In Production Podcast

Nick Melnychuk
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  • Economía y empresa

Most AI conversations happen in boardrooms. This one happens where AI actually runs. In Production is the podcast for CTOs, CIOs, CISOs, CDOs, and tech founders who have moved beyond the theory. The executives and builders making consequential decisions about AI inside real organizations, under real pressure, with real accountability. Every episode, host Nick sits down with a practitioner who has actually shipped AI into production, fought the organizational battles, navigated the resistance, and earned the right to an opinion. Not consultants. Not analysts. Not keynote speakers. The engineers, executives, and leaders who carry the scars and the wisdom to prove it. ______________________________ What we cover: - The gap between the AI demo and the AI deployment, and why most enterprises never cross it - What experienced leaders actually discovered when they tried to automate decisions that lived entirely in someone's head - Why data readiness, legacy infrastructure, and organizational alignment kill more AI programs than bad technology ever will - What genuine AI transformation looks like across financial services, manufacturing, healthcare, legal intelligence, and enterprise software - The honest conversation every technology executive needs to have internally before a single vendor is engaged or a single model is touched ______________________________ Who you will hear from: Guests on In Production have shipped AI at organizations across financial services, banking, healthcare, manufacturing, entertainment, and enterprise technology. They have led teams of hundreds, managed P&Ls built on data products, launched agentic systems inside regulated industries, and navigated the full arc from whiteboard to incident report to scaled deployment. These are not people with opinions about AI. These are people with earned experience in AI. The distinction is everything, and you will feel it in every conversation. ______________________________ Who this is for: - CTOs and CIOs navigating enterprise AI adoption with zero tolerance for hype and full accountability for outcomes - CISOs who need to understand how AI reshapes their risk landscape before the landscape reshapes itself - CDOs building the data foundations that determine whether AI programs succeed or quietly collapse - Tech Founders building in the enterprise AI space who want to understand precisely how sophisticated buyers think and decide - Engineering Leaders who have been asked to deliver on AI and want the honest, unfiltered roadmap from the people who have already done it - Investors who want clear signal on where enterprise AI is actually landing versus where the pitch decks say it will ______________________________ Why In Production? In software, being in production means one thing. It is real. It is live. It has to work. No more pilots running in isolation. No more proof of concepts that never ship. No more AI strategies that exist only inside a presentation. This show is named after the only moment that matters. The moment AI stops being a promise and becomes a system that real organizations depend on, every single day. That is the conversation we are here to have. ______________________________ New episodes weekly. Hosted by Nick, Enterprise AI professional with deep experience across LLMs, blockchain, and large-scale technology, now building the most rigorous and honest conversation in enterprise AI. Subscribe wherever you listen to podcasts. ______________________________ The views and opinions expressed in this podcast are those of the individual guests and do not represent the positions of their employers or affiliated organizations.

  1. hace 2 días

    Everyone's Cutting Heads Before the AI Even Works with Conrad Bedard

    Conrad Bedard has rebuilt the procurement function from scratch at 5 companies across banking, insurance, and telecom. He's sat on both sides of the table, buying the technology and governing the risk, and he sees enterprises making the same mistake with AI that manufacturing made with robotics 20 years ago: cutting capacity before the tool can absorb it. In this conversation, Conrad and Nick get into why source-to-pay transformations took almost two years to get approved (and why that was the point), what goes wrong when companies bolt new technology onto broken processes, and why it's almost never procurement that kills an AI deal. They dig into the maturity-jump problem, organizations trying to leap from level 2 to level 5 in one move, and where a regulated buyer should actually start. Essential listening for procurement leaders, CFOs, and anyone in a regulated industry being told to "do more with less" with AI. Conrad's LinkedIn https://www.linkedin.com/in/conrad-bedard/ Takeaways Procurement plays a crucial role in providing total value to the business, beyond cost savings and contract administration.Implementing AI in procurement requires a focus on data quality, process remodeling, and change management. Chapters 00:00 — Intro: 16 years in procurement, buy side and sell side00:56 — Walking into orgs that treat procurement as a cost center01:23 — What the first 90 days actually look like03:40 — Falling into procurement by accident07:13 — Assessing the lay of the land in a new organization09:55 — The #1 implementation failure: new tech on old processes11:32 — "Dirty data in is dirty data out"13:51 — Why approval took almost two years (and why that mattered)16:47 — The robotics parallel: buying the shiny thing too early18:56 — Why governance has to make AI defendable and defensible20:10 — Why procurement rarely says no, but risk and IT do21:44 — The maturity-jump problem: level 2 to 5 in one leap22:20 — Will AI replace junior and mid-level roles?22:56 — Evolving roles vs. eliminating them25:36 — Where to actually start: process, intake, low-risk tasks25:52 — Start small, then go complex

    29 min

  2. hace 5 días

    The AI Said His Work Was "Too Good" to Touch with Paul Paskovatyi

    Paul Paskovatyi has 25 years as a business analyst and over 40 years in IT, and he's watched every trend arrive as "the future" and then settle into where it actually fits. So when AI hit, he didn't jump. He tested it inside a bank that's actively replacing its core platform and running two AI tools day to day. What he found: his hand-written user stories score 90%+ on the bank's own rating system; AI-generated ones from raw input barely hit 75%. One tool even rejected his work for being "too good" to bother editing. In this episode, Paul and Nick get honest about AI's real ceiling (around 25% time saved), where it genuinely shines (compliance document scanning), where it consistently makes things worse (front-end logic, diagrams), and why the 75% it can't touch is the real career moat. A grounded, hype-free conversation for business analysts, engineering leaders, and anyone in regulated industries weighing what AI can actually do. Paul's LinkedIn https://www.linkedin.com/in/paulpaskovatyi/ Takeaways AI's role in business analysis is limited, and human interaction remains crucial for capturing requirements.AI's impact in highly regulated industries, such as compliance and governance, is significant but not without limitations.Appreciating the value of non-automated work is essential, as AI cannot fully replace human roles. Chapters 00:00 — Intro: 25 years in BA, 40+ years in IT02:29 — Why he calls himself old school, and stays careful02:53 — First encounter with AI: through his kids' homework04:48 — The two AI tools he uses in the bank (AI Ignite, Rovo)06:00 — When the AI rejected his story for being "too good"06:15 — The experiment: AI-from-raw vs. manual, and what developers said11:27 — "AI is a helper, not a driver"11:42 — Requirements feeding compliance, audit, and governance12:13 — Where AI shines: scanning documents for compliance14:30 — Where it fails: front-end requirements and stripped diagrams15:23 — The cases where AI always fails17:39 — Advice to BAs: ask what's in it for your actual work first20:14 — Why the BA role isn't going away25:49 — The honest ceiling: ~25% time saved26:53 — The junior-to-senior pipeline problem27:24 — Why the 75% AI can't do made him value his work more28:00 — The scoring gap: 90%+ manual vs. 75% AI-assistedarding

    29 min

  3. 14 may

    Innovation Theater ≠ Production with Sobhan Khani

    Most enterprises aren't failing at AI because the technology isn't ready. They're failing because nobody wants to admit the process isn't ready. Sobhan Khani is President and Partner at Plug and Play — the largest corporate innovation platform in the world. 550 corporate partners. 100,000+ startups in their database. 800 people across 70 cities. He has watched IoT, FinTech, crypto, and now AI cycle through the enterprise conversation from both sides of the table. In this episode, Sobhan and Nick go into the real reasons AI pilots fail to reach production — and what the programs that actually ship do differently. What you'll hear: ↳ Why McKinsey's stat — 60–70% of enterprises running AI agents, less than 5% seeing results — is a process problem, not a model problem ↳ The top-down vs. bottom-up debate: why both camps are getting results, and what that tells you about AI transformation complexity ↳ The undocumented workaround problem — every enterprise process has a real version and a documented version, and AI finds the gap on day one ↳ Why trying to fit AI into the workflow you already have typically doesn't work — and what Microsoft's Chief Scientist office said about it ↳ The internal champion pattern: what separates the deployments that survive the middle section from the pilots that quietly die ↳ The budget shift thesis: what happens to headcount and software spend when agents can scale without FTE cost ↳ The CTO rule from a seed-stage startup that every mid-market company should borrow before their next hire This is not a conversation about AI potential. It's a conversation about why the gap between the demo and the deployment is still killing programs at Fortune 500 companies — and what the people actually navigating it are doing differently.

    19 min

  4. 11 may

    You Can't Block What's Already on Their Phones with Justin Lahullier

    Most enterprises treat AI compliance like a wall-building problem. Block the tools. Write the policy. Wait for vendors to be HIPAA-ready. And while they're waiting, their employees are already using ChatGPT on their phones. Justin La Julière has been the CIO and CISO at Delta Dental in New Jersey and Connecticut for over 25 years — running IT and security simultaneously in one of the most regulated environments you can operate in: dental insurance, PHI everywhere, state and federal compliance on every AI decision. Chapters 00:00 Introduction and Transformation Journey05:59 Rethinking Processes and Creating New Value12:03 Compliance and Governance in AI Initiatives18:01 Measuring AI Success and ROI23:52 Navigating Compliance and Cultural Change And he's one of the furthest along. In this episode, Justin shares the exact architecture and cultural playbook he used to drive AI adoption inside a regulated insurance company — without waiting for perfect governance conditions, without blocking tools employees were already using anyway, and without the kind of top-down IT mandate that kills adoption before it starts. What we get into: We talk about how Justin's team built a PII/PHI filtering layer on the backend of their internal AI environment — so employees could put anything they wanted in the tool without having to classify their own data first. The cognitive load of "wait, is this PHI?" was killing adoption. Removing that question changed everything. We talk about AI hours — a cadence Justin runs every three weeks where practitioners from across the business (not IT people) demo what they're actually building with AI. Two-thirds of the company shows up. Not because they were told to. Because someone from provider relations showed them something useful and they wanted in. We talk about governance with real teeth: a framework that forces a two-page business case before any AI initiative gets resourced, and a discipline of killing projects early — before they become someone's baby — so they don't survive on organizational inertia long after they've stopped delivering value. We talk about what Justin calls the "green glass risk" — the danger that your entire team only knows what's inside the building, and never brings back signal from outside. And why staying at the tip of the spear on new technology has been his personal operating principle for a quarter century. Justin's read on where regulated industries are right now: the compliance and legal teams that are sitting on the sidelines aren't protecting the organization — they're falling behind it. The gap between companies that have started and companies that haven't is widening faster than most executives realize. His single piece of advice for a mid-market healthcare or insurance company just getting started: don't try to sprint before you walk. Get safe tools in people's hands. Build cross-pollination. Let practitioners talk to practitioners. The technology will keep improving — culture is the only thing you actually have to build yourself. Justin Lahullier is on LinkedIn and responds to messages. If you're a CIO, CISO, or technology leader in a regulated industry navigating any of this, he's worth reaching out to. In Production is the podcast for technology and AI leaders who have moved past the theory — and are doing the hard work of deploying AI in environments where failure has real consequences. New episodes drop regularly. Subscribe wherever you listen.

    30 min

  5. 7 may

    AI Doesn't Create Data Problems. It Amplifies Them with Lance Harlan

    Most banks greenlighting AI use cases in 2025 think they're making a technology decision. They're not. They're making a data accountability decision — and most of them aren't ready for it. Lance Harlan is the Data Governance Program Manager at Trustmark Bank and the author of the KISS Data Success Guide series. His career didn't follow a straight line into governance — he went from auto mechanics to the Marine Corps, through defense contracting, IT infrastructure, and systems administration before landing in strategy and data governance. That non-linear path is precisely what makes his perspective valuable. Every insight he brings has been earned, tested in practice, and stress-tested against the organizational reality of a financial institution that's been operating for over 100 years. In this episode of In Production, Nick and Lance go deep on what enterprise AI governance actually looks like inside a regulated institution — not the framework version, the real version. What we cover: The gap between paper-based governance and how people actually work on Thursday afternoon — and why most programs are built in a vacuum that doesn't survive contact with reality. Why the KISS mindset (Keep It Stupidly Simple) isn't a framework — it's a filter. If your governance program isn't easy to understand, easy to explain, and easy to teach, it won't be adopted. How Lance structured data contracts at Trustmark to keep authority with the people who already had it — and why that's the only way to avoid governance becoming an enforcement burden that nobody follows. The hard truth about AI governance in banking: you can check every box in ISO 42001, satisfy NIST AI RMF, align with OCC guidance — and still have an AI system making decisions that no one in your organization can fully explain. Compliance is not effectiveness. The 4 questions every bank leader must answer before greenlighting any AI use case — starting with: "Is the data driving this decision actually under governance yet?" Why AI doesn't create data problems — it amplifies them. Inconsistent data quality, unclear ownership, and lack of governance don't disappear when you add AI. They scale. What separates the banks that will win with AI in 5 years from those that won't. The answer isn't speed, budget, or vendor selection. It's whether the organization started with ownership and accountability before they touched the first model. Why "accountability without understanding is just documentation" — and the difference between knowing who owns a decision and whether that person actually understands what they own. The line from this episode worth sitting with: "Just because you implemented AI to make decisions, you lose the right to say we didn't know." Guest: Lance Harlan — Data Governance Program Manager, Trustmark Bank. Author of the KISS Data Success Guide series on South Tech and Medium. Lance writes to work through governance challenges in real-world institutional environments, publishes only after testing what works, and connects with practitioners on LinkedIn. 📎 Connect with Lance on LinkedIn 📚 https://www.linkedin.com/in/lancewharlan/ Leading with AI Agents by Reddy Mallidi — Lance's current read and recommendation for this episode. In Production is for CTOs, CIOs, CISOs, CDOs, and technology leaders who have moved past the theory. Every episode is a conversation with someone who has deployed AI in the real world — regulated industries, complex infrastructure, real stakes. No keynote speakers. No consultants with opinions. Practitioners only.

    24 min

  6. 4 may

    The Demo Is Easy. Production Is a Job with Debasish Bhattacharjee

    Most enterprises don't fail at AI because their models are bad. They fail because they mistake capability for readiness. Debasish Bhattacharjee, Engineering Leader who has built and scaled AI systems across Fortune 500 organizations including Oracle, IBM, Broadcom, and SAP, has shipped seven production AI systems across Fortune 500 organizations — systems that collectively drive over $65 million in annual savings. Not lab experiments. Real deployments across expense management, procurement, HR, and customer support. Built with teams of 12 to 15 engineers. Shipped under a quarter. In this episode of In Production, Debasish breaks down exactly what separates the pilots that looked impressive from the systems that businesses actually trust — and why the gap almost always has nothing to do with the model. What we cover: ↳ Why his first AI deployment was off by 400% — and why the model had nothing to do with it. The data was the monster. Cleanup took four months. The model was ready in six weeks. ↳ The question that paused a $3M AI roadmap three weeks before launch. One room. Capable executives. Complete silence. What that silence revealed about organizational readiness. ↳ Why governance fails most enterprises — and what it looks like when it's built correctly. The difference between a permission gate and a feedback system. ↳ The triple lens: advisor, builder, operator. What each teaches you that the other two can't — and why the operator lens is the one most organizations are missing. ↳ The hidden 20% that lives in people's heads. Why asking employees to document what they do doesn't work — and what actually surfaces the undocumented rules before they become production incidents. ↳ Shadow mode as a process audit tool. How running AI silent alongside human decisions for 2–4 weeks before any automation reveals the broken process underneath — and why that's where the real savings are. One procurement deployment: $2.1M recovered from process redesign alone, before the AI system drove another $7–8M on top. ↳ The metric that doesn't lie. Why human override rate — and how it changes over time — tells you more about your AI initiative than accuracy, uptime, or any demo metric. ↳ Vendor lock-in and the pivot conversation. How to walk into a CTO's office and say "we made the right call with the information we had — the market moved faster than our assumptions" — and why that framing gets respected. ↳ What Debasish would tell any leader who just got the mandate to take AI from pilot to production scale: start with shadow mode, not automation. If you're a CTO, CIO, or engineering leader who has been handed an AI mandate and is trying to figure out what separates the programs that ship from the ones that quietly die — this conversation is the one. Debasish is reachable on LinkedIn at linkedin.com/in/debasishtech.

    30 min

  7. 1 may

    When the Plan Meets Friction: ERP, AI & Public Sector Transformation with Brian Bowles

    Brian Bowles has no IT background. He came from law enforcement and the Coast Guard — environments where the plan falls apart the moment it meets friction, and you push through anyway. He's now the Director of Nevada's Office of Project Management, overseeing Core NV: the State's $200M ERP transformation and one of the rare large-scale government tech projects that actually hit its phase one timeline. In this episode, Brian gets specific about what makes public sector transformation succeed — and fail. Nevada tried this once before. It didn't work. This conversation is about what changed the second time. Chapters 00:00 Introduction and Project Overview06:36 Teamwork and Urgency in Project Execution14:14 Phase One Completion and Maintenance19:30 AI in ERP Transformation28:00 Future State of Government and AI in ERP We cover: → Why most ERP projects in the public sector fail before the technology is ever a factor — and the single leadership condition that determines whether a program survives contact with the organization → How Nevada broke a core rule of project management (skipping the business process analysis) and what they discovered when they went into discovery anyway: processes that existed nowhere on paper, passed down from predecessor to predecessor for decades → The decision that changed the program's trajectory — telling every agency in the State that the process would change to fit the system, not the other way around. And what happened when they tested whether any laws or regulations actually needed to change (they didn't) → What an integrated team actually looks like in practice — contractors, vendors, system implementers, and State employees treated as a single unit with shared accountability. Why Nevada's first attempt failed partly because it didn't do this → The honest mistake: no plan for production support between phase one go-live and end of phase two. What it cost them in phase two capacity, and why the State's biennial legislative cycle made it nearly impossible to course-correct quickly → Where Brian sees AI landing inside a modernized ERP environment — not as a chatbot, but as a policy enforcement layer embedded directly into financial and HR workflows. The use case: a brand-new employee on day one gets the same institutional knowledge guardrails as a 25-year veteran → What Brian would tell other states that no vendor will ever say to them — one piece of advice about the relationship between a government and its system implementer that most jurisdictions get completely wrong Brian's perspective is different from most guests on this show. He's not from the private sector. He's not selling a platform. He's a public servant running a program with real consequences — for State workers, for Nevada's citizens, and for the future of how government uses technology. If you work in enterprise transformation, regulated environments, or anywhere the question is how to get a large, risk-averse organization to change — this one's worth your time. Guest: Brian Bowles, Director — Nevada Office of Project Management Find Brian: linkedin.com/in/brian-bowles | opm.nv.gov

    27 min

  8. 29 abr

    From Goldman Sachs to Carlyle: What 30 Years of Applied AI Actually Teaches You with Dean Barr

    Dean Barr has been deploying AI longer than most people in this space have been paying attention to it. He started in the 90s — building neural networks and genetic algorithms inside Goldman Sachs's financial trading division. He went on to launch and run a quantitative hedge fund for 11 years, managing long-short equity assets with AI at the core. That fund was eventually sold to a bank. He filed the first machine learning patent ever issued in the United States. In 2019, he started collaborating with researchers at OpenAI — back when the lab was still largely unknown outside a small circle of practitioners. Most recently, he served as Head of Applied AI globally and Chief Data Scientist at Carlisle, where he architected and built the firm's entire investment AI infrastructure. Chapters 00:00 The Evolution of AI in Finance07:10 AI's Impact on Private Markets13:41 Responsibility and Accountability in AI23:07 The Future of AI and Recursive Self-Improvement28:07 Organizational Transformation with AI Dean calls himself an applied AI researcher. Not a theorist. Not a consultant. Someone who has taken these models into real environments, tested them against real constraints, and found out what actually holds. In this episode, we go deep on what that experience actually looks like. Dean breaks down why the proof of concept phase is a trap — and why skipping straight from experimentation to production readiness is the only move that leads anywhere. He explains the capabilities gap he saw forming early: not between humans and AI, but between what these models can actually do and how organizations are using them. His read then, and now, is that we're not even close to the ceiling. We talk about what it takes to deploy AI inside regulated financial environments — private equity specifically — where there is no portfolio effect to absorb mistakes, and the fiduciary accountability sits entirely with a human being. Dean walks through the architecture he built at Carlisle: citation-grounded outputs, observation checkpoints at every stage of the pipeline, and a footnote agent trained on forensic accounting that caught an ASC 606 violation buried on page 933 of a data room — in 4 hours, not 3 weeks. We also get into the organizational side of transformation. Why end users have to be part of the build process from day one. Why most enterprises skip the observation layer and pay for it later. And why Dean sees recursive self-improvement — not AGI debates — as the real frontier executives should be thinking about right now. If you work in asset management, financial services, or any regulated industry trying to move AI past the pilot stage, this conversation is the one. Dean can be reached at dean@dsconsult.ai

    28 min
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Most AI conversations happen in boardrooms. This one happens where AI actually runs. In Production is the podcast for CTOs, CIOs, CISOs, CDOs, and tech founders who have moved beyond the theory. The executives and builders making consequential decisions about AI inside real organizations, under real pressure, with real accountability. Every episode, host Nick sits down with a practitioner who has actually shipped AI into production, fought the organizational battles, navigated the resistance, and earned the right to an opinion. Not consultants. Not analysts. Not keynote speakers. The engineers, executives, and leaders who carry the scars and the wisdom to prove it. ______________________________ What we cover: - The gap between the AI demo and the AI deployment, and why most enterprises never cross it - What experienced leaders actually discovered when they tried to automate decisions that lived entirely in someone's head - Why data readiness, legacy infrastructure, and organizational alignment kill more AI programs than bad technology ever will - What genuine AI transformation looks like across financial services, manufacturing, healthcare, legal intelligence, and enterprise software - The honest conversation every technology executive needs to have internally before a single vendor is engaged or a single model is touched ______________________________ Who you will hear from: Guests on In Production have shipped AI at organizations across financial services, banking, healthcare, manufacturing, entertainment, and enterprise technology. They have led teams of hundreds, managed P&Ls built on data products, launched agentic systems inside regulated industries, and navigated the full arc from whiteboard to incident report to scaled deployment. These are not people with opinions about AI. These are people with earned experience in AI. The distinction is everything, and you will feel it in every conversation. ______________________________ Who this is for: - CTOs and CIOs navigating enterprise AI adoption with zero tolerance for hype and full accountability for outcomes - CISOs who need to understand how AI reshapes their risk landscape before the landscape reshapes itself - CDOs building the data foundations that determine whether AI programs succeed or quietly collapse - Tech Founders building in the enterprise AI space who want to understand precisely how sophisticated buyers think and decide - Engineering Leaders who have been asked to deliver on AI and want the honest, unfiltered roadmap from the people who have already done it - Investors who want clear signal on where enterprise AI is actually landing versus where the pitch decks say it will ______________________________ Why In Production? In software, being in production means one thing. It is real. It is live. It has to work. No more pilots running in isolation. No more proof of concepts that never ship. No more AI strategies that exist only inside a presentation. This show is named after the only moment that matters. The moment AI stops being a promise and becomes a system that real organizations depend on, every single day. That is the conversation we are here to have. ______________________________ New episodes weekly. Hosted by Nick, Enterprise AI professional with deep experience across LLMs, blockchain, and large-scale technology, now building the most rigorous and honest conversation in enterprise AI. Subscribe wherever you listen to podcasts. ______________________________ The views and opinions expressed in this podcast are those of the individual guests and do not represent the positions of their employers or affiliated organizations.

Información

  • Creador
    Nick Melnychuk
  • Años de actividad
    2 k
  • Episodios
    19
  • Clasificación
    Apto
  • Copyright
    © 2026 Nick Melnychuk
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    In Production Podcast