Episode Summary

Everyone wants faster automation and better AI outcomes, but few organizations stop to examine the data foundations underneath them. In this episode of Automate or Die Trying, host Will talks with Keith Belanger about why acceleration alone is not a strategy—and why AI will amplify both the strengths and weaknesses already present in your data ecosystem.

Drawing on three decades of experience in data architecture, including leading a 300+ terabyte migration to Snowflake in a highly regulated insurance environment, Keith explains why many enterprise data failures are not dramatic breaches but quiet, cumulative problems that build over time. He shares lessons from large-scale modernization efforts, the importance of data modeling and architectural discipline, and why organizations often create technical debt by prioritizing speed over design.

The conversation explores how AI agents interact with enterprise data, why governance and testing must become continuous practices, and how organizations can detect schema drift, data quality issues, and hidden risks before they impact business decisions. Keith also discusses the growing role of DataOps, the importance of embedding architectural intent into AI-assisted development, and why human oversight remains essential even as automation becomes more powerful.

If you're responsible for data engineering, AI initiatives, security, governance, or enterprise architecture, this episode offers practical insights into building data platforms that can scale safely while remaining trustworthy and AI-ready.

Key Topics Discussed

• Why “acceleration is neutral” in data and AI
• Lessons from a 300+ TB Snowflake migration
• The hidden cost of cumulative data failures
• Data contracts, schema drift, and governance
• AI-ready data and enterprise trust
• Continuous testing and observability
• DataOps automation and CI/CD for data platforms
• Human oversight in AI-assisted engineering
• Balancing speed, security, and architectural discipline
• Building resilient foundations for enterprise AI

Guest Information

• Keith Belanger – Field CTO, DataOps.live
• LinkedIn: Keith Belanger LinkedIn Profile

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Automate or Die Trying empowers leaders to adopt AI securely, move faster with confidence, and transform the future of work.

Episode Summary:

In this episode of Automate or Die Trying, host Wil Ramos sits down with Christopher Moravec, CTO and founder of Dymatic, to explore what it actually looks like to deploy AI agents into real-world government, GIS, and public sector workflows.

Christopher shares how Dymatic helps cities and agencies modernize permitting, infrastructure management, zoning feedback, and operational processes using AI-powered mapping and automation systems. From analyzing traffic plans to processing thousands of public comments, he explains how location-aware AI is transforming how governments operate at scale.

The conversation also dives into Christopher’s personal AI ecosystem, including his autonomous AI agent named “Jaws” — an assistant running from a Mac Mini in his workshop that manages tasks, reviews emails, writes newsletters, controls environmental systems, and is now learning to operate inside a physical robot body it helped code itself.

Christopher breaks down the practical realities of deploying AI into production environments, including why most organizations are giving agents too much access, how prompt injection attacks actually work, and why layered security and restricted permissions matter more than flashy demos. He also explains how AI guardrails fail in practice, how poison-pill data attacks could shape future disinformation campaigns, and why AI systems need multiple layers of validation, auditing, and monitoring.

They also explore the future of operating systems, autonomous workflows, physical AI agents, and how AI may eventually replace traditional applications with dynamic, generated interfaces tailored to each user in real time.

For leaders experimenting with agentic AI, Christopher emphasizes the importance of starting small, focusing on measurable workflow improvements, and limiting AI access to only the systems and permissions truly required.

Key Takeaways:

• AI agents should start with narrowly scoped tasks before expanding autonomy
• Most AI security failures come from giving agents unnecessary permissions
• Prompt injection attacks remain one of the biggest risks in production AI systems
• Layered validation and monitoring are essential for safe AI deployment
• AI systems consuming public data can become vulnerable to disinformation poisoning
• Autonomous agents require immutable logging and continuous auditing
• The future operating system may revolve around AI-generated interfaces instead of apps
• AI-assisted automation can dramatically reduce repetitive operational workloads
• Physical AI agents and embodied systems are becoming increasingly practical
• The best AI deployments solve boring operational problems that save real time

Connect with Christopher Moravec:

• LinkedIn: https://www.linkedin.com/in/christophermoravec

Websites:

• https://moravec.net
• https://moraveclabs.com

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Automate or Die Trying empowers leaders to adopt AI securely, move faster with confidence, and transform the future of work.

Episode Summary:

In this episode of Automate or Die Trying, host Wil Ramos sits down with Lawrence Pingree, Head of Security and AI Research at SACR (Software Analyst Cyber Research), to explore how AI agents are fundamentally reshaping cybersecurity, governance, and enterprise defense strategies.

Lawrence shares insights from a career spent defining major cybersecurity categories including EDR, content disarm and reconstruction, cloud incident response automation, and generative AI runtime defense. Drawing from his experience at Gartner, SonicWall, and SACR, he explains how industry terminology becomes standard practice and why language itself plays a critical role in advancing cybersecurity innovation.

The conversation dives deep into the rise of agentic AI and why traditional security architectures are struggling to keep pace with machine-speed automation. Lawrence introduces concepts like identity dark matter, Unified Agentic Defense Platforms (UADP), and Just-in-Time Trust, describing how non-human identities and autonomous AI agents are creating entirely new operational and governance challenges for modern enterprises.

They also unpack how runtime security is evolving beyond traditional EDR models, why organizations must rethink prevention versus detection, and how AI systems introduce additive risks on top of existing human vulnerabilities. Lawrence explains why future security strategies will require real-time behavioral scoring, federated trust systems, and continuous runtime inspection across AI workflows and agent communications.

For leaders navigating the next wave of AI adoption, Lawrence emphasizes the importance of practical security design, runtime visibility, identity management, and building defenses that can adapt as fast as the technology itself evolves.

Key Takeaways:

• AI agents are introducing new forms of identity risk and “identity dark matter”
• Traditional zero trust models are struggling to keep pace with agentic AI systems
• Runtime security and real-time prevention are becoming critical for AI defense
• Unified Agentic Defense Platforms (UADP) aim to standardize AI runtime protection
• AI vulnerabilities are additive to existing human and infrastructure security risks
• Non-human identities now vastly outnumber human identities in enterprise environments
• Behavioral scoring and federated trust models may become essential for AI governance
• Security automation has always existed, but AI dramatically increases its scale and speed
• Prompt injection, credential leakage, and agent misalignment are emerging enterprise risks
• Future security strategies must prioritize runtime visibility and adaptive prevention

Connect with Lawrence Pingree:

• LinkedIn: https://www.linkedin.com/in/lawrencepingree

Websites:

• https://lawrencepingree.com
• https://geek-guy.com
• https://iuniverse.com

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Automate or Die Trying empowers leaders to adopt AI securely, move faster with confidence, and transform the future of work.

Episode Summary:

In this episode of Automate or Die Trying, host Wil Ramos sits down with Jon G Shende, CTO of Data Security & AI and Global VP of SecOps at Sutherland, to explore what it really takes to secure AI at enterprise scale.

Jon shares how he helped rebuild a modern AI-driven security stack in partnership with Google Security, retiring legacy systems and standing up a new, machine-learning-powered operation from the ground up. He also reflects on his early work coining “cloud forensics as a service,” a concept that anticipated many of today’s cloud security challenges.

The conversation examines the rise of agentic AI and why traditional governance models are no longer sufficient. Jon explains how AI is becoming the new insider threat, not because it’s malicious, but because of the level of access, autonomy, and trust organizations are giving it.

They also unpack real-world lessons from large-scale security transformations, including the challenges of aligning stakeholders, managing risk, and ensuring data integrity across complex systems. Jon emphasizes that while automation and AI create massive opportunities, they also require strong fundamentals in access control, policy enforcement, and auditability.

For leaders building in today’s AI-driven world, Jon highlights the importance of collaboration, practical security design, and responsible adoption of emerging technologies.

Key Takeaways:

• AI is becoming the new insider threat due to its trusted access and speed at scale
• Governance must evolve to operate at the workflow level, not just static control points
• Overprivileged AI agents increase the risk of data exposure and misuse
• Automation without strong fundamentals can amplify security failures
• The ROI of security lies in preventing incidents, not generating revenue
• Large-scale transformations require alignment across technical and business stakeholders
• Great leadership is about enabling teams, not taking the spotlight

Connect with Jon G Shende:

• LinkedIn: https://www.linkedin.com/in/jonshende
• Website: https://cloudfraas.com
• Blog: https://jonshende.blogspot.com/view/magazine

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Automate or Die Trying empowers leaders to adopt AI securely, move faster with confidence, and transform the future of work.

Episode Summary:

In this episode of Automate or Die Trying, host Wil Ramos sits down with Ryan Donahue, Senior Director of Cybersecurity at Comcast. Ryan leads vulnerability management, asset inventory, compliance, and fraud detection across one of the largest and most complex networks in the world, supporting millions of internal assets and more than 100 million customer devices.

Ryan shares how his background as a software engineer shaped his approach to security leadership. Instead of relying solely on vendor tools, he often builds internal systems to solve security challenges at massive scale, including credential management platforms handling billions of transactions per day and automation frameworks designed to protect telecom infrastructure.

The conversation explores the realities of securing large-scale telecom systems, where pushing updates or configuration changes can impact millions of devices simultaneously. Ryan discusses the challenge of maintaining visibility across massive infrastructure and how his team works to identify unknown assets and reduce operational risk.

Ryan also explains why automation must be implemented carefully in security environments. While automation enables scale, poorly designed systems can amplify vulnerabilities. His approach focuses on building secure defaults into platforms so engineering teams can move quickly while maintaining strong security standards.

For security leaders and engineers, Ryan emphasizes the importance of strong debugging skills, clear security metrics, and designing platforms that make security scalable across large engineering organizations.

Key Takeaways:

1. Security at telecom scale requires deep engineering and custom tooling
2. Asset visibility is the foundation of an effective security program
3. Automation must be designed carefully to avoid amplifying risk
4. Security platforms should simplify protection for developers
5. Security reporting should focus on patterns rather than raw vulnerability counts
6. Strong debugging and system-level thinking remain essential skills

Connect with Ryan Donahue:

1. LinkedIn: https://www.linkedin.com/in/ryan-donahue-28324a5
2. Website: https://rydonahue.com

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Automate or Die Trying empowers leaders to adopt AI securely, move faster with confidence, and transform the future of work.

Episode Summary:

In this episode of Automate or Die Trying, host Wil Ramos sits down with Art Pugach, CTO of Key Associates and Microsoft MVP, recognized for his work with M365 Co-Pilot. Art shares insights from his 27-year career building ERP, CRM, AI, and automation solutions, and discusses how organizations can move beyond hype to apply AI practically.

The conversation explores the importance of understanding human workflows before deploying automation, practical examples of AI creating knowledge bases and streamlining order processing, and the role of AI as an assistant rather than a replacement. Art also highlights lessons learned from early ERP implementations, managing AI credit limits, and showing employees how to effectively use AI tools.

For business leaders, IT professionals, and tech enthusiasts, Art emphasizes starting with use cases, combining human oversight with AI agents, and leveraging automation to free up resources for higher-value work.

Key Takeaways:

• AI and automation succeed when human workflows are well-defined
• Co-Pilot and AI agents can dramatically reduce repetitive tasks
• Knowledge bases and help desk automation save months of work in days
• Human oversight is critical; AI cannot read minds
• Understand AI licensing and credit limits to avoid hidden costs
• Focus on practical use cases to demonstrate value

Connect with Art Pugach:

• LinkedIn: https://www.linkedin.com/in/art-pugach-8144b236
• Key Associates: https://www.keyassociates.com
• Podcast: When in Doubt Reboot – https://whenindoubtreboot.com

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Automate or Die Trying empowers leaders to adopt AI securely, move faster with confidence, and transform the future of work.

Episode Summary:

In this episode of Automate or Die Trying, host Wil Ramos sits down with David VonThenen, an AI/ML engineer and systems architect focused on building reliable AI systems that move beyond demos into real-world production.

David shares insights from his background in infrastructure and distributed systems, explaining why strong architecture, observability, and governance are critical when deploying AI at scale. The conversation explores hybrid retrieval approaches beyond simple vector search, the rise of multi-agent AI systems, and the growing need for monitoring and safeguards as AI tools become more autonomous.

For founders, engineers, and technical leaders, David emphasizes a practical approach: design systems carefully, prioritize reliability, and continuously validate AI models in production environments.

Key Takeaways:

• Start with solid system architecture before scaling AI models
• Hybrid retrieval strategies often outperform pure vector search
• Multi-agent architectures allow modular, scalable AI workflows
• Observability and governance are critical for production AI systems
• Adversarial prompts highlight the need for strong safeguards
• Infrastructure design still matters as much as model quality

Connect with David VonThenen:

• LinkedIn: https://linkedin.com/in/davidvonthenen
• GitHub: https://github.com/dvonthenen
• Twitter/X: https://twitter.com/dvonthenen
• Personal Website: https://davidvonthenen.com

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Automate or Die Trying empowers leaders to adopt AI securely, move faster with confidence, and transform the future of work.

Episode Summary:

In this episode of Automate or Die Trying, host Wil Ramos sits down with Oleg Danyliuk, CEO of Duanex and CTO of Insyghtful.ai. Oleg combines nearly two decades of enterprise software experience with a deep focus on AI-driven automation and real-time insights, helping businesses scale efficiently while keeping human judgment in the loop.

Oleg shares his journey from a Java developer in Ukraine to leading distributed engineering teams across continents, building high-load banking systems, ticketing platforms, and enterprise service buses. He discusses founding Duanex in 2016 to provide clients with high-value automation solutions, emphasizing transparency, delivering real outcomes, and prioritizing client relationships over short-term revenue.

The conversation explores how Oleg applies enterprise-grade rigor to AI agent development. He walks through his work deploying AI for lead validation at Duanex and developing Insyghtful.ai, a platform that reads the emotional temperature of sales calls in real-time, providing actionable guidance to reps while balancing speed, latency, and usability. Oleg explains the challenges of working with incomplete or messy data, handling human-AI collaboration, and managing distributed teams under extreme conditions, including operating with developers in active war zones in Ukraine.

Oleg also shares insights on scaling AI responsibly: start small, focus on meaningful impact, avoid automating everything, and use external APIs like OpenAI’s Gemini to test quickly before committing to full custom models. He emphasizes that AI should augment human intelligence rather than replace it, highlighting the ongoing need for maintenance, monitoring, and cultural context when interpreting emotional signals in conversations.

For founders, technical leaders, and AI enthusiasts, Oleg’s advice is clear: focus on solving high-value problems incrementally, maintain transparency with clients, and continuously validate your models in production to ensure real-world efficacy.

Key Takeaways:

1. Start small with AI automation—test single steps before scaling
2. Use APIs for low-cost experimentation before building custom models
3. Real-time AI requires low latency (sub-second) for actionable insights
4. AI augments humans; it does not replace nuanced human judgment
5. Distributed engineering teams can maintain high performance even under extreme conditions with proper infrastructure and human connection
6. Data quality is an ongoing effort, not a one-time cleanup
7. Transparency with clients builds long-term trust and drives referrals

Connect with Oleg Danyliuk:

1. LinkedIn: https://www.linkedin.com/in/oleg-danyliuk
2. Company: https://duanex.com

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Automate or Die Trying empowers leaders to adopt AI securely, move faster with confidence, and transform the future of work.

Episode Summary:

In this episode of Automate or Die Trying, host Wil Ramos sits down with Alessandro Pireno, a fractional CTO and CPO, board advisor, and AI, data, and go-to-market leader who helps early-stage startups scale. Alessandro has led product engineering and sales engineering teams at Snowflake and Surreal DB. His unique path spans applied physics, engineering, product leadership, and sales, giving him a 360-degree perspective on building, selling, and scaling technology.

Alessandro shares insights from his experience helping startups navigate technical product commercialization, particularly in the AI infrastructure space. He explains common pitfalls, including products that technically excel but fail to align with customer value or economic impact. He highlights how startups can distinguish between “nice-to-have” features versus core value drivers, emphasizing the importance of translating technical capabilities into measurable outcomes for customers.

The discussion explores the critical role of sales engineers in driving adoption, not just demos, and how cross-functional collaboration between sales and engineering teams accelerates success. Alessandro also dives into key differences between CIO and CISO perspectives, how AI has reshaped executive priorities, and why understanding the true customer pain is more valuable than superficial feature adoption.

He shares real-world examples of when beautiful UX or “pretty packaging” can mislead buyers, stressing that delivering the right solution—even if it looks less polished—creates real value and long-term trust. He also emphasizes the importance of leveraging proprietary data effectively as a competitive advantage in AI product development.

For technical leaders—CTOs, VPs of engineering, and founders building AI or data-driven products—Alessandro’s principle is clear: stress-test assumptions, focus on delivering measurable impact, and use cross-functional experience to bridge the gap between engineering, product, and sales.

Key Takeaways:

1. AI adoption alone does not guarantee differentiation; understand real customer impact
2. Translate technical features into measurable business value, not just performance metrics
3. Strong collaboration between sales engineers and sales teams accelerates adoption
4. CIOs often focus on cost optimization, while CISOs prioritize risk management
5. Proprietary data is the “crude oil” that makes AI-powered products unique
6. Avoid prioritizing aesthetics over function when delivering solutions
7. Early-stage startups benefit from fractional leadership with a cross-functional lens

Connect with Alessandro Pireno:

1. LinkedIn: https://www.linkedin.com/in/apireno
2. Website: https://pireno.com

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Automate or Die Trying empowers leaders to adopt AI securely, move faster with confidence, and transform the future of work.

Episode Summary:

In this episode of Automate or Die Trying, host Wil Ramos sits down with Abukari Einus, Co-Founder & CEO of FoodQwik. Abukari has built across multiple domains, from healthcare systems at First Line 24, decentralized finance at NorthStar, to robotics at Three Farm State Farming.

The conversation explores Abukari’s journey shipping full-stack systems, smart contracts, automated deployments, and managing Linux servers while solving real-world problems in emerging markets. They discuss how FoodQwik uses AI to personalize food recommendations for dietary and allergy needs, creating a more mindful and health-conscious food ordering experience in Ghana.

Abukari shares lessons learned from building in highly regulated and technical domains like healthcare and DeFi, emphasizing accountability, automation, and iterative user feedback. He also highlights the operational and logistical challenges of running a food tech platform in Northern Ghana and how his team overcomes them to ensure timely delivery.

The discussion touches on AI-driven personalization, the importance of conversational interfaces in food tech, and how FoodQwik differentiates itself from general ordering platforms by focusing on users with niche dietary needs. Finally, Abukari offers advice for builders in emerging markets: keep shipping, iterate with users, and embrace automation to scale effectively.

Key Takeaways:

1. Building technology in emerging markets requires iterative user feedback and adaptability
2. AI can tailor food ordering to dietary, allergy, and health needs
3. Accountability and security lessons from DeFi and healthcare transfer to consumer tech
4. Automation is critical for small teams with limited margins for error
5. Operational logistics are a key challenge in scaling food delivery in Africa
6. Differentiation comes from focusing on niche needs and user experience

Connect with Abukari Einus:

1. LinkedIn: https://linkedin.com/in/abukari-einus-913721177
2. GitHub: https://github.com/blackdante101

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Automate or Die Trying empowers leaders to adopt AI securely, move faster with confidence, and transform the future of work.