Episode Summary: In this compelling conversation, Nitin Mittal shares insights from his unique position as the AI strategy leader across Deloitte's global operations.

From scaling AI implementations across Fortune 500 companies to navigating the rapid evolution from predictive AI to generative AI, Nitin discusses the practical realities of enterprise AI adoption. He explores the critical importance of trust frameworks, the emerging role of agentic AI, and why he believes we're entering a transformative period where AI will fundamentally reshape how organizations operate and compete.

Key quotes

"Trust is not just a nice-to-have in AI — it's the foundation. Without it, even the most sophisticated AI system will fail to deliver value."

"We've moved from asking 'Can AI do this?' to 'How quickly can we scale AI to do this across our entire organization?'"

"The organizations that will win with AI aren't necessarily those with the best technology, but those with the best change management and cultural readiness."

"Agentic AI represents a fundamental shift—we're moving from AI as a tool to AI as a colleague."

Top themes

1. Trust is foundational - Organizations must establish robust trust frameworks before scaling AI

2. Culture drives adoption - Technology alone isn't enough; successful AI transformation requires cultural change

3. Generative AI is transformative - The shift from predictive to generative AI represents a step-change in enterprise capabilities

4. Agentic AI is emerging - Autonomous AI agents will be the next major wave of innovation

5. Change management matters - The human side of AI transformation often determines success or failure

   
   

About the guest

• Career path from engineering to leading global AI strategy at Deloitte

• Transition from traditional consulting to AI-focused leadership

• Nitin works with Fortune 500 companies to navigate the complexities of enterprise AI adoption. His focus on trustworthy AI and practical implementation has made him a sought-after voice on the future of AI in business.

  Nitin Mittal on LinkedIn - linkedin.com/in/nitinmittal0101

  Deloitte AI Institute - deloitte.com

#AI

#ArtificialIntelligence

#GenerativeAI

#AgenticAI

#EnterpriseAI

#DigitalTransformation

#Leadership

#Deloitte

#TrustworthyAI

In this episode of AI Leadership Lab, host Ryan Heath interviews Umesh Sachdev, CEO of Uniphore, live from the World Economic Forum in Davos. As the leader of a company serving over 2,000 customers globally, Umesh shares critical insights about the shift from AI experimentation to real business impact in 2026. The conversation explores how C-suite leaders are moving beyond the novelty of GPUs and LLMs to focus on outcome-as-a-service models, the importance of cost optimization across different AI use cases, and why the pace of decision-making has become the defining factor separating AI leaders from laggards.Key TakeawaysThe Era of AI Pilots is Over: Outcomes Matter NowIn 2026, the conversation has shifted from which GPU or LLM to use to what business transformation AI delivers. Companies that have figured out how to use AI as a growth enabler are starting to break away from the pack. One Size Does Not Fit All in AIDifferent use cases require different AI architectures. A real-time call center assistant needs sub-second response times with high-capacity GPUs, while CFO automation tasks can tolerate three-minute responses using smaller models on lower-capacity hardware. The key is matching infrastructure costs to the specific outcome required, rather than applying a uniform approach across all AI initiatives.AI Agents Must Work Within Existing WorkflowsThe thinking has evolved: companies need consistency for tasks repeated thousands of times daily. The 2026 breakthrough is making AI agents work reliably within current business structures rather than forcing organizational redesign.Open, Sovereign Architecture is Non-NegotiableClients demand flexibility to avoid vendor lock-in and the ability to adapt as new innovations emerge. More critically, especially outside the US, geopolitical developments are driving demand for sovereign AI architectures that ensure access cannot be cut off by any single government action. Speed of Decision-Making Defines AI LeadershipThe traditional playbook of research, analysis, and committee-based decisions is being discarded. CEOs across Fortune 500 companies recognize that moving at the speed of AI is essential to satisfy investors and Wall Street. The gap between companies that can execute with agility and those that cannot is widening dramatically.Chapter Timestamps[00:00] The Davos Reality Check: AI ROI in 2026[01:16] From Pilots to Business Transformation[01:34] Outcome-as-a-Service Business Model[02:03] Matching AI Architecture to Use Cases[03:00] Workflow and Organizational Design[04:25] Uniphore’s Product Roadmap and Platform Strategy[06:21] From Novelty to Business Basics[07:00] Leadership in the AI Revolution[08:30] Bringing the Workforce Along[09:37] Humans, Agents, and Sustainable Jobs[11:43] Near-Term Job Displacement vs Long-Term OpportunitiesAbout the GuestUmesh Sachdev is the CEO of Uniphore, a global AI platform company serving over 2,000 enterprise customers. Under his leadership, Uniphore has developed the Business AI Cloud, an open and sovereign platform that delivers enterprise-grade AI solutions with a focus on business outcomes rather than technical specifications. The platform runs multiple types of compute and LLMs, offering clients the flexibility to choose their technology components while maintaining security, scalability, and sovereignty.Connect with Umesh & UniphoreUniphore Website: https://www.uniphore.comAbout 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 HeathWebsite: RyanHeathConsulting.comResources MentionedUniphore Business AI Cloud - Open and sovereign AI platform that encompasses multiple types of compute and LLMs, delivering enterprise-grade security and scalability - https://www.uniphore.com

In this episode of AI Leadership Lab, host Ryan Heath sits down with Philip Rathle, Chief Technology Officer at Neo4j, to explore how graph databases are revolutionizing AI infrastructure and enterprise knowledge systems.

Philip reveals why understanding the relationships between data points is more powerful than having all the facts, and how companies like Google built trillion-dollar businesses on graph algorithms. From explaining knowledge graphs in plain language to discussing how graph-based retrieval can make AI more trustworthy and explainable, this conversation delivers actionable insights for leaders seeking to build more effective AI systems.

Takeaways

Relationships Matter More Than Facts

Understanding connections between data points often reveals more than the data itself. Philip demonstrates this with a striking example: knowing how friends-of-friends-of-friends behave is a better predictor of someone's behavior than having comprehensive facts about that individual person. This principle applies across business contexts, from customer 360 systems to organizational analysis.

The Real vs. Declared Org Chart

Graph technology can reveal an organization's true power structure by analyzing email patterns, Slack messages, and information flows. Companies are using this to identify single points of failure—like one person receiving all questions on a critical topic—and to facilitate warm introductions by mapping who knows whom across company boundaries.

Graph RAG Delivers Better Results with Less

By combining knowledge graphs with language models, companies are achieving superior answers while using two-thirds less data in context windows. This "graph RAG" approach queries a knowledge graph first, then feeds only the most relevant results to the model, resulting in faster responses, lower costs, and reduced energy consumption.

AI Systems Need Knowledge Layers, Not Just Language Models

Language models alone have fatal flaws for enterprise use: they hallucinate, lack company-specific data, operate as black boxes, and can't discern what information is appropriate for which purpose. Successful AI implementations complement LLMs with knowledge graphs that provide exact, explainable results while maintaining the context and causality that business users understand.

Explainability is the Path to Trust and Adoption

Graph-based systems enable accountability by providing traceable answers.

Timestamps

[00:00] Introduction

[01:12] Philip's journey from consulting to graph databases

[04:00] Facebook and Google as graph pioneers

[05:18] What is a knowledge graph?

[07:44] The true org chart: mapping real power structures

[09:30] Making AI more explainable and trustworthy

[14:13] Build vs. buy considerations for graph technology

[16:07] How graphs will reshape AI infrastructure

[18:08] Graph RAG and the future of AI applications

[20:00] Human impact: accountability and agency in AI

About the Guest

Philip Rathle is the Chief Technology Officer at Neo4j, a company that has been pioneering graph database technology and knowledge graphs for AI applications. Philip's career began in consulting, where he quickly became convinced that data serves as a mirror of business operations — the better your data, the better handle you have on your business. He built United Airlines' first passenger 360 system.

Connect with Philip & Neo4j

Neo4j Website: https://neo4j.com

LinkedIn: Search for Philip Rathle, CTO at Neo4j

Support the Show

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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/