Episode Overview

In this episode of AI Leadership Lab, host Ryan Heath sits down with Ryan Steelberg, CEO of Veritone, to explore the practical realities of deploying AI in enterprises. With a deep history in ad tech and in structuring previously unstructured audio and video data, Steelberg offers a grounded perspective on AI adoption that cuts through the hype. From discussing the critical importance of data infrastructure to sharing insights on ROI measurement and the mistakes companies make when integrating AI, this conversation provides essential guidance for leaders who want AI solutions that actually work—not just shiny marketing promises.

Key Takeaways

Focus Data Infrastructure, Forget AI Magic

Most organizations struggle with basic data management and cloud migration before they can meaningfully apply AI. Companies must understand and embrace their data journey first—there's no skipping this step, regardless of how advanced the AI tools promise to be.

AI is a Tool, Not a Solution

When evaluating AI products, redact every mention of "AI" from the marketing literature and ask: why are you buying this software? The AI is just a component, like an engine in a car. Focus on whether the solution satisfies your well-defined needs, not whether it's labeled as "next generation" or "future proof."

Track Everything to Improve Everything

Smart AI deployment requires comprehensive tracking of how users interact with applications. This data reveals whether bottlenecks stem from the AI model itself or the application layer, enabling companies to improve both the technology and the workflow continuously.

Customized ROI Metrics Matter

ROI metrics must be tailored to specific use cases and business models. What drives value for a sports organization (speed to market for content) differs radically from what matters to a media company (ad revenue optimization), even when using the same technology stack.

Combine Experience with Fresh Perspective

Organizations need both veterans who understand traditional processes and newcomers who organically embrace AI tools, and communicate naturally with data.

Regulated Environments Require Specific AI Approaches

In secure or air-gapped environments like Department of Defense networks, you cannot invoke third-party AI models. Everything must be containerized and deployable within the secure environment.

Key Quotes

"Imagine taking a piece of marketing literature and redacting any word that mentions AI. Why are you buying this software solution?"

"Don't ever throw away your ore. You don't know where the gold or diamonds are gonna be materialized or processed through."

Chapter Timestamps

[00:00] Veritone's AI journey from ad tech origins

[02:04] Bringing structure to unstructured data

[04:02] Deploying AI in regulated industries

[05:17] Product roadmap evolution and customer feedback

[08:00] Common mistakes in AI integration

[10:06] Skills and upskilling challenges

[12:25] Measuring ROI in AI deployments

[16:00] Surprising customer use cases

[21:00] Smart questions for evaluating AI products

About the Guest

Ryan Steelberg is the CEO of Veritone. Steelberg's journey into AI began with a fundamental problem: how to target ads against audio and video content in an increasingly organic media ecosystem. This challenge led Veritone to develop sophisticated capabilities in transcription, object detection, and machine vision to bring structure to unstructured media content.

Under Steelberg's leadership, Veritone's major clients include NBCUniversal, iHeartMedia, the US Tennis Association, CNBC, and the Department of Defense.

Connect with Ryan & Veritone

https://www.veritone.com

https://linkedin.com/in/ryansteelberg/

About AI Leadership Lab

AI Leadership Lab interviews C-suite leaders about their AI journeys, covering what's working, where they're getting stuck, how they pivot, and the lessons they take from the losses.

Host: RyanHeathConsulting.com

In this episode of AI Leadership Lab, host Ryan Heath sits down with Dan Neely, CEO and co-founder of Vermillio, an AI platform for protecting and monetizing intellectual property.

Recorded live from Web Summit in Lisbon, this conversation tackles the critical challenge facing every creator in the AI age: how to protect your likeness and work and capitalize on new monetization opportunities.

From explaining the concept of likeness rights to discussing neural fingerprinting technology, Dan offers practical insights for any creator, IP owner (or organization that needs to use them) on how to navigate the intersection of AI, intellectual property, and co-creation.

Key Takeaways

Likeness is the New Frontier of IP Protection

Most creators focus on protecting their output (music, films, scripts etc) but overlook their likeness: their image, voice, and name.

In an AI world where anyone can prompt "create a song in the style of [creator name]," likeness becomes a critical asset requiring protection. This isn't just for famous creators; it matters for every person whose likeness can be synthetically recreated.

Protection gives options for Monetization

Once you've protected your likeness, you gain complete control over whether and how to monetize it. You can choose never to allow its use, or you can participate in the economics of AI-generated content. The key insight is seeing that this can deliver passive income — even at a tiny royalty rate — when you consider there are across trillions of AI transactions.

The Industry Needs Third-Party Infrastructure

Traditional fingerprinting and watermarking don't work in today's AI world. Neural fingerprinting technology offers an alternative, especially when it can detect what percentage of someone's IP exists in AI outputs, from 1% to 100%.

Studios, Platforms, and Creators Face Unclear Responsibility

The industry is still debating who bears responsibility for protecting talent: Is it studios who hire actors, platforms that enable content creation, or individual creators themselves?

Likeness rights have traditionally only been negotiated for specific projects (like marketing a movie), creating complexity as AI enables infinite use cases. The market is currently in a "land grab" phase similar to early internet advertising.

Co-Creation Will Democratize Creative Expression

The most exciting development is enabling fans to co-create with the content and creators they love—at scale and with proper licensing. This democratizes creativity, allowing people who couldn't previously draw or make music to create in amazing ways, while ensuring creators participate in the economic value generated by their likeness and work.

Chapter Timestamps

[00:00] First steps for protecting creative work and likeness

[02:33] Deep fakes and AI disruption with Sora

[04:42] Monetizing creative work beyond traditional models

[07:40] The maturity curve for understanding likeness rights

[10:03] Trace ID system and neural fingerprinting technology

[12:42] Advice for those overwhelmed by AI choices

[15:18] What's exciting about the future of AI co-creation

About the Guest

Dan Neely is the CEO and co-founder of Vermillio, a leading rights management platform that protects creators' work and likeness. His company has developed neural fingerprinting technology that can detect IP ingredients in AI-generated outputs in any given creation.

He has worked directly with major artists like David Gilmour of Pink Floyd to allow fans to engage with their favorite creators in licensed, economically fair ways.

Connect with Dan Neely & Vermillio

https://time.com/7012738/dan-neely/

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

About AI Leadership Lab

AI Leadership Lab interviews C-suite leaders about their AI journeys, covering what's working, where they're getting stuck, how they pivot, and the lessons they take from the losses.

Host: Ryan Heath

Website: RyanHeathConsulting.com

In this episode of AI Leadership Lab, host Ryan Heath sits down with Zak Ali, US General Manager of Finder, a fintech firm, to explore how the rise of AI-powered answer engines is fundamentally reshaping digital marketing and web traffic.

As ChatGPT and similar tools increasingly provide instant answers without requiring clicks to websites, Zak offers practical insights on adapting to this post-click economy, providing a roadmap for marketers and business leaders navigating the transition from traditional SEO to answer engine optimization.

Key Takeaways

The Post-Click Economy is Here

The future belongs to content requiring genuine human experience, expertise, and authentic perspectives that AI cannot replicate. Traffic patterns are fundamentally shifting away from simple fact-based queries toward content where real human insight adds irreplaceable value.

Small Language Models Are the Future

Rather than relying on massive general-purpose AI trained on the entire internet, specialized small language models (SLMs) trained on curated datasets deliver better, more efficient results. This approach addresses both environmental concerns around energy consumption and accuracy issues, while making AI more accessible and practical for specific use cases like medical diagnosis or financial analysis.

Authenticity Becomes Competitive Advantage

As AI-generated content floods the digital landscape with sameness, authentic human experiences and genuine perspectives will stand out more than ever. Companies and creators who lean into showcasing real expertise, original thinking, and unique voices will differentiate themselves in an increasingly homogenized content environment.

The Value Exchange Must Rebalance

AI systems cannot train themselves on their own output without degrading quality—they need human-created content. As AI potentially puts creators out of business, the value exchange will eventually tip back toward content creators, similar to how platforms like Cloudflare are introducing pay-per-crawl models that compensate publishers when AI systems access their content.

Smaller Players Can Win Through Agility

While large organizations may secure lucrative licensing deals with AI companies, smaller publishers and businesses have the advantage of nimbleness. They can adapt quickly to new formats, experiment with emerging platforms, and pivot strategies without the bureaucratic inertia that slows down major corporations in responding to rapid technological change.

AI Literacy Requires Immediate Investment

The lack of basic AI and media literacy represents a critical vulnerability, especially as countries like China invest heavily in teaching AI skills from elementary school. Success in the AI era requires intentional retraining programs and education initiatives rather than assuming market forces will naturally help workers adapt to displacement.

Episode chapters

[00:00] Welcome and the birth of a new industry

[02:46] How AI is touching every industry simultaneously

[03:16] The death of informational queries and web browsing

[05:50] Will AI need to pay creators like Google News?

[10:18] The post-click economy and digital ecosystem changes

[12:15] Authenticity as the antidote to AI sameness

[13:00] Privacy concerns and the ethics of AI data usage

[16:26] Who deserves credit in the age of AI-generated content

[23:22] What excites and worries Zak about AI's future

[26:04] Media literacy and AI fact-checking on social platforms

About the Guest

Zak Ali is the US General Manager of FinTech Finder, where he leads strategy and has become a leading voice in answer engine optimization (AEO), helping organizations adapt to a world where AI provides instant answers without requiring users to visit websites.

With deep expertise in SEO, digital marketing, and fintech, Zak brings a pragmatic perspective to the AI transformation.

Finder:⁠ https://www.finder.com⁠

Connect with Zak Ali on LinkedIn: ⁠https://linkedin.com/in/zak-ali-ab267777/⁠