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Big Agile helps organizations deliver tangible business outcomes, not just output, through agile training, executive coaching, and transformation services that cut waste, boost impact, and align teams around what truly matters.

A product leader told me last week that she approves code she could not read if her life depended on it. Then she added,...
06/04/2026

A product leader told me last week that she approves code she could not read if her life depended on it. Then she added, almost apologizing, "and now a lot of it is written by AI."

She is not behind. She is most product and engineering leaders right now.

Here is the part nobody hands you. In October, Ox Security analyzed more than 300 repositories and found AI-generated code is not more buggy per line than human code. The trouble is that AI repeats the same structural shortcuts over and over, and it strips out the slow-downs (review, debugging, the careful second pass) that used to catch them before customers did.

You do not have to learn to read code to manage that. You have to learn the handful of predictable ways AI code goes wrong, and ask about them in the rooms you are already in.

So I translated all ten patterns Ox Security documented into plain language, with one question you can ask in a sprint review for each. Things like fake test coverage that lifts the dashboard without testing anything real, and code that worked on someone's laptop but ignores production.

You will not sound like an engineer. You will sound like a leader who knows where the soft spots are.

If you lead a product or engineering org and you are funding AI-assisted delivery, you cannot personally inspect; this one is for you.

Link in comment below

A product leader told me last week that she approves code she could not read if her life depended on it. Then she added,...
06/03/2026

A product leader told me last week that she approves code she could not read if her life depended on it. Then she added, almost apologizing, "and now a lot of it is written by AI."

She is not behind. She is most product and engineering leaders right now.

Here is the part nobody hands you. In October, Ox Security analyzed more than 300 repositories and found AI-generated code is not more buggy per line than human code. The trouble is that AI repeats the same structural shortcuts over and over, and it strips out the slow-downs (review, debugging, the careful second pass) that used to catch them before customers did.

You do not have to learn to read code to manage that. You have to learn the handful of predictable ways AI code goes wrong, and ask about them in the rooms you are already in.

So I translated all ten patterns Ox Security documented into plain language, with one question you can ask in sprint review for each. Things like fake test coverage that lifts the dashboard without testing anything real, and code that worked on someone's laptop but ignores production.

You will not sound like an engineer. You will sound like a leader who knows where the soft spots are.

If you lead a product or engineering org and you are funding AI-assisted delivery you cannot personally inspect, this one is for you.

A new MIT/Wharton paper landed last week (May 27, 2026) that puts hard numbers on the productivity paradox I have been w...
06/03/2026

A new MIT/Wharton paper landed last week (May 27, 2026) that puts hard numbers on the productivity paradox I have been writing about all last week.

The authors measured AI coding productivity across 100,000+ GitHub developers, tracing the effect from individual keystrokes all the way to shipped software.

The pattern they document is the cleanest version of an argument I have been making the long way around.

When developers adopt AI coding agents:

Lines of code: +741%
Pull requests: +65%
Releases: +20%

The engine got faster. The car did not get faster because nothing else in the production system scaled with it. The bottleneck moved from writing code to reviewing, integrating, and shipping it.

The paper's deepest finding is a structural parameter. The elasticity of substitution between AI output and human effort in software production is 0.25. In plain English, that means AI and humans are strong complements in this work, not substitutes. You cannot solve a review bottleneck by adding more AI.

You can only solve it by upgrading the review.

This is the productivity paradox with the kind of rigor most operating dashboards will never reach.

Source: Demirer, Musolff, and Yang, "Writing Code vs. Shipping Code: Productivity Effects Across Generations of AI Coding Tools," MIT and Wharton, May 2026.

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6843118

Ask the person who has done the job for fifteen years to explain how it works, and watch what happens.They start strong....
06/03/2026

Ask the person who has done the job for fifteen years to explain how it works, and watch what happens.

They start strong. Three steps in: "well, it depends." A few steps later: "oh, except for the corporate accounts." And then they tell: "I don't really think about it, I just do it."

That person is the most valuable source of truth in the building, and they still cannot fully tell you what they know.

We keep treating that gap as a tooling problem. It is not. Talking to computers has been getting easier for seventy years, from punch cards to plain English. Talking to humans about what they actually need has not gotten easier at all.

AI is crushing the cost of building and barely touching the cost of understanding. Which means the expensive problem, knowing what to build, is now the only one left standing. And cheap building makes it more tempting to skip.

The scarce skill is no longer coding. It is not even prompting. It is knowing and being able to say what you actually need.

New on the blog: why understanding the problem is still the hard part, and how to get good at the part that stays human.

Read the full breakdown: https://big-agile.com/blog/talking-to-computers-is-getting-easier-talking-to-humans-still-hard

A common misconception about an AI-augmented CSPO is that it is a course on prompt engineering.It is not.The AI generate...
06/02/2026

A common misconception about an AI-augmented CSPO is that it is a course on prompt engineering.

It is not.

The AI generates the first draft. That part takes minutes. The actual course is what happens next. Your team translates that draft onto chart paper, debates it, marks it up, throws half of it away, and produces artifacts you can defend to a stakeholder who has never heard of generative AI.

The walls of sticky notes are the actual course.

We give you seven pre-engineered prompts so you do not waste class time tuning prompts. You spend that time on the harder work: deciding what is actually true about your product, your users, and your real team capacity.

Link in the first comment: 👇

Quick gut check: an AI tool writes a feature, it passes all the tests, it ships, and a week later it exposes customer da...
06/01/2026

Quick gut check: an AI tool writes a feature, it passes all the tests, it ships, and a week later it exposes customer data. Who's accountable?

If you had to pause, that pause IS the problem.

AI didn't remove accountability from our teams. It diffused it across so many hands that nobody's quite sure where their job ends and the AI begins. Meanwhile the data shows security findings climbing 10x and architectural flaws surging while everyone's dashboard looks great.

Lance Dacy walks through why this is a leadership design problem (not a developer one) and a dead-simple fix you can try with one team this week.

Watch or read the full episode. Link's in the comments 👇

There are three ways to be dangerous with AI as a product professional.𝗗𝗮𝗻𝗴𝗲𝗿𝗼𝘂𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝗮𝗻𝗱 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆.You trust ...
05/31/2026

There are three ways to be dangerous with AI as a product professional.

𝗗𝗮𝗻𝗴𝗲𝗿𝗼𝘂𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿𝘀𝗲𝗹𝗳 𝗮𝗻𝗱 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆.
You trust output you do not understand. You share AI-generated analysis without verifying it. You build roadmap decisions on confident-sounding hallucinations. This is not a beginner mistake. It happens to experienced professionals because the output looks authoritative.

𝗗𝗮𝗻𝗴𝗲𝗿𝗼𝘂𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗰𝗮𝗿𝗲𝗲𝗿.
You watch AI reshape how discovery works, how customer feedback gets synthesized, how backlogs get refined, and you keep doing what you have always done. You tell yourself you will get up to speed when things settle down. They do not settle down.

𝗗𝗮𝗻𝗴𝗲𝗿𝗼𝘂𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻.
You learn the mechanism. You prompt with precision. You build AI-powered workflows into your actual product process. You lead your team through the transition with clarity. You move faster, decide smarter, and outperform peers who are still guessing.

Our AI for Product Managers workshop is built around the third one.

Register: https://big-agile.com/ai-for-product-management

Wrapping a Friday with Stakeholders ;)
05/29/2026

Wrapping a Friday with Stakeholders ;)

95% of professionals affirm agile, but only 7% of organizations do it well.That is the data from Forrester's State of Ag...
05/29/2026

95% of professionals affirm agile, but only 7% of organizations do it well.

That is the data from Forrester's State of Agile Development 2025. Fifteen years of practice, overwhelming agreement that it matters, and only seven out of every hundred organizations have achieved full proficiency.

The conversation about this gap usually splits two ways. Some leaders say agile failed at scale and we should move on. Others say agile is fine and the 93% just need to try harder.

Both reads are wrong, and the data points to a third explanation that almost nobody is naming.

In this week's Next-Gen Agility, I work through what the seven percent figured out about the relationship between agility and architecture, why most enterprises are stuck oscillating between "go fast" and "add governance" indefinitely, and the three questions you can bring to your next leadership conversation to tell you which structures in your organization are still earning their keep.

If you have ever watched your team get caught between "we need to move faster" and "we need more oversight" inside the same quarter, this one is for you.

New edition of Next-Gen Agility is live. Link in the first comment.

A Product Owner sits in sprint planning on a Wednesday morning. The new AI prioritization tool tells her to push a featu...
05/28/2026

A Product Owner sits in sprint planning on a Wednesday morning. The new AI prioritization tool tells her to push a feature down two slots. Her senior engineer leans forward and says, "I think that's wrong. Three customers called this out in the last month."

She has the tool. She has her engineer's gut. They do not agree. The room goes quiet, waiting for her to make a call. And she freezes.

That scene is playing out in product organizations everywhere right now. The AI is faster than the gut. The gut has been right before. Nobody knows which one to trust on this particular Wednesday, and nobody has a habit for resolving it.

Most teams default to one of two failures.

Blind compliance: rubber-stamping the AI output because "the model said so."

Read more: https://big-agile.com/blog/when-ai-says-x-and-your-gut-says-y-the-three-question-test

Blind rejection: dismissing the AI output because "the model couldn't possibly understand our domain."

Neither is the answer. The third path is honest interrogation, and it is a learned skill, not a personality trait.

In this week's AI blog, I walk through the three-question framework you can put to work on the next AI output that conflicts with your judgment, plus the four traps that quietly turn AI tools into either oracles or ornaments.

If you are leading a team that uses AI tools but has not yet figured out how to disagree with them well, this one is for you.

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