Let’s Solve IT! Podcast

IT leaders, are you ready to turn strategy into results? Welcome to Let’s Solve IT!, the podcast designed exclusively for IT decision-makers who are ready to tackle today’s toughest challenges head-on. If you’re facing IT challenges, you’re not alone. This bi-weekly podcast dives into real IT challenges from AI adoption to cybersecurity risks with candid conversations, lessons learned, and practical solutions. Hosted by Matt Brown, Sr. Executive Director at NetApp, Let’s Solve IT! helps you bridge the gap between strategy and execution. Listen now to gain practical insights, tackle complex challenges, and deliver real results.

Let’s Solve IT! Podcast is a news podcast hosted by Unknown Host, with 14 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Unknown Host hosts Let’s Solve IT! Podcast, a news show with 14 episodes published.

Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.

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  Episode #14

  Ep. 14 - Are you bringing a dull knife to your data strategy?

  Sep 9, 202615 minS1

  A strong data strategy isn’t built when the project begins; it’s built in the preparation that comes before it. In this episode of Let’s Solve IT!, Matt Brown is joined by Spencer Hamons, Executive Architect at NetApp, who explains why organizations must measure, classify, and understand their data before investing in infrastructure. The discussion covers how to identify hot, warm, and cold data, why storage performance matters more than ever as SSD prices rise, and how telemetry and observability help organizations make smarter infrastructure decisions. In this episode you’ll learn: Why AI projects fail without data preparation The “sharpen the knife” approach to data strategy Hot, warm, and cold data explained Measuring performance before making infrastructure decisions SSD price increases and the return of tiered storage Telemetry and observability for smarter storage planning Optimizing AI training, RAG, and inference data Why culture matters as much as technology Continuous improvement for AI infrastructure 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/hamons/ AI, artificial intelligence, data strategy, storage, enterprise storage, data management, observability, telemetry, infrastructure, GPUs, RAG, retrieval augmented generation, inference, AI infrastructure, SSD, hard drives, hybrid cloud, object storage, NetApp, CIO, IT leadership, data architecture, data center modernization, AI adoption

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  Episode #13

  Ep. 13 - The hypervisor Odyssey: Are you letting a good crisis go to waste?

  Aug 26, 202614 min

  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

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  Episode #12

  Ep. 12 - Is zero downtime becoming your biggest cybersecurity risk?

  Aug 12, 202613 minS1

  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

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  Episode #11

  Ep. 11 - Are you spending your precious tokens on trash?

  Jul 29, 202614 min

  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

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  Episode #10

  Ep. 10 - Is AI a Technical Wave or a Cultural Tsunami?

  Jul 15, 202612 min

  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.

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