arxiv: 2605.03800 · v1 · submitted 2026-05-05 · 💻 cs.SE · cs.AI

AI Advocate: Educational Path to Transform Squads to the Future

Carla Soares , Gabriel Moreira , Ana Paula Camargo , Fabio Henrique Scacabarozi , Nicole Davila , Marselle Silva This is my paper

Pith reviewed 2026-05-07 15:53 UTC · model grok-4.3

classification 💻 cs.SE cs.AI

keywords AI Advocateshuman-AI collaborationsoftware development squadsupskillingexperience reporthybrid teamstechnology transformation

0 comments

The pith

Training select professionals as AI Advocates drives the shift of traditional software squads to hybrid human-AI structures.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents an experience report from a Brazilian technology company that trained certain professionals, called AI Advocates, to help move software development squads from conventional setups to ones built around human and AI collaboration. It positions this upskilling as the main mechanism that sparks both cultural changes in team practices and technical gains in productivity. A reader would care because many teams now face pressure to incorporate AI without losing their existing workflows, and the report offers concrete lessons and challenges drawn from the process.

Core claim

The upskilling of XPTO professionals, referred to as AI Advocates, acts as a catalyst for cultural and technical transformation that enables the transition of traditional software development squads into hybrid structures centered on collaborative work between humans and Artificial Intelligence.

What carries the argument

The AI Advocate educational path, which selects and trains professionals to embed AI collaboration practices inside existing squads.

If this is right

Squads that adopt the AI Advocate role will integrate AI tools more effectively into daily work.
Cultural resistance to AI decreases as Advocates model collaborative practices.
Technical productivity rises through structured human-AI handoffs within teams.
Other companies can anticipate the same training challenges when scaling similar programs.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

The program may require adjustments for companies operating outside Brazil or in non-private settings.
Success metrics beyond lessons learned, such as before-and-after productivity data, would strengthen the case for wider use.
The Advocate role could eventually become a permanent position rather than a temporary bridge.

Load-bearing premise

The experiences from training AI Advocates in one private Brazilian technology company supply lessons that apply to software development teams more generally.

What would settle it

A controlled comparison showing that squads in other companies reach comparable hybrid human-AI collaboration without any dedicated AI Advocate training program would undermine the claim that such upskilling serves as the necessary catalyst.

read the original abstract

This paper analyzes the strategic education process aimed at transitioning traditional software development squads into hybrid structures centered on collaborative work between humans and Artificial Intelligence (AI). In a context where human-AI collaboration can significantly increase productivity, this study explores how the upskilling of XPTO professionals, referred to as AI Advocates, acts as a catalyst for cultural and technical transformation. The objective is to present an experience report on the education and enablement process of AI Advocates within a private Brazilian technology company, highlighting key lessons learned and identified challenges.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

This is a single-company experience report on training AI Advocates with no metrics or controls to back the claim that it catalyzed real transformation.

read the letter

The paper walks through an internal training program at one Brazilian tech firm that designates certain developers as AI Advocates to help squads blend human work with AI tools. It covers the program's structure, some lessons on rollout, and a few challenges like skill gaps or pushback from teams. That kind of concrete description from practice is the main thing it offers, and it can give other companies a starting template for similar upskilling efforts if they are already thinking along these lines. The authors stick to what they observed without overclaiming theory or new methods, which keeps it grounded as an experience report. The central assertion that these Advocates act as a catalyst for cultural and technical shifts is stated clearly but rests entirely on the authors' narrative of the process. There are no before-and-after numbers, no productivity measures, no survey results, and no comparison to squads without the program, so the transformation part stays untested. The single-firm setting also means readers have to judge transferability themselves, with little detail on company scale or exact outcomes. This kind of report is mainly useful for industry practitioners or educators who want practical ideas on AI adoption in software teams rather than for researchers seeking generalizable findings or strong evidence. It is coherent on its own terms and engages honestly with the practical side of the topic. A serious editor could send it for peer review as an experience report, with the expectation that referees would push for clearer limitations and any available supporting details.

Referee Report

2 major / 2 minor

Summary. The manuscript is an experience report describing the education and enablement process for 'AI Advocates' (upskilled professionals) at a single private Brazilian technology company. It claims that this upskilling serves as a catalyst for transforming traditional software development squads into hybrid human-AI collaborative structures, while presenting lessons learned and challenges encountered.

Significance. The topic of integrating AI into software engineering teams is timely. As a descriptive account of a real-world training initiative, the paper could offer practical guidance to industry practitioners if the reported experiences are detailed and contextualized. However, the absence of quantitative metrics, controls, or validation limits its contribution to the scholarly literature in software engineering.

major comments (2)

[Abstract] Abstract: the central claim that 'the upskilling of XPTO professionals, referred to as AI Advocates, acts as a catalyst for cultural and technical transformation' is presented as an observed outcome but is not accompanied by any productivity deltas, adoption rates, survey scores, pre/post comparisons, or other indicators anywhere in the manuscript.
[Lessons learned and outcomes] The reported outcomes and lessons learned sections: the attribution of squad-level transformation to the AI Advocate program rests on descriptive narrative from one company without controls, longitudinal tracking, or external benchmarks, leaving the causal link untested.

minor comments (2)

[Conclusion] The manuscript would benefit from an explicit limitations subsection discussing the single-company scope and lack of generalizability.
[Introduction] Key terms such as 'hybrid structures' and 'AI Advocate' roles should be defined with concrete responsibilities in the early sections for clarity.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We thank the referee for their constructive feedback on our experience report. We address the major comments point by point below, acknowledging the qualitative nature of the study while revising the manuscript to better contextualize our claims.

read point-by-point responses

Referee: [Abstract] Abstract: the central claim that 'the upskilling of XPTO professionals, referred to as AI Advocates, acts as a catalyst for cultural and technical transformation' is presented as an observed outcome but is not accompanied by any productivity deltas, adoption rates, survey scores, pre/post comparisons, or other indicators anywhere in the manuscript.

Authors: We agree that the abstract presents the catalyst role as an observed outcome without supporting quantitative indicators. As an experience report from a single organization, the manuscript draws on qualitative observations and internal narratives rather than measured deltas or surveys. We will revise the abstract to use more cautious phrasing, such as 'we observed that the upskilling contributed to cultural and technical transformation,' explicitly framing it as descriptive rather than causally validated. This change will be incorporated in the revised version. revision: partial
Referee: [Lessons learned and outcomes] The reported outcomes and lessons learned sections: the attribution of squad-level transformation to the AI Advocate program rests on descriptive narrative from one company without controls, longitudinal tracking, or external benchmarks, leaving the causal link untested.

Authors: The attribution of transformation is based on descriptive narrative and observations within one private Brazilian company, without controls, longitudinal tracking, or external benchmarks, as is standard for experience reports in software engineering. We will revise the lessons learned and outcomes sections to explicitly state these limitations, including the single-company scope and absence of formal validation methods, to ensure readers understand the scope of the findings. No new empirical data will be added, as it was not collected. revision: partial

standing simulated objections not resolved

Providing quantitative metrics such as productivity deltas, adoption rates, survey scores, pre/post comparisons, controls, or external benchmarks, as these were not part of the original program design or data collection.

Circularity Check

0 steps flagged

No circularity in descriptive experience report

full rationale

The paper is a single-company experience report that narrates an upskilling program and lists lessons/challenges. It contains no equations, no quantitative predictions, no fitted parameters, and no load-bearing self-citations or uniqueness theorems. The central claim is offered as an observational summary of their process rather than a result derived from any internal construction, so the derivation chain is empty.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The paper is an experience report without formal models, mathematical derivations, or quantitative analysis. No free parameters, axioms, or invented entities are introduced.

pith-pipeline@v0.9.0 · 5389 in / 965 out tokens · 54657 ms · 2026-05-07T15:53:42.260753+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

7 extracted references · 2 canonical work pages

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