
In Production Podcast
Claim This Podcastby Nick Melnychuk
Podcast Overview
<p>Most AI conversations happen in boardrooms. This one happens where AI actually runs.</p><p>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.</p><p>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.</p><p>______________________________</p><p>What we cover:</p><p>- The gap between the AI demo and the AI deployment, and why most enterprises never cross it</p><p>- What experienced leaders actually discovered when they tried to automate decisions that lived entirely in someone's head</p><p>- Why data readiness, legacy infrastructure, and organizational alignment kill more AI programs than bad technology ever will</p><p>- What genuine AI transformation looks like across financial services, manufacturing, healthcare, legal intelligence, and enterprise software</p><p>- The honest conversation every technology executive needs to have internally before a single vendor is engaged or a single model is touched</p><p>______________________________</p><p>Who you will hear from:</p><p>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.</p><p>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.</p><p>______________________________</p><p>Who this is for:</p><p>- CTOs and CIOs navigating enterprise AI adoption with zero tolerance for hype and full accountability for outcomes</p><p>- CISOs who need to understand how AI reshapes their risk landscape before the landscape reshapes itself</p><p>- CDOs building the data foundations that determine whether AI programs succeed or quietly collapse</p><p>- Tech Founders building in the enterprise AI space who want to understand precisely how sophisticated buyers think and decide</p><p>- Engineering Leaders who have been asked to deliver on AI and want the honest, unfiltered roadmap from the people who have already done it</p><p>- Investors who want clear signal on where enterprise AI is actually landing versus where the pitch decks say it will</p><p>______________________________</p><p>Why In Production?</p><p>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.</p><p>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.</p><p>______________________________</p><p>New episodes weekly.</p><p>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.<br /></p><p>Subscribe wherever you listen to podcasts.</p><p>______________________________</p><p>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.</p>
Language
🇺🇲
Publishing Since
3/25/2026
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Recent Episodes

July 14, 2026
Fraudsters drive sports cars. Healthcare drives a school bus with Akhil Pandit Pautra
<p>Akhil Pandit Pautra(<a rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/akhil-panditpautra/" target="_blank">https://www.linkedin.com/in/akhil-panditpautra/</a>) 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.<br /></p><p>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.<br /><br />00:00 | Intro and guest introduction<br />00:44 | Fraudsters adopt AI faster than the organizations detecting it<br />01:18 | Why this is an accountability problem, not technology<br />01:59 | The industry shifts from can we to should we<br />08:49 | The damage that happens between detection and action<br />09:03 | A signal is not evidence, the check engine light<br />10:12 | Governance as what lets organizations move confidently<br />12:11 | Deciding which signals actually deserve attention<br />12:37 | The hidden cost of every audit and intervention<br />14:52 | People do not ask about algorithms, they ask can I trust this<br />15:15 | AI as decision support, not decision replacement<br />16:10 | Moving from needle in a haystack to guided search<br />17:24 | Why e-pharmacies change where the risk lives<br />20:44 | The 90 day starting point, curiosity before AI<br />21:11 | A three step framework for responsible adoption<br />26:06 | AI as a GPS, why judgment outlasts intelligence<br />26:56 | The coming fight over data and model sovereignty<br />28:03 | AI changes how we work, not why we do it<br />29:16 | Closing exchange and where to reach Akhil</p>

July 7, 2026
The model is the easy part. Owning it for six months is the job with Tamojit Saha
<p>Tamojit Saha (<a rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/sahatamojit/" target="_blank">https://www.linkedin.com/in/sahatamojit/</a>) 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.<br /></p><p>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.<br /><br />00:00 | Intro<br />02:06 | Why the enterprise AI bottleneck moved from technology to people<br />03:23 | Technology scales fast, human adoption lags, speed learners win<br />06:01 | From project thinking to product thinking, the living asset<br />08:36 | Why protecting the status quo creates organizational debt<br />09:43 | Challenging your assumptions before the market forces it<br />11:20 | When a model fails the explainability test in healthcare<br />12:09 | Compliance and lawyers question the decision before data scientists<br />15:04 | Budgeting to build AI but never to operate it<br />17:19 | The interview tell separating tool users from production owners<br />18:04 | Owning a model means responsibility after 90 days<br />18:53 | Why training does not equal transformation<br />19:34 | Redesigning the work so people can fail safely<br />20:34 | Why practical experience beats certifications for new talent<br />22:05 | What universities miss: system thinking over technology thinking<br />24:28 | Closing advice: stop treating AI as only technology<br />25:42 | There is no shortcut around the fundamentals<br />27:32 | Where to reach Tamojit, and closing thoughts</p>

June 30, 2026
The Demo Never Made It to Production. His Did. With Marc Rohde
<p>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.<br /></p><p>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.<br /></p><p>If you're a CTO, CIO, or operations leader trying to turn AI pilots into real production wins, this is the playbook.<br /><br />Marc's LinkedIn <a rel="noopener noreferrer nofollow" href="https://www.linkedin.com/in/marcrohde/" target="_blank">https://www.linkedin.com/in/marcrohde/</a></p><p></p><p>Takeaways</p><ul><li>Unifying business functions for AI deployment</li><li>Starting small and scaling AI solutions</li></ul><p></p><p>Chapters</p><ul><li>00:00 — Intro: the Chief Business Integration Officer role explained</li><li>03:39 — What had to happen internally for the transformation to work</li><li>04:09 — The original bet: data-driven decisions, 13 years ago</li><li>05:07 — Migrating off legacy ERP to a cloud platform</li><li>07:09 — How the Microsoft Copilot decision actually got made</li><li>09:16 — "I don't care if it's the best LLM" — outcomes over technology</li><li>10:30 — AI for me vs. AI for we</li><li>13:36 — The institutional knowledge problem</li><li>16:48 — Where to push on resistance, and where to step aside</li><li>19:31 — AI as an intern with PhD knowledge and no street smarts</li><li>20:17 — The question almost nobody asks: "What did I do wrong?"</li><li>24:23 — From proof-of-concept to production: pick a reasonable goal</li><li>24:55 — The one-question bot that handled 25% of email</li><li>25:41 — AI order entry: 10% of orders, zero human touch</li><li>26:32 — The ROI reframe: don't think headcount, think capacity</li><li>28:34 — Closing: ground every decision in business outcomes</li></ul>
22 total episodes available
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- What is In Production Podcast?
<p>Most AI conversations happen in boardrooms. This one happens where AI actually runs.</p><p>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.</p><p>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.</p><p>______________________________</p><p>What we cover:</p><p>- The gap between the AI demo and the AI deployment, and why most enterprises never cross it</p><p>- What experienced leaders actually discovered when they tried to automate decisions that lived entirely in someone's head</p><p>- Why data readiness, legacy infrastructure, and organizational alignment kill more AI programs than bad technology ever will</p><p>- What genuine AI transformation looks like across financial services, manufacturing, healthcare, legal intelligence, and enterprise software</p><p>- The honest conversation every technology executive needs to have internally before a single vendor is engaged or a single model is touched</p><p>______________________________</p><p>Who you will hear from:</p><p>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.</p><p>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.</p><p>______________________________</p><p>Who this is for:</p><p>- CTOs and CIOs navigating enterprise AI adoption with zero tolerance for hype and full accountability for outcomes</p><p>- CISOs who need to understand how AI reshapes their risk landscape before the landscape reshapes itself</p><p>- CDOs building the data foundations that determine whether AI programs succeed or quietly collapse</p><p>- Tech Founders building in the enterprise AI space who want to understand precisely how sophisticated buyers think and decide</p><p>- Engineering Leaders who have been asked to deliver on AI and want the honest, unfiltered roadmap from the people who have already done it</p><p>- Investors who want clear signal on where enterprise AI is actually landing versus where the pitch decks say it will</p><p>______________________________</p><p>Why In Production?</p><p>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.</p><p>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.</p><p>______________________________</p><p>New episodes weekly.</p><p>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.<br /></p><p>Subscribe wherever you listen to podcasts.</p><p>______________________________</p><p>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.</p> - How often does this podcast release new episodes?
This podcast updates daily.
- Where can I listen to this podcast?
This podcast is available on 4 platforms including Apple Podcasts, Spotify, and more. You can also use the RSS feed directly.
- Does this podcast accept guests?
Yes, this podcast regularly features guests.
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