Podcast
OPEN Tech Talks: AI worth Talking| Artificial Intelligence |Tools & Tips
Hosted by Kashif Manzoor · EN
"Open conversations. Real technology. AI for growth."
Open Tech Talks is your weekly sandbox for technology: Artificial Intelligence, Generative AI, Machine Learning, Large Language Models (LLMs) insights, experimentation, and inspiration. Hosted by Kashif Manzoor, AI Evangelist, Cloud Expert, and Enterprise Architect, this Podcast combines technology products, artificial intelligence, machine learning overviews, how-tos, best practices, tips & tricks, and troubleshooting techniques.
Whether you're a CIO, IT manager, developer, or just curious about AI, Open Tech Talks is for you, covering a wide range of topics, including Artificial Intelligence, Multi-Cloud, ERP, SaaS, and business challenges. Join Kashif each week as he explores the latest happenings in the tech world and shares his insights to help you stay ahead of the curve.
Here's what you can expect from Open Tech Talks Conversations:
• How organizations scale AI beyond pilots
• Where AI implementations break down
• Governance, risk, and maturity in GenAI systems
• Career evolution in the age of AI
The podcast is available on all major platforms, including Spotify, Apple, and Google.
Each episode of the podcast is about 30 minutes long.
"The views expressed on this Podcast and blog are my own and do not necessarily reflect those of my current or previous employers."
20episodes
Episodes
Newest firstAll episodes
How Small Businesses Can Win with AI Agents Before Enterprises Do with Anne Cantera
2w ago00:25:21Tap to summarizeArtificial Intelligence is no longer just for large enterprises with massive IT budgets. Today, even the smallest businesses can automate repetitive work, improve customer service, and reclaim valuable hours using AI. Chapters: 00:00 Introduction to Anne Cantera 01:12 The Role of AI in Small Businesses 03:43 Identifying Problems in Small Businesses 06:41 Implementing AI Solutions 10:24 Tech Stack and Feasibility for Small Businesses 12:30 Driving Towards Workable AI Products 13:43 Adoption Challenges and Experiences 15:43 Measuring ROI in AI Implementations 18:07 Advice for Small Business Owners 19:40 Educating Yourself on AI 22:43 Public Perception of AI 24:09 Conclusion and Contact Information Episode # 146 Today's Guest: Anne Cantera, Founder, Elementyl Intelligence She is an AI and voice technology expert, conversation designer, and founder of Elementyl Intelligence Website: Elementyl Intelligence LinkedIn What Listeners Will Learn: Why small businesses may adopt AI faster than large enterprises How to identify business problems that AI can solve A practical framework for assessing AI opportunities When AI is the right solution and when it isn't How voice agents can automate customer interactions The benefits of AI-powered inbox and workflow automation How custom AI solutions are becoming affordable for SMEs Why ROI should drive every AI investment The importance of AI literacy for business owners How to become a better AI operator. Why monitoring and continuous improvement matter after AI deployment. Common mistakes businesses make when implementing AI. How to avoid misinformation and AI hype. Resources: Elementyl Intelligence Voice of AI - Free AI Education Platform Elemental Intelligence - AI Consulting for Small Business Reuven Cohen - Trusted AI Educator
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The Future of AI Coding: Multi-Model Agents, Enterprise AI & Developer Productivity with Emilie Schario
3w ago00:31:49Tap to summarizeArtificial Intelligence has completely transformed software development. What started with autocomplete has evolved into AI coding agents capable of building entire applications from simple prompts. But as organizations adopt these tools, new challenges emerge: cost, security, vendor lock-in, enterprise governance, and the future role of software engineers. Today's conversation isn't about one product. It's about understanding where software development is heading over the next few years. Joining me is Emilie Schario, VP of Engineering at Kilo, where she works on agentic engineering and multi-model AI developer tools. We discuss how enterprises should think about AI coding assistants, why relying on a single AI model may not be the future, how engineering teams should prioritize product development, and what both junior and senior developers need to do to stay relevant. Whether you're a developer, engineering leader, CTO, startup founder, or simply curious about AI transforming software engineering, this episode is packed with practical insights. Let's get started. Chapters: 00:00 Introduction to Emily Sherio and Kilo Code 04:13 The Role of Kilo in Developer Productivity 07:52 Choosing the Right AI Models for Development 12:24 Building Features that Matter 16:38 The Importance of Code Review and Feature Management 19:24 The Future of Development and AI Integration 25:59 The Necessity of Learning Coding Principles 30:16 Enhancing Security in Coding Tools Episode # 194 Today's Guest: Emilie Schario, VP of Engineering, Kilo She works on agentic engineering and multi-model AI developer tools Guests Links: LinkedIn What Listeners Will Learn: Why AI coding is moving toward multi-model architectures. The advantages and risks of proprietary AI coding tools. How organizations can reduce AI infrastructure costs. Why vendor lock-in is becoming an AI challenge. The future of developer productivity. Why code review is becoming more important than writing code. Product management lessons every startup should know. When to remove features rather than add more. Enterprise AI security challenges. How organizations can adopt AI without compromising security. Why prompt engineering is becoming a fundamental skill. Advice for junior developers entering the AI era. The importance of mentoring the next generation of engineers. Resources: Kilo - AI Developer Platform Prompts for Mere Mortals by Florian Hines
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AI Skills Are the New Competitive Advantage with John Munsell
4w ago00:25:46Tap to summarizeOne lesson I've learned throughout my career, from enterprise architecture to cloud transformation and now Generative AI, is this. Every technology revolution follows the same pattern. At first, companies invest in technology. Later, they realize they should have invested in people. I've seen organizations spend millions on software while allocating almost nothing to learning. Then six months later they ask, "Why aren't people using the new platform?" The answer usually isn't difficult. People don't resist technology. People resist uncertainty. When employees understand how AI helps them become better, not replaced, they become curious. Curiosity leads to experimentation. Experimentation builds confidence. Confidence creates innovation. That's how real transformation happens. Today's conversation is about something that many organizations overlook. Not AI models. Not software. Not hype. We're talking about AI capability, the skill that will define the winners of the next decade. Let's begin. Episode # 193 Today's Guest: John Munsell, Chief Executive Officer, Bizzuka, Inc. John Munsell is the co-founder of Bizzuka, an AI consulting firm focused on artificial intelligence strategy and implementation. With a career spanning over 25 years in marketing, software development, financial services, and sales, John brings a wealth of experience to the AI industry. Website: Bizzuka What Listeners Will Learn: Why AI success depends more on people than technology The importance of AI skills across an organization Why giving employees AI tools isn't enough How organizations can measure AI capability The hidden cost of poor AI adoption Why AI training delivers long-term business value Common mistakes leaders make during AI transformation Practical ways to improve workforce AI readiness Why experimentation matters more than perfection How AI can increase productivity across every role Resources: Bizzuka
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AI Success Starts Before AI: Fix Your Knowledge, Strategy, and Governance First with Abby Clobridge
Jul 500:25:09Tap to summarizeOne thing I've noticed after working with enterprise customers for more than 20 years is this. When AI projects fail, people usually blame the model. They blame ChatGPT. They blame the vendor. They blame the technology. But after sitting in hundreds of customer meetings, I rarely found AI itself to be the problem. The real problem was hidden somewhere else. The data was scattered. Nobody knew which document was the latest. Every department had its own tools. Everyone wanted AI, but nobody agreed on the business problem they were trying to solve. I remember one customer asking me, "Can AI search all of our knowledge?" My first question wasn't about AI. It was, "Where is your knowledge?" The room became quiet. Because the answer was: "It's everywhere." That conversation completely changed how I think about enterprise AI. Today we're going to discuss something that doesn't get enough attention. Not prompts. Not models. Not the latest AI announcements. We're talking about the foundation that determines whether AI becomes a competitive advantage—or an expensive experiment. Let's get started. Episode # 192 Today's Guest: Abby Clobridge, Founder and Fractional CIO of FireOak Strategies She's spent her career working with nonprofits, NGOs, foundations, and mission-driven businesses to solve a problem almost every organization faces but few can articulate: how to make technology actually work for people. Website: FireOakStrategies What Listeners Will Learn: Why AI projects fail before the first prompt is written The hidden connection between knowledge management and AI success How messy data reduces AI accuracy What AI readiness really means Why governance matters more than buying new AI tools How small businesses can compete with AI Practical ways organizations are using AI for automation Common mistakes leaders make when adopting AI How to build an AI strategy that lasts beyond today's hype The role of IT leaders in successful AI transformation Resources: FireOakStrategies
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AI Can't Replace Your Brand Story with Mona Bavar
Jun 2800:26:17Tap to summarizeAI has changed the conversation. Not because machines suddenly became intelligent, but because millions of ordinary people suddenly gained access to something that felt extraordinary. I still remember when ChatGPT first became available. Like many people, I opened it with curiosity. I wasn't looking for shortcuts. I wanted to understand what was actually happening. Every week since then, I have spoken with founders, researchers, engineers, and business leaders from around the world. One thing became very clear. Technology changes quickly. People don't. Many businesses are still asking the wrong question. "How can AI do my work?" Instead, we should ask, "How can AI help me become better at the work only I can do?" That's a very different conversation. I've learned that AI can generate content. It can analyze data. It can write code. But it still cannot replace purpose. It cannot replace your experiences. It cannot replace your story. The companies that will succeed over the next few years won't simply use more AI. They will know how to combine technology with authenticity. Today's conversation is exactly about that. Not just where AI is going… …but where we, as humans, need to grow alongside it. Let's begin. Episode # 192 Today's Guest: Mona Bavar, Founder, BlueApples.ai She is a cultural innovator blending ancient wisdom with cutting-edge AI to transform how businesses tell their stories and shape the future. Website: BlueApples.ai What Listeners Will Learn: Why AI should amplify your uniqueness instead of replacing it How businesses can keep their authentic brand voice in the AI era Why knowing your "why" matters more than learning prompts Practical ways entrepreneurs can start using AI without losing their identity The future of AI in governance, search, agents, and business How to prepare for 2026 without chasing every new tool Resources: BlueApples.ai
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In the Age of AI, Human Growth Matters More Than Ever with Carlee Wolfe
Jun 2100:25:13Tap to summarizeFor the last two years, almost every conversation in technology has been about AI. New models. New tools. New agents. New automation. And honestly, I understand the excitement. I've spent more than 20 years working in technology, cloud, AI, and enterprise transformation. I've seen many waves of innovation. But this one feels different. Yet something has been bothering me. Every conference I attend, every LinkedIn post I read, every discussion I have with business leaders seems focused on one question: "What can AI do?" Very few people are asking: "What should I become?" A few years ago, if someone wanted career growth, the advice was simple. Work hard. Gain experience. Move to the next role. Today, that roadmap is disappearing. The world is changing faster than job descriptions can keep up. And that's why this conversation matters. Because the future may not belong to the people with the best title. It may belong to the people who remain curious, adaptable, connected, and willing to keep learning. In this episode, we're not talking about prompts or models. We're talking about people. Because while AI is changing work, humans are still responsible for creating meaning. Episode # 191 Today's Guest: Carlee Wolfe, Leadership Strategist, Talent Advisor, and Coach She is a strategist, connector, and coach helping people and organizations thrive through change. With 20+ years of leading talent, culture, and transformation across global brands like Under Armour, Hyatt Hotels, Apollo Education Group, and the U.S. Olympic & Paralympic Committee Website: LinkedIn What Listeners Will Learn: Why curiosity is becoming more valuable than expertise The real meaning of networking in the AI era How leaders can create a culture of learning Why do many professionals feel stuck in their careers How AI is changing career growth and workplace expectations Practical ways to continue learning despite a busy schedule How to prepare for the future of work without fear Why are human skills becoming more important as AI advances Resources: LinkedIn
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AI Safety, AGI, and the Next Decade with Dr. Craig Kaplan
Jun 1400:30:28Tap to summarizeFor most of my career, technology felt predictable. A new software platform arrived. A new programming language appeared. A new cloud service changed how we deploy applications. Every wave of technology helped people work faster. But AI feels different. Over the last two years, I have watched professionals across industries experience something I have never seen before. People are not simply using a new tool. They are having conversations with technology. A marketer can generate campaigns. A consultant can build frameworks. A developer can create applications in hours instead of weeks. And every week, the systems become smarter. Personally, I have experienced this while building AI frameworks, experimenting with coding agents, and working with organizations trying to adopt Generative AI. Many times I have found myself staring at a screen thinking: "How did it do that?" Not because the output was perfect. But because the pace of improvement was faster than expected. This raises an important question. If AI is becoming more capable every month, how do we ensure we build systems that remain useful, trustworthy, and safe? That is exactly what we explore in today's Open Tech Talks conversation with Dr. Craig Kaplan. Episode # 190 Today's Guest: Dr. Craig A. Kaplan, Inventor of the designs and Technologies that enable safe SuperIntelligence. He is a pioneer in artificial intelligence and the inventor behind technologies designed for safe Superintelligence. For more than four decades, he has worked at the intersection of intelligent systems, ethics, and innovation, developing architectures that help AI evolve safely and remain aligned with human values. Website: SuperIntelligence YouTube: iStudios What Listeners Will Learn: How AI evolved from symbolic systems to Generative AI The difference between AI, AGI, and Superintelligence Why are many AI researchers concerned about AI safety Enterprise AI risks leaders should understand today Why AI agents are becoming the next major AI wave The rise of multi-agent and collective intelligence systems How organizations can design safer AI solutions Why AI is shifting from a tool to a digital coworker The future impact of AI on jobs and knowledge work Practical guidance for responsible AI adoption Resources: SuperIntelligence
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Everyone Wants AI But Few Know Why with Kevin Carlson
Jun 700:26:24Tap to summarizeFor many years, technology projects were relatively predictable. A new system was implemented, a process was automated, or an application was modernized. The challenges were technical, but the path was usually clear. Then Generative AI arrived. I still remember some of the early conversations with technology leaders. Almost every discussion had the same underlying question: "How quickly can we adopt AI?" Yet very few people were asking a more important question: "Why are we adopting AI?" Throughout my career in enterprise technology, ERP, cloud, and AI transformation, I've seen organizations succeed when they focus on solving real business problems. I've also seen companies chase trends because everyone else was doing it. Today's conversation reminded me that technology leadership is no longer about buying the latest tool. It's about balancing innovation, security, business value, and human judgment. As AI becomes part of every organization, the challenge is not whether to adopt it. The challenge is adopting it thoughtfully. Episode # 188 Today's Guest: Kevin Carlson, TechCXO Partner Kevin Carlson is a seasoned tech exec and a go-to expert on AI's real-world impact within businesses. He's been a CTO or CISO four times over, working across different industries in both North America and Europe, so he brings a genuinely practical viewpoint to how AI is changing business and the world. Website: TechCXO What Listeners Will Learn: Why do many AI initiatives fail despite large investments How technology leaders should balance innovation and business value The difference between AI hype and AI outcomes Practical approaches for introducing AI into organizations Why starting small often leads to bigger success Common mistakes enterprises make during AI adoption How security leaders should think about AI risks Data privacy considerations when using public AI models Why governance matters more than ever How AI is changing the role of developers Why communication and product thinking are becoming critical skills The rise of AI-assisted software development Resources: TechCXO
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Beyond ChatGPT: The Future of Context-Aware AI with Martin Lucas
May 1000:20:44Tap to summarizeOne thing I have realized after years of working in AI, enterprise systems, ERP, and now Generative AI, is that technology alone never changes industries. What changes industries is understanding people. The problem today is not a shortage of content. There is no shortage of tools. It is not even a shortage of AI models. The real problem is relevance. Why do people ignore most advertisements? Why do customers disconnect from brands? Why do organizations create more AI-generated content but still fail to create engagement? Because human decision-making is emotional, contextual, irrational, and deeply personal. And that is why today's conversation is important. For years, the world focused on machine learning models, automation, and now Generative AI. But very few people are asking a deeper question: Can AI actually understand human intent, context, and decision-making? Today's guest, Martin Lucas, has spent years exploring exactly that through deterministic AI and decision science. And personally, this topic resonates with me deeply. Because while building AI adoption frameworks and helping organizations modernize, I constantly see one challenge repeated everywhere: Companies are automating communication…but not improving understanding. They are generating more…but connecting less. This episode is not just about AI technology. It is about human behavior, trust, context, branding, creativity, and the future relationship between humans and intelligent systems. Let's dive in. Episode # 188 Today's Guest: Martin Lucas, Inventor of Deterministic AI He is the inventor of deterministic AI and decision science, proven across more than 100 global brands with results up to 76% above market performance. Website: Deterministic AI What Listeners Will Learn: What deterministic AI means in simple language Why traditional LLMs still struggle with consistency and context The difference between content generation and true understanding Why most ads and marketing messages fail today How human emotions influence decision-making Why AI-generated content often feels repetitive and disconnected How brands can create stronger emotional relevance with customers Why curiosity is essential for creativity and innovation The future relationship between AI, creativity, and human psychology How startups can build stronger brand positioning using behavioral understanding Resources: Deterministic AI
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How GenAI Is Changing Surveys, Research, and Product Validation with Sharif Amlani
May 300:26:16Tap to summarizeOne of the biggest shifts I'm seeing right now is not only how AI is changing work, but how it is changing the way we test ideas. In the past, if a founder, researcher, product manager, or strategist wanted to validate an idea, the process was slow. Build a hypothesis. Run surveys. Wait for responses. Clean the data. Analyze it. Then maybe discover the question itself was not strong enough. Now, with GenAI, that whole cycle is being challenged. And this connects directly with my own work as well. When I work on AI strategy, GenAI maturity, or enterprise adoption roadmaps, the hardest part is often not the technology. The hardest part is asking the right question before building the solution. That is why today's conversation is important. Because we are moving from AI as a content generator to AI as a thinking partner. A system that can help researchers, founders, and teams test assumptions, explore user behavior, and sharpen decisions before spending time and money in the wrong direction. Today, I'm joined by Sharif Amlani, who brings together political science, research methods, data analysis, and generative AI to build tools for synthetic respondents and AI-powered research analysis. This is a conversation about research, validation, synthetic data, agents, and what happens when GenAI becomes part of the thinking process itself. Let's get into it. Episode # 187 Today's Guest: Sharif Amlani, Founder, HumanAI Sharif Amlani is the Founder and CEO of HumanAI, a UC Berkeley startup using generative AI to transform how we do research, analyze data, and expand what we know about the world around us. Website: HumanAI What Listeners Will Learn: How GenAI is changing research, surveys, and analysis What synthetic respondents are and where they can be useful Why AI-generated responses should support-not replace-real human validation How founders can test ideas earlier, before spending money on surveys Why talking to users remains the most important startup habit How AI agents can support analysis and reporting workflows Why consistency matters more than intensity when building a startup How market feedback can reveal a different customer than originally expected Resources: HumanAI
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