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."
12episodes
Episodes
Newest firstAll episodes
Beyond ChatGPT: The Future of Context-Aware AI with Martin Lucas
4w ago00:20:44Tap 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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The Hidden Challenges of AI Adoption in Enterprises
Apr 1900:28:00Tap to summarizeOver the past year, something has become very clear. AI is not just a technology shift. It is a leadership test. Across enterprises, startups, and even governments, the same pattern keeps repeating: Leaders are being pushed to act fast Teams are overwhelmed with change And yet, clarity is missing From the outside, it looks like a technology race. But from inside organizations, it feels very different. It feels like: uncertainty pressure and a constant question - "Are we doing enough?" In conversations with CIOs, architects, and business leaders, one thing stands out: The real challenge is not adopting AI. The real challenge is leading through it. That's why this episode matters. Chapter List: 00:00 Introduction to Silicon Valley Executive Academy 01:37 Understanding the Silicon Valley Playbook 03:20 The Impact of AI on Leadership 05:25 Leading Through AI Transformation 09:45 Managing Pressure as a Leader 11:21 Driving Growth with a Healthy Culture 13:39 Common Challenges for Executives 16:00 The Role of Emotional Intelligence in Leadership 17:20 Micro Joy Method for Leaders 18:58 Building Trust as a Leader 19:54 Identifying Red Flags in Leadership 21:20 Evolving Leadership Models 23:53 Advice for Emerging Leaders Episode # 186 Today's Guest: Victoria Mensch, CEO & Founder, Silicon Valley Executive Academy An executive leadership coach and strategist with over 25 years of experience in Silicon Valley's high-tech sector. With a PhD in Psychology and an MBA from UC Berkeley. Website: Executive Silicon Valley What Listeners Will Learn: Why AI adoption is fundamentally a leadership challenge How pressure and hype impact executive decision-making The difference between transformation and patching processes with AI Why culture and team alignment matter more than tools How leaders can manage uncertainty without burning out teams What early-career professionals should focus on in an AI-driven world Why trust, courage, and clarity are becoming core leadership traits
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What I've Learned Helping Enterprises Adopt GenAI
Apr 500:18:16Tap to summarize80% of enterprise AI projects never reach production. After two decades helping enterprises adopt new technology, Kashif Manzoor breaks down the five failure modes killing enterprise AI initiatives, introduces the GenAI Maturity Framework, and shares three questions every CTO should ask before approving their next AI project. Episode #: 185 In this episode, you'll learn: The 5 failure modes killing enterprise AI initiatives The GenAI Maturity Framework (6 dimensions, 6 levels) 3 questions every CTO should ask before their next AI initiative Why the gap between perceived and actual AI maturity is where POCs go to die Practical actions you can take this week TIMESTAMPS: 0:00 - The POC graveyard (a real conversation) 1:30 - Welcome + Why this episode exists 3:30 - My journey: Oracle → Cloud → GenAI 7:00 - The 80% problem: Why enterprise AI fails 10:00 - Failure Mode 1: The Strategy Gap 12:30 - Failure Mode 2: The Architecture Gap 15:00 - Failure Mode 3: The Governance Gap 17:00 - Failure Mode 4: The Talent Gap 19:00 - Failure Mode 5: The Measurement Gap 21:00 - The GenAI Maturity Framework (6 levels explained) 24:00 - 3 Questions Every CTO Should Ask 26:30 - What's coming next 28:00 - Subscribe + Connect
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Could Living Neurons Power the Future of AI with Ewelina Kurtys
Mar 1500:26:54Tap to summarizeOver the last couple of years, most of my conversations around AI have been about capability. How fast models are improving. How agents are becoming more autonomous. How enterprises can adopt GenAI safely. How teams can redesign workflows around intelligence. But this week, I found myself thinking about something deeper. Not what AI can do. But what does AI cost? And I don't just mean money. I mean energy. I mean infrastructure. I mean the hidden assumptions underneath the current AI boom. Because when we talk about the future of AI, most people immediately jump to models, chips, data centers, agents, and software stacks. But as someone who works closely with organizations trying to operationalize AI in the real world, I keep coming back to a harder question: What happens when the current compute model itself becomes the bottleneck? This is not a question most teams are asking yet. But it is a question serious builders should start paying attention to. This week, while reviewing different enterprise AI patterns and thinking through long-term architecture choices, I realized that much of the current AI conversation still happens within the assumptions of silicon, scale, and software abstraction. But what if the next major shift is not a better model? What if it is a different computing substrate altogether? That's exactly why today's conversation is important. Because this episode is not about another AI app. It is not about another wrapper. It is not about another productivity layer. It is about something much more fundamental: What might come after silicon, and how should we think about it today? Chapters: 00:00 Introduction to Ewelina Kurtis and Final Spark 00:52 Understanding Living Neurons and Their Potential 02:44 The Vision Behind Final Spark 05:34 Current Progress and Future Goals 08:27 Collaborations and Research Opportunities 11:17 Programming Living Neurons 14:02 Ethical Considerations in Biocomputing 16:59 Benefits of Biocomputing for Society 19:39 Advice for Aspiring Bioengineers 22:30 Commercial Aspects of Final Spark 24:24 Investor Insights and Future Directions Episode # 184 Today's Guest: Dr. Ewelina Kurtys, Scientist from FinalSpark Website: FinalSpark What Listeners Will Learn: Why the future of AI may require rethinking computation itself, not just models How energy efficiency is becoming a core strategic issue in AI What biocomputing means in simple terms How living-neuron-based computing differs from traditional silicon-based systems Why future AI progress may depend on alternative hardware paradigms How emerging scientific computing trends should matter to enterprise AI leaders today Why staying ahead in AI means looking beyond current tools and architectures Resources: FinalSpark
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How Attackers Use AI And Why Your Defenses Might Still Fail with Adriel Desautels
Feb 2200:25:09Tap to summarizeEpisode # 183 Today's Guest: Adriel Desautels, Founder & CEO, Netragard Adriel is a leader in cybersecurity with over 20 years of experience. Adriel founded Secure Network Operations and the SNOsoft Research Team, whose vulnerability research helped shape modern responsible disclosure practices. He later launched Netragard, pioneering Realistic Threat Penetration Testing, which he now call Red Teaming, and expanding into a broad range of security services. Website: Netregard X/Twitter: Netregard What Listeners Will Learn: Why "AI penetration testing" is often closer to automated scanning than real offensive testing How AI changes security risk mainly through volume and speed, not necessarily sophistication Where organizations get misled into a false sense of security Why "preventing breach" is unrealistic and why limiting damage paths matters more What cybersecurity professionals should focus on to stay relevant in the LLM era How AI may influence vulnerability research, but still struggles with novel exploitation thinking Resources: Netregard
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Why 95% of AI Pilots Fail and How to Be in the 5% with Mindaugas Maciulis
Feb 700:29:51Tap to summarizeWelcome to Open Tech Talks. Quick note before we start, thank you. The messages, the feedback, the "keep this practical" reminders… they've been incredibly helpful. Open Tech Talks has always been a weekly sandbox for technology insights, experimentation, and inspiration—with one objective: learn, test, and share what's real. Now, a personal moment from this week. A few days ago, I sat with a business owner who said something that stuck with me: "AI is everywhere… but I don't know where to start without breaking my business." And that's the truth for most companies, especially small businesses. Because "start with AI" sounds simple… until it touches real operations: leads that go cold, follow-ups that don't happen, teams that feel overwhelmed, tools that multiply, processes that nobody can explain clearly. Most AI projects don't fail because the model is weak. They fail because the process is unclear, the team is overloaded, and the strategy is missing. Let's begin. Episode # 182 Today's Guest: Mindaugas (Min) Maciulis, Founder & CEO of Strategic AI Advisors He works with CEOs, COOs, and operating partners in the $20M–$250M range who are ready to go beyond pilots and turn AI into real EBITDA growth. His proven 90-day sprint framework, AImpact OS, delivers measurable lifts across productivity, customer service, and sales. Website: Strategic Advisors What Listeners Will Learn: Identify the best "starting point" for AI using business pain, not hype Understand why AI pilots fail mostly due to adoption (not technology) Learn a practical approach to simplify workflows before adding automation See how SMBs can move faster than enterprises in the AI era Understand the difference between augmentation and transformation with AI Learn how to avoid tool overload and focus on measurable outcomes Resources: Strategic Advisors
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AI Is Creating Technical Debt Faster Than You Think with Maxim Silaev
Jan 3000:32:32Tap to summarizeThis week, I've been thinking about something slightly uncomfortable. Last weekend, I was reviewing one of my older architecture diagrams from five years ago. A cloud-native migration plan I was deeply proud of at the time. It was clean. Structured. Scalable. And then I asked myself: If I were to rebuild this today in the era of generative AI… Would I build it the same way? The honest answer? No. Not because it was wrong. But because our assumptions have changed. Two years ago, AI was a feature. Today, AI is shaping architecture decisions. We're not just designing systems anymore. We're designing systems that design, generate, predict, and automate. And here's the tension I keep seeing in enterprise conversations: Everyone wants AI. But very few are asking: "What technical debt are we creating while chasing it?" That's why today's conversation matters. Today, I'm joined by Maxim Salav, based in Australia, someone who works deeply in enterprise architecture and technical debt remediation. And this episode is not about hype. It's about responsibility. Because AI doesn't remove architectural complexity. In many cases, it amplifies it. Let's get into it. Chapters 00:00 Introduction to Technical Debt and Architecture 01:34 The Impact of AI on Technical Debt 04:12 Generative AI and Architectural Challenges 08:40 Adopting AI in Organizations 12:26 Building AI Strategies and Governance 17:33 Data Quality and AI Integration 22:43 Guardrails for AI Adoption Episode # 181 Today's Guest: Maxim Silaev, Technology Advisor and Enterprise Architect He is a technology advisor and enterprise architect with more than two decades of experience working with high-growth companies, complex systems, and business-critical platforms. Website: Arch-Experts What Listeners Will Learn: What technical debt really means in the AI era How generative AI can unintentionally increase hidden system risk Why architecture remains critical despite AI coding tools The importance of governance and verification layers in AI systems How large enterprises are cautiously integrating AI Why strategy must precede AI deployment The evolving role of enterprise architects in AI-native environments Resources: Arch-Experts
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Simplify Your Tech Stack and Scale Faster with Kara Williams
Jan 2500:23:43Tap to summarizeChapters 00:00 Introduction to Kara Williams 01:53 Kara's Coaching Journey and Entrepreneurial Background 03:20 The Importance of a Simplified Tech Stack 05:51 Common Mistakes in Tech Selection 07:09 Exploring AI in Business 08:16 Creating the Proof First GPT 10:47 Learning and Executing with AI 12:04 Common Challenges Faced by Entrepreneurs 13:50 Guiding New Entrepreneurs 14:59 Misconceptions About Low Ticket Offers 16:18 Refining Messaging and Offers 17:29 The Role of Automation in Business 18:34 Understanding Automation Needs 19:36 Testing Freebies and Building Relationships 20:29 Lessons Learned in Business 21:20 Future Plans and Refinements 22:31 Final Tips for Entrepreneurs Episode # 180 Today's Guest: Kara Williams, Founder, GHL Mastery Academy She is the founder of GHL Mastery Academy, where she helps CEOs stop being the bottleneck in their business by turning their VA, OBM, or EA into a trained backend powerhouse. Website: Kara Williams Youtube: GHL Mastery Academy What Listeners Will Learn: Why "cheap tool stacking" quietly becomes expensive (money + time + broken trust) How to think about systems like a real business owner (not a hobbyist) Why reliability matters more than feature-count in early-stage tech stacks How entrepreneurs can use AI to validate offers before building full courses or funnels What automation is actually for: visibility, testing, and removing blind spots How to simplify business operations without losing flexibility or creativity Resources: Website: Kara Williams
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Building Startups in the AI Era Lessons from 30 Years of Venture Capital with Scott Kelly
Jan 1800:29:14Tap to summarizeWelcome back to Open Tech Talks, and thank you, genuinely, for the continued support, messages, and thoughtful feedback. This show has been running for years now, and what keeps it meaningful is the shared curiosity of this community. We're in a very different phase of the AI journey. The conversation has clearly moved past "Can we build this?" Now it's about "Should we build this?", "Is this sustainable?", and "Does this actually create value?" Over the last year, I've personally noticed something interesting while working with enterprises, founders, and investors: AI has lowered the cost of building but raised the cost of judgment. It's easier than ever to create products, prototypes, and even companies. But deciding what's worth building, when to raise capital, and how to scale responsibly has become harder, not easier. That's why today's conversation matters. This episode is not about chasing trends or predicting the next AI unicorn. It's about long-term thinking, founder discipline, and understanding capital, timing, and execution in an AI-driven world. Today's guest has spent decades working across venture capital, startup growth, and exits through multiple technology cycles and brings a grounded perspective that's especially valuable right now. Let's welcome Scott Kelly to Open Tech Talks. Chapters 00:00 Introduction to Scott Kelly and His Ventures 02:00 The Transformative Impact of AI 04:03 Successful Investments and Entrepreneurial Journeys 05:53 Lessons for Entrepreneurs and Pitching Tips 10:06 Navigating the AI Landscape in Startups 11:52 Industry Applications of AI 14:54 Pitch Events and Investor Engagement 17:03 Investor Perspectives on New Technologies 19:52 Advice for Aspiring Entrepreneurs Episode # 179 Today's Guest: Scott Kelly, Founder & CEO, Black Dog Venture Partners He has been working on both sides, with entrepreneurs and investors alike, for more than three decades. Harnessing his innovative skills, vast experience training thousands of salespeople, and tapping into his vast network of investors. Website: Black Dog Venture Partners Youtube: VC FastPitch What Listeners Will Learn: How AI is changing the economics of building and scaling startups Why many founders may not need venture capital as early as they think Lessons from past technology cycles that still apply in the GenAI era How investors evaluate AI-driven businesses beyond surface-level hype Why timing, discipline, and execution matter more than tools What founders often misunderstand about pitching, capital, and exits How AI lowers build costs but raises the importance of strategic judgment Resources: Website: Black Dog Venture Partners YouTube: VC FastPitch
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