Let’s Solve IT! Podcast

Ep. 9 - Are you building AI to fire me?

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?  

Ep. 8 - Is your IT support strategy creating a security risk?

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. 

Ep. 7 - Should you buy your way out of a supply chain crisis?

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

Ep. 6 - Do your employees have the right AI tools to succeed?

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  
 

Ep. 5 - More Tools. Less Output? The Truth About Developer Productivity

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 

Ep. 4 - Does your data center need to go nuclear to support AI?

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/ 

Ep. 3 - Why are ERP implementations CIO career killers?

Why do so many ERP transformations derail careers at the highest level? 
It’s not the technology. It’s everything else.  In this episode of Let’s Solve IT!, Matt Brown sits down with NetApp CIO Hem Nerkar to confront one of the most uncomfortable truths in enterprise IT: ERP transformations don’t start by failing systems—they start by breaking the people leading them. 
Because what looks like a software upgrade is actually a high-risk, multi-year bet on your company’s culture, operations, and future.  In this episode, you’ll learn: • Massive financial investment with no immediate payoff • Organizational resistance at every level • Hundreds of systems and stakeholders in play • And a clock that never stops ticking while the business keeps running  Hem Nerkar pulls back the curtain on what really makes these transformations so dangerous and what separates those projects that succeed from those that quietly unravel careers.  This isn’t about features. This isn’t about vendors.  This is about pressure, risk, and leadership when there is no safe path forward. 
Let’s talk about what that really means. Let’s Solve IT! 
 
Learn More Here: 
IT case studies | NetApp 
 
Connect with us!
https://www.linkedin.com/in/cmattbrown 
https://www.linkedin.com/in/hem-nerkar-445b731 

Ep. 2 - How to afford AI and the people to run IT

AI budgets are blowing up. Hardware prices are all over the place. And executives want ROI yesterday. 
So here’s the real question nobody wants to say out loud: How are you supposed to afford the tech and the people to run it? 
In this week’s episode of Let’s Solve IT!, host Matt Brown and Stephan Stelter, NetApp Manager, Solutions Engineering, confront the tension IT leaders feel every day: innovation pressure vs. finite budgets. They get into FOMO-driven spending, AI productivity myths, and resource scarcity.  
Rather than defaulting to layoffs or across‑the‑board cuts, this conversation explores a smarter path forward: reallocating resources, retasking talent, and placing the right workloads in the right environments to maximize impact without burning out teams or budgets. 
If you’re feeling squeezed between transformation goals and headcount limitations, this episode is for you.  
Let’s talk about what that really means, and Let’s Solve IT! 
 
Learn more about the world of NetApp on NetApp: IT case studies | NetApp
 

Ep. 1 - When AI convenience becomes a business risk

AI is everywhere — recording meetings, transcribing conversations, summarizing discussions, and storing more data than ever before. But as AI-powered productivity tools become the norm, how do organizations protect themselves from growing security and legal risk? 
 In this first episode of Let’s Solve IT!, host Matt Brown sits down with NetApp CISO Gavin Guttersen to explore a sobering reality: AI tools can create powerful productivity gains and unexpected exposure.  From litigation risk to data governance gaps, from “AI companions” in meetings to agents talking to agents, this conversation dives into the uncomfortable truth: Security through obscurity is gone.  In this episode, you’ll learn: 
How AI transcription and meeting recording tools create unexpected security and legal exposure 
Where data governance breaks down as enterprise AI adoption accelerates 
What CISOs, IT leaders, and business teams must consider in an always‑recorded environment 
If you’re responsible for AI security, data governance, or risk management, this episode sets the foundation for the conversations organizations can’t afford to avoid. 
Let’s talk about what that really means. Let’s Solve IT! 
 
Learn more about the world of NetApp on NetApp: IT case studies | NetApp 
 
Connect with us! 
https://www.linkedin.com/in/cmattbrown 
https://www.linkedin.com/in/gaving