In Production Podcast

Nick Melnychuk
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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.

  1. 5d ago

    Everyone is racing into AI. The bill arrives in production with Peter Hondrogiannis

    Peter Hondrogiannis(https://www.linkedin.com/in/peter-hondrogiannis-3045ab58/) has spent close to 26 years in banking and financial services, with nearly two decades in fraud and risk, across TD Bank, NTT (formerly Millennium One Solutions), and now Equifax, where he leads AI enablement. He runs AI into production for a living, which is exactly why he is telling senior leaders to slow down. The industry is moving at what he calls the AI gold rush, and leaders are getting burned in post production because the architecture, the tokenization costs, the agentic resources, and the platform fees were never mapped before the first model was touched. One leader found his AI implementation now costs more than the human-in-the-loop it replaced. The conversation gets specific: the due diligence questions to answer before you engage a vendor, why a fraud and risk mindset catches security and privacy gaps a pure technologist misses, how orchestrated hybridity and an LLM-as-judge keep a human as the final arbiter on complex cases, and why AI theater (high adoption, shallow utility) quietly stalls organizations while the metrics still look green. Worth the listen for any CTO, CIO, or CISO being pushed to move faster on AI than the economics can justify. Timestamps: 00:00 | Intro 00:13 | Peter's path from banking into fraud and risk 03:46 | The AI gold rush and why speed burns leaders 04:16 | The costs teams skip: architecture, tokens, agentic resources 05:30 | When AI ends up costing more than the humans 06:01 | Tokenization economics you have to model first 07:11 | The fraud mindset edge over a pure technologist 08:40 | White hat your own AI before you ship it 10:53 | Logic flattening when you force a veteran's judgment 11:31 | Orchestrated hybridity keeps the human as arbiter 12:10 | LLM as a judge and institutional memory 12:30 | Human overrides are training signal, not failure 14:29 | The translation layer between C-suite and frontline 15:38 | Stealth failure mode and the 60% correction tax 17:33 | The resistance nobody expects: intellectual atrophy 17:48 | Reframing AI from skip the work to force multiplier 19:48 | Beating FOMO: learn to crawl, then walk, then run 21:35 | Why swinging for the fences backfires 24:05 | AI theater versus real force multiplication 27:19 | Voice agents handling 95% of call types 28:05 | Eating the elephant one bite at a time 29:05 | Where to reach Peter

  2. Jul 14

    Fraudsters drive sports cars. Healthcare drives a school bus with Akhil Pandit Pautra

    Akhil Pandit Pautra(https://www.linkedin.com/in/akhil-panditpautra/) is a pharmacist with an MBA who has seen healthcare from three sides: the front line as a pharmacist and pharmacy manager, the regulatory side through committees of the Ontario College of Pharmacists, and now the payer side, running healthcare benefits, fraud prevention, and AI-enabled risk management at one of the largest PBMs in Canada. He opens with the asymmetry that keeps risk leaders up at night. Fraudsters adopt AI faster than the organizations trying to catch them, not because their technology is better, but because they answer to no one on privacy, fairness, or the cost of being wrong. His reframe is that this is an accountability problem, not a technology problem, and that a detected signal is not the same thing as evidence to act. The conversation covers the gap between detection and action and the real damage that happens inside it, why more information often creates less clarity and how to decide which signals actually deserve attention, why every audit carries a hidden cost to providers and patients, and a concrete 90-day starting point that puts curiosity and data readiness ahead of buying any model. Akhil closes on why judgment, not intelligence, becomes the scarce resource, and why the coming fight is over data and model sovereignty. Worth the listen for any CISO, CDO, or head of risk in a regulated industry deciding how to move on AI without breaking the trust they are there to protect. 00:00 | Intro and guest introduction 00:44 | Fraudsters adopt AI faster than the organizations detecting it 01:18 | Why this is an accountability problem, not technology 01:59 | The industry shifts from can we to should we 08:49 | The damage that happens between detection and action 09:03 | A signal is not evidence, the check engine light 10:12 | Governance as what lets organizations move confidently 12:11 | Deciding which signals actually deserve attention 12:37 | The hidden cost of every audit and intervention 14:52 | People do not ask about algorithms, they ask can I trust this 15:15 | AI as decision support, not decision replacement 16:10 | Moving from needle in a haystack to guided search 17:24 | Why e-pharmacies change where the risk lives 20:44 | The 90 day starting point, curiosity before AI 21:11 | A three step framework for responsible adoption 26:06 | AI as a GPS, why judgment outlasts intelligence 26:56 | The coming fight over data and model sovereignty 28:03 | AI changes how we work, not why we do it 29:16 | Closing exchange and where to reach Akhil

  3. Jul 7

    The model is the easy part. Owning it for six months is the job with Tamojit Saha

    Tamojit Saha (https://www.linkedin.com/in/sahatamojit/) is a technology leader who started his career as a telecom developer and now leads engineering, software development, digital transformation, and AI initiatives across complex, highly regulated environments in healthcare. He sits on Northeastern's program advisory committee, and a significant part of his work is figuring out how AI will reshape engineering, leadership, and workforce capabilities over the next few years. His core argument in this conversation: most enterprises no longer have a technology gap. The cloud, the models, the data platforms are all accessible right now. The real constraint has moved to organizational capability, and the rare people who can own AI in production, not just build it, are the bottleneck almost nobody is budgeting for. The conversation gets specific about what that ownership actually looks like. Tamojit breaks down the interview tell that separates tool users from production owners (people who talk about monitoring, drift detection, and incident management rather than just model architecture), why owning a model means taking responsibility after 90 days and after six months, and why most teams budget to build AI but fail to budget to operate it, with governance as the real long-term investment. He also covers explainability in regulated healthcare, where compliance officers and lawyers, not data scientists, are the first to ask why a decision was made, and why training does not equal transformation unless people get to apply it to real work and fail safely. Worth the listen for any CTO, CIO, CDO, or engineering leader stuck between pilot and production who has realized the technology can be figured out, but the people to hand it to cannot. 00:00 | Intro 02:06 | Why the enterprise AI bottleneck moved from technology to people 03:23 | Technology scales fast, human adoption lags, speed learners win 06:01 | From project thinking to product thinking, the living asset 08:36 | Why protecting the status quo creates organizational debt 09:43 | Challenging your assumptions before the market forces it 11:20 | When a model fails the explainability test in healthcare 12:09 | Compliance and lawyers question the decision before data scientists 15:04 | Budgeting to build AI but never to operate it 17:19 | The interview tell separating tool users from production owners 18:04 | Owning a model means responsibility after 90 days 18:53 | Why training does not equal transformation 19:34 | Redesigning the work so people can fail safely 20:34 | Why practical experience beats certifications for new talent 22:05 | What universities miss: system thinking over technology thinking 24:28 | Closing advice: stop treating AI as only technology 25:42 | There is no shortcut around the fundamentals 27:32 | Where to reach Tamojit, and closing thoughts

  4. Jun 30

    The Demo Never Made It to Production. His Did. With Marc Rohde

    Most enterprise AI dies in the demo. Marc Rohde, Chief Business Integration Officer at Andis, runs it in production: 10% of order volume goes from email to warehouse with no human intervention, and a single support bot answers 25% of email volume perfectly. In this episode, Marc and Nick unpack what actually had to happen first. The unglamorous answer: 13 years of data cleansing before AI existed in its current form. They get into why his biggest wins were small and pointed (not big and ambitious), the difference between "AI for me" and "AI for we," and the problem he's still trying to solve, capturing institutional knowledge before it walks out the door with a 15-year employee. If you're a CTO, CIO, or operations leader trying to turn AI pilots into real production wins, this is the playbook. Marc's LinkedIn https://www.linkedin.com/in/marcrohde/ Takeaways Unifying business functions for AI deploymentStarting small and scaling AI solutions Chapters 00:00 — Intro: the Chief Business Integration Officer role explained03:39 — What had to happen internally for the transformation to work04:09 — The original bet: data-driven decisions, 13 years ago05:07 — Migrating off legacy ERP to a cloud platform07:09 — How the Microsoft Copilot decision actually got made09:16 — "I don't care if it's the best LLM" — outcomes over technology10:30 — AI for me vs. AI for we13:36 — The institutional knowledge problem16:48 — Where to push on resistance, and where to step aside19:31 — AI as an intern with PhD knowledge and no street smarts20:17 — The question almost nobody asks: "What did I do wrong?"24:23 — From proof-of-concept to production: pick a reasonable goal24:55 — The one-question bot that handled 25% of email25:41 — AI order entry: 10% of orders, zero human touch26:32 — The ROI reframe: don't think headcount, think capacity28:34 — Closing: ground every decision in business outcomes

  5. Jun 26

    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

  6. Jun 23

    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

  7. May 14

    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.

  8. May 11

    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.

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About

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.

Information

  • Creator
    Nick Melnychuk
  • Years Active
    2026
  • Episodes
    23
  • Rating
    Clean
  • Copyright
    © 2026 Nick Melnychuk
  • Show Website
    In Production Podcast