Yuval Yeret: Scaling AI from Activity to Impact

Yuval Yeret

Yuval Yeret hosts Scaling AI: From Activity to Impact, conversations for technology, product, and business leaders asking a hard question: how do we turn all this AI activity theater into real business impact? Yuval and his guests explore what needs to change in how organizations choose, fund, learn, and work with AI — including how native AI capabilities can help scale AI itself. Building on its roots in scaling with agility, the podcast applies adaptive, product-oriented thinking to the biggest operating-model challenge facing organizations today.

  1. Oct 1

    Leading in the Fog (using Agility) w/ Brad Farris

    When nobody can predict the next five months, let alone five years, what does it mean for a leader to be agile? Brad Farris coaches owners and CEOs of professional services firms through what he calls a polycrisis: AI reshaping the work, shifting expectations between owners and the people who deliver it, and geopolitical shocks on top. We talk about what agility looks like in practice for a managing partner, not a product team: admit you don't know how this will go, stay anchored on the mission and the people you serve, agree on outcomes before debating how, and take the smallest step that moves you toward them. Then we turn to the harder question for anyone who built their reputation in product and engineering: how do you bring these ideas to the rest of the organization without arriving as the person who is there to fix them? Key takeaways: Start by admitting "I don't know how this is going to go." Without that, planning is pretending.Get agreement on the mission and outcomes before anyone jumps to the how.In the fog, take the smallest step toward the outcome, check what you learned, and repeat. More people running experiments means more information.Don't show up as the fixer. Sit on the same side of the table and let people create the solution through small experiments.Move from expectations to agreements: both sides can negotiate, and that is the first step toward trust.Agile principles fit well beyond product and engineering, but distill them to first principles and leave the mandates behind. Chapters: 00:00 Fog, uncertainty, and the need for agility01:09 Brad's path from consulting to executive coaching02:22 The polycrisis facing professional services firms05:30 What agility means in practice for a CEO06:27 Admitting "I don't know" and renewing the mission08:16 Outcomes as the North Star10:13 Developing your company like a product13:39 Small experiments when you can't see ahead16:07 Bringing agility beyond product and engineering20:03 Getting on the same side of the table22:41 Consulting vs. coaching: who owns the problem24:29 Agile coaches and leader accountability29:54 Co-creation and accountability34:31 What leaders do between sessions36:05 Agreements vs. expectations37:49 Principles without the mandates40:02 Where to find Brad About Brad: Brad Farris is an executive coach at Anchor Advisors in Chicago. For 25 years he has worked with leaders of growing businesses, mostly professional services and creative firms in the $2M to $50M range. Join Brad's weekly email, The Conversation: https://anchoradvisors.com/join-the-conversation/ Brad on LinkedIn: https://www.linkedin.com/in/bradfarris/ Brad on why there are no generalized solutions: https://anchoradvisors.com/there-are-no-generalized-solutions/ Further reading from Yuval on developing your company like a product: https://yuvalyeret.com/category/develop-the-company-as-a-product/ Scaling AI: From Activity to Impact — yuvalyeret.com Insights (and help/advice) on scaling AI from activity to impact — yuvalyeret.com/insights Yuval's Linkedin – https://www.linkedin.com/in/yuvalyeret/

    Leading in the Fog (using Agility) w/ Brad Farris

  2. Sep 22

    AI Is Rewriting Professional Services: When Faster Implementation Moves the Bottleneck

    AI can significantly compress enterprise software implementation timelines. That does not eliminate the constraint. It moves it. Julie Hodge, AI Strategy & Operations Leader at Zuora, joins me to discuss how AI is changing professional services, customer onboarding, and the relationship between product and services. We use Zuora's Milo implementation agent and digital quote-to-cash twin as a concrete example, then follow the implications downstream: customer testing and change capacity can become the next bottleneck, customer-facing experts can become builders, and faster teams still need shared direction and lightweight governance. Key takeaways: • Start with the implementation bottlenecks customers already feel. • Digital twins create an early working model from existing business inputs. • When everyone can build, the product–services boundary starts to blur. • Faster engineering can move the bottleneck into customer testing, training, or change readiness. • Tiny teams still need shared context, priorities, and traceability. • Human effort alone is a poor test for whether an AI-generated change needs governance. Chapters: 00:00 Why AI matters in professional services 01:49 Where enterprise implementation time goes 03:27 Start with customer-visible bottlenecks 04:32 Milo, digital twins, and the first working tenant 07:02 Mapping the customer onboarding value stream 08:38 Product, engineering, and services working together 11:38 When everyone can build 13:25 Engineering stops being the only constraint 16:38 Prioritizing the services AI backlog 17:06 When delivery outpaces customer capacity 18:00 Following the moving constraint 20:14 Using AI to build AI solutions 22:19 Ownership across product and services 23:34 Speed without abandoning governance 24:08 What project management survives AI acceleration 27:02 Tiny teams and coordination costs 29:17 Start with one or two visible wins About Julie: Julie Hodge is an AI Strategy & Operations Leader at Zuora. Her work focuses on how AI operates inside the enterprise and how professional-services teams turn AI capabilities into faster, more dependable customer outcomes. Julie on LinkedIn: https://www.linkedin.com/in/jdhodge Learn about Zuora Milo: https://www.zuora.com/products/milo/ Scaling AI: From Activity to Impact — yuvalyeret.com Insights (and help/advice) on scaling AI from activity to impact — yuvalyeret.com/insights Yuval's Linkedin – https://www.linkedin.com/in/yuvalyeret/

    AI Is Rewriting Professional Services: When Faster Implementation Moves the Bottleneck

  3. Sep 14

    Tiny Teams, Same Dependencies: Can AI Flatten Your Organization?

    Consulting firms are telling engineering leaders that coding agents mean you can flatten the org, cut coordination layers, and move to autonomous tiny teams. But in the enterprise trenches, most teams aren't decoupled feature teams—they are system teams with heavy cross-team dependencies. 10x-ing code generation without fixing organizational coupling doesn't eliminate scaling overhead; it just floods downstream queues with unintegrated PRs and dependency waits. In this solo episode, Yuval Yeret breaks down what scaling frameworks (SAFe, LeSS) actually do in the age of AI, why procedural coordination collapses while structural complexity remains, and how to use coding agents at your system bottlenecks to continuously descale without breaking delivery. Notable Quotes: "If your organization requires 4 portfolios, 8 value chains, and 16 pods to align before a single customer epic can ship, 10x-ing code generation just produces more unintegrated PRs and longer dependency queues. That isn't throughput—it's activity theater." "Scaling frameworks exist for one reason: to manage the coordination overhead forced on you by your current dependencies. As long as those dependencies exist, you need to manage them somewhere." "Don't just create smaller teams, call them tiny, and assume they will 10x without fixing the architecture and the system around them." Links & Resources: - Read the companion article: https://yuvalyeret.com/blog/most-of-your-scaling-apparatus-is-now-optional - Organizing teams around outcomes: https://yuvalyeret.com/blog/when-and-why-do-we-need-a-product-operating-model - Kevin Fox's Blue Light (Theory of Constraints): https://theoryofconstraints.blogspot.com/2007/06/toc-stories-2-blue-light-creating.html Scaling AI: From Activity to Impact — For leaders trying to turn AI activity into real business impact. Insights (and help/advice) on scaling AI from activity to impact — yuvalyeret.com/insights Yuval's Linkedin – https://www.linkedin.com/in/yuvalyeret/

  4. Aug 11

    Developers of the System, Not the Code: Inside Next Insurance's Agentic DLC w/ Shay Mandel

    "We are now developers, not of the code. We are developers of the system." Most AI coding rollouts end up with faster typing and the same delivery system. Shay Mandel's team at Ergo-Next Insurance went after the system instead. They broke the whole product development lifecycle into skills an agent runs — problem definition, PRD, engineering review, technical design, implementation, testing, code review — and told everyone to stop writing the artifacts and start fixing the thing that writes them. 00:00 Welcome, and why this conversation 00:31 Shay Mandel, Ergo-Next Insurance, and 200 people building product 03:46 What you should get out of this conversation 05:14 Breaking the development lifecycle into skills 06:37 The engineering review skill that runs before engineering reviews 08:38 How the engineers actually feel about it 10:40 Developers of the system, not of the code 11:11 Auto-improvement loops: pointing an agent at a KPI 14:21 Where humans still decide, and where they don't 21:21 Digital twins of your best insurance experts 22:20 Ask-engineering and ask-product agents across time zones 24:21 What changed in how the work is managed, and what didn't 29:24 Epic-level Kanban and flow metrics for human and agent handoffs 30:39 Advice for leaders starting the journey 31:32 Setting expectations: 60% to 95%, and why the last 5% needs a human 33:29 When you tell a PM to open a terminal, you lose them 35:24 Avoiding skill sprawl: one shared brain, not 200 37:44 Beyond product and engineering: actuaries, finance, HR "They are not supposed to write anything on their own anymore. They just need to fix the skills or fix the context." - Shay Mandel "The initial version will probably be sixty or seventy percent accurate, and we'll get to ninety or ninety-five. The extra five percent is why we need a human in the loop." - Shay Mandel "You need to think from a product perspective, even about your AI harness and your AI capabilities." - Yuval Yeret Links and resources Shay on LinkedIn: https://www.linkedin.com/in/shaymandel/ Product Leaders AI meetup: https://luma.com/ProductLeaders.ai Yuval's Linkedin – https://www.linkedin.com/in/yuvalyeret/ Scaling AI: From Activity to Impact — yuvalyeret.com/insights Don't Redesign Your Process Yet. Change Who Writes the Artifacts. – https://yuvalyeret.com/blog/change-who-writes-the-artifacts-before-your-process How Next Insurance Broke Its Whole Lifecycle Into Agent Skills – https://yuvalyeret.com/blog/how-next-insurance-broke-its-lifecycle-into-agent-skills

    Developers of the System, Not the Code: Inside Next Insurance's Agentic DLC w/ Shay Mandel

  5. Jul 28

    Beyond Token Caps w/ Tomer Elias: How Enterprises Actually Measure AI Impact

    Most enterprise AI conversations get stuck on the wrong number: how many tokens an employee should be allowed to burn. Tomer Elias argues the cap is the least interesting question in the room. The hard part is attribution, knowing whether any of that spend moved a business metric at all. We compare notes on what enterprises are actually doing right now, why AI keeps exposing organizational problems that predate it, and what has to be in place before "AI impact" means anything. About the Guest Tomer Elias is a product executive with more than 15 years leading startups from zero to one and one to 100, through unicorn and IPO stages. His focus has been AI and data throughout. He was part of the first AI lab in Israel, helped build a cybersecurity unicorn, and served on a committee defining agentic identity standards alongside OpenAI, AWS, and Cloudflare. He is currently mapping how enterprises adopt AI and where they sit on the maturity curve. Chapters 00:00 Why this conversation 00:54 Tomer's background: AI labs, a cyber unicorn, agentic identity standards 02:26 What actually changed with gen AI: ambiguity 02:50 Human operating systems and agent operating systems 06:17 Setting a token cap, and why the number isn't the point 07:19 Measuring outcomes was broken long before AI 08:43 Activity, output, outcome, impact: what the work really looks like 10:55 AI surfaces the organizational DNA you never fixed 11:19 The factory lens: an industrial engineer's view of the enterprise 14:31 Goldratt and the constraint: where AI actually pays 15:56 Two levels of observability: spec conformance vs. value 19:00 A worked example: meeting transcripts as context 20:33 Kill criteria and staged funding for AI experiments 22:41 Build vs. buy, and the question nobody asks first 25:09 The rise of the business engineer 25:50 The digital twin that knows your stack 28:06 Data infrastructure: the real enterprise blocker 30:05 Closing the loop: telemetry, usage, decisions 34:12 Psychological safety and toxic token usage 39:32 Security guardrails: MCP safety, DLP, sandboxes 42:54 Who builds beyond product and engineering? 46:54 Teaching product skills instead of staffing PMs Notable Quotes "AI puts the credit card at the hand of the employees. But without the oversight and training, costs can spiral. But high usage doesn't really mean a bad thing. It's not good or bad. The question is what's the impact that you get out of that." — Tomer Elias "Once you implement AI in your organization, it surfaces your DNA and the organizational culture that didn't change for a while and now needs to change if you really want to push impact with AI." — Tomer Elias "Any improvement away from the bottleneck or from the constraint is meaningless, and any improvement directly at the constraint is a real multiplier." — Yuval Yeret "If it's a healthy DNA, people would feel safe to experiment. And if it's a toxic culture, you would get toxic token usage and activity theater." — Yuval Yeret Links and Resources Tomer Elias on LinkedIn: https://www.linkedin.com/in/tomer-elias1 If you're working on turning AI activity into business impact, I write about this every week in the Scaling w/ Agility newsletter. 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/ How to Measure AI Impact Beyond Token Caps – https://yuvalyeret.com/blog/how-to-measure-ai-impact-beyond-token-caps Is Your Agent Harness Still Learning? Check Its Last Updated Time. – https://yuvalyeret.com/blog/your-agent-config-is-standard-work

    Beyond Token Caps w/ Tomer Elias: How Enterprises Actually Measure AI Impact

  6. Jun 2

    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 or 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.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/ Agility Might Have Been Waiting for AI – https://yuvalyeret.com/blog/agility-might-have-been-waiting-for-ai

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

  7. May 27

    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 Join the Activity to Impact Conversation on Linkedin Goal-Based Loop Engineering: From Spec-Driven to Outcome-Driven – https://yuvalyeret.com/blog/ai-agent-completion-goals-aim-at-outcomes

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

  8. May 21

    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.

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

Yuval Yeret hosts Scaling AI: From Activity to Impact, conversations for technology, product, and business leaders asking a hard question: how do we turn all this AI activity theater into real business impact? Yuval and his guests explore what needs to change in how organizations choose, fund, learn, and work with AI — including how native AI capabilities can help scale AI itself. Building on its roots in scaling with agility, the podcast applies adaptive, product-oriented thinking to the biggest operating-model challenge facing organizations today.

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