Big Agile with Lance Dacy

About Big Agile with Lance Dacy

Most agile advice is theater. Big Agile isn't. Enterprise agile consultant Lance Dacy takes the beliefs the agile world treats as settled, velocity, psychological safety, AI governance, org design, and pressure-tests them against how work actually moves inside real companies. No ceremony worship, no framework dogma. Just systems thinking, hard research, and two decades inside organizations where the standups are perfect and the roadmap still slips. For Scrum Masters, Product Owners, coaches, and leaders who want agility that's real, not performed.

Publisher

Big Agile

Category

business

Language

en

Explicit

No

First episode

6 Jul 2026

Latest episode

2 Nov 2026

Latest episodes

18 episodes in the feed.

1. 

   2 Nov 2026

   Why Your Fastest Reviewer Is Slowing Everyone Down

   This episode is about code review, and what happens when your whole review process runs through one person. I worked with a team whose senior engineer reviewed every pull request for four teams. Nothing merged without her. Her manager called her amazing, and she is. She also hadn't written a line of product code in a week. AI is now sending far more code into that same line. I dig into what a 2026 benchmark says about how long AI-generated pull requests wait for review, what Don Reinertsen and DORA say about batch size, and why hiring a second hero doesn't remove the dependency. If one person on your team went on vacation tomorrow, how many reviews would stop? Get the Big Agile newsletter: https://big-agile.com/newsletter (https://big-agile.com/newsletter) Workshops and consulting: https://big-agile.com/events (https://big-agile.com/events) Send us your team's questions: https://big-agile.com/questions (https://big-agile.com/questions)

2. 

   26 Oct 2026

   Why Your Best Metric Is Hiding Your Worst Problem

   This episode is about steering your team with a set of signals instead of a single number. A team I worked with went from releasing once a quarter to shipping 30 times a day. That chart earned a leadership slide. Their incident rate climbed too. It got a 2 a.m. phone call instead. The metric wasn't wrong. It was alone. I dig into what DORA's own guide says about metrics that pull against each other, and why a 1992 article about airplane cockpits still fits engineering dashboards. If your team is being praised for one number right now, what is it costing the people who produce it? Get the Big Agile newsletter: https://big-agile.com/newsletter (https://big-agile.com/newsletter) Workshops and consulting: https://big-agile.com/events (https://big-agile.com/events)

3. 

   19 Oct 2026

   Skipped Refactoring Is a Loan

   Your team's velocity chart has never looked better. So why does Lance Dacy think that team might be borrowing? In this episode, Lance looks at what happens to a code base when new code gets cheap to generate and refactoring quietly stops. It starts with one team: roadmap on track, stakeholders happy, one refactor in six weeks. Then a customer gets billed on the wrong day, and the fix doesn't stick. If you lead a team that's shipping faster than ever, this one is for you. The cost is real. It just never shows up on the chart.

4. 

   12 Oct 2026

   Why Your AI ROI Slide Can’t Show a Loss

   Your AI coding tools made the team busier. Can your ROI slide prove they made the business better? In this episode, Lance Dacy looks at why so many AI ROI numbers fall apart in front of a CFO. It starts at a board meeting. The VP of engineering puts up one big green number. The CFO asks a single question about it, and nobody in the room can answer. If you're the one building the slide that goes upstairs, this one is for you. The problem is hiding in the numbers you're proudest of.

5. 

   5 Oct 2026

   Why Your Team Codes Faster but Ships No Sooner

   AI coding tools made your developers faster. So why isn't anything reaching customers sooner? In this episode, Lance Dacy tracks down where that speed actually went, and why most leadership dashboards can't see it. It starts in a staff meeting. A VP is beaming over a chart that's way up, until the product leader asks how much faster the team is shipping. Not one day. If your teams are producing more than ever and delivery hasn't budged, this one is for you. The fix is simple, and it's going to feel backwards.

6. 

   28 Sept 2026

   Why Perfect Scrum Events Can Still Miss Every Date

   A coach is working with four teams. Every Daily Scrum stays inside the time box. Every retrospective produces action items. Sprint reviews are well attended, and people actually make decisions in them. By every measure that coach was hired to improve, the teams are thriving. They've also missed every delivery date this quarter. So the coach does what the job seems to ask for: a fresh retro format, a framework refresher, tighter facilitation. The events get healthier. The dates keep slipping. In the final episode of Q3, Lance Dacy digs into what the latest State of Agile survey says about coaches, a role now being pulled in several directions at once, and what it can't say about which of those directions has a future. He brings in DORA's 2025 research on AI and delivery, and a 2009 book that saw this problem coming long before AI sped everything up. His argument: the coach doesn't need a better checklist. The coach needs to look somewhere the checklist never points. If your events look great and your dates don't, this is the episode to start with.

7. 

   21 Sept 2026

   Why Your AI Training Is Making Your Team Less Safe

   A team finished an AI course feeling great. Clean prompts, certificates, real progress from where they started six weeks earlier. Two weeks later, the model handed one of them an answer that was confident, specific, and completely wrong. It cited a number that didn't exist. It went into a document, and the document went to a client before anyone caught it. When Lance asked how it slipped through, the answer was simple: nobody questioned it. It looked fine. That's the gap this episode is about: the space between using an AI tool and knowing when not to trust it. Lance Dacy makes the case that most AI training is quietly solving the wrong problem. It builds fluency, which is getting cheaper every month, while skipping the skill that's actually scarce and getting more valuable. He digs into why a certificate on the wall can leave a team less safe, and what a program has to do differently before the training ever changes what people do at their desks. If you're spending money teaching your team to use AI, this is the question to sit with first.

8. 

   14 Sept 2026

   Will AI Replace Product Managers? It Already Took Half

   A product manager sits down for backlog refinement. A year ago that meant estimating, clarifying, dragging cards and tidying columns nobody reads. Today she describes a feature out loud and has three working prototypes before lunch. So the posts go up again. Product management is finished. Somebody go update the org chart. The administrative half of the role really is evaporating, and most of it should. Nobody grew up dreaming of grooming a backlog. The trouble is that the busy work is what filled the calendar, and a full calendar looked like a job. In this episode, Lance Dacy argues the replacement case makes one specific mistake: it treats the tasks and the role as the same thing. The tasks were only ever how the role showed up when building software was slow. Take the slowness away and what is left is the part that was always the job, and it is now more expensive to get wrong, not less. He walks one real customer request all the way through to show exactly where the judgment calls land, and closes with a one-week exercise that makes the change visible on your own calendar. Progress over perfection.

9. 

   7 Sept 2026

   AI Makes Bad Requirements More Expensive

   Somebody on your team opens a ticket. It says "add a way for customers to export their data." One line. How hard could that be? By lunch there is a working export button, passing tests, and a pull request waiting for review. It is fast, it is confident, and it is the wrong feature. Here is the uncomfortable part: nothing failed. The code compiled. The tests passed. The reviewer approved it, because review checks the code against the ticket. Nobody was checking the ticket against reality. Previously, a vague requirement like that was survivable, and not because anyone was careful. Because building was slow enough to catch it. That filter is gone. So the expensive part of the work has moved. It used to be building. Now it is deciding, and almost nobody has reorganized around that. In this episode, Lance Dacy argues that AI did not create your requirements problem. It removed the delay that was hiding it. He also shares the one-page approach he now uses to force the decision out into the open before anyone writes a line of code, including the one field almost every team skips. Progress over perfection.

10. 

    31 Aug 2026

    Why Your AI Pilot Died After the Demo

    The AI demo works on the first try. Someone leans over and says "this changes everything." There's applause, a follow-up meeting goes on the calendar, and then everyone leaves. Six months later, nobody can tell you what happened to the tool. Lance Dacy calls that place pilot purgatory, and in this episode he digs into why AI pilots stall after the demo. His argument: pilots almost never die on the model. They die on ownership, the part nobody was assigned once the applause stopped. Lance names the gap in five loops, who runs it, who owns the outcome, who can pause or kill it, who gets paged when it breaks, and how you'll know it worked. None of the five is about the model. Every one is about people making a decision out loud. He also makes the case for designing a pilot backward from production instead of forward from the demo, because a pilot built to survive operations is less impressive in the room and far more likely to still be running in September. Along the way he pulls in the data with the caveats it deserves: Deloitte's State of AI in the Enterprise 2026 (access up 50% in a year while daily use stays flat, and only a quarter of organizations shipping 40% or more of their experiments) and KPMG's Q2 2026 AI Pulse (a third of leaders overriding AI outputs with no formal criteria). The episode ends with the week's tool, the Pilot-to-Production Readiness Checklist, and a test you can run on any pilot this week: name who runs it in production, then name who can kill it. If either answer takes more than a few seconds, you've found the work. Subscribe to the Big Agile newsletter at https://big-agile.com/newsletter (https://big-agile.com/newsletter )and explore workshops and consulting at https://big-agile.com/events (https://big-agile.com/events).

11. 

    24 Aug 2026

    More Dashboards, Worse Outcomes: The Visibility Paradox

    Even the dashboards have a dashboard now. Somewhere in your organization a team added a batch of new ones this quarter, and meanwhile a monitor in IT ops has been frozen on a session-timeout screen since March that nobody has noticed. That contradiction is the whole episode. We've never had more visibility into how we build software, and the people running those systems say delivery keeps getting harder, not easier. Lance Dacy calls this the visibility paradox, and he walks through why both things are true at once. Drawing on Digital.ai's 18th State of Agile Report, two years of DORA research on AI and delivery stability, DORA's 2026 "instability tax," and Mark Graban's work on signal versus noise, he makes a simple case: a metric only earns its place if it changes a decision, and AI has removed the cost that used to kill the weak ones. The practical core is a tool Lance calls the dashboard-to-decision audit: one record per metric, five lines each. Name it, name who actually reads it, name the last decision it changed, ask what breaks if it disappeared tonight, then keep it, cut it, or merge it. He runs it live on the executive summary dashboard, makes the case that this is leadership work rather than team housekeeping, and closes with a challenge you can run before your next review. Subscribe to the Big Agile newsletter at https://big-agile.com/newsletter (https://big-agile.com/newsletter) and explore workshops and consulting at https://big-agile.com/events (https://big-agile.com/events).

12. 

    17 Aug 2026

    Why More AI Code Made Your Delivery Slower

    Your team shipped more code last quarter than in any previous quarter, and delivery is slower than it was last spring. Both things are true, and they are related. Nobody stopped working. The work stopped moving. It is sitting in the review column, and it has been sitting there long enough that nobody on the team remarks on it anymore. In this episode Lance Dacy works through what happens when generation gets cheap and verification does not. GitClear's 2026 research puts roughly a quarter of detected commits as AI-assisted, with two-week churn running about 15% above its 2023 level. The 2025 Stack Overflow survey found that the number one developer frustration, at 66%, is output that is almost right but not quite, with 45% saying debugging AI-generated code takes longer. And DORA reversed part of its 2024 finding: throughput flipped positive in 2025, while delivery instability did not improve. Lance's argument is that review is not a step in your process, it is a queue, and queues behave in ways that make this worse than it looks. Drawing on Donald Reinertsen's 2009 book The Principles of Product Development Flow, he covers why this inventory is invisible on every report you own, why queue size spikes rather than climbs as you approach full capacity, and what rate matching means when your fast sender is an AI and your slow receiver is a human reviewer. Then the practical part: four numbers that tell you how many AI-assisted items your team can genuinely verify in a week, worked through with a real example, plus where to put the limit. The answer is counterintuitive. You cap generation to protect verification, and you set that cap light rather than tight. Closing thought: verification is not overhead added to the real work. It is the work. The scarce person on your team is no longer the one who can produce a plausible change, it is the one who can look at a plausible change and tell you whether it is right. Your homework is two numbers. What you generated last week, and what you can actually review. If they are not close, you do not have a productivity story. You have a queue. Sources referenced: GitClear 2026 code quality research, Stack Overflow 2025 Developer Survey, DORA 2024 and 2025 reports, Donald Reinertsen, The Principles of Product Development Flow (2009). Weekly newsletter: https://big-agile.com/newsletter (https://big-agile.com/newsletter) Workshops and consulting: https://big-agile.com/events (https://big-agile.com/events)

13. 

    11 Aug 2026

    Using AI Is Not Delegating to It

    84% of developers use AI. 46% don't trust it. Both can be true, because using a tool and handing it work are two different relationships. Lance Dacy breaks down a five-rung delegation ladder and the one question that decides which rung a task belongs on.

14. 

    3 Aug 2026

    Human in the Loop Is Not a Strategy

    "A human will remain in the loop." It's in most AI policies, and it usually protects nothing. Lance Dacy explains why AI oversight fails at agent speed, the four postures every decision already falls into, and how to test whether your named human can actually stop the line.

15. 

    27 Jul 2026

    Attend an Agile Conference Like an Analyst, Not a Fan

    Inspired fades over the flight home. Changed shows up in your next planning session. Lance Dacy breaks down how to attend an Agile conference like an analyst instead of a fan, with three questions that turn a week on the floor into one real decision.

16. 

    20 Jul 2026

    Owning AI Is Not the Same as Controlling It

    Two rooms, same company, same morning. In the boardroom: "We're all in on AI." Two floors down, in a standup, someone asks the only question that matters: "So what does that mean for us by Friday?" Nobody has an answer. That gap is the whole problem. The pressure to "get AI" has moved to the top of the org. BCG's AI Radar 2026 found 72% of CEOs now say they're the main decision maker on AI (double from a year ago), and half believe their own job depends on getting it right. Meanwhile Grant Thornton's 2026 survey found 78% of leaders aren't confident they could pass a basic AI governance check in 90 days. Ownership went up. Control didn't follow. In this episode, Lance Dacy explains why owning AI and controlling it are not the same skill, and walks through the four-part translation leaders can use to convert board-level pressure into something teams can safely act on: one observable outcome, a clear safe-to-try line, named decision rights, and a no-blame way to surface the misses. In this episode: Why "use AI to move faster" is useless to a team, and the one-sentence rewrite that fixes it How to draw a safe-to-try line so people stop guessing with customer data Who needs the authority to say yes, and why naming them unfreezes the team A one-sentence exercise to run in your next leadership meeting Resources mentioned: • BCG AI Radar 2026 (Jan 2026) • Grant Thornton 2026 AI Impact Survey — the "AI proof gap" Newsletter: https://big-agile.com/newsletter (https://big-agile.com/newsletter) Events & Consulting: https://big-agile.com/events (https://big-agile.com/events) Connect with Lance: https://www.linkedin.com/in/lancedacy/ (https://www.linkedin.com/in/lancedacy/)

17. 

    13 Jul 2026

    Why Your AI Productivity Metrics Can't Survive One Question

    Somebody in your org just approved an AI budget on a chart that says productivity is up 30%. Compared to what? Measured how? That's the question most AI productivity numbers can't survive — and it's not because the people building them aren't smart. The number was never built to be pressed on. In this episode, Lance Dacy breaks down why your green dashboard is measuring AI-flavored activity, not real progress. He walks through the 2025 METR study where experienced developers felt about 20% faster with AI while the clock showed them 19% slower, the follow-up that collapsed because developers refused to work without AI, and what DORA's latest research says about shipping more while breaking more. Then he hands you the "gut check": three questions every AI metric has to survive before you trust it, plus a 10-minute experiment to run on your own dashboard this week.

18. 

    6 Jul 2026

    Why AI Made Your Team Louder, Not Better

    Your team bought the AI licenses, the demo looked incredible, and six weeks later you're shipping the same confusion, just faster. AI didn't fix your team. It made it louder. Lance Dacy breaks down why AI amplifies whatever your system already was, and how to read the results.

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