This episode challenges a core assumption most tech leaders still operate under: that intelligence is our primary advantage. As AI systems begin to outperform humans in reasoning and decision-making, this conversation reframes AI not as another step in the industrial revolution, but as the start of an intelligence revolution. For CTOs and business leaders, this shift forces a deeper question. If machines can think, what is left for humans to lead?

You’ll learn why performance-driven organizations are hitting a ceiling and why continuing to optimize for efficiency alone will limit long-term value. The discussion explores a transition already underway, from purely economic thinking toward purpose, connection, and responsibility. It also highlights how board-level conversations are evolving, moving from short-term business cases to broader value-based decisions that shape the future of the organization and society.

The episode also brings this down to action. It outlines how leaders and teams can respond today through experimentation, curiosity, and intentional time spent on change rather than just execution. You’ll walk away with a clearer perspective on how to position your organization in a world where AI handles more of the thinking, and where leadership is defined by the ability to rethink strategy, culture, and human contribution at a fundamental level.

Key Takeaways

• AI is replacing not just manual work but cognitive work, forcing leaders to redefine human value beyond intelligence

• Performance-driven cultures are reaching their limits; future organizations must prioritize meaning, connection, and long-term value

• Strategy must shift from pure business cases to value cases, balancing today’s results with tomorrow’s impact

• Leaders should foster experimentation by allocating time for learning, not just execution, to unlock AI’s real potential

• Organizations that combine human strengths with AI effectively will outpace slower, legacy-driven competitors

• The most critical leadership skill ahead is not control, but curiosity, responsibility, and the ability to rethink assumptions

Margôt van Brakel is a keynote speaker, author, and advisor with more than 20 years of experience guiding organizations through complex change. She works with boards and executive teams on the human side of AI — exploring how organizations can use technology as a means, not an end, and find the right balance between people and machines. Her work focuses on what it means to keep human value at the center of organizational transformation. Because this is not the next wave in the industrial revolution — it is a new one. And that changes everything about the questions leaders need to ask.

00:00 Margôt Journey

05:42 Treating People as Machines

12:38 AI's Societal Impact

12:06 Ad

20:32 What is Your Responsibility?

26:00 What is Homo Conexus?

39:01 Ad

38:12 The Ugly Phase of Transformation

42:25 A Hopeful Perspective!

• Website: https://margotvanbrakel.com

• LinkedIn: https://www.linkedin.com/in/margotvanbrakel/

• Instagram: https://www.instagram.com/margotvanbrakel/

Colin shares the raw, behind-the-scenes reality of moving from a cushy engineering role at Microsoft to the high-stakes environment of a Silicon Valley accelerator. He discusses the pivotal "moment of liberation" when he admitted his initial startup idea was failing, a realization that cleared the path for him to co-found a viral sensation overnight.

The conversation dives deep into the intersection of technology and user psychology. Colin explains how understanding human behavior allowed him to scale a product to 17 million users and later lead growth for a live-streaming giant in Asia with over 100 million users. He breaks down why technical leaders must look beyond the code to understand the "psychological levers" of their customers, whether they are building B2C dating apps or complex B2B platforms.

Mark and Colin also explore the evolving role of the CTO in the age of AI. Colin argues that as AI makes the digital landscape noisier and more automated, the competitive advantage shifts to leaders who can empathize and connect with people. This episode is a masterclass in growth strategy, the psychology of negotiation, and why "judgment" is the most valuable asset a tech leader can bring to the boardroom.

1. The Power of Admitting Failure: Admitting a product is failing is often the necessary liberation to clear the path for a high-growth idea.
2. Psychology Over Code: Growth is driven by understanding the psychological levers of acquisition and retention rather than just adding features.
3. The AI Human Advantage: As AI automates digital noise, the primary competitive edge for leaders shifts toward human empathy and judgment.
4. Strategic Friction: Real growth involves framing user choices and occasionally adding "good friction" to nudge people toward higher-value actions.
5. Team Empathy as a Lever: Success requires framing technical decisions with the team's emotions in mind to ensure smooth execution.
6. Pricing Strategy as Growth: Behavioral triggers, like the "second cheapest bottle" effect, can be applied to tech products to increase revenue.

00:00 The 14 Year-old Company Owner

05:47 Launching DOWN

12:54 Ad

13:26 The Controversial "Bang With Friends"

17:42 "Outrageous Startup Growth"

24:18 Ad

24:29 Psychology as a Foundation!

28:14 Standing Out Through The AI Noise

30:10 Past Advice and Future Plans!

33:22 Behavioral Improvement and Empathetic Approach

Colin Hodge is a seasoned entrepreneur and growth expert with over 17 years of experience scaling businesses to over 100 million users. His expertise lies in organic growth, user psychology, and disruptive marketing. Colin co-founded and grew 'Bang with Friends' (later DOWN) to over 6 million users organically before selling it, and later re-acquired and grew it into a top 5 US dating app. He has also served on the board of a Silicon Valley social media company, contributing to over $200 million in annual revenue, and acted as Chief Growth Officer for 17Live, Asia's leading live-streaming app, where he led its US launch. Colin's approach is rooted in understanding user psychology to drive authentic, sustainable growth, and he is passionate about sharing his lessons with fellow founders and marketers.

1. Website: https://colinhodge.com/
2. LinkedIn: https://linkedin.com/in/ckbhodge
3. Instagram: https://instagram.com/ckbhodge
4. Colin's Book: Outrageous Startup Growth: Uncovering the Secrets of User Psychology to Scale Your Success: https://www.amazon.com/Outrageous-Startup-Growth-Uncovering-Psychology/dp/1394387334

Most AI projects don’t fail because of bad models. They fail because the data underneath is broken.

Matt Soltau breaks down why “AI readiness” is really a data problem and why most organizations are building on fragile, disconnected systems that can’t survive outside a demo. From hidden data silos to untraceable pipelines and compliance risks, this conversation exposes the real reason AI initiatives stall and what CTOs must fix before scaling anything to production.

You’ll learn how to move from spaghetti architecture to controlled, traceable data flows, why integration strategy matters more than the latest AI tool, and how to build a foundation that actually supports long-term AI value. If your board is pushing for AI but your systems feel messy underneath, this episode will show you where to start and what to avoid.

1. Why most AI pilots fail to deliver business impact despite working in sandbox environments
2. The hidden risk of disconnected data pipelines and how they break AI in production
3. Early warning signs your data foundation is not ready for AI
4. Why point-to-point integrations create long-term complexity and technical debt
5. How to design scalable data architecture using integration layers instead of “spaghetti systems”
6. A practical starting point: how to move from one controlled use case to enterprise-wide AI adoption

Matt Soltau is a Global Director of Strategy & Operations at IntelliPaaS, an AI‑ready data integration and workflow automation platform for enterprises and regulated organizations. With nearly a decade spent connecting siloed systems, automating complex processes and making data trustworthy for AI. He helps leaders turn fragile tech stacks into secure, compliant workflows that actually scale and drive real business results.

00:00 Dangerous AI Assumption

09:37 Why Pragmatic Solutions aren't Enough

15:08 Ad

15:40 How Important is Compliance?

22:29 When Does the Problem Start?

28:59 Working with Enterprise Organizations

34:17 Dealing with Compliance Complexity

39:07 Ad

39:19 Expensive Integration Mistakes

46:26 Controlling Your Data Foundation!

1. Website: https://www.intellipaas.io/
2. LinkedIn: https://www.linkedin.com/in/soltaumatt/
3. YouTube: https://www.youtube.com/@IntelliPaaS

What happens when you take a trust-based, offline financial system and turn it into software that people depend on with their money? In this episode, Erioluwa shares the real pressure behind building a fintech startup in Nigeria, where every product decision, line of code, and feature trade-off directly impacts user trust and financial safety.

For startup founders, this conversation goes beyond fintech. It reveals what it actually takes to move users from familiar, manual systems into digital products, how to balance growth with risk, and why sometimes the best product decision is not building a feature at all. From early traction challenges to scaling a lean engineering team, this episode breaks down the realities of building something people rely on financially, not just functionally.

You’ll also hear how trust becomes the real product, why simplifying solutions often beats complex engineering, and what changes when your users’ money, not just their attention, is on the line.

1. Why fintech changes engineering standards completely and why “good enough” code is never acceptable
2. How to balance product growth with fraud prevention and system risk
3. A practical approach to deciding which features to build or reject based on risk vs return
4. How to scale a lean engineering team without introducing unnecessary process overhead
5. Why raising the bar for users can be more effective than building complex safeguards
6. The shift from writing code to leading people, making trade-offs, and owning outcomes

Erioluwa Asiru is a software engineer, technology leader, and mentor, serving as CTO and Cofounder of Circle Funds. She leads the company’s technical vision, building scalable financial infrastructure and products that expand access to funding and wealth-building opportunities. Her work combines hands-on engineering with strategic leadership, ensuring reliable systems and strong product execution.

Alongside her role as a founder, Erioluwa is a lead mentor at Data Epic, where she guides cohorts through intensive six-month programs in data and software engineering. She is passionate about developing practical skills, fostering confidence, and helping aspiring professionals transition into successful tech careers.

Erioluwa has delivered a wide range of impactful solutions, including digital savings platforms, creator financing systems, admin portals, and automated data analysis tools. Driven by innovation, education, and economic empowerment, she is committed to building technology that creates real opportunities while mentoring the next generation of tech talent.

00:00 What is Ajo?

06:56 Ad

07:28 Working in Nigerian Financial Technology

13:35 Fraud, Features, and Leadership

22:51 Ad

23:03 Complicated Solutions and Inspiration

26:38 The Future of Circle Funds

29:46 Mentorship Experience

31:05 You Need a Community!

1. Website: https://circlefunds.io
2. LinkedIn: https://www.linkedin.com/in/asiru-erioluwa/

Most AI initiatives don’t fail because of weak models. They fail because they collapse under real-world complexity. This episode breaks down how CTOs and business leaders can design AI systems that survive messy data, unpredictable users, and production reality.

You’ll learn how to shift from “build a model” thinking to system-level strategy, manage AI as a high-risk investment, and align business expectations with technical uncertainty.

Key Takeaways

• Why AI success depends more on system design than model performance

• How to build guardrails, monitoring, and rollback into AI from day one

• The “VC mindset” for AI investments and why most initiatives will fail before one works

• How to align CEOs and boards around uncertainty, cost, and probabilistic outcomes

• The real reason AI breaks in production and how to anticipate failure modes early

• What skills modern AI teams need as roles shift from building models to orchestrating systems

Sairam Sundaresan is an AI Engineering Leader with over 15 years of R&D experience. Currently leading autonomous driving research at Valeo, he has a history of innovation at Intel Labs and Qualcomm, backed by a portfolio of patents and publications.

Beyond his technical roles, Sairam is deeply invested in the growth of the AI ecosystem. He has mentored countless engineers, served as a Lead at the Frontier Development Lab (FDL) applying AI to space science, and actively advises business leaders on AI adoption.

He is also the author of the book 'AI for the Rest of Us' (Bloomsbury) and writes Gradient Ascent, an illustrated newsletter read by over 25,000 subscribers. Through these channels and his LinkedIn following of over 100,000, he is one of the industry's most trusted voices for making AI accessible.

00:00 Industry Real World Failures

05:26 Reliability of AI Models

11:24 Ad

11:57 "AI for the Rest of Us"

16:28 Starting With AI

24:54 Explaining AI to the Board

27:37 Ad

27:48 AI Unrealistic Expectations

32:42 What kind of People does AI Demand?

36:49 Keeping Up with AI

39:15 AI Predictions

• Website: https://newsletter.artofsaience.com

• LinkedIn: https://www.linkedin.com/in/sairam-sundaresan/

• Instagram: https://www.instagram.com/artofsaience/

• YouTube: https://www.youtube.com/@artofsaience

• His Book: https://www.amazon.com/AI-Rest-Us-Illustrated-Introduction/dp/B0F29THNLT/

Most AI strategies fail before anything ships, not because of the tech, but because leaders never test assumptions against reality. In this episode, CTOs will learn how to ground AI strategy in real user behavior, reduce risk early, and avoid costly AI failures before they scale.

Jill Stover-Heinze breaks down how generative AI changes the risk model, why non-deterministic systems demand new leadership thinking, and how CTOs can turn governance, user research, and risk into a competitive advantage instead of a bottleneck.

Key Takeaways

1. Why most AI strategies fail before build and how to validate ideas against real user behavior
2. How generative AI changes risk and why non-deterministic systems require new leadership thinking
3. How to use risk and governance as a design tool instead of a compliance exercise
4. Why product-market fit still kills AI initiatives and how to avoid building the wrong thing
5. What to ask your board and teams to move fast without falling into AI hype and costly mistakes

Jill Heinze helps product leaders make smarter AI decisions through strategic intelligence and ground truth research. As founder of Saddle-Stitch Consulting, she brings 20 years of user research and competitive intelligence experience to help organizations navigate AI uncertainty, revealing what competitors miss and avoiding expensive mistakes before they happen. She serves as Responsible AI Program Director for The American College of Financial Services and hosts Responsible Tech Talks on LinkedIn Live.

00:00 The Ground Truth

04:38 AI's Hidden Consequences (NIST)

12:47 Ad

13:19 Approaching AI as a CTO

18:45 FOMO in the Corporate World

22:57 Keeping Up with AI

28:42 Ad

28:53 Effectively Using AI

37:27 Talk to Your People!

1. Website: https://www.saddlestitchconsulting.com
2. LinkedIn: https://www.linkedin.com/in/jill-stover-heinze/
3. Instagram: https://www.instagram.com/jill_saddlestitchconsult/
4. YouTube: https://www.youtube.com/@JillHeinze-SaddleStitchConsult
5. Facebook: https://www.facebook.com/profile.php?id=61581363390571
6. TikTok: https://www.tiktok.com/@jill_saddlestitchconsult

AI is moving from copilots to autonomous agents, and most tech leaders are not prepared for what that shift means. Craig walks through the real risks behind superintelligence, why AI checking AI is becoming inevitable, and how CTOs and CIOs can design safer, more resilient systems before autonomy outpaces human oversight

Rather than focusing on hype, this episode dives into the alignment problem, the limits of guardrails, and why monolithic black box models may be the wrong long term architecture.

You will hear a practical path forward for tech leaders who are already overwhelmed by AI generated code, agent frameworks, and rapidly evolving models

If you are leading engineering, AI, or technology strategy, this episode will challenge how you think about safety, governance, autonomy, and the future role of the CTO in an AI driven world.

Key Takeaways

1. Why AI checking AI is not optional as code generation and autonomy scale
2. What the alignment problem really means for enterprise technology leaders
3. The limits of guardrails and why prevention at design stage beats patching at deployment
4. How democratic architectures of multiple agents can reduce systemic risk
5. Why vendor agnostic, multi model strategies increase resilience and strategic control
6. How to embed company values into AI systems through training, memory, and architecture
7. What P(doom) represents and why many leading researchers assign it far higher risk than most executives assume

Dr. Craig A. Kaplan is a renowned expert in artificial intelligence, artificial general intelligence, and superintelligence, with a focus on collective intelligence and quantitative modeling. He is the Founder of Superintellligence.com and CEO and founder of iQ Company, a consulting firm dedicated to advanced AGI and SI systems. Previously, he founded PredictWallStreet, a financial services firm that powered top hedge fund performance by leveraging the collective intelligence of retail investors. Dr. Kaplan has authored a book, published extensively in scientific journals, and holds numerous patents on AI-related technologies.

00:00 How far is AGI?

06:55 What is P(doom)?

16:43 AI Reviewing AI Output

20:54 Dealing with Bad Actors (Human or AI)

25:36 Approaching AI as a Small Scale CTO

30:43 Democracy of AI Agents

35:15 AI Safety Conferences

40:03 AI Models, Open-Source or Big Company?

45:05 Is AI Adoption Keeping Up?

1. LinkedIn: https://www.linkedin.com/in/craigakaplan
2. Website: https://www.superintelligence.com
3. Website: https://iqco.com
4. YouTube: https://www.youtube.com/@iqstudios1

AI is not just another tool. It is reshaping leadership, strategy, and the future of work.

Stephanie Sylvester explains why most AI initiatives fail, why middle management is most at risk, and how CTOs and CIOs can use AI to create faster strategy clarity, stronger teams, and real business impact. If you are leading AI inside your organization, this conversation will challenge how you think about automation, culture, and staying relevant in an AI-driven economy.

Key Takeaways

• Why AI initiatives fail when treated like software instead of a leadership and culture shift

• How C-level leaders can use AI to create faster strategy clarity and execution

• Why middle management roles are most exposed and how to reposition them around coaching and people development

• How AI can improve team performance and reduce internal friction

• Why clean data and context determine whether AI creates value or chaos

• How tech leaders should think about personal employability and continuous learning in an AI-driven economy

Stephanie is a technology leader who helps organizations turn complex AI and software problems into clear, actionable strategies. With a track record of partnering closely with CTOs and engineering teams, she specializes in scaling systems, improving developer velocity, and aligning technical roadmaps to business outcomes. On CTO Compass, she’ll share practical insights from the field on building resilient architectures, navigating AI adoption, and leading teams through rapid change.

00:00 Managing AI with a Human Perspective

06:06 Avatar Buddy

14:04 Automation using AI

24:47 Getting People Into AI

31:05 Working with Smaller Systems

35:16 C-level People and Personal Growth

43:42 What Does the Future Look Like?

• Website: https://avatarbuddy.ai

• LinkedIn: https://www.linkedin.com/in/stephanie-a-sylvestre

• Instagram: https://instagram.com/avatar_buddyai

• YouTube: https://www.youtube.com/channel/UC7WJI0FupsR6CTMmI6-MEeQ

• Website: https://tairi.co

• LinkedIn: https://www.linkedin.com/in/mwormgoor

• Instagram: https://www.instagram.com/markwormgoor

• Business Email: [email protected]

CTOs and CIOs are facing relentless delivery pressure as teams shrink, AI accelerates expectations, and urgency becomes the default operating mode. In this episode, you will learn how constant exhaustion and apathy signal deeper leadership risk, why staying hands-on quietly destroys strategic capacity, and how small changes in delegation, prioritization, and ownership can help tech leaders move from reactive sprinting to sustainable executive leadership.

Key Takeaways

• Why rising delivery pressure is burning out even highly capable CTOs and CIOs

• How the hands-on fixer mindset breaks down at scale and increases risk

• The early warning signs of burnout most tech leaders ignore until it is too late

• How to delegate real ownership without losing control or accountability

• What sustainable, long-term tech leadership looks like in high-velocity environments

Andrew Hinkelman is a certified Executive Coach and recent CTO and COO. He has more than 25 years of experience in the Technology and Financial Services industries across roles in IT, Product Management, and Business Intelligence along with consulting and fractional roles.

Andrew helps high-achieving executives—CEOs, CTOs, and their teams—master the balance between operational demands and strategic growth. He uses an "inside-out" framework with his clients, meaning that we must understand self-leadership and self-improvement before we can effectively lead others.

Andrew has worked with global brands and high-growth companies like AWS, Sony, AirBnB, LinkedIn, Stripe, Coinbase, Reddit, Twitch, Tripadvisor, Hello Fresh, and Gilead, helping their leaders overcome challenges, foster collaboration, and elevate their impact.

Andrew lives in the Seattle area with his family where he spends an enormous amount of time on a bike or paddleboard and sneaking out to the mountains.

00:00 What makes a Successful CTO

05:48 "I need to fix everything"

10:56 Delivery Expectations and Overwork

21:54 What Burnout Looks Like

26:40 When Standard Training Isn't Working

33:05 Bringing People into the Culture

37:54 TAKE CARE OF YOURSELF!

• Website: https://ctolunches.com/

• Website: https://www.pri-1.com/

• LinkedIn: https://www.linkedin.com/in/andrewhinkelman/

• Website: https://tairi.co

• LinkedIn: https://www.linkedin.com/in/mwormgoor

• Instagram: https://www.instagram.com/markwormgoor

• Business Email: [email protected]

Most CTOs and CIOs are not behind on AI because they chose the wrong model. They are behind because their data, culture, and workflows are not ready.

In this episode of The CTO Compass, Mark Wormgoor speaks with Santosh Kaveti, CEO of ProArch, about what it really takes to scale AI in large enterprises. They break down why AI pilots stall, why data lakes did not solve data readiness, and how tech leaders can move from experimentation to real business impact in 90 days or less.

The conversation also covers Microsoft Copilot, enterprise data governance, AI security and compliance, and how CIOs and CTOs should respond when boards demand AI results.

If you are a CTO or CIO under pressure to deliver AI outcomes instead of demos, this episode offers a practical, no-hype playbook.

Key Takeaways for CTOs and CIOs

1. Why AI adoption fails without strong data readiness and ownership
2. What “data as a product” looks like in practice
3. How to move from AI pilots to production in four to six weeks
4. Why Copilot often struggles and how to unlock real productivity gains
5. How to measure AI ROI beyond licenses and usage metrics
6. Why model-agnostic architecture matters for enterprise AI

With over 18 years of experience as a technologist, entrepreneur, investor, and advisor, Santosh Kaveti is the CEO and Founder of ProArch, a purpose-driven enterprise that accelerates value and increases resilience for its clients with consulting and technology services, enabled by cloud, guided by data, fueled by apps, and secured by design.

Santosh’s vision and leadership have propelled ProArch to become a dominant force in key industry verticals, such as Energy, Healthcare & Lifesciences, and Manufacturing, where he leverages his expertise in manufacturing process improvement, mentoring, and consulting.

00:00 Knowing When You're in the Wrong Direction

07:44 What is Data Readiness in AI?

12:49 Where Do You Start with AI Data?

17:43 AI Implementation in Your Business

26:11 Microsoft Copilot is Overtaking!

37:04 AI Enterprise Adoption

43:53 Leveraging AI for Big Changes

1. Website: https://www.proarch.com/
2. LinkedIn: https://www.linkedin.com/in/santoshkaveti/

• Website: https://tairi.co

• LinkedIn: https://www.linkedin.com/in/mwormgoor

• Instagram: https://www.instagram.com/markwormgoor

• Business Email: [email protected]