AI models are becoming commoditized, but deploying AI systems that deliver real ROI remains hard. In this episode, Mehmet sits down with Bryan Wood, Principal Architect at Snorkel AI, to unpack why data-centric AI, evaluation, and domain expertise are now the true differentiators.

Bryan shares lessons from working with frontier AI labs and highly regulated enterprises, explains why most AI projects stall before production, and breaks down what it actually takes to deploy AI safely and at scale.

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👤 About the Guest

Bryan Wood is a Principal Architect at Snorkel AI, where he works closely with frontier AI labs and enterprises to design high-quality, AI-ready datasets and evaluation frameworks.

He brings over 20 years of experience in financial services, with a unique background spanning banking, engineering, and fine art. Bryan specializes in data-centric AI, programmatic labeling, AI evaluation, and deploying AI systems in high-compliance environments.

https://www.linkedin.com/in/bryanmwood/

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🧠 Key Takeaways

• Why AI success is less about models and more about data and evaluation

• How enterprises misunderstand ROI and why most projects stall before production

• The difference between benchmark performance and real-world trust

• Why evaluation must be bespoke, not off-the-shelf

• How frontier labs approach data as true R&D

• Why partnering beats building AI entirely in-house today

• What’s realistic (and unrealistic) about autonomous agents in the near term

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🎯 What You’ll Learn

• How to move from AI experimentation to production deployment

• How to design data that reflects real enterprise workflows

• How to identify where AI systems actually fail, and why

• Why regulated industries are proving grounds, not laggards

• How startups can overcome data and talent constraints

• Where AI is heading beyond today’s LLM plateau

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⏱️ Episode Highlights & Timestamps

00:00 – Introduction & Bryan’s background

02:30 – Why data is now the real AI bottleneck

05:00 – Models are commoditized. So what actually matters?

07:45 – Why AI evaluation is harder than building AI

11:30 – Enterprise misconceptions about AI readiness

15:10 – Hallucinations, RAG failures, and finding the real problem

18:40 – Why most AI projects fail to show ROI

22:30 – Partnering vs building AI in-house

26:00 – AI in regulated industries: myth vs reality

30:10 – Startups, cold start problems, and data moats

33:40 – Scaling data operations with small teams

36:00 – What’s next: agents, data complexity, and AI timelines

39:00 – Final thoughts and where AI is really heading

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📌 Resources Mentioned

• Snorkel AI – Data-centric AI and programmatic labeling: https://snorkel.ai/

• Enterprise AI evaluation frameworks

• Frontier AI lab research practices

• MIT studies on AI ROI and enterprise adoption

Live events generate massive attention, yet most venues have no idea who is actually attending. In this episode, Mehmet Gonullu sits down with Matt Zarracina, CEO and Co-Founder of True Tickets, to unpack the hidden infrastructure problem behind ticketing, identity, and audience ownership.

Matt shares how legacy ticketing systems optimized for transactions, not relationships, and why “shadow audiences” have become one of the biggest blind spots in live event tech. The conversation spans SaaS innovation in legacy industries, blockchain learnings, AI-driven personalization, and what it truly takes to build mission-critical infrastructure at scale.

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About the Guest

Matt Zarracina is the CEO and Co-Founder of True Tickets, a ticket custody and identity platform helping venues understand who is actually attending their events.

His background spans the U.S. Naval Academy, helicopter aviation, systems engineering, an MBA, M&A consulting at Deloitte, and corporate innovation leadership before founding True Tickets full-time in 2018.

https://www.linkedin.com/in/zarracina/

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Key Takeaways

• Why most venues only know 30–40% of their real audience

• How “ticket custody” differs fundamentally from ticket sales

• Why legacy ticketing systems were never designed for identity or post-sale visibility

• The real reason ticket resale abuse and bots persist

• How data unlocks personalization, donor growth, and long-term audience relationships

• Why mission-critical SaaS cannot “move fast and break things”

• Where AI fits next: fraud detection, pricing intelligence, and behavioral patterns

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What You’ll Learn

• What the “shadow audience” really is and why it matters

• How True Tickets integrates into legacy ticketing systems without replacing them

• Why frictionless UX is not always the goal and what “optimal friction” means

• How venues can reclaim ownership from secondary markets

• Lessons from building SaaS inside conservative, legacy industries

• Why consultants and operators can become strong founders

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Episode Highlights & Timestamps

(Approximate, optimized for Spotify & YouTube chapters)

• 00:00 – Introduction and Matt’s unconventional journey

• 03:45 – The origin of True Tickets and discovering ticketing’s blind spot

• 07:30 – Defining the “Shadow Audience” problem

• 10:45 – Bots, resale markets, and why legislation alone fails

• 14:00 – Real-world example: turning attendees into donors

• 17:45 – What True Tickets actually does under the hood

• 21:30 – SaaS in legacy industries and mission-critical systems

• 26:00 – Balancing security, friction, and user experience

• 30:45 – The future of ticketing: data, AI, and personalization

• 35:00 – Global expansion and market opportunity

• 38:30 – Founder lessons from consulting to scale-up CEO

• 43:30 – Final reflections and where to learn more

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Resources Mentioned

• True Tickets Website: https://www.true-tickets.com/

• ROI Calculator and Product Demo (available on True Tickets’ site)

• Super Founders by Ali Tamaseb

In this episode of The CTO Show with Mehmet, I’m joined by Ahikam Kaufman, Co-Founder and CEO of Safebooks.ai, a seasoned finance executive turned entrepreneur with deep experience across startups, public companies, and large-scale acquisitions.

We explore why finance has lagged behind other functions in digital transformation, how AI is fundamentally reshaping financial governance, and why the modern CFO is becoming a transformation leader, not just a financial steward.

This conversation goes beyond buzzwords and dives into real-world problems: broken audit trails, fragmented systems, compliance risk, and how AI agents can finally deliver real-time financial truth.

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👤 About the Guest

Ahikam Kaufman is the Co-Founder and CEO of Safebooks.ai.

He began his career in accounting, served as a CFO in Silicon Valley startups, experienced multiple acquisitions including by Hewlett-Packard and Intuit, and spent over a decade as an entrepreneur.

Today, Ahikam is focused on modernizing the Office of the CFO by applying AI to financial data governance, auditability, and compliance at scale.

https://www.linkedin.com/in/ahikam-kaufman-688310/

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🎯 Key Topics Covered

• Why finance was never designed for today’s data complexity

• The two biggest blind spots in modern financial organizations

• What “audit trail” really means and why it’s so hard to achieve

• How AI agents bridge structured system data and unstructured documents

• From quote to cash: tracing transactions across fragmented systems

• Why compliance failures are often data problems, not intent problems

• The evolving role of the CFO in the AI era

• Where humans still matter and where machines outperform

• Why AI makes regulation easier to meet, not harder

• Practical advice for founders building in finance and compliance

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🧠 Key Takeaways

• Finance teams deal with massive data but are not trained as data teams

• Fragmented systems create hidden compliance and cash-flow risks

• AI can monitor 100% of financial transactions, not just samples

• Real-time governance is now technically possible for the first time

• CFOs are becoming transformation leaders, not just scorekeepers

• The future of finance is continuous, automated, and exception-driven

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🎓 What You’ll Learn

• How AI changes financial accuracy from “material” to near-perfect

• Why most financial errors happen even when teams do “everything right”

• How AI reduces headcount pressure without removing human oversight

• What founders must understand before building in fintech or compliance

• How finance can finally get its own “single pane of glass”

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⏱️ Episode Highlights (Timestamps)

• 00:00 – Ahikam’s journey from CFO to AI founder

• 05:00 – The two unsolved problems in corporate finance

• 09:30 – Why audit trails break across modern systems

• 14:00 – What really goes wrong when financial data is wrong

• 18:30 – How AI understands contracts and financial documents

• 24:00 – Humans vs machines in financial decision-making

• 30:00 – The CFO’s evolving role in AI transformation

• 36:00 – Regulation, compliance, and AI realities

• 43:00 – Advice for founders building in finance

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🔗 Resources Mentioned

• Safebooks.ai

• Topics: AI agents, financial audit trails, CFO transformation, data governance