Tech Leads Club published a dedicated site, ai-adoption.techleads.club, with only two public pages and a single content barrier: sign-up. It looks simple. Behind the simplicity, it is one of the best-designed technical community funnels to appear in Brazil in 2026: an opinionated framework, a readiness diagnostic in 9 questions, a lead magnet with semantic qualification via dropdowns, and the diagnostic result as an immediate reward for creating an account.
This post breaks down the entire site: the 7 phases of the framework grouped into 4 movements (Diagnose, Structure, Execute, Scale), the 9 dimensions measured by the diagnostic, the 3 answer levels (Low, Medium, High) and what they say about the reader before they even click submit, and the sign-up form that serves simultaneously as a content gate and as a qualification instrument. Anyone who works on AI adoption in engineering should look at this at least as a communication reference, maybe as a benchmark of product execution.
The two public pages, the site's whole route
The site is minimalist by choice, not by shortage. Only /framework and /diagnostico are accessible without login. The framework page is an opinionated presentation of the method: a short hero, a block summarizing 4 movements, a breakdown of the 7 phases, a link to the full guide (the community's main asset) and a final CTA pointing to the diagnostic. The diagnostic page is the questionnaire itself, which triggers the sign-up form right after the ninth question.
The top menu exposes three items: Diagnostic, Framework, Log in. The flow is clear and one-directional: read the framework, take the diagnostic, create an account, see the result, join the community. No blog, no glossary, no institutional page. The absence of peripheral items is not a shortage of content, it is a funnel decision.
The framework: 4 movements for 7 sequential phases
Tech Leads Club groups the 7 phases into 4 movements with verb-like names: Diagnose, Structure, Execute, Scale. Each movement defines a moment in the journey, and each phase carries a concrete objective and advancement criteria.
Diagnose (Phase 1)
Phase 1, Organizational and Engineering Diagnostic. Measures the readiness of the organization and of engineering for AI adoption, identifying gaps in culture, operational maturity and technical capability. It is the base on which all the following decisions will be made. Without this phase, the rest of the framework operates in the dark.
Structure (Phases 2 and 3)
Phase 2, AI Enablers Team. Defines the roles, responsibilities and ceremonies of the squad responsible for the adoption journey. Phase 3, Pilot Team Definition. Selects the squad where the first real validation in production happens, with scope, success criteria and a follow-up cadence. The structural premise is hard: no pilot starts without an explicit owner of the strategy.
Execute (Phases 4, 5 and 6)
Phase 4, Removal of Organizational and Technical Bottlenecks. Eliminates impediments that restrict adoption at scale. Phase 5, Progressive AI Adoption in the Development Flow. Integrates AI incrementally, delivering measurable gains in productivity and quality. Phase 6, Governance and Standardization. Establishes policies, standards and controls for responsible, traceable and consistent use. Order matters: governance without adoption is theater, adoption without unblocking is friction.
Scale (Phase 7)
Phase 7, Organizational Scale. Expands AI adoption to all engineering teams, operationalizing governance and learnings. This phase only makes sense after there is a proven pilot, bottlenecks resolved and standards established. Tech Leads Club is explicit in positioning scale as a consequence, not as a starting point. Anyone who tries to scale before phase 1 is building on emptiness.
The diagnostic: 9 questions, 3 levels, 1 implicit ranking
The questionnaire is the central asset. There are 9 questions, each with 3 options (Low, Medium, High), presented with dense descriptions that work as an evaluation rubric. The dimensions measured are not obvious until you read all of them in sequence:
- Squad autonomy: the ability to lead features end to end without external approvals.
- Adoption speed: the time between identifying a new practice and formally experimenting with it in the team.
- Operational ownership: the squad detects, resolves and learns from incidents, or delegates to SRE and ops.
- Deploy frequency and automation: monthly and manual versus daily and automated with rollback.
- Test coverage and quality accountability: separate QA versus quality gates in the pipeline.
- CI feedback loop: time from commit to production signal, proactive versus reactive observability.
- Architecture health and productivity: technical debt, onboarding time, reproducible local environment.
- Current AI use in the flow: none, individual, standardized across the team, or integrated in multiple stages.
- Seniority composition: the proportion of senior and staff engineers capable of autonomous technical decisions.
Notice that 8 of the 9 questions measure engineering maturity that is independent of AI: autonomy, deploy, testing, on-call, CI, architecture, seniority. Only question 8 is AI-specific. The implicit thesis is strong and correct: the AI adoption bottleneck is almost never AI, it is the engineering that AI will amplify. Anyone who answers Low on 6 of the 9 questions does not have an AI problem, they have an engineering problem, and no copilot will solve that.
Sign-up as the single gate, and as a qualification instrument
After answering the 9 questions, the site displays a sign-up screen. The text is direct: 'Create your account to access the full result and the AI adoption framework journey.' Without signing up, the reader answers 9 questions and is left without a score. This is the only friction point in the whole site, and it happens at the point of the user's greatest cognitive commitment, they have just thought about the state of their own engineering in detail.
The form has 10 fields, all with consistent internal IDs:
name,email,company,role: basic identification, with generic placeholders (Ana Silva, Tech Lead) that suggest the ICP.company_size,engineering_size: two size dropdowns, with buckets that cover everything from startup (1-10) to large company (1000+, 300+ engineers).ai_usage: a dropdown with 4 semantically loaded values,none,individual,team,organization. These are exactly the same maturity levels that question 8 of the diagnostic measures. In other words, the system cross-references implicit self-assessment (the answer to question 8) with explicit declaration (the form field), and has material to detect inconsistencies between what the leader says and what the team actually practices.passwordandpassword_confirmwithautocomplete='new-password': confirms that the act is creating an account, not logging in.consent_marketing: an opt-in checkbox, unchecked by default, aligned with LGPD.
No field has the HTML required attribute. Validation is done via JavaScript on submit. The fields send data to the API with values in English even though the front end is in Portuguese, a clear sign that the backend is internationalizable and that the team thought about separating presentation from data from the start. For a product that still looks pre-launch (a community under construction), that is a relevant level of care.
What this reveals about the community model
The site's construction answers a well-defined product question: how to capture technical leaders who are on the right problem, with enough qualification for the community to be relevant, without burning attention on a generic marketing landing page. The answer is triangular:
- Open opinionated content (the framework page) attracts those who are already on the problem. It does not convert skeptics, and that is deliberate.
- A free 9-question diagnostic makes the reader invest cognitive time before the gate. Nobody finishes 9 dense questions and gives up at the sign-up: the psychological cost is already sunk.
- Sign-up as a reward, not as a toll. The gate's text sells the result, not the community. The community comes later, as a discovery, further down the funnel.
The lead magnet is the diagnostic, but the product being sold is the thesis, and the authority of whoever built it. For anyone thinking about building a vertical technical community around a strong position, this is a clear execution reference.
Summary: what the site does well, ordered by importance
- It states an opinionated thesis about AI adoption. Sequential phases, no shortcut.
- It delivers the essential content without login. The full framework is on the public page.
- It uses the diagnostic as a self-assessment mechanism that is valuable on its own. Even those who do not finish the sign-up leave thinking.
- It qualifies the lead via semantic dropdowns that serve the backend, without an extra questionnaire.
- It treats sign-up as value delivery, not as marketing friction.
The result is an almost invisible funnel, in two pages, that fulfills the function of marketing, qualification, community and product at the same time. If you are building something in the AI adoption space in engineering, it is worth taking the diagnostic (it is free and the result is honest), reading the framework page (the opinion is clear, you can agree or disagree on that basis), and looking at the form's HTML calmly, especially the values of the ai_usage dropdown. The construction is discreet, and precisely for that reason instructive.