AI Hype vs. Real Value

AI Hype vs. Real Value

Author: Demetrios July 24, 2026 Duration: 42:37

Manish Dasaur is a Managing Director at PwC with over 20 years in data and AI, having helped 100+ clients navigate AI disruption and extract real business value from data, AI, and agentic AI initiatives. In this episode, he breaks down why most enterprise AI programs stall — and the playbook the winners are using instead.


Huge thanks to PwC for supporting this episode!


💰 The 30% benchmark — What "good" actually looks like: real efficiency gains clients are reporting across engineering, finance, HR, and supply chain

🔄 Workflows, not use cases — Why isolated pilots and POCs never show up in EBITDA, and how end-to-end workflow redesign does

🧪 Champion vs. challenger — Running a control group against your AI-automated process so ROI is demonstrated, not guessed

📞 Why customer care agents are still freaking hard — Context, CDP integration, billing systems, and voice-to-voice latency

💸 Tokenomics & FinOps — Consumption-based cost surprises, model selection, prompt engineering, and enforcing cost-per-workflow budgets

🔍 Auditing agentic behavior — Using AI to test AI, the missing "SOC 2 for agents," and certifying agents for sensitive use cases

👤 Human in the loop as an evolving scale — From reviewing 50% of outputs down to 10% as trust builds

🧠 88% do AI, 33% scale it — Building a culture of innovation, and why AI usage is showing up in performance reviews

💼 Jobs, reskilling & the operating model reset — Why 75%+ of jobs will be reskilled, not replacedIf you're an AI leader, platform engineer, or exec trying to turn AI experiments into P&L impact, this one's for you.


Links & Resources:

Connect with Manish: https://www.linkedin.com/in/manishdasaur/

PwC AI: https://www.pwc.com/us/en/tech-effect/ai-analytics.html

PwC's 2026 AI Business Predictions: https://www.pwc.com/us/en/tech-effect/ai-analytics/ai-predictions.html


Timestamps:

[00:00] AI Hype vs Business Value

[00:44] API Spend Tracker Widget

[02:18] Tokenomics and FinOps for AI

[06:19] Measuring AI Impact Objectively

[11:07] AI in Support Workflows

[18:05] AI Innovation Culture

[27:16] MCP Servers and SOC 2

[29:14] Human in the Loop in evolving scale

[35:44] AI and Workforce Efficiency

[39:59] AI Transformation and Mindset

[42:19] Wrap-up


Hosted by Demetrios, MLOps.community is a space for honest, meandering talks about the real work of making artificial intelligence systems actually work. This isn't about hype or theoretical papers; it's about the messy, practical, and often surprising journey of taking models from a notebook into a live environment. You'll hear from engineers and practitioners who are in the trenches, discussing the tools, the frustrations, and the occasional breakthroughs that define the day-to-day. The conversations are deliberately relaxed, covering everything from traditional machine learning pipelines to the new world of large language models and even the intangible "vibes" of team culture and process. Each episode peels back a layer on what "production" really means, whether that involves deploying a predictive service, managing an agentic system, or maintaining reliability as everything scales. Tuning into this podcast feels like grabbing a coffee with colleagues who aren't afraid to dig into the technical nitty-gritty while keeping the tone conversational and accessible. It's for anyone who builds, manages, or is just curious about the operational backbone that allows AI to deliver value, offering a grounded perspective often missing from the broader conversation.
Author: Language: en-us Episodes: 50

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