The CTO Compass

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]

Revenue systems are becoming a major source of complexity in modern SaaS and AI companies. As CPQ, pricing, quoting, and billing evolve, they often slow decisions and execution instead of enabling growth.

In this episode of The CTO Compass, Tina Kung, Founder and CTO of Nue, shares lessons from building revenue systems at Salesforce, Zuora, and Neocrm. The conversation explores why revenue infrastructure shapes strategic clarity and boardroom impact, why most enterprise AI stops at summaries, and how CTOs can design systems that scale without slowing the business down.

You'll learn:

1. Why CPQ and quote-to revenue systems often slow decisions as SaaS and AI companies scale
2. How pricing flexibility and personalization create hidden complexity that impacts execution and strategy
3. Why revenue systems should be treated as core technical infrastructure with direct boardroom impact
4. What most enterprise AI gets wrong by stopping at summaries instead of removing operational work
5. How CTOs can simplify revenue workflows to improve clarity without triggering a large transformation project

00:00 Handling Business Overload

07:09 Simplifying the Process

14:47 The Hurdles of Implementation

18:31 Problems with Old Contracts

23:11 OpenAI's Strict Timeline

25:54 Complexity under Simplicity

31:26 Implementing AI

35:40 What's Next?

Tina Kung has over 20 years of experience in the opportunity-to-cash space.

Before co-founding Nue.io, she served as VP of Products and Engineering at Neocrm — the largest CRM in China — and held engineering leadership roles at Salesforce CPQ, Zuora, and Oracle CPQ.

As Nue’s CTO, Tina is reimagining how modern businesses run revenue. She founded Nue to solve the painful complexity she witnessed firsthand at companies like Salesforce and Zuora.

Her vision is clear: to build a unified, AI-powered platform that makes revenue operations elegant, intelligent, and beautifully simple.

Under her technical leadership, Nue is powering the next generation of SaaS and AI companies with a Salesforce-native quote-to-revenue platform that unifies pricing, quoting, billing, and analytics in real time.

1. LinkedIn: https://www.linkedin.com/in/tinakung/
2. Website: https://nue.io

Most CTOs and tech leaders don’t think they’re the bottleneck. They think they’re being responsible: staying close to the details, protecting quality, moving fast.

But from a buyer’s perspective, that same behavior often is a sign of risk: decisions that don’t scale, teams that depend too heavily on one person, and execution that quietly slows down as the organization grows.

In this episode of The CTO Compass, Mark sits down with David Hori, an operator-turned-acquirer with more than 25 years of experience scaling companies and taking three startups to exit. Today, David doesn’t just advise leaders, he buys businesses. That gives him a rare view on what breaks as companies scale, and why strong leaders so often become the constraint without realizing it.

Together, they unpack:

1. how CTOs and tech leaders unintentionally become bottlenecks
2. the difference between delegation and abdication
3. how to work effectively with visionary CEOs without drowning in continuously changing priorities
4. why AI often amplifies leadership problems instead of fixing them
5. and what buyers look for when evaluating whether a business can scale or exit

00:00 When the Leader is the Bottleneck

07:58 Being Open to Change

15:36 Handling Visionaries

19:12 How to Approach Delegation

26:27 AI in Today's Business Landscape

37:14 Is Your Business Going to be Bought?

39:55 Realizing Where You're at!

David buys profitable local businesses, typically with 10-200 employees or $3–$15M in revenue. He is not private equity chasing a quick flip. He focuses on preserving what founders built, including their people, culture, and legacy.

He also advises a small number of established owners, but he thinks like a buyer, not a consultant. That means direct, honest assessments with no busywork and no slide-deck frameworks that fail in practice. Just proven systems that help owners step back or exit entirely.

Whether he is buying a business or advising an owner, David brings an operator’s perspective. Fast decisions, practical solutions, no fluff.

1. LinkedIn: https://www.linkedin.com/in/iamdavidhori/
2. Instagram: https://www.instagram.com/toplineops/
3. Topline Operators: https://www.toplineops.com/
4. YouTube: https://www.youtube.com/@toplineops