From time to time, the industry has a breakthrough, and things change. Sometimes the improvement is incremental, and other times it is very disruptive. Not all change stays forever; actually, new technologies tend to die sooner than old inventions like dishes, forks, knives, spoons, glasses, and many more. I remember web services dominating the corporate universe until rest came and put them to almost extinction. I remember EJB rise and fall like a flash, Netscape, and many others. Those transformations are not new, and they do happen from time to time. Before AI, we had other movements and other breakthroughs like DevOps, Cloud Computing, Agile, Mobile Phones, the Internet, and the Personal computer. AI, perhaps, is the most disruptive force we have seen so far. No other technology or movement has such mystique as AI does. Some call it the Genie; others, the Revolution of the machines (Skynet); others think it's AGI already. One interesting effect we see at this point is that AI blended with engineering, like good coffee, wine, and whiskey (blend). AI offers a dual transformation effect because it can and will disrupt and change (if it succeeds) how we work and the products we build. AI is not a panacea; it's not a silver bullet, yet the hype has run strong over the last 2 years (2025 and 20024).
Transformations: Just add another tool
As I mentioned in my previous post, Agents & Workflows, it's much easier for us as humans and companies to just add to what we always did, but now with a new tool. For sure, I can make a new version of this picture, but now with an agent using AI to chop the tree :-)
Tweet I make 8th Jan 2026 https://x.com/diego_pacheco/status/2009186168378957890Transformations: The Waste
I was always a big believer in Lean. Not only by the work of Dr Deming, but also the influence that he had on Agile, DevOps, Lean Hospitals, Lean UX, Lean StartUp, and I hope it influences AI. Lean exists in markets and industries where there is a lot of money and a lot of waste. Pretty sure AI qualifies, and we need Lean AI.
It's so easy to do things that don't make sense and keep repeating them over and over. For instance, I was never a big fan of Scrum, always preferred XP and Kanban. Scrum has this thing called planning poker; the whole idea of scrum was to work without dates. Therefore, you dont give date estimates, and then you ship software every spring in a regular cadence, and if it is not done in this ship, it goes into the next one. Decades have passed, and companies are still working with dates. Lean/Kanban has always been much better at handling dates. The reality is that companies still work with dates, and they do planning poker, for what? The date is already set. What is the point? The original scum: Story points were used to manage capacity, but that utility is lost when you have a date and need to ship on that date. Let me be absolutely clear, I always find this waste. But now it will be much worse.
No - It does not work like that
We don't need story points anymore; we did not need them before. Considering AI agents, it's even more of a waste to do it so. This and many other war crimes happened and still happen because companies don't do retrospectives, and all SDLCs are written in stone, while they should be organic and alive, and keep changing and evolving all the time.
What almost nobody realized is that Agile was cool and awesome 20-25 years ago because it put engineers at its heart. AI and Agents have engineers. I hope we have a new boom and a new renaissance where we rethink how we work, and we shall adopt Lean and remove waste.
Transformations: What does it mean?
Have you ever tought what it means to transform? What it means to make things different. IF transform means rename the things we always did (re-brand) I tell you, this is not transformation. IF transformation means adding a tool, that's not transformation. IF transformation means: An AI Team, an AI Department, and an AI Enterprise tool, that's not transformation.
Transform means:
- Change the organization believes
- Change the organization's values
- Change the organizational structures
- Change the organization reporting
- Change the organization's ways of working
- Change the organization's products
IF these changes are not happening, there is no transformation. Perhaps we can say there is optimization, which is to do more with less, or luckily do more with more. Optimization is good for companies and adds value, don't get me wrong, but it's not as good as deep transformations.
Transformations: What does it mean to manage?
Lean believes in the empowerment of the people. Lean believes in effective learning. Lean is about:
For AI Transformations to be effective, we need to measure the entire e-2-e value stream. Otherwise, we can easily be fooling ourselves on 2-10x developer productivity and still deliver on the exact same pace, so we are expending more money because of tokens, and we have more output, but do we have more value?
Having an AI Agent in production is not necessarily valuable (it's more costly and can easily be wasted). Again, we need to learn to see what value is and also learn what waste is. Does anyone remember when people went all in on Microservices and never bothered to isolate databases? Does anyone remember the mess that was created?
Distributed monoliths! I would never forget, and AI can do the same with MCP.
What is behind this 2 assumptions that define Lean? What is management, and what are you managing? Traditional methods hold that you need to manage people; Lean holds that you need to manage the work, which is very different. IF we go back to the industrial revolution, managers were the ones who knew what the others needed to do, and they were in charge of the clock.
Way before AI changed, unfortunately, most companies did not realize it. Now with AI agents, how much do you need your manager telling you what to do, versus you chatting with Claude code to figure out? AI Agents can kill several wastes we have in software engineering. However, AI will also introduce waste.
AI Agents can answer hard questions, can get code done, and ship features very fast. You don't need a manager to tell you how to fix a bug in React; you can ask the AI Agent. IF Claude's code will be the master un-blocker while doing code, why do we need a scrum master to run daily meetings of 1 hour? We don't. We did not before AI, and now even less.
Still believe AI cannot self-manage, it needs guidance, especially because:
- We need to discover what kind of product we will build and how to add value (interactive process)
- We need security experts, AI is not good at multi-system design, and security is hard.
- AI sucks on the architecture of multiple systems, we need critical thinkers, we need architects.
Management needs to change, needs to be about enabling, coaching, and mentoring others. Traditional management makes no sense with AI Agents. Do we need to be in physical offices if AI Agents run in the cloud? Many things can and should be re-assessed.
Transformations: More Risk or More Deployments?
For the sake of argument, let's say AI Agents allow us to be much more productive. Let's say, before we would take 2 weeks to finish 10 items, we can do 10 items in 1 week. Well, now we have a dilemma: what do we do?
More Changes: It's the "easy" thing to do. Just give more work to engineers, and they can do double the work. However, it's not so simple to double the work; product and UX need to be ready for that, otherwise we risk shipping half-baked products. Plus, this is the worst option because the more software there is, the greater the risk. DevOps has the principle of small deltas; we should be deploying to production more often and with smaller deltas because thats reduces the risk.
More Deploys: More deploys means smaller deltas and therefore less risk. Giving smaller deltas makes rollback easier and makes it easier to figure out which change breaks production. Bigger buckets of change will also mean more risk because now, what is there? what that broke it? We need to keep in mind that with AI Agents, people are by nature paying less attention to the code than before.
Transformations: Buy vs Build
Before AI Agents, companies always preferred to buy software. Because it took a long time and would always be a big commitment to build software beyond your core business. However, AI Coding agents are changing that; now building internal tools is not as expensive, and it's much more accessible to build them that change how people work.
Building an internal tool is a great way to change how we work, IF the tools consider new ways of working. IF the tools are just a "faster" way to do the same as before, that is not really a transformation.
Transformations: Real Discovery vs Requirements
Lean understands that requirements are just decisions made by someone. Requirements are lies; there is no such thing as requirements. What we actually do is:
- Our current Thesis
- Our current Assumptions
- Our current Visions
- Our current Attempt
- Our current premises
- Our current experiments
Requirements are often wrong. Because it's hard to get things right, especially in complex software that can take months or years to complete. We often have all the wrong assumptions about requirements because we think they are correct, and therefore, we just need to "clarify" them. However, in reality, requirements are arbitrary, they are pure form and shape of waterfall (huge anti-pattern).
We need real science; real science does not work with requirements, it works with experiments. Thats the big shift we need to do.
Spec-Driven Development (SDD) is just the AI version of waterfall. IF we use AI agents correctly, we can work closely and very tightly with product analysts, UX designers, and architects/Engineers and figure out what via learning by experiments and doing.
Transformations: Hard Reality
Most of the transformations fail. Most of these transformations failed:
- Agile Transformations fail (become JIRA + Scrum as water-scrum-fall).
- DevOps Transformations fail (become centralized ops called the devops team)
- Digital Transformation fails (becomes a bloated mobile application with iframes)
I hope AI transformations are different, and that we learn from past failed transformations and actually do the things that are right and matter, no matter if they are AI or not. IF AI is the excuse to do the right things, so be it. But we already know that
95% of generative AI pilots failed.
IMHO, it's back to the learning; we are still learning how to learn effectively in companies.
How can we do better?
Transformations are not easy. Because people have egos, they have all sorts of political issues. Here are some actions to try:
- Create an experimentation culture: Where people are doing POCs all the time and discussing ideas, running experiments instead of making permanent changes up front.
- Keep Going: It's easy to establish momentum when you are aligned with your company and the industry trends; however, there will be harder times, where excitement will be gone, that's the time to continue doing the right things, even with less excitement.
- Do Retrospectives: It's easy to move fast, especially with AI Agents, but people need to be able to digest things, make sure you have regular retrospectives, and people internalize learnings and failures - otherwise the same mistakes will repeat over and over.
Cheers,
Diego Pacheco
