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forcing autonomous solutions, this conversation exposes the urgent tensions between technological capability and outdated oversight systems.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

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/⁠

Ryan Heath interviews Peter Kant, CEO and co-founder of Enabled Intelligence, about revolutionizing AI data labeling through neurodiverse talent.

Peter shares how his company solves a critical bottleneck in AI development — high-quality labeled training data — while creating meaningful employment for neurodiverse individuals and people with disabilities.

From achieving 95% accuracy rates compared to the industry standard of 70% to developing thin AI models for edge deployment, this conversation reveals how diversity in human cognition creates more robust, efficient, and representative AI systems that benefit both national security and commercial applications.

Key Takeaways

Neurodiversity Drives AI Quality and Efficiency

Enabled Intelligence's workforce is over 50% neurodiverse or persons with disabilities, leveraging hyperfocus, pattern recognition, and attention to detail — delivering 95% accuracy in data labeling versus the 70% industry standard, Kant says, while processing data two to three times faster than typical workforces.

High-Quality Training Data Reduces AI Costs Dramatically

Better labeled data consumes less compute power. When training data contains errors, AI systems must learn workarounds, while representative, accurately labeled data creates lighter, more efficient models that can operate at the "edge" without massive infrastructure.

Brain Diversity Creates More Representative AI

Successfully mimicking human thought through AI means mimicking more than software developers from Stanford. By incorporating neurodiverse perspectives in data labeling, Enabled Intelligence's training data better represents the spectrum of human cognition, resulting in more reliable AI models.

Specialized AI Tools Are the Growth Frontier

Enabled Intelligence has expanded into model fine-tuning and development, creating purpose-built, lightweight AI tools for specific business needs, from proposal writing to electronic medical record analysis.

Professional Workforce Model Pays Off

Higher labor costs in the U.S. are offset by high retention rates, and low error rates, which delivers enough efficiency and stability to make the economics work.

Hyperspectral Imaging Unlocks Hidden Intelligence

By combining hyperspectral satellite imagery — capturing roughly 220 different light spectra — with AI analysis, previously impossible applications become feasible. From identifying lithium mines and monitoring deforestation to detecting camouflaged military assets, AI now processes what was impossible or previously very labor-intensive to identify.

Chapter Timestamps

[00:00] Introduction and company mission

[02:00] Origin story at Stanford Research Institute

[04:00] The data labeling bottleneck problem

[06:00] Israeli cyber battalions inspiration

[08:00] Economics of neurodiverse workforce

[10:00] Accuracy rates and efficiency gains

[13:00] Model fine-tuning and specialized AI

[17:00] Hyperspectral imagery explained

[22:00] Company expansion

[24:00] Recruiting and training approach

Peter Kant's computing background is grounded at Stanford Research Institute (SRI International), where Peter identified a critical gap in the AI ecosystem: the lack of access to reliable, accurately labeled training data, particularly for classified and sensitive applications.

Drawing inspiration from Israeli Defense Forces' cyber battalions that employed neurodiverse soldiers, Peter built Enabled Intelligence with a workforce that is majority neurodiverse or people with disabilities. The company has expanded beyond data labeling into AI model fine-tuning and development, creating specialized, lightweight AI tools for both defense and commercial applications. The company recently doubled in size over two months and is expanding operations from its base to St. Louis, with interest from NATO countries.

Connect with Peter Kant

https://enabledintelligence.net/

https://www.linkedin.com/in/peterkant4/https://enabledintelligence.net/our-people/

In this episode of AI Leadership Lab, host Ryan Heath sits down with Dan Morrison, CEO of StoryVenture and strategic communications professor at Johns Hopkins University, to explore how storytelling remains humanity's most powerful tool even as AI transforms the communications landscape.

Drawing from his career spanning Bloomberg, IBM, the State Department, OECD, and Pew Research Center, Dan argues that AI hasn't changed the fundamental principles of persuasion that Aristotle identified 2,000 years ago.

 From discussing the parallels between the dot-com bubble and today's AI revolution to exploring the US-China AI narrative competition, this conversation offers communicators a roadmap for leading their organizations through technological transformation while keeping human creativity at the center.

Key Takeaways

Storytelling Fundamentals Never Change

Despite AI's transformative power, the core elements of persuasive storytelling — Aristotle's ethos, pathos, and logos —remain relevant today. Humans must still craft authentic narratives with clear beginnings, middles, and ends that tap into emotion, establish credibility, and demonstrate logic.

AI Should Be Your Second Draft, Not Your First

The most effective use of AI in creative work comes in the middle of the process, not at the beginning or end.

Technology Wins Battles, Narratives Win Coalitions

In the global AI race between the US, China, and Europe, technical superiority alone isn't enough. The US may have advantages in external trust and coalition-building despite internal polarization, while China's infrastructure and scale advantages are offset by challenges in persuading other nations to adopt its AI vision. Europe risks repeating internet-era mistakes by over-regulating without matching innovation.

Domain Expertise Becomes More Valuable, Not Less

AI doesn't diminish the value of years of professional experience — it amplifies it. Seasoned professionals can immediately spot AI hallucinations and guide AI tools more effectively through sophisticated prompting.

Communicators Have a Once-in-a-Generation Leadership Opportunity

Communications professionals should seize the moment by becoming their organization's leaders in AI experimentation and positioning themselves at the center of organizational transformation.

Build Trust Before You Need It

In an era of rapid misinformation spread, organizations must proactively control their narratives. When attacks come, third-party validators who already trust you will come to your defense — but that trust must be earned long before a crisis hits.

Chapter Timestamps

[02:00] Where data and diplomacy intersect

[04:00] What doesn't change with AI in storytelling

[06:00] Using AI in the creative writing process

[09:00] Writer's block and AI as a thinking partner

[11:00] Trust and credibility in legacy institutions

[15:00] The US-China AI narrative competition

[18:00] Europe's challenge: Innovation vs. regulation

[20:00] Finland's approach to combating misinformation

[23:00] Essential skills for communicators in the AI era

About the guest

Dan Morrison is the CEO of StoryVenture, a strategic communications firm, and teaches strategic political communications and persuasion at Johns Hopkins University. His career has consistently positioned him at the intersection of data, diplomacy, and storytelling, spanning roles as a financial journalist at Bloomberg (where he covered the dot-com boom and bust), speechwriter and communicator at IBM, and communications leadership positions at the OECD in Paris, the U.S. State Department, and Pew Research Center.

Dan brings a unique perspective to AI's impact on communications by drawing parallels between today's AI revolution and the internet era. His teaching focuses on timeless persuasion principles while helping students and clients navigate how AI tools can enhance rather than replace human creativity. Dan is also a novelist and screenwriter

⁠LinkedIn⁠

⁠StoryVenture⁠

In this episode of AI Leadership Lab, host Ryan Heath sits down with Larissa Schneider, COO and co-founder of Unframe AI, to discuss how her company is revolutionizing enterprise AI adoption. Larissa shares the origin story of Unframe, their unique "try before you buy" approach, and how they're helping Fortune 500 companies move from one AI use case to 17+ in just a few quarters.

Key Topics:

• Why most AI vendors are taking value instead of providing it

• The "Lego building blocks" approach to enterprise AI

• How to get unstuck from data paralysis

• Moving from 18-month consulting engagements to 45-minute discovery calls

• Building a customer-first culture in a global startup

• The future of AI in the workplace

More about the guest: Larissa Schneider is the COO and co-founder of Unframe AI, a platform that helps enterprises implement AI solutions quickly and efficiently. Previously, she worked at Noname Security and spent six years at Nutanix in enterprise sales and marketing. Larissa and her co-founders launched Unframe in early 2024 to address the gap between AI hype and actual enterprise value delivery.

Timestamps

[00:00] Intro - The try-before-you-buy AI model

[01:03] The origin story of Unframe AI

[03:05] The "Lego building blocks" approach to AI

[05:34] Breaking through data paralysis

[07:31] The overwhelming AI landscape in 2025

[09:30] Balancing hype with engineering truth

[12:19] How enterprises interface with the Unframe platform

[14:00] What Unframe got wrong initially and how they pivoted

[15:59] Building a customer-first culture

[17:40] Global adoption trends and surprises

[20:00] Ethical considerations and responsible AI

[22:01] The future of AI leadership, AI use in board meetings

[24:25] Where AI will be in 3-5 years

Key Quotes

"There's no other industry that can say, sure, I'll build it for you. You can try it and only if you like it, you'll pay me on an outcome-based pricing model."

"If a human can interact with it, the AI is smart enough to figure it out as long as it's tailored in the right direction."

"Software should fit the humans and the enterprise processes that they have evaluated, not the other way around."

Resources & Links

• Unframe AI: https://www.unframe.ai/

• Connect with Larissa Schneider: https://www.linkedin.com/in/schneiderlarissa/

• Ryan Heath Consulting: ryanhealthconsulting.com

In this episode of AI Leadership Lab, host Ryan Heath sits down with Miriam Vogel, President and CEO of EqualAI, to explore the critical intersection of AI governance, literacy, and trust.

With half of Americans more concerned than excited about AI, Miriam offers a roadmap for building responsible AI systems that deserve public trust.

From discussing the litigation boom around AI to sharing real-world examples of companies getting it right, this conversation provides actionable insights for leaders navigating the AI revolution.

Trust is Earned, Not Assumed

AI adoption requires governance and transparency. Companies that deploy AI responsibly—with CEO involvement and clear accountability—see significantly higher trust and adoption rates than those that don't.

AI Literacy is Essential

Most people don't realize they're already using AI daily through GPS, news feeds, and streaming services. Closing the literacy gap requires acknowledging fears, explaining mitigation strategies, and demonstrating realworld benefits.

Speed Up Responsibility, Not Just Innovation

Rather than slowing down AI development, leaders should accelerate responsible practices. Existing laws already apply to AI, and litigation has increased six-fold over six years—expected to double or more.

Good AI Hygiene is Universal

Smart companies across industries—from banks to consumer goods—are converging on similar best practices: transparency, accountability, employee involvement, and continuous monitoring for model drift and new use cases.

Leadership Matters

Organizations that involve senior executives in AI rollout, prioritize employee upskilling, and treat workers as ambassadors rather than obstacles see dramatically better outcomes in both adoption and innovation.

Miriam Vogel is the President and CEO of EqualAI, a nonprofit organization dedicated to promoting artificial intelligence governance. A former policymaker, lawyer, and general counsel, Miriam brings practical expertise in helping senior executives, boards, and organizations implement responsible AI practices. She works with leading companies across industries—from financial institutions to consumer goods—advising on governance frameworks, risk mitigation, and building trust through transparency and accountability.

Miriam is the co-author of "Governing the Machine" (released October 28th), which examines AI gone wrong while spotlighting governance done right, showing that we don't need to slow innovation—we need to speed up responsibility. The book distills lessons from Microsoft, Google DeepMind, PepsiCo, and Accenture, as well as regulators from Singapore to the United States, giving executives a concrete, global playbook for safe, effective adoption. Her work emphasizes the inseparability of AI governance and AI literacy, viewing them as "hand in glove" necessities for successful AI adoption.

EqualAI Website: https://www.equalai.org/about-us/leadership/miriam-vogel/

Book: "Governing the Machine" - https://www.bloomsbury.com/au/governing-the-machine9781399426275/

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

How AI Will Replace Your Financial Advisor

In this episode of AI Leadership Lab, host Ryan Heath sits down with Fahad Hassan, CEO and co-founder of Range, to discuss how his company is building a fully autonomous AI-powered wealth management platform.

Fahad shares why he spent five years deeply understanding the financial advisory industry before automating it, how Range is disrupting century-old fee structures, and why he believes 99.9% of Americans won't need human financial advisors within the next few years.

Key Topics:

• The "Uber to Waymo" journey: Starting with human advisors to build fully autonomous AI

• Why the asset-based fee model is broken and how Range is fixing it

• Building AI agents that check each other's work for compliance and accuracy

• Navigating AI innovation in a heavily regulated industry

• Why radical transparency is Range's competitive advantage

• The path to a fully autonomous wealth management system by 2027-2028

Guest Bio

Fahad Hassan is the CEO and co-founder of Range, an AI-powered wealth management platform that's reimagining financial advice for everyday Americans. Rather than rushing to build an AI solution, Fahad and his co-founder David spent five years hiring financial advisors, getting SEC registered, and deeply understanding the wealth management ecosystem before systematically automating it. Range is backed by Gradient Ventures (Google's AI-focused fund) and Caffeinated Capital, and is on a mission to make high-quality financial advice accessible, transparent, and affordable through AI.

Timestamps

[00:00] Intro - Why AI is better than humans at financial advice

[01:00] How AI is embedded throughout Range's platform

[01:58] The five-year journey to understand wealth management first

[03:00] The Robinhood analogy: From stockbrokers to automation

[05:00] Why Range still has human advisors (for now)

[06:00] The horse-and-buggy to car transition

[07:00] Disrupting the percentage-of-assets fee structure

[09:00] Why the billable hours model won't survive AI

[12:00] The personalization advantage AI has over human advisors

[13:00] Radical transparency as a core value

[16:00] Building customer advocates who fight for you

[17:00] What AI laws should look like

[18:00] Why Gradient Ventures and Caffeinated Capital invested

[19:00] The bold pitch: No more humans in wealth management

[21:00] The 2025-2030 tsunami of AI transformation

[22:00] Future fundraising and doubling down on technology

Key Quotes

"Humans lose money left and right. They're wrong all the time. And the worst part about it is you can't back into why they gave you that decision. With technology you can do that."

"Most Americans end up paying their financial advisor $250,000 to $300,000 over the course of their lifetime in fees. And most advisors are parking you in the S&P 500."

"We're not gonna augment anything. We may do that temporarily, but our belief is in a fully autonomous agentic AI wealth system."

"I think the same tsunami is gonna happen between 2025 and 2030. You're gonna wake up and all of a sudden, no more flip phones. You don't have a choice."

"Software should be radically transparent. We have our pricing in big size 18 font right on range.com. You will not find that on Fidelity, Schwab... Their pricing is buried in size 8 font on page 27."

Extra resources

• Range: https://www.range.com/public/about-us

• Connect with Fahad Hassan: https://www.linkedin.com/in/fahadrange/

• Ryan Heath Consulting: https://www.ryanhealthconsulting.com

  
  

In this episode of the AI Leadership Lab, host Ryan Heath engages in a thought-provoking conversation with Ryan Steelberg, CEO of Veritone, a leading enterprise AI software company. The discussion delves into the evolution of AI in the ad tech industry, exploring how cognitive AI models have transformed the landscape of media and advertising.

Steelberg shares insights into the importance of structuring data and the role of AI in solving complex problems for enterprises. Listeners will gain valuable perspectives on the challenges and opportunities presented by AI in regulated industries and network-isolated environments.

Steelberg emphasizes the need for a clear understanding of business needs when integrating AI solutions, cautioning against the allure of shiny new technologies without a solid foundation. This episode is a must-listen for anyone interested in the intersection of AI, media, and business innovation.