is your team experimenting with or evaluating the use of AI for these less-defined, more creative tasks?  
While you don't see engineering roles being supplanted, you acknowledged that lower-skilled functions might see a reduction [13:07]. What is your organization's strategy for reskilling or transitioning employees in those potentially affected roles?  
You emphasized the importance of making AI an "engineering reflex" [11:28]. What specific metrics or qualitative indicators do you use to measure this cultural shift and the level of AI fluency within your teams?  

What if your biggest security vulnerability isn’t a hacker, but your support strategy? 
 In this episode of Let’s Solve IT!, Matt Brown sits down with Mike Eubanks, Senior Director of IT Operations at NetApp, to explore why reactive IT support is becoming a growing business risk. From technical debt and aging infrastructure to ransomware and expanding attack surfaces, they discuss why proactive operations, observability, and AI are becoming essential tools for modern IT organizations. 
 You’ll hear: 
Why reactive IT support is becoming a growing security risk in the age of ransomware and cyber threats 
What a technical debt, aging infrastructure, and poor technology hygiene expand an organization’s attack surface 
The role of observability, AI, and proactive operations in identifying and resolving issues before they impact the business 
Practical strategies for reducing risk, improving security posture, and shifting IT support from firefighting to prevention 
 Support teams rarely get recognized when nothing breaks, but that’s exactly the point. The real challenge in modern IT isn’t responding to disasters faster. It prevents outages, ransomware attacks, and operational disruptions before the business ever feels the impact. 
 You are not alone. Let’s Solve IT! 
 
Learn More: 
IT case studies | NetApp 
 
Connect with us:
Matt Brown | LinkedIn  
Michael Eubanks | LinkedIn 
 
Below is a summary of this episode’s transcript:  
What does it really mean to make IT support proactive in an era defined by AI? 
In this episode of Let’s Solve IT!, host Matt Brown sits down with Mike Eubanks, Senior Director of IT Operations at NetApp, for a candid, real-world conversation about why traditional IT support models are breaking—and what it takes to evolve them. 
Key topics: 
Why unsupported systems and legacy applications create hidden security vulnerabilities 
How observability enables predictive, proactive IT support 
The role of AI in correlating data, reducing troubleshooting time from hours to seconds 
How to build a secure AI environment with governance and guardrails 
Why culture is critical to shifting from reactive to proactive operations 
The importance of failing fast, iterating quickly, and empowering teams to act early 
 
If your team is still waiting for tickets to come in, this conversation will challenge you to rethink what modern IT support should look like—and how to get ahead of risk before it disrupts the business. 
At the center of the discussion is a fundamental shift: IT support can no longer afford to be reactive. While business leaders continue to invest in innovation and AI-driven transformation, support organizations are expected to operate like a utility—always on, always available, and invisible when working well. But under the surface, aging infrastructure, unsupported systems, and growing data complexity are creating a constant stream of hidden risk. 
Notable Quotes: 
“Being secure requires a different focus." (1:58) - Stated when explaining growth of security risks forced IT and the business to change their approach. AI is fundamentally changing both sides of the equation. On one hand, it’s accelerating innovation and enabling faster insights. On the other, it’s amplifying security risks, exposing new ways for bad actors to identify and exploit weaknesses. 
"You have to listen to the experts. You have to let them plan and understand the plan and then communicate that plan." (2:45) - Said while describing his leadership philosophy in IT support, emphasizing collaboration and expertise.  
“We don't look for things that are broken. We look for things that are breaking and AI helps us do that." (5:22) - This was Mike's explanation of the shift from traditional monitoring to proactive observability. He shares how his team is shifting from traditional monitoring to modern observability, using AI-powered analytics to identify patterns, detect anomalies, and predict issues before they impact users. 
“Unsupported breeds the problem. What that means is that they don't support and they don't provide security patches and those types of things anymore, which creates a huge risk if you are keeping privileged company data or your company secrets on an old server that has an aging OS that's out of support and is not receiving patches on a regular basis or at all.” (2:16) Mike explains how legacy hardware and applications are no longer just performance liabilities—they are critical security vulnerabilities, especially when they fall out of vendor support and stop receiving patches.” 
"What you have to do is first of all, you have to create a culture... where it's okay to fail. Just fail fast and learn and iterate, iterate. Don't wait until everything's perfect to release or you'll never release." (12:39) - Stated when discussing the importance of creating a culture that encourages rapid innovation and is not paralyzed by the fear of failure. 
 
How do leading IT organizations get ahead? 
Shift to modern observability with: 
AI-driven correlation of massive data sets, reducing troubleshooting time from hours to seconds 
A next-generation Network Operations Center (NOC) model, combining real-time visibility with intelligent diagnostics 
The ability to trace issues across services, pinpoint root causes, and feed insights directly to engineering teams for permanent fixes 
Continuous feedback loops that turn incidents into long-term improvements 
 
Mike also highlights how cloud architectures are changing the game, enabling organizations to eliminate downtime entirely in some cases by shifting workloads, rebuilding environments, and avoiding traditional patching cycles. 
But technology alone isn’t enough. 
A major theme throughout the episode is culture. Moving from reactive to proactive support requires a mindset shift across the organization: 
Teams must be trained to seek out risks before they surface 
Leaders must encourage experimentation and remove the fear of failure 
Organizations must adopt a “fail fast, learn fast, iterate” approach to keep pace with rapid change 
Continuous learning is essential, especially as AI capabilities and threats evolve at unprecedented speed 
Mike emphasizes that many teams still operate with a “if it’s not broken, don’t fix it” mentality—which is no longer viable in a modern IT environment. The new mandate is clear: identify risks early, act sooner, and build systems that improve continuously. 
Practical advice for IT leaders:  
Invest in a strong observability foundation 
Build secure AI environments with governance and guardrails 
Stay current on emerging technologies and evolving threat landscapes 
Create a culture that prioritizes proactivity over perfection 
Ultimately, this episode reframes IT support as a strategic capability—not just a cost center. The organizations that succeed will be the ones that can anticipate issues, reduce risk, and maintain resilience in a system that never stops moving. 
If your IT team is still operating in reactive mode, this conversation will push you to rethink what’s possible—and what’s required—to stay ahead. 

The technology industry is facing an ever-increasing supply chain crisis, and AI is rapidly making it worse. What started as a hardware shortage is now forcing enterprises to rethink storage, data center capacity, procurement strategy, and whether their infrastructure is truly prepared for the next wave of demand. 
 In this episode of Let’s Solve IT!, Matt Brown speaks with NetApp Technical Evangelist and The STEMINISTS co-host Phoebe Goh about a question many IT and engineering leaders are facing: Should you buy your way out of a supply chain crisis? 
The conversation explores why throwing money at the problem and panic-buying hardware may make things worse. As AI demand explodes, enterprises are discovering that you can’t simply hoard your way out of a supply chain crisis, especially when the real problem is how data, storage, cloud, and infrastructure strategy are being managed in the first place. 
You’ll hear: 
Why AI is increasing pressure on enterprise storage and data center capacity 
How the DRAM shortage and SSD supply chain challenges are affecting infrastructure decisions 
Why buying more hardware may not solve the real problem 
How AI is changing the value of cold data and historical data 
Why storage teams need to think more strategically about data mobility, security, and performance 
How cloud, tiering, and modernization can help organizations stay flexible 
Why IT strategy, procurement, sustainability, and infrastructure planning must be connected 
Because the real question may not be whether you can buy your way through the crisis. 
It may be whether that decision prepares you for what comes next. 
You are not alone. Let’s Solve IT! 
 
Learn More 
IT case studies | NetApp 
 
Check out The STEMINISTS Podcast: 
The STEMINISTS Podcast | Phoebe Goh and Mekka Williams 
 
Connect with us!
https://www.linkedin.com/in/cmattbrown 
Phoebe Goh | LinkedIn 
 
AI data center, enterprise storage, storage infrastructure, supply chain crisis, DRAM shortage, SSD shortage, data center capacity, data mobility, cloud strategy, infrastructure modernization, IT strategy, storage optimization, AI infrastructure, cloud tiering, data center sustainability, AI workloads, enterprise AI, hybrid cloud, storage performance, data management

AI is no longer just a productivity tool. It is quickly becoming one of the biggest operational, security, and governance challenges enterprises have faced since the rise of the cloud. 
In this episode of Let’s Solve IT!, Matt Brown speaks with NetApp IT Director of Enterprise Architecture and AI, Paul Carau, about the uncomfortable reality many technology leaders are now facing: Employees are adopting AI faster than most organizations can govern it. 
The conversation explores why AI success requires far more than simply deploying the latest toolset. Leaders must now navigate employee personas, data access, governance, change management, overlapping AI capabilities, and the growing pressure to move faster without losing control of the environment. 
You’ll hear: 
Why uncontrolled AI adoption is becoming a major enterprise risk 
How shadow AI is creating new challenges of security and governance 
Why employee personas matter when deploying AI at scale 
The growing complexity caused by overlapping AI platforms and tools 
How organizations can balance innovation speed with operational control 
Why AI governance must evolve alongside business expectations 
Because the real question may no longer be whether your company is using AI. 
It may be whether you still control how it’s being used. 
Let’s talk about what that means, and Let’s Solve IT! 
 
Resources just for you:
IT case studies | NetApp 
 
Connect with us!
https://www.linkedin.com/in/cmattbrown 
https://www.linkedin.com/in/paul-carau  
 

Are your technology investments driving developer productivity—or just adding complexity? 
In this episode of Let’s Solve IT!, Matt Brown sits down with Mekka Williams, Director of Innovation and Solutions and co-host of The STEMINISTS podcast, to unpack one of the biggest disconnects in modern IT: the gap between investment and impact. 
They explore why more tools don’t always lead to better outcomes, what developer productivity really looks like from a business perspective, and how leading organizations are aligning people, processes, and technology to drive measurable results. 
You’ll hear: 
Why time-to-value continues to fall short of expectations
How DevOps complexity is slowing teams down
What meaningful productivity measurementlookslike 
Where AIdelivers real valueand where it introduces risk 
Why progress starts with action, not perfection
Because improving productivity isn’t a one-time fix—it’s an ongoing business priority. 
You are not alone. Let’s Solve IT! 
 
Learn More 
IT case studies | NetApp 
 
Also make sure you check out The STEMINISTS Podcast | Phoebe Goh and Mekka Williams 
 
Connect with us! 
https://www.linkedin.com/in/cmattbrown 
Mekka Williams | LinkedIn 

It sounds extreme, but it’s not as far off as you might think. 
In this episode of Let’s Solve IT!, Matt Brown talks with Ralph Renne, Sr. Director, Workplace Experience, to break down what’s really happening behind the scenes: massive increases in power demand, the shift to water-cooled environments, and why most data centers aren’t built for what’s coming next. 
 
In this episode, you’ll learn: 
What’s driving these new AI infrastructure demands 
Why this isn’t just an IT problem, it’s a facilities and business problem 
The real timelines and costs to get ready
And what you should be doing right now to prepare
Because this isn’t a future problem. 
It’s already here. 
And if you’re not planning for it, chances are, you're already behind. 
You’re not alone. Let’s Solve It! 
 
Learn More 
IT case studies | NetApp 
 
Connect with us!
https://www.linkedin.com/in/cmattbrown 
https://www.linkedin.com/in/ralph-renne-36ab0a6/