What happens when AI moves from theory to practice? Season one explored the messy, human answers.

From launching startups to reshaping industries, the first season of Artificial Insights dug into AI in practice, not just in theory. Guests shared hard-won lessons about why simplicity often beats sophistication, how to lead responsibly through uncertainty, and why AI’s real value lies in solving the right problems.

Daniel looks back at highlights from every conversation:

• Anwar Jeffrey on why “smart enough” beats over-engineered. Listen to the full episode: https://rss.com/podcasts/manaryhaus/1635320/
• Jake Walker on starting with the problem before the technology. Listen to the full episode: https://rss.com/podcasts/manaryhaus/1654888/
• Mandip Kanjiya on AI as the next phase of programming. Listen to the full episode: https://rss.com/podcasts/manaryhaus/1674607/
• Prerna Kaul on upskilling and augmenting human work. Listen to the full episode: https://rss.com/podcasts/manaryhaus/1697847/
• Peter Carr on why most tech projects fail. Listen to the full episode: https://rss.com/podcasts/manaryhaus/1720663/
• Addy Bhatia on the ethics of chatbots in mental health. Listen to the full episode: https://rss.com/podcasts/manaryhaus/1742470/
• Chris Wynder on why AI won’t take your job, but someone using it will. Listen to the full episode: https://rss.com/podcasts/manaryhaus/1766286/
• Kris Braun on where expert judgment still matters most. Listen to the full episode: https://rss.com/podcasts/manaryhaus/1788572/

It’s a chance to revisit the season’s biggest insights, and a reminder of how AI is changing not only what we build but also how we think about work, trust, and human judgment.

🔑 What You’ll Learn in This Episode

• ✅ Why AI products succeed when they solve problems simply
• ✅ How to balance opportunity and responsibility in AI projects
• ✅ What “augmentation” really means for workers
• ✅ Why ethical design matters in mental health AI
• ✅ Where human expertise remains essential, even in an AI-first world

🔗 Resources & Links

• 🤝 Connect with Daniel on LinkedIn: https://www.linkedin.com/in/danielmanary/
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

🎄 Looking back helps us see ahead. Share this episode with someone reflecting on AI’s role in their own work.

What does it mean to keep human judgment at the center when AI can handle more of the work?

For Kris Braun, co-founder and CTO of RunQL, the answer lies in augmentation rather than abdication. With experience launching serverless products at Google and founding multiple ventures, Kris has seen how tools can help or hinder depending on how they’re used.

At RunQL, his team is building AI-powered workflows that support data professionals while keeping their expertise in the loop.

In this episode, Daniel and Kris explore why access to data alone isn’t enough, how business intelligence failed to eliminate the need for interpretation, and why leaders who hand off decision-making risk losing the ability to make good judgments.

They talk about the three levels of AI adoption: augmentation, automation, and abdication, and why the last one poses the greatest risk to both companies and our humanity. Along the way, they discuss code as disposable, how AI changes software development, and the balance between speed and flexibility when building products.

🔑 What You’ll Learn in This Episode

• ✅ Why BI didn’t replace the need for data experts
• ✅ The difference between augmentation, automation, and abdication
• ✅ How AI can shorten cycles between business questions and insights
• ✅ Why domain expertise remains essential in applying AI effectively
• ✅ What’s changing for developers coding with AI

🔗 Resources & Links

• 🤝 Connect with Kris on LinkedIn: https://www.linkedin.com/in/krisbraun/
• ✨ Explore RunQL: https://runql.com
• 🌐 Google for Startups Accelerator Canada: https://startup.google.com/accelerator
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Think about something new? Share this episode with someone navigating the balance between AI assistance and human judgment.

Is AI a threat to jobs, or is the real risk failing to learn how to use it?

Chris Wynder thinks the second. With a PhD in developmental neurobiology and a career that’s spanned biotech, consulting, and enterprise software, Chris has seen how each wave of technology shifts work. Now as Director of the Developer Program and Product Marketing at OpenText, he helps thousands of developers and partners navigate automation and AI.

In this episode, Chris and Daniel discuss why hospitals still rely on DOS servers for life-critical machines, what distinguishes personal productivity tools from business productivity tools, and why trust is the deciding factor for AI adoption in sensitive fields like healthcare. Chris explains why smaller, specialized models can be more accurate and efficient than massive LLMs, and why hallucinations aren’t just inconvenient but dangerous in high-stakes domains like healthcare.

He also shares how AI has changed his own work, from supporting his writing with dysgraphia, to simplifying market research, to reshaping how he communicates with developers. For Chris, AI is not about replacement but about enabling people to do more with confidence. The real advantage goes to those willing to upskill and adapt.

🔑 What You’ll Learn in This Episode

• ✅ Why smaller, specialized models often outperform large LLMs in critical industries
• ✅ The key difference between personal and business productivity tools
• ✅ How trust shapes AI adoption in healthcare and enterprise contexts
• ✅ Ways AI can augment, rather than replace, professional expertise
• ✅ Why upskilling is the best defense against job disruption

🔗 Resources & Links

• 🤝 Connect with Chris on LinkedIn: https://www.linkedin.com/in/christopher-r-wynder-phd/
• 🌐 Explore OpenText: https://www.opentext.com/
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Found this helpful? Share it with someone weighing AI’s risks and opportunities at work.

AI can make mental health and wellness apps scalable… but should it?

Founder Addy Bhatia argues the point isn’t to mimic a therapist or build a “friend.” It’s to listen well, respect boundaries, and integrate care. In this episode, Addy shares why Suno avoids avatars and pronouns, why the logo is an ear, and how the product sits alongside real relationships rather than replacing them. He walks through the pivot from consumer app to working with therapists and clinics, and why “get to wow faster” matters in a crowded market.

Daniel and Addy also dig into the unglamorous decisions that shape trustworthy tools: encrypting user data, anonymizing before model calls, storing data where customers require it, and choosing different models for different jobs.

Addy explains how empathy benchmarks guide chat interactions, while smaller models handle proactive, asynchronous nudges like check‑ins after a poor night’s sleep. The result is a support mechanism that aims to help people notice patterns, prepare for tough days, and bring better context into therapy sessions.

🔑 What You’ll Learn in This Episode

• ✅ Why Suno is designed as a companion that supports therapy, not a substitute
• ✅ How “getting to wow faster” improves adoption
• ✅ Practical privacy choices: encryption, anonymization, data residency
• ✅ When to favor high‑EQ models for chats and lighter models for automation
• ✅ Founder habits that protect focus time and reduce calendar overload

🔗 Resources & Links

• ✨ Suno Wellness: https://suno.chat
• 🧩 Suno for Providers: https://www.suno.chat/providers
• 📚 EQ‑Bench 3 leaderboard (emotional intelligence in LLMs): https://eqbench.com
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Building in this space? Share the episode with your clinical or product team and ask one question: where should AI scale support, and where should humans stay front and center?

Most technology pilots never make it past the trial stage. What separates the few that succeed from the many that stall?

Peter Carr, associate professor at the University of Waterloo, has spent years helping leaders navigate that shift. His work spans the Faculty of Engineering and the Conrad School of Entrepreneurship and Business, where he teaches and researches how organizations can adopt technology responsibly. He has advised dozens of companies on digital transformation, AI adoption, and the cultural and process changes needed to make it real.

In this conversation, Daniel and Peter talk about why 70% of technology projects fail, and what it takes to close that gap. They explore how executives can align business and technology priorities, why collaboration across silos is essential, and how organizations can build the confidence to adopt AI and other emerging tools. Peter also shares examples from BDC, Interac, and manufacturing firms moving from firefighting to predictive maintenance.

🔑 What You’ll Learn in This Episode

• ✅ Why most tech projects stall at the pilot stage
• ✅ How to build a framework for digital transformation
• ✅ What cultural and leadership shifts matter most for AI adoption
• ✅ Why training and upskilling are critical for future competitiveness
• ✅ How executives can separate noise from real strategic signals

🔗 Resources & Links

• 🤝 Connect with Peter on LinkedIn: https://www.linkedin.com/in/pdcarr/
• 🌐 Digital Transformation at WatSPEED: https://watspeed.uwaterloo.ca/programs-and-courses/course-digital-transformation.html
• ✨ BDC (Business Development Bank of Canada): https://www.bdc.ca/
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Found this useful? Share it with someone navigating digital transformation in their company.

Where’s the line between empowering workers with AI and replacing them?

Prerna Kaul has faced that question firsthand. While launching Walmart’s first Canadian mobile app, she met customers worried that automation might erase human jobs. Years later at Moderna, she led the build of an internal generative AI platform that supported 750 custom GPTs to help the company pursue an ambitious goal of 15 vaccines in five years. Today she’s applying those lessons at Panasonic Well, shaping AI for family wellness.

In this conversation, Daniel and Prerna talk through her journey from engineer to product manager, why engineers often become product thinkers, and what it takes to build AI products that both deliver value and maintain trust. Prerna shares the importance of prototyping in GenAI, how 120+ customer discovery interviews guided her platform design, and what it looks like to quantify impact when metrics aren’t straightforward.

🔑 What You’ll Learn in This Episode

• ✅ How to address fears of automation while rolling out new tech
• ✅ Why prototyping and scope are essential for GenAI accuracy
• ✅ What it took to launch 750 GPTs at Moderna
• ✅ How engineers grow into product leaders
• ✅ Why defining success metrics is harder in AI than in B2C

🔗 Resources & Links

• 🤝 Connect with Prerna on LinkedIn: https://www.linkedin.com/in/prernakkaul
• ✨ Learn more about Panasonic Well: https://well.panasonic.com/
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Know someone wrestling with the balance between automation and human work? Share this episode with them!

The views expressed in this podcast are based on Prerna’s professional experiences and expertise. They do not represent the views of her employer.

AI can save money and time. Or it can generate a $20,000 bill overnight. What makes the difference?

Mandip Kanjiya has shipped AI-powered products in HR, insurance, and identity verification. Some worked. Some didn’t. Each one taught clear lessons for leaders who want AI to help, not hurt. In this conversation, Mandip shows why an “advisory layer” helps teams avoid leaking sensitive data, how on-device processing cuts costs and compliance exposure, and where using proven models beats trying to build your own.

Daniel and Mandip get specific: claims triage that moved faster once the workflow was redesigned, HR document generation that stayed private by templating inputs, and KYC checks that extracted only the fields needed on the user’s device. They also talk about where things went wrong and got expensive, including the real-world example of a cloud bill that spiked to $20K in a day and a routing project that failed because bad data looked clean.

🔑 What You’ll Learn in This Episode

• ✅ How to design an AI “advisory layer” that protects company and client data
• ✅ When edge processing reduces latency, spend, and compliance risk
• ✅ The difference between building AI and using AI to build solutions
• ✅ Why enabling policies beat fear-based restrictions for employee adoption
• ✅ How data quality and validation guard against confident, wrong outputs

🔗 Resources & Links

• 🌐 Communitech: https://www.communitech.ca/
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Thinking about bills? Share this with a colleague who signs the cloud bills and start a conversation about your edge strategy.

The hardest part of AI isn’t the technology. It’s knowing when not to use it.

Jake Walker has led products at Toyota, Otto Motors, DarwinAI, and Sportsnet. Across each role, he has seen that success does not come from using AI everywhere, it comes from matching the right technology to the problem. Sometimes that means deep learning, but often the solution is much simpler.

In this episode, Daniel and Jake explore the difference between capability, feature, and technology. Jake explains why voice and vision need AI, but why many manufacturing and streaming challenges can be solved without it. He shares how product managers should think of themselves as “value managers,” how to weigh ROI against timelines, and why being second to market can still succeed if you build distinct competencies.

🔑 What You’ll Learn in This Episode

• ✅ Why AI is a tool, not a strategy
• ✅ How to choose between AI and simpler technologies
• ✅ What makes a product manager a “value manager”
• ✅ How timelines and willingness to pay shape product decisions
• ✅ Why competitors’ strengths create urgency in your roadmap

🔗 Resources & Links

• 🤝 Connect with Jake on LinkedIn: https://www.linkedin.com/in/jakewalker123/
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Learn something new? Share this episode with a teammate who thinks “AI everywhere” is the answer.

What if the smartest move in AI isn’t chasing the shiniest model, but keeping things simple enough to work in production?

Anwar Jeffrey has lived that lesson. As co-founder and CTO of Glowstick, he built a revenue operations assistant that helps post-sales teams detect expansion opportunities, churn risks, and customer needs hidden in everyday conversations. But instead of leaning on expensive neural nets or large language models, his team focused on what he calls “dumb AI”: intent detection, sentiment, and similarity signals combined in pragmatic ways that delivered real value.

In this conversation, Daniel and Anwar unpack what it takes to bring an AI product from idea to adoption. They talk about why data is always messier than you expect, how to earn trust by meeting users inside the tools they already use, and why simplicity often beats sophistication when it comes to scaling.

🔑 What You’ll Learn in This Episode

• ✅ Why Glowstick chose simple, explainable AI over LLMs
• ✅ How messy customer data shapes product architecture
• ✅ What makes generalizability and scalability different challenges
• ✅ Why user trust depends on where insights live, not just how accurate they are
• ✅ How “dumb AI” can reach production faster and survive long enough to matter

🔗 Resources & Links

• 🤝 Connect with Anwar on LinkedIn: https://www.linkedin.com/in/anthonyajeffrey/
• 💻 Explore Azimuth (open-source tool Anwar’s team used): https://github.com/Servicenow/azimuth
• 💻 Explore Baal (Bayesian Active Learning): https://github.com/baal-org/baal
• 📩 Subscribe to the Artificial Insights newsletter for key takeaways: https://manary.haus/podcast/#haus
• 👉 Have a guest in mind? Reach out to Daniel at [email protected]

💬 Learned something here? Share this episode with someone building AI who might need permission to keep it simple.

Host Daniel Manary delves into the essential aspects of creating AI products that meets people's needs. Daniel explores the motivation behind building impactful AI solutions, then questions one of the most groundbreaking technologies in our lifetime about what makes it so impactful.