Recorded live from the Ai4 conference floor in Las Vegas, Sam sits down with Dilo Wijesuriya, President & COO of ARC Document Solutions, to talk about the unglamorous but essential layer of enterprise AI: getting decades of paper archives into a format models can actually learn from.

What's Covered:

The Technology — ARC holds patents for OCR on wide-format documents (architectural drawings, engineering blueprints) that standard scanning tools can't accurately process, backed by a 200-person engineering team in India.

Security & Compliance — SOC 2, SOC 3, ISO 27001, and HIPAA compliant, running on AWS — built for regulated industries like healthcare and financial services.

Will Paper Disappear? Dilo's view: not for a long time. Most organizations' most critical institutional knowledge still exists only on paper, meaning today's LLMs simply can't learn from it yet.

The Book Destruction Debate — A direct response to recent controversy over companies destroying physical books after digitizing them, and why ARC's non-destructive robotic scanning preserves originals for high-value collections at universities, libraries, and museums.

Looking Ahead — Why Dilo believes the next competitive advantage for most enterprises isn't a better model — it's finally accessing the data already sitting in their own archives.

Key Quote: "The challenge isn't finding more data. It's making existing information accessible."

Connect with Dilo and ARC:

Dilo Wijesuriya: https://www.linkedin.com/in/dilo-wijesuriya/

ARC Document Solution: https://www.e-arc.com/

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#Ai4Conference #DocumentDigitization #AIReadyData #EnterpriseAI #OCR #AsembleAI

This episode was recorded live from the Ai4 conference podcast pavilion, where host Mac Goswami sat down with Kathryn Harrison, Global VP of Strategy in AI Commercialization at Concentrix — and an exited founder who built and sold the B2B SaaS platform MakePay, founded Deep Trust Alliance, and previously helped lead IBM Blockchain — about what it actually takes to turn AI into measurable value across a global enterprise.

What's Covered:

Humans Plus AI, at Global Scale — Concentrix runs customer and technical support across 75 countries and 150 languages. Kathryn makes the case that the future workforce isn't AI replacing people — it's humans plus AI and automation — and what that looks like across 400,000 employees with segmented AI access.

Three Rules for Commercializing AI — Kathryn's framework for doing it at scale: start with outcome-based use cases, redesign the work instead of bolting AI on, and build in guardrails, integration, compliance, observability, and humans-in-the-loop. Plus why she frames "tokenomics" as capital allocation.

The Agentic Operating System — How Concentrix uses agentic workflows to recruit and onboard 50,000 hires a year, with a 21-day implementation goal — a real production system, not a demo.

From Pilots to ROI — Why most AI stalls before it delivers, how to actually measure return, and where enterprise AI spend most often goes wrong.

The Deepfake Threat — Drawing on her work founding Deep Trust Alliance, Kathryn on the rise of deepfake-driven fraud, how it differs from traditional cybersecurity, and the broader societal risks.

The Coming Shakeout — Orchestration across messy client tech stacks, consolidation among AI startups, and where Kathryn sees AI and automation heading next.

Connect with Kathryn:
LinkedIn: https://www.linkedin.com/in/kathrynannharrison/
Concentrix: https://www.concentrix.com/
Deep Trust Alliance: https://www.deeptrustalliance.org/

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#Ai4Conference #AICommercialization #Deepfakes #AgenticAI #Concentrix #AsembleAI

This episode was recorded live from the Ai4 conference podcast pavilion, Sam sat down with Alex Zhavoronkov, Founder & CEO of Insilico Medicine, about what it actually takes to turn AI-generated molecules into approved drugs.

What's Covered:

From Laughed-Out-of-the-Room to 33 Candidates — Alex pitched generative AI for drug design in 2015 and got dismissed. Today: 33 developmental candidates in six years, zero failed toxicity studies, and deals with Eli Lilly, Takeda, Servier, and SK Bio at a pace of nearly one per month.

The Real Bottleneck — "It's not about a story. Many people in our field love to tell a story, but they don't have a single drug in the clinic discovered by AI." Alex's direct take on separating hype from results in AI drug discovery.

A Lucky Breakthrough — The story of how Insilico stumbled onto a novel, non-opioid pain mechanism that outperformed morphine in animal testing — now targeting a $70 billion market.

Why Abu Dhabi — Not for speed, but for geopolitical neutrality. Alex explains why Insilico built a 60-person AI lab in the UAE, and how two drugs now trace their origin to the Middle East for the first time in modern history.

Quantum-Generated Drugs — A December 2025 Nature Biotechnology cover story: a molecule generated on a real IBM quantum computer, validated experimentally, with the University of Toronto.

Pharmaceutical Superintelligence vs. AGI — Where Alex thinks AI drug discovery already stands, and why he draws a hard line between a useful scientific partner and the "conscious AI God" version of AGI.

Key Quote: "In terms of pharmaceutical superintelligence, we're very close to being there. In terms of AGI - the future AI God - we're still not there, and we might never get there."

Connect with Alex:

LinkedIn: https://www.linkedin.com/in/zhavoronkov/

Insilico Medicine Website: https://insilico.com/

Alex's published manuscript about longevity medicine in the Nature journal: https://www.nature.com/articles/s43587-020-00020-4

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#Ai4Conference #InsilicoMedicine #DrugDiscoveryAI #Longevity #AsembleAI

Shadow AI is costing organizations $10.3 million a year—more than malicious insider threats combined. Employees are using AI tools nobody approved, on data nobody's tracking, and most leadership teams have no idea it's happening at this scale.

Banning AI doesn't work. You can't solve this with another policy PDF nobody reads. You need real behavioral change.

In this episode, hosts Sam Dey and Mac Goswami sit down with Kate Marshall-founder of TheGrai and author of AI at Work—to expose why most enterprise AI rollouts fail at the most critical layer: getting people to actually adopt and stick with new tools and processes.

What You'll Learn:

🔹 The $10.3M Shadow AI Problem — What that number actually represents and why banning AI just drives it underground

🔹 The Maturity Model Trap — Why organizations get stuck between Level 1 (Awareness) and Level 2 (Shadow AI), with leadership presenting vendor demos while employees silently use unapproved tools

🔹 Why Generic Training Fails — The fatal flaw of all-hands lunch-and-learn sessions and what role-specific, sticky AI training actually looks like in practice

🔹 The Habit Layer™ Framework — Kate's proprietary methodology for turning one-time training into lasting behavior change

🔹 Data Hygiene as the Foundation — Why cleaning up your downloads folder, emails, and redundant files is where AI transformation actually begins

🔹 The Book: AI at Work — Why Kate wrote a 3-chapter workbook for non-technical professionals instead of another theory-heavy guide

Kate's Closing Insight: "Adoption is not a training day. It's a habit. You have to give employees not just access to tools, but time, space, and role-specific guidance to actually learn how to use them."

Key Takeaway: The gap between knowing about AI and actually using it effectively is the difference between organizations that transform and those that waste millions on failed pilots.

Connect with Kate Marshall: Website: katemarshall.ai LinkedIn: https://www.linkedin.com/in/kate-b-marshall/ Book: AI at Work

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#ShadowAI #AIAdoption #HabitLayer #AIatWork #ChangeManagement #EnterpriseAI #AsembleAI

"AI native software development" gets thrown around everywhere right now—and almost nobody can define it clearly. Not a chatbot bolted on. Not Copilot autocomplete. We mean production-grade systems where AI agents write, orchestrate, and ship the work end-to-end.

In this episode, hosts Sam Dave and Mac Goswami sit down with Mohamed Faker, Engineering Leader, Financial Services AI at Vanguard Group and co-founder/CTO of Hirin, a fractional leadership hiring platform built almost entirely by orchestrating specialized AI agents.

Key Insights:

• What AI-Native Actually Means — Every line of code in Hirin was AI-produced. Mohamed's role: architect, decision-maker, final say on direction—not the one typing code.
• From Solo Orchestrator to Manager of Agents — How he evolved from manually prompting individual AI chats (architect, UX expert, engineer) to building agent hierarchies with sub-agents and dedicated "audit" agents reporting directly to him.
• Where Agents Fail — Spotting when an agent burns tokens without progress, takes conversations sideways, or simply isn't suited to the task—and knowing when to stop.
• Validation at Scale — Building internal "audit department" agents that verify other agents did exactly what was asked, nothing more, nothing less.
• Product Management Is the New Core Skill — Knowing how to break down features, prioritize by dependency and complexity, matters more than knowing how to code.
• Biggest AI Adoption Mistakes — Rushing to adopt AI without defining real ROI, plus strategies that fail because the workforce isn't trained or willing to execute them.
• Human-AI Collaboration — Why the human must always stay in the loop as critical thinker and decision-maker, even as the agent-to-human ratio shifts dramatically.

The Horse-and-Carriage Analogy — Entire industries can disappear in 15 years, but the people who adapted earned more by managing the new technology rather than resisting it.

Mohamed's takeaway: "The future is you managing a subset of AI agents. Think about it-you're going to have multiple versions of yourself working together."

Connect with Mohamed Faker: https://www.linkedin.com/in/mohamed-faker/

Check out Hyern: https://hyern.com/

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#AINative #MultiAgentAI #SoftwareDevelopment #AIAdoption #ProductManagement #AsembleAI

97% false positives. Millions of alerts daily. Security tools that can't keep up. The threat landscape has outpaced traditional security operations—and Agentic AI is the answer.

In this episode, hosts Mac Goswami and Sam Dey sit down with Ramya Ganesh, Top 50 Women Cybersecurity Leads in the US and AI leader at Cisco, to break down how autonomous AI agents are transforming cybersecurity from detection to response.

Key Insights:

Multi-Agent Systems Beat Single Models — Like a hospital with specialists, multiple focused agents outperform one generalist AI. Modular, scalable, explainable, resilient.

The Future SOC — Not humans vs. AI, but humans supervising teams of AI agents handling continuous telemetry while analysts focus on strategic decisions.

Agentic AI vs. AI-Assisted Tools — Speed, autonomy, and cross-system correlation distinguish today's agentic platforms from yesterday's alert dashboards.

POC to Production — Most AI initiatives fail because they start with technology, not business problems. Success requires measurable metrics and governance discipline before deployment.

For Women in Tech — Stay curious, experiment, share what you build publicly. Imposter syndrome is real but community and visibility accelerate growth.

Ramya's takeaway: "The companies seeing the greatest AI success aren't those with the most advanced models—they're the ones with the strongest discipline around AI adoption."

Connect with Ramya: https://www.linkedin.com/in/ramya-ganesh-082bb231/

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#AgenticAI #Cybersecurity #WomenInTech #SOC #AsembleAI

What does it take to call out billion-dollar healthcare AI companies when the system is rigged against whistleblowers?

In this episode of Inside Assemble AI, hosts Sam Day and Mac welcome Sergei Polevikov, PhD-trained data scientist, AI entrepreneur, author of the widely-read Substack newsletter AI Health Uncut, and co-host of Digital Health Inside Out. Sergei has spent years investigating irregularities in healthcare AI, from inflated product claims and misleading adoption reports to the structural VC incentives that allow fraud to fester.

This is one of our most candid conversations yet — covering the 10 patterns that predict healthcare AI failure, why the real AI adoption rate in healthcare is nowhere near what industry reports claim, and why human-in-the-loop remains an essential safeguard regardless of how capable foundation models become.

TOPICS COVERED:

→ How Sergei went from healthcare AI founder (WellAI / Chart2Chart) to fraud investigator — and why transparency, not scandal, drives his mission.

→ His 10 healthcare tech failure patterns, including: the Chinese wall between management and teams, investors-as-customers conflicts of interest, smoke-and-mirrors technology, champagne-and-cocaine financial mismanagement, toxic code of silence, founder extortion, and celebrity protection schemes.

→ Why surveys from firms like Menlo Ventures and McKinsey dramatically overstate AI adoption — and what US Census Bureau data covering 30,000+ smaller healthcare organisations actually shows.

→ The structural reason why incumbents like Epic, Optum, and Cigna are disincentivised to build genuinely innovative AI products — and why startups like Abridge are winning despite the odds.

→ What's genuinely working in healthcare AI right now: AI scribes (done well), drug discovery, genomics, and protein structure modelling.

→ His advice for founders entering the healthcare or pharma space: protect your mission when VC money arrives, read every clause in your operating agreement, and choose partners who care about patients — not just their LPs.

RESOURCES & LINKS: 1. "AI Health Uncut" Substack: FixHealth.ai

2. Advancing AI in Healthcare: A Comprehensive Review of Best Practices: https://www.sciencedirect.com/science/article/abs/pii/S0009898123003212

3. "Digital Health Inside Out" podcast: https://www.youtube.com/@DigitalHealthInsideOut

CONNECT WITH ASSEMBLE AI:

Subscribe on Apple Podcasts, Spotify, iHeartRadio, and Amazon Music. Follow our YouTube channel and Substack newsletter for more deep dives into AI's real impact across industries. Have a topic you'd like us to explore? Reach out — we welcome new voices and fresh perspectives.

Keywords: healthcare AI, AI fraud, digital health, VC pump and dump, Babylon Health, Olive AI, Theranos patterns, AI scribes, Epic health, healthcare startup, AI adoption, human in the loop, AI compliance, healthcare innovation

68.5 billion euros in EPL betting annually. 1.4 million data points per match. Soccer sits at the absolute center of the AI revolution, and it's transforming the world's most popular sport from officiating to tactical analysis.

In Episode 2 of our "AI in Sports Analytics" series, hosts Sam Dave and Mac Goswami explore how AI fundamentally changed soccer from 2020-2025. Revolutionary Technology:

Semi-Automated Offside Detection (EPL 2024-25): Calibrated cameras + AI algorithms measure player positions with centimeter-level precision. Pioneered at 2022 Qatar World Cup, now standard across elite leagues. Processes data faster than humans, eliminating decades of controversial calls.

Player Tracking: Optical systems track each player 25x/second, detecting invisible tactical patterns. Game-changer: Standard TV footage now generates tracking data previously requiring expensive dedicated cameras. Smaller-budget teams access insights once reserved for Barcelona, Manchester City, Bayern Munich.

Match Prediction: 69-78% accuracy with ensemble models. Challenge: Soccer is harder to predict than basketball/baseball due to lower scoring and higher randomness. One lucky deflection can decide a match despite dominating possession. Real-World Impact:

Tactical Analysis (March 2025 study): Real-time computer vision tracks all players, ball, formations simultaneously. Coaches see which tactical adjustments opponents made in the 67th minute three weeks ago and how they affected passing networks.

Large Events Model (2024): Deep learning framework simulates games from any state. Test tactical approaches against AI-simulated opponents before stepping onto the pitch.

Economic Impact: Sports analytics market: $1.03B (2024) → $2.61B (2030). AI-powered betting analytics provide sophisticated predictions. The Reality:

AI reveals tactical sophistication fans never saw. That perfect through ball required reading three defenders' positioning, understanding striker's running profile, executing with millimeter precision. AI helps us see genius, not replace it.

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Next: Baseball AI revolution

#SoccerAnalytics #AIFootball #EPL #SportsAnalytics #AsembleAI