In this episode, Yasmeen Ahmad, Managing Director of Product Management for Data & AI Cloud at Google Cloud, reveals why 80–90% of enterprise data is "dark" and untouched — and how Google Cloud is building the tools to finally unlock it. Yasmeen shares how BigQuery's new Knowledge Engine captures the invisible business context that human analysts have always carried in their heads, and why this semantic layer is the real unlock for enterprise AI in 2026.

Yasmeen breaks down how enterprises are scaling from 50 to 2,000 autonomous AI agents, why continuous evaluation (not unit testing) is the only way to keep agents trustworthy, and what Google learned from seeing 50% of its own code now written by AI. She also explains why 95% of AI pilots produce zero measurable ROI — and why companies that partner with a platform like Google Cloud see dramatically different results. Plus, her contrarian take on governance: it's not the brake, it's what lets you drive 150 mph into the bend with confidence.

Key Topics Covered

• Why 80–90% of enterprise data is "dark" unstructured data that GenAI can finally unlock
• How BigQuery's Knowledge Engine captures the invisible business context analysts carry in their heads
• The semantic layer: why the next big unlock is context, not just more powerful models
• How enterprises are scaling from 50 to 2,000 autonomous AI agents
• Intent-driven agentic AI: giving agents outcomes instead of step-by-step instructions
• Why continuous evaluation is replacing traditional unit testing for AI agents
• Google's internal AI adoption: 50% of code written by AI, 10% engineering efficiency gains
• Why 95% of AI pilots produce zero ROI and what changes that outcome
• AI governance as an accelerator — the "brakes that let you drive 150 mph" framework
• Why culture and founder mentality matter more than technology budget for AI success

Episode Timestamps

00:00 - Introduction and welcome

00:50 - Being Scottish in Silicon Valley and the power of community

03:13 - The career thread: curiosity, pivots, and getting outside your comfort zone

06:20 - What makes data fascinating: the hidden stories inside numbers

07:48 - Why data is the lifeblood of enterprise AI

10:19 - 80–90% of enterprise data is "dark" and untouched

12:59 - What BigQuery actually does (explained simply)

14:50 - The invisible work: knowledge layers and business semantics

17:38 - The agentic AI moment: agents that think, plan, and execute

20:41 - From 50 to 2,000 autonomous agents inside enterprises

22:07 - Why you can't evaluate AI agents like traditional software

25:46 - Signals of AI readiness: Google's 50% AI-written code and Honeywell's 30% efficiency gains

30:21 - Why 95% of AI pilots produce zero ROI

35:37 - Governance as a speed accelerator, not a brake

39:53 - Who's best poised to win: culture over budget

45:59 - Why do you do what you do?

Yasmeen's Socials:

LinkedIn — https://www.linkedin.com/in/yasmeenahmaduk/

Partner Links

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In this episode, we break down the "Agentic Brain" — the architecture behind AI agents that can think, plan, and execute multi-step workflows with minimal human oversight — and why the agentic AI market is about to explode from $7.8 billion to over $52 billion by 2030. We reveal how the smartest enterprises are already deploying autonomous agents that handle everything from customer service to internal operations, and why only 14% of companies have production-ready agentic solutions despite all the hype.

We go deep on what "bounded autonomy" actually means in practice, how multi-agent systems work (inquiries surged 1,445% in just one year), and why Gartner predicts 40% of enterprise apps will embed AI agents by the end of 2026. We also cover the real-world examples of companies using agents to handle 80% of customer service autonomously, the difference between "agentic AI" and a chatbot with extra steps, and what operators and executives need to understand before deploying agents inside their organizations.

Key Topics Covered

• What the "Agentic Brain" is and how it differs from traditional chatbots and copilots
• Why the agentic AI market is projected to grow from $7.8 billion to $52 billion by 2030
• How multi-agent systems work: planning, reasoning, tool use, and memory
• Why only 14% of companies have production-ready agentic solutions despite massive investment
• The concept of "bounded autonomy" and why it matters for enterprise deployments
• How some companies are already using AI agents to handle 80% of customer service autonomously
• Why multi-agent system inquiries surged 1,445% in one year
• Gartner's prediction that 40% of enterprise apps will embed AI agents by end of 2026
• The difference between real agentic AI and hype — how to tell what's actually production-ready
• What executives and operators need to know before deploying AI agents in their organizations

Episode Timestamps

00:00 - Introduction and meet the Agentic Brain team

00:39 - Today's agenda: three practical AI agents

01:09 - Agent 1: Personalized learning agent

03:05 - How the learning agent works

09:25 - Live demo: testing the learning agent

16:50 - Future of personalized learning with AI

24:51 - Agent 2: Sales analysis agent

28:31 - Demo: Sales performance dashboard

32:41 - Call quality and feedback analysis

46:18 - Real-world impact: proactive team management

49:26 - Agent 3: Customer service agent

50:52 - Multi-agent systems and scaling

78:55 - Closing remarks and thanks

Partner Links

Work with Agentic Brain — https://cal.com/forms/44f75adf-4741-4c5e-88d5-3249592ada99

Book Enterprise Training — https://www.upscaile.com/

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This week on AI News in 5: OpenAI kills Sora just six months after launch — blindsiding Disney and losing a billion-dollar deal. Amazon drops $50 billion on OpenAI, putting Microsoft on the warpath. Apple dumps OpenAI and partners with Google to rebuild Siri. OpenClaw becomes the fastest-growing open-source project in history. And a hacker infects a popular AI tool downloaded 3.4 million times a day.

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About The AI Why with Liam Lawson:

The AI Why breaks down what's actually happening in AI — who's building it, how it's being implemented at scale, and why the people building it do what they do. New episodes every Tuesday (AI News in 5) and Thursday (founder and exec interviews).