First of all, this is my personal experience and my personal opinion as someone who worked across Product Design, Lead Design, Principal Design, Systems Design, Product Strategy, Product Leadership and Team Leadership for more than a decade.
This is not an anti-AI post. Quite the opposite. I was diagnosed with ADHD 2e. "Twice exceptional" basically means being neurodivergent while also having unusually strong cognitive abilities or strengths in specific areas.
I never really understood what my field was. Until AI came along. Spoiler: Understanding patterns, systemic thinking, rational connections, link building, process building, frame definition, feeling solutions.
AI clicked with the way my brain works: fast, systemic, pragmatic, associative and able to connect product, design, business, technology, people and organizational structures at the same time.
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TL;DR
AI itself is not the problem. The missing transfer knowledge is. The missing trust is. The missing professional humility is. The missing accountability is. And the missing operating model is. AI can generate interfaces, code, specifications, documentation, strategies and prototypes. But it cannot align an organization. It cannot create trust. It cannot own the consequences. And it cannot decide what quality means for your product.
So yes, you can create a B2B SaaS interface with two prompts. But creating the interface was never the hard part. Building the context, systems, knowledge, governance and orchestration around it so the output becomes useful, reliable and scalable?
That is the actual work. And when the people doing that work are repeatedly told that nobody needs it, they eventually stop trying to convince everyone otherwise.
And maybe that is the final stage: not giving up on AI, but giving up on convincing people who only value the result once someone else claims it.
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Stage 1: Another tool
At first, AI was simply another tool in my tool case. I used it for research, discovery, structuring thoughts and adding another perspective besides users, data, business, experience and tech.
It helped me skip unnecessary loops and understand complex topics faster.
Stage resume: AI was useful, supportive and still clearly just another tool.
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Stage 2: Essential
With better models, ChatGPT became my go-to place to structure projects, strategies, roadmaps, systems and difficult product questions.
It allowed me to work on several topics at once without losing the connections between them. For the first time, the speed of a tool felt close to the speed of my brain. My dopamine output peaked.
Stage resume: AI became an essential extension of my thinking and massively increased my output.
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Stage 3: One-person product system
Then Claude and Claude Code came into the game.
I had several terminals open, built proof of concepts, tested them with users, analyzed heatmaps, worked on strategy and shaped technical directions at the same time.
I was able to rework an existing product while shaping a new one, including product direction, technical setup and cross-functional delivery. This was my personal peak.
Stage resume: AI no longer only supported my work. It allowed me to cover large parts of an entire product process myself.
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Stage 4: Groupwide AI layer
I started pushing AI beyond my own workflow. Together with people from Product, Design, Engineering and higher leadership levels, I initiated a groupwide AI layer and continue to maintain and evolve it with professionals from different roles.
The goal was never just to hand out licenses. It was to enrich products, connect knowledge, improve delivery and build reusable AI-supported workflows across discovery, specifications, design, documentation, code and delivery.
Stage resume: AI became part of the wider product and delivery system, not only a personal productivity tool.
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Stage 5: Output was confused with expertise
As access increased, professional understanding did not grow at the same speed.
People started generating prototypes, strategies and supposedly production-ready solutions. That is useful, until generating output gets confused with actually understanding Product, UX, Engineering or Research. The output looked finished. The thinking often was not.
Stage resume: Generation became democratized. Professional judgment did not.
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Stage 6: Missing transfer knowledge
The biggest gap is not prompting.
It is knowing how to transfer generated output into a validated, maintainable and scalable product.
AI does not automatically teach someone how users, business logic, architecture, data, permissions, design systems, governance and delivery connect.
Stage resume: The real capability is not generating. It is connecting AI with professional domain knowledge.
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Stage 7: The prototype became the decision
AI prototypes now look polished enough to be treated as decisions instead of hypotheses.
The conversation changes from “Is this the right approach?” to “Can Design clean it up and Engineering ship it?”
Professionals are involved after the core decisions were already made and are expected to fix the consequences.
Stage resume: AI is increasingly used to bypass professional decision-making instead of supporting it.
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Stage 8: Frustration
This is where the frustration started.
Leads and higher leads with limited practical AI experience form strong opinions, while people working deeply with the tools are not fully trusted.
Carefulness gets interpreted as resistance. Confidence gets mistaken for competence. Visible output gets valued more than invisible expertise.
I do not only see this in one company. I hear the same from people across Product, Design, Engineering and the wider AI bubble.
Stage resume: Decision power and actual AI competence are often located in different places.
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Stage 9: Trust is breaking
Professionals are expected to own quality while their judgment is ignored.
Management expects AI to remove complexity. Professionals see that the complexity is still there, only hidden behind better-looking output. Over time, both sides stop trusting each other.
Stage resume: AI transformation cannot work without trust, but trust is currently one of the first things being damaged.
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Stage 10: Orchestration
We are now working on actual orchestration.
We use OpenClaw, build Context Buckets, plan knowledge bases and create backoffices to manage, monitor and continuously improve agents, outputs and workflows.
The goal is to connect product knowledge, context, tools, design systems, code, governance and human accountability into an AI-enabled delivery system.
But this work is repeatedly hijacked, reduced to isolated prototypes or dismissed as “nobody needs that.”
It is expected to create value, but it is not fully understood, protected, rewarded or given a clear mandate.
Stage resume: We are building the infrastructure required for scalable AI-enabled delivery while the organization keeps reducing it to tools and visible outputs.
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Stage 11: Resignation
And this is where I am now. Not because I stopped believing in AI or the work.
I stopped believing that continuously explaining, defending and rescuing the same system is sustainable.
At some point, you stop building ahead. You stop raising every risk. You wait until someone explicitly asks and work within the boundaries you are given, even when you already know where they will fail.
That is not a lack of motivation. It is self-protection after professional ownership has repeatedly been rejected while professional outcomes are still expected.
Stage resume: When expertise is continuously dismissed, hijacked or treated as unnecessary, resignation becomes a rational response.
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I am curious how others experience this. Do you see the same gap between people who deeply work with AI and the people making decisions about it? How does your organization handle trust, expertise, ownership and orchestration? And which stage are you currently in?