Let’s Solve IT! Podcast

By NetAppNews Technology Tech News

Let’s Solve IT! Podcast episodes

November 04Ep. 18 - Is bad data really an IT problem?
Artificial intelligence has raised the stakes for data quality, but is bad data really an IT problem?
In this episode of Let’s Solve IT!, host Matt Brown sits down with NetApp Senior Vice President and Chief Information Officer Hem Nerkar to challenge one of the biggest misconceptions in enterprise AI. While IT builds and manages the technology that powers AI, the business owns the data that determines whether AI produces meaningful results.
Hem explains why improving data quality requires more than better technology. It demands executive sponsorship, business ownership, governance frameworks, trusted data sources, and continuous collaboration across the organization. Together, they explore how NetApp approaches data stewardship, measures success, establishes accountability, and creates the discipline needed to turn enterprise data into a strategic asset.
In this episode, you’ll learn:
Why bad data is a business challenge, not just an IT problem
How business and IT share responsibility for building trusted, AI-ready data
Why data governance and stewardship are essential for accurate AI and analytics
How to identify trusted data sources, data lineage, and ownership across the enterprise
Practical first steps CIOs and business leaders can take to improve data quality without trying to fix everything at once
Why data quality is an ongoing discipline, not a one-time project

Whether you’re leading AI initiatives, modernizing your data strategy, or trying to improve trust in analytics, this conversation offers practical guidance on where to start and why data governance is becoming one of the most important business capabilities in the AI era.
You are not alone. Let’s Solve IT.

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IT case studies | NetApp

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https://www.linkedin.com/in/hem-nerkar-445b731

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Episode overview
In this episode of "Let's Solve IT!," host Matt Brown talks with Hem Nerkar, Senior Vice President and Chief Information Officer at NetApp, about a question Matt Brown says he hears constantly from customers: when AI or a dashboard produces a bad result, is that automatically an IT problem? Hem Nerkar's answer sets the frame for the entire conversation. IT manages the technology infrastructure, but the business owns the data, including how that data gets organized and used to achieve company goals.
Hem Nerkar explains why this question has become urgent now. The "AI age" has intensified the stakes around data quality, since AI hallucination traces back, in many cases, to poor underlying data. Hem Nerkar describes NetApp's operational response: a data governance framework built on partnership between IT and business units like finance, sales, and product teams. In this model, IT acts as a "caretaker" and "coordinator," providing the architecture and tools, while business leaders own the data, decide acceptable accuracy levels, and sit as named stewards within the governance structure.
Hem Nerkar also walks through concrete mechanics: identifying true data sources (as opposed to second or third-hop copies that get "contaminated" as they move between systems), maintaining an extensive catalog of data entities and their owners, and consolidating sources through enterprise resource planning (ERP) implementation. Hem Nerkar closes with advice for CIOs starting this work: acknowledge the issue, quantify the challenge, and build the discipline step by step.
Key takeaways
The following takeaways summarize Hem Nerkar's guidance on shared data ownership and governance for IT leaders.
IT manages the technology infrastructure, but the business owns the data, including how it's organized and used toward company goals.
The maturing AI age has made data quality more urgent, since AI hallucination often traces back to the quality of the underlying data.
Data accuracy is a business decision, not a CIO decision. The business must determine what accuracy percentage is acceptable for its goals.
A formal data governance framework works best when it includes named stewards from each business unit, such as finance, sales, and product teams.
IT's role in data governance is to coordinate, not own. IT builds the framework, tools, and architecture, while business units own the data itself.
Identifying the true, original source of data is critical, since customer and product information often becomes distorted as it passes through multiple systems.
NetApp consolidated data sources through an ERP implementation, reducing the number of places data was flowing from.
NetApp maintains an extensive data catalog tracking entities, key attributes, business owners, and data stewards. That catalog requires constant maintenance, not a one-time setup.
Getting business leaders aligned starts with empathy. Business owners have their own day-to-day priorities, so IT needs to show how the data challenge affects those priorities directly.
Incremental success starts with acknowledging there's an issue, quantifying the challenge, and building visibility with executives step by step.
Topics and themes
This episode covers the shared responsibility model for enterprise data quality, framed around governance structure, source management, and the AI-driven urgency behind it.
Shared data ownership: the distinction between IT's infrastructure responsibility and business's data ownership.
AI as a driver of data quality urgency: hallucination and other AI maturity issues traced back to poor data.
Data governance framework: structure, stewards, and cadence built in partnership between IT and business units.
Business measurement of accuracy: acceptable data accuracy levels as a business decision, not an IT decision.
Data source identification: distinguishing original data sources from downstream, contaminated copies.
Data lineage and architecture: tracking how data moves across systems as a foundation for governance.
Data cataloging: maintaining a living record of entities, attributes, owners, and stewards.
ERP consolidation: reducing the number of data sources through enterprise resource planning implementation.
Business and IT partnership: framing governance as joint ownership rather than an IT-only mandate.
Incremental CIO strategy: a step-by-step approach to acknowledging, quantifying, and addressing data challenges.
Notable quotes
"IT is responsible for managing the technology infrastructure, but business really owns the data with respect to how that data is organized and how it's getting used for our business and achieving company's goals." (1:12)
"We are in this AI age, but as AI gets matured, AI has a lot more issues right now. Hallucination is one of the big ones. One of the main reasons is the data, and that's where the data is even more important right now than it was ever before." (2:15)
"IT's job is to make sure that we have that governance framework established in the organization with all of this key owners sitting there with us, we being the caretaker as IT, making sure that we have that process laid out, the governance laid out, cadence laid out." (3:54)
"The challenge could be that the data goes through multiple hops. The customer information flows from one system to another system, and that's where a lot of contamination happens." (7:10)
"It needs to be constantly care and feeding." (12:02)
"Incremental success starts with acknowledging there is an issue, identifying where the challenges are, quantifying the challenges, focusing on the right area step by step, getting those visibility to execs and all of the team members and continue to work on it." (13:51)
15 min
October 21Ep. 17 - If AI has all the answers, do we still need the experts?
Can AI help us find answers faster without replacing the human experience, context, and trust needed to solve technical problems?

Recorded at NetApp INSIGHT 2026, host Matt Brown joins NetApp’s Drew Claybrook and Lex Rutledge to explore why people and communities remain essential in an AI-driven world.

They discuss how experts turn information into practical guidance, from understanding a customer’s environment to checking whether an AI-generated answer is relevant. They also share what makes technical communities valuable: trusted relationships, shared experience, and the freedom to ask questions.

In this episode, you’ll learn:
Why understanding the person and their environment leads to better guidance
How experts help determine whether information is current and relevant
Why checking the sources behind AI-generated answers matters
How community leaders can make asking questions easier
Where AI can support communities and help experts share their knowledge

As AI becomes part of how we work, how can we keep human experience and connection at the heart of solving problems?

You are not alone. Let’s Solve IT!

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Drew Claybrook | LinkedIn
Lex Rutledge | LinkedIn

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Drew Claybrook and Lex Rutledge on why human expertise survives the AI era
Episode overview
In this episode of "Let's Solve IT!," recorded at NetApp Insight, host Matt Brown talks with Drew Claybrook, Technical Marketing Engineer at NetApp, and Lex Rutledge, A-Team Program Manager at NetApp, about whether AI's growing capabilities make human experts unnecessary. The episode opens with Matt Brown's framing question: "If AI has all the answers, do we still need the experts?" Drew Claybrook and Lex Rutledge answer no on both counts, arguing that AI depends on human-generated content to function and that trust, built through years of community participation, cannot be replicated by AI.
Drew Claybrook draws on six and a half years managing NetApp's community to argue that AI systems need human experts to create the content AI learns from in the first place. Drew Claybrook also points to a NetApp community platform migration completed in July, where archived content from 2018 and earlier still surfaces in AI-generated answers, a sign that users follow reference links back to original expert sources rather than trusting AI summaries outright. Lex Rutledge draws on the NetApp A-Team, a subject matter expert program established in 2013, to argue that trust accumulated over time gives human experts a credibility AI cannot match, particularly for attendees who travel to NetApp Insight specifically for face-to-face answers.
The conversation also covers why some technical professionals avoid brand-owned communities. Drew Claybrook explains that community members often fear admitting a knowledge gap in a space where an admin or colleague might recognize their name, driving them toward anonymous platforms like Reddit and Discord instead. Both guests close by affirming that human connection, highlighted by lessons from the COVID-19 pandemic, remains necessary even as AI use grows across daily life.
Key takeaways
The following points capture the practical arguments Drew Claybrook and Lex Rutledge made about AI, trust, and community management.
AI systems require human-generated content as training material, meaning human experts remain foundational to AI's functioning, not optional to it.
Face-to-face interaction at events like NetApp Insight gives attendees answers to complex questions that AI cannot provide, according to Lex Rutledge.
Human tone and implied context, available in person or on a call, often get lost in text-based community platforms like forums, Reddit, or Discord.
Trust built over years of community participation cannot currently be replicated or replaced by AI, per Lex Rutledge.
AI systems can surface outdated information, prompting users to second-guess AI answers and seek out the original expert source instead.
Technical professionals often avoid posting in identified, brand-owned communities out of fear of admitting a knowledge deficiency, preferring anonymous platforms like Reddit or Discord.
The NetApp A-Team, established in 2013, retains many of its original members, demonstrating that long-term expert engagement is achievable.
Community managers should actively invest resources in their communities, including organizational buy-in and direct access to internal experts, rather than treating communities as passive assets.
Curating and updating technical knowledge requires human expertise to judge relevance and accuracy, a task AI can support but not fully replace.
Topics and themes
The episode centers on the relationship between AI and human expertise within technical communities, covering trust, platform strategy, and community management practices.
AI as a tool, not a replacement: AI supports expert work but does not substitute for human judgment or community trust.
Trust as the foundation of community value: trust built over time distinguishes human expert networks from AI-generated answers.
AI limitations and hallucinations: outdated or inaccurate AI responses drive users back to verified expert sources.
Platform diversification: NetApp maintains presence across owned forums, Discord, and Reddit to meet users where they are.
Anonymity and professional reputation: technical professionals' reluctance to admit knowledge gaps in identified brand communities.
The NetApp A-Team program: a subject matter expert network established in 2013, identifiable by branded shirts at events.
Community management strategy: the need for organizational buy-in, resource investment, and reciprocal value exchange with community members.
Human connection post-pandemic: COVID-19 as a reference point for why in-person and human interaction retain value.
Notable quotes
"You've got to have the content there for AI to learn from it."—Drew Claybrook (0:42)
"A lot of them I hear in the hallways today, some of them come here so they can have that face-to-face conversation to get that answer, to go into the complex question that they've not been able to get anywhere else except for that human connection when they come to Insight every year."—Lex Rutledge (1:11)
"Trust is incredibly important. And I think without that trust that communities bring with time and the expertise that those communities have, AI can't replicate that or replace that by any means right now."—Lex Rutledge (3:31)
"People are afraid to admit a knowledge deficiency. So instead they'll go to somewhere like Reddit or to Discord where they can be a little bit more anonymous."—Drew Claybrook (7:36)
"They're second guessing the AI response. They're looking for what the expert actually said, not how it was summarized."—Drew Claybrook (6:52)
"As humans, I think we just want the human connection because we have emotions."—Lex Rutledge (8:44)
14 min
October 07Ep. 16 - Are you building bots when you really need an AI intern?
AI agents can follow instructions, but how do they learn your organization's unwritten rules?
Matt Brown welcomes Robert Vaughn, IT AI domain owner at Intel, to explore the “AI intern”: a metaphor for systems that use institutional knowledge and ongoing feedback to understand better what matters.
They discuss the difference between AI reduction and synthesis, how risk compounds across agentic workflows, and why human oversight remains essential. Discover why building more useful, trustworthy enterprise AI takes more than connecting bots.
In this episode, you’ll learn:
Why institutional knowledge is a missing piece in enterprise AI
How feedback helps AI account for changing priorities, roles, and information
Why synthesis introduces greater risk than summarizing or extracting information
How human oversight can help AI support higher-value decisions
How complementary retrieval pipelines can combine documented information with organizational context
You are not alone. Let’s Solve IT.

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IT case studies | NetApp

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https://www.linkedin.com/in/robert-lawson-vaughn-7a65103/

Agentic AI, AI agents, AI intern, enterprise AI, institutional knowledge, organizational context, AI awareness, feedback loops, human oversight, human-in-the-loop, AI risk management, trustworthy AI, retrieval-augmented generation, RAG pipelines, AI reasoning, AI synthesis, IT automation, Robert Vaughn, Matt Brown, Intel, NetApp, Let’s Solve IT!
15 min
September 23Ep. 15 - Is your telemetry all cost and no action?
Organizations are collecting more telemetry than ever, but is all that data actually delivering value?
Matt Brown sits down with Marty Mayer, Senior Director of Product Management at NetApp, to explore how NetApp turns massive amounts of telemetry into actionable insights. Marty shares how Active IQ uses data from more than 300,000 systems across 24,000 customers to improve support, identify risks, optimize infrastructure, and help customers get more from their NetApp investments.
They also discuss the importance of continually evaluating what data is worth collecting, how Value Insights can help quantify business outcomes, and how AI is moving telemetry beyond recommendations toward automated action.
In this episode, you’ll learn:
Collect telemetry with a clear use case—not simply because you can.
NetApp Active IQ receives roughly 10 TB of telemetry each month from more than 300,000 systems across 24,000 customers.
Telemetry can deliver value far beyond break/fix support, including security, capacity optimization, planning, and customer success.
Measuring how data is actually being used helps determine what should be retained, archived, or pruned.
The next evolution is moving from insight to recommendation to automated action through AI agents.
Start small with AI, establish guardrails, test with trusted users, and expand based on feedback.
The quality and accessibility of your data ultimately determine the value AI can deliver.
You are not alone. Let’s Solve IT.

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IT case studies | NetApp

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https://www.linkedin.com/in/martymayer/

Telemetry, Active IQ, AI, AIOps, AI agents, data strategy, data analytics, IT operations, infrastructure optimization, automation, predictive insights, customer support, Value Insights, storage efficiency, capacity planning, cybersecurity, NetApp
14 min
September 09Ep. 14 - Are you bringing a dull knife to your data strategy?
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.

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IT case studies | NetApp

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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
15 min
August 26Ep. 13 - The hypervisor Odyssey: Are you letting a good crisis go to waste?
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

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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
15 min
August 12Ep. 12 - Is zero downtime becoming your biggest cybersecurity risk?
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!
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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
13 min
July 29Ep. 11 - Are you spending your precious tokens on trash?
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.

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IT case studies | NetApp

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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
15 min
July 15Ep. 10 - Is AI a Technical Wave or a Cultural Tsunami?
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.
13 min
July 01Ep. 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?
15 min

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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. 

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