Episode Overview

In this episode of AI Leadership Lab, host Ryan Heath speaks with Pari Parchi, Founder and CEO of Panorama Aero, about the critical infrastructure challenges facing America's airspace.

With the US still operating on World War II-era radar systems while drones proliferate and autonomous flight technology advances, Pari reveals where the private sector may need to take more airspace management into its own hands. From the regulatory gridlock preventing counter-drone technology to the looming pilot shortage forcing autonomous solutions, this conversation exposes the urgent tensions between technological capability and outdated oversight systems.

Key Takeaways

America's Airspace Runs on World War II Technology

U.S. airspace management still relies on infrastructure dating to World War II, with radar systems and radio control as the foundation. Most aircraft landings remain VFR (visual flight rules), meaning pilots land by sight rather than automated systems. Since the 2003 ATC NextGen bill aimed at modernization, only 16% of initiatives have been completed.

The Drone Regulation Paradox

If someone flies a drone into your backyard to look through your windows, shooting it down is illegal — but the drone operator usually faces no penalty. This regulatory gap, primarily under Federal Communications Commission jurisdiction, leaves Americans vulnerable to privacy violations and potential security threats. The U.S. is up to two years behind Ukraine, Israel, and China in drone and counter-drone technology development, partly because we're not dealing with these threats daily.

The Private Sector Will Lead Airspace Security

With federal agencies stretched thin and regulatory changes moving slowly, private sector organizations are developing their own airspace protection systems. Companies are deploying counter-drone sensors to protect critical infrastructure, airports, public events, and private property. While they may not be able to shoot down unauthorized drones, they can identify operators, track license plates, and locate individuals for enforcement action.

The Pilot Shortage Will Force Autonomous Flight

At $1,000 to $1,500 per day, human pilot costs for the smallest aircraft can be economically infeasible: think four- or six-seater eVTOL vehicles and flying cars. The global pilot shortage is therefore increasingly the inevitability of autonomous flight. The transition will likely start with reducing commercial aircraft from two pilots to one, with AI serving as a "backseat driver" co-pilot.

Humans and Machines See the Airspace Differently

While AI can handle routine flight paths, human pilots provide irreplaceable value during emergencies, mechanical failures, and unexpected weather conditions. Having physical presence in the aircraft versus ground-based command and control is like attending the Super Bowl in person versus watching on TV.

Special Mission Aircraft Protect More Than We Realize

Turboprop aircraft and business jets serve critical public safety functions: surveillance, reconnaissance, mapping, medevac, and firefighting. These "special mission" or "multi-mission" aircraft use the airframe as a technology chassis, implementing specialized equipment for essential operations. The complexity and cost of maintaining these assets is widely underestimated.

About the Guest

Pari Parchi and Panorama Aero specialize in the acquisition and management of specialized aerospace assets. Through defense, aerospace, and early-stage investing experience, Pari brings a unique global perspective to airspace management challenges, having lived and worked across four continents.

Panorama Aero focuses on special mission and multi-mission aircraft — turboprop aircraft and business jets modified for specific purposes including surveillance, reconnaissance, mapping, medevac, firefighting, and other critical operations.

LinkedIn: linkedin.com/in/pariparchi

Company: panorama.aero

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