Avoiding AI Theater: Strategies for Real Impact

Why the Pivot? Tracing the Line from Scaling Agility to AI Impact

In this special episode, host Yuval Yeret flips the script with guest Philip Morgan, an expert on positioning and point of view. They discuss the recent rebranding of the podcast and dive deep into the striking parallels between historical Agile Theater and the emerging risks of AI Theater. Discover why measuring raw activity—like tokens consumed or lines of code written—is a trap, and how organizations can shift toward measuring tangible business outcomes and flow efficiency. You'll also learn about Unreasonable Agility and what happens when R&D goes on the offense. Chapters 00:08 The Agile Shift in AI and Podcast Rebranding 01:07 Philip Morgan's Background in Positioning 02:36 Yuval's Role as a Plumber for Engineering Pipelines 06:53 How Pain and Opportunity Drive Change (Gillette & Biotech Examples) 15:51 Introspective and Compound Engineering 17:08 Exploring AI Theater vs. Agile Theater 21:44 Shifting Bottlenecks: From Coding to Code Review and Adoption 28:04 The Trap of Vanity Metrics and Token Maxing 33:26 Moving from Output to Outcomes and Business Impact 40:50 Breaking the Status Quo and Resisting AI Mandates 56:21 Unreasonable Agility: Taking R&D on the Offense Notable Quotes "If you want to succeed with it, find somebody who talks about the principles, who understands it in depth, who can make changes while still being aligned to the spirit." - Yuval Yeret "People will find a way to use AI without really getting any value. They will find a way to generate a lot of activity, but not really create any change in the impact." - Yuval Yeret "There's a sense in which it seems like Agile was sort of waiting for AI." - Philip Morgan Ready to stop measuring tokens and start measuring real business outcomes? Reach out to Yuval at yuvalyeret.com [yuvalyeret.com] or yuval@yeretagility.com [yuval@yeretagility.com]to discuss how to build unreasonable agility into your organization. About PhilipPhilip Morgan is a positioning and point-of-view expert who helps independent service providers thrive by leveraging effective market positioning. He is the author of multiple books on the topic and shares his insights freely at ⁠philipmorgan.net⁠ [https://philipmorgan.net/].Scaling AI: From Activity to Impact — yuvalyeret.com The Scaling w/ Agility Newsletter – yuvalyeret.com/insights Yuval's Linkedin – https://www.linkedin.com/in/yuvalyeret/

Claude's /goal Feature Just Exposed A Real Challenge With AI Agents

Claude and Codex have a relatively new feature — `/goal` — that lets you set a completion condition and keep the AI running autonomously until it's met. This capability exposes a real challenge with agentic AI and explains why its shifting the bottleneck to its assymetric capabilities in different stages and domains of value creation. 00:09 The new /goal capability — what it does and how it works 02:07 What the official examples reveal: all output, no outcome 03:02 The missing examples — what outcome-oriented goals would actually look like 04:00 Output vs. outcome: why the gap matters for AI impact 04:45 The real bottleneck — observability and closing the feedback loop 05:45 Live demo: setting an outcome-oriented /goal on a blog post 08:30 What the loop did — changes made, open items, what's next Are your AI goals output-oriented or outcome-oriented? What are you doing to enable outcome-oriented goals in your AI sessions? And as a final thought - What would happen if your human teams were empowered to seek a /goal? Insights on Scaling AI from Activity to Impact [yuvalyeret.com/insights] Join the Activity to Impact Conversation on Linkedin [https://www.linkedin.com/in/yuvalyeret/]

You can 10x engineering and still not 10x the business

AI coding assistants are genuinely good now — coding, debugging, tests, docs. But faster engineering output doesn't automatically become faster business impact. AI has quietly moved the bottleneck: from building working software to validating whether that software creates value for anyone who adopts it. This episode uses flow thinking, cumulative flow, and the theory of constraints to help leaders see where AI speed is creating congestion — and where human + AI effort should be aimed next. Key takeaways:• AI creates speed, not automatic value — speed only counts if it improves end-to-end flow• AI impact is asymmetric: engineering scales faster than discovery, adoption, and value validation• Local productivity can create system-level congestion once the bottleneck moves• Tech debt hides the new bottleneck — engineering absorbs extra capacity into easy-to-validate cleanup• Inventory is the signal: watch where work waits, loops, or gets reworked• Subordinate human and AI effort to the current constraint — don't spread enablement evenly Chapters:00:00 — The promise of AI in engineering02:04 — Why AI's impact is asymmetric05:02 — Spotting the moved bottleneck07:56 — From activity to impact: visualizing flow10:19 — Driving adoption, not just output13:15 — Subordinating people and AI to the constraint15:47 — Continuous improvement and real value Monday morning diagnostic — pick one AI initiative and ask: What outcome should it improve? Where does work wait or get reworked? If this team gets 2x faster, which group becomes the constraint — and what AI support should be redirected toward them? "You can 10x engineering and still not 10x the business. Don't force everybody to scale - help the constraint scale." If this helped, share it with a leader trying to turn AI activity into real business value.

Avoiding AI Theater: Strategies for Real Impact

Yuval Yeret explores the evolution of AI adoption in organizations, from activity and output to impactful results. He discusses common pitfalls of AI theater, the importance of focusing on real impact, and strategies to leverage AI for organizational bottlenecks. 00:00 The Journey from AI Activity to Impact 03:59 Understanding AI Output and Its Limitations 07:24 Achieving AI Impact Across the Organization Dive deeper into how to shift AI from Activity to Impact [https://yuvalyeret.com/category/ai-activity-to-impact/] Follow Yuval on Linkedin [https://www.linkedin.com/in/yuvalyeret/]for insights on AI adoption and impact, AI-native organizational strategy, AI bottlenecks, scaling AI intelligently, AI in engineering, and beyond.

Beyond AI Hype: Building an AI-Powered Organization w/ Kumar Venugopal, CTO of Zoetis

The new season of the podcast explores a question that's top of mind for many technology, product, and business leaders these days - How do you scale AI from Activity to Impact? What should your "ways of working" look like to enable you to build great products with AI? To use AI to become a 10x organization? Today's conversation is with a CTO who's working in the trenches to scale the impact of AI on their organization. Kumar Venugopal, CTO of Zoetis, the world's largest animal health company, is redesigning how the entire organization makes decisions, builds products, and defines roles — with AI at the center. In this episode you'll learn what it actually takes to move from personal productivity to organizational transformation, how Zoetis is targeting a 26-day infrastructure workflow down to 2-3 days using a three-agent pipeline, why design thinking is the skill that separates useful AI output from useless output, and why agile matters more in the AI era, not less. "The real opportunity with AI isn't strictly automation. It's how you embed it into how we make decisions." — Kumar Venugopal "We are doubling down on Agile. Even the agentic approach has to be built with a minimum viable product approach, with proper user stories. The cycles are a lot faster, but the process doesn't go away." — Kumar Venugopal Chapters: 00:00 Introduction — from 8086 to the AI era 03:01 Why this AI wave feels systemically different 05:30 AI in veterinary diagnostics and decision-making 08:12 The four levels of AI integration 11:01 Infrastructure automation — 26 days to 2-3 days 13:57 Design thinking as the missing AI skill 16:29 Build vs. buy — can vibe coding replace SaaS? 19:26 Who does this work and what's the real constraint 21:58 Three competencies every employee needs 25:11 Why Agile is more important now, not less 27:45 Scaling AI beyond the technology organization 30:30 Kumar's three-step change model for leaders Follow Kumar on LinkedIn [https://www.linkedin.com/in/itleaderdna/] for practitioner-level insights from inside a live AI transformation. https://yuvalyeret.com/category/genai/