00 SLR - AI and Software Product Management

https://gemini.google.com/app/5a81c0887dad125c

Criterio → Especifico da gestao de produto

Não está preparado para isto aquin Q1: Does the use of AI-enabled positioning and product definition using generative AI tools (compared to traditional methods) lead to the creation of better software products through deeper market understanding and more effective strategies, as perceived and measured by individuals contributing to product management activities?

Ultimas notas… antes de submeter

Nao esquecer de mencionar que em muitas situacoes foi colocado custo como, mas sem uma mensuração efetiva.

The Artificial Intelligence Revolution in New-Product Development https://ieeexplore.ieee.org/document/10330569

Evaluation of Generative AI-Assisted Software Design and Engineering: A User-Centered Approach First Online: 01 June 2024

New generation of tools with combined with other techniques Requirements .. upper level, not lower level Methodology

LLM-Based Simulation Scenario Aided Generation Method ChatGPT Spring AI-Driven

Go beyong, can be use, how good is it using by that, and what it takes to rearch full potential.

Mudar intro, com base no artigo abaixo 979-8-8688-0950-7.pdf -

Plan

help my do a systematic literature review of AI impact on Software Product Management.

Let’s break down how to conduct a systematic literature review (SLR) on the impact of AI on Software Product Management. This is a complex process, but I can guide you through the key steps.

Phase 1: Planning and Preparation

1. Define Your Research Question(s): Be precise! Instead of “AI impact on SPM,” consider something like:

   • “How does AI influence the process of requirements elicitation in software product management?”

   • “What are the documented benefits and challenges of using AI-driven tools for product roadmap planning?”

   • “How does the adoption of AI-powered analytics impact product success metrics?”

   • “What ethical considerations arise when using AI in software product management decisions?”

     IIDEA -> Using all the keywords of ISPMA? Product Strategy, Positioning ..

   A well-defined question will focus your search and analysis.

2. Identify Keywords and Search Strings: Based on your research question, generate relevant keywords and combine them into search strings. Use Boolean operators (AND, OR, NOT) to refine your search. Examples:

   • (“artificial intelligence” OR “machine learning” OR “deep learning”) AND (“software product management” OR “product roadmap” OR “requirements engineering” OR “product strategy”)
   • “AI” AND (“product manager” OR “product owner”) AND (“impact” OR “effect” OR “influence”) AND (“software development”)
   • (“AI-driven” OR “AI-powered”) AND (“product analytics” OR “customer insights”)

   Consider synonyms and variations of your keywords. Also, think about acronyms (e.g., “AI” instead of “Artificial Intelligence”).

3. Select Databases and Resources: Choose relevant academic databases and resources. Examples:

   • Computer Science: ACM Digital Library, IEEE Xplore, ScienceDirect, SpringerLink, Web of Science
   • Business/Management: Scopus, EBSCOhost, ProQuest, Emerald Insight
   • General: Google Scholar (use with caution due to its broad scope)

   Consider specialized databases related to software engineering or product management if they exist.

4. Define Inclusion and Exclusion Criteria: These criteria will determine which articles you include in your review. Examples:

   • Inclusion:
     • Peer-reviewed journal articles and conference proceedings
     • Empirical studies (quantitative, qualitative, or mixed methods)
     • Articles published within a specific timeframe (e.g., the last 10 years)
     • Articles written in a specific language (e.g., English)
     • Articles specifically addressing the impact of AI on software product management (not just general product management).
   • Exclusion:
     • Grey literature (e.g., blog posts, white papers, unless they are from reputable organizations and contain rigorous research)
     • Opinion pieces or editorials (unless they are highly influential and contribute significantly to the debate)
     • Articles not directly related to your research question
     • Duplicate articles

5. Develop a Data Extraction Form: Create a structured form to extract relevant information from the selected articles. This will ensure consistency in your data collection. Include fields like:

   • Author(s)
   • Year of Publication
   • Title
   • Journal/Conference
   • Research Question
   • Methodology
   • Key Findings related to your research question
   • AI techniques used (if applicable)
   • Software product management aspects impacted
   • Limitations of the study

Phase 2: Conducting the Review

6. Search and Identify Potential Studies: Execute your search strings in the chosen databases. Keep track of the number of results from each database.

7. Screening: This is a multi-stage process:

   • Title and Abstract Screening: Quickly review the titles and abstracts of the retrieved articles to eliminate irrelevant studies.
   • Full-Text Screening: For articles that pass the initial screening, read the full text to determine if they meet your inclusion criteria. This is the most time-consuming step.

8. Quality Assessment: Evaluate the methodological quality of the included studies. Use established quality assessment tools or checklists relevant to the study design (e.g., for randomized controlled trials, surveys, case studies). This helps you assess the reliability and validity of the findings.

9. Data Extraction: Use your pre-defined data extraction form to extract relevant information from the included studies. Be thorough and consistent.

10. Data Synthesis: This is where you synthesize the findings of the included studies. Look for patterns, themes, and discrepancies. You can use various methods for data synthesis, such as:

    • Narrative Synthesis: A descriptive summary of the findings.
    • Meta-Analysis (if applicable): A statistical analysis of the combined data from multiple studies (only possible if the studies are sufficiently similar).
    • Thematic Analysis: Identifying recurring themes in the data.

Phase 3: Reporting the Results

11. Write the Review Report: Your report should clearly describe the entire review process, including your research question, search strategy, inclusion/exclusion criteria, quality assessment, data extraction, and data synthesis. Present your findings in a clear and organized manner.

12. PRISMA Flow Diagram: Include a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram to visually represent the study selection process.

13. Discuss Limitations: Acknowledge any limitations of your review, such as the limited number of studies, the quality of the included studies, or the potential for publication bias.

Tools that can help:

• Rayyan QCRI: A free web-based tool for screening abstracts and full texts.
• Covidence: A paid software for managing systematic reviews.

Key Considerations for AI in SPM:

• Ethical Implications: AI in SPM raises ethical questions regarding bias in algorithms, data privacy, and job displacement. Pay attention to these in your review.
• Practical Applications: Focus on how AI is being used in real-world SPM scenarios.
• Future Research Directions: Identify gaps in the existing research and suggest areas for future investigation.

This is a comprehensive overview. Remember to consult established guidelines for conducting systematic literature reviews (e.g., the PRISMA guidelines). This process is iterative, so be prepared to refine your search strategy and inclusion criteria as you go along. Good luck!

TODO

e storing. Organized knowledge can be stored in folders with electronic documentation, databases,or project archives. However, the rapidly evolving generative technologies encourage considering more advanced methods such as prompt retrieval (Cheng et al., 2023), document embeddings (Sinoara et al., 2019), or generative model weights pretrained on a

Entrega

• Lista de palavras em ingles que indicam usam de IA

• Grafico incluindo o tipo de evaluation que foi feito

• Review coisas de LLM, em especial aquele gemini RQ e ver se tem algo a ser incluido no future research opportunities

• In order to have a well defined scope of activities.. we used the ISPMA framework. (ASK Hans-Berndt)

• Missed good definitions of evaluation that could be shared among studies

• Add prisma ref

• Core Activitives of PM (List them) - OUTPUTS vs OUTCOMES

• Justify PM Artifact?

• If the focus became LLM, is the text aligned with it?

• Teve pesquisas selecionadas interessantes Getting Inspiration for Feature Elicitation: App Store- vs. LLM-based Approach | Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering

• Automatic stats from MD

• Table - incluir inclusion/exclusion - criteria - documentar isto

• Table - Quality assessment

  • Check is keywords are present in the paper!

• Generative artificial intelligence in innovation management: A preview of future research developments - ScienceDirect

Theory Background

SPM Book: https://drive.google.com/file/d/1RnLL2IINrv68BvR8ILx7cjAULHGup8jp/view LLM version: https://notebooklm.google.com/notebook/f6f77edd-05aa-4263-a663-757aa22fef80

Ideas

SLR from Gemini: https://gemini.google.com/app/652ad21a63c3da19

• Objectives:
  • Not focusing on the how… focusing on the what… Not the underlying techniques
  • A look in the future, future looking
  • Future looking - Assuming the challenges, limitations and risks will be assumed - how the future of product management looks like ai-augument?
  • Starting point → The artifacts will still exist
• Scope
  • Core artifacts of product managers - https://gemini.google.com/app/04cde786b8f15d7a - user scenarios, stories, and storyboards
    • journey mapping
    • Know your customer
    • Emulating Users/Consumers
  • In the value chain, AI will transform development.. for example, from user stories to UML diagrams.. the impact is considerable … boundaries.. if development is more faster, information about use comes.. what will be the bottleneck?
  • What you don’t need?
• Context:
  • Something we can agree on .. and product will continue to have a roadmap? a product will continue to requirements? a product will continue to have feedback? the product will continue to have market considerations, competitors, etc.
  • Two moments.. somebody still managing the product.. vs no one management the product (assumption)
  • Other assumption: if we find novel things about what they are doing, we bring two contributions… one is that they can become better over time and overcome it’s actual limitations, risks, etc, and second, thinking forwardly, we can, based on the current stage, think forward on what could be the next things to happen.
  • select the things Generative AI can do that is not “automating” the work, it is augmenting the work, or even doing things a product manager couldn’t do without AI?
  • What you can do vs what you can achieve? https://gemini.google.com/app/dd39d21c2a92212c
  • Must include MultiModal, Agents…
• Categories
  • Value Stream Think… the categories
  • Could it be something like Product Management → Users/Consumers → Getting Data when you do not have, or want to expand
    • User Persona from application usage, application logs
  • Have consumers to your app, to better understand or find novel uses
  • Product Management → Data … Analyins,,, obtaining.. data is very generic..
  • Product Management → Product … enhance it’s communication… documentation…
  • PM → Stakeholders… managing then…
• Roadmap
  • Roadmap - O que um roadmap.. como se constroi um .. um dos pontos principais, e as estimativas… de prazo.. tempo.. clareza de prioridades… entao teremos fazermos roadmap em menos tempo, menos esforco? mas eles seram melhores? mais faceis de tomada de decisao, como uma re-priorizacao ao longo da jornada?

Hu, J. (2023). Revolutionizing product management with GPT. Mind the product. https://www. mindtheproduct.com/a-deep-dive-into-how-ai-can-help-product-managers-succeed/.

Potential places for Publication

https://icsob2025.github.io/ResearchPapers/

Knowledge about use cases

Enhancing PM with AI ai

Paper

Edit: https://onedrive.live.com/?id=root&cid=AE2CB29636ADBD03&qt=mru

https://onedrive.live.com/personal/AE2CB29636ADBD03/_layouts/15/Doc.aspx?resid=AE2CB29636ADBD03!see3ec90bc5be45fcab02dc84ff03f36f&cid=AE2CB29636ADBD03&migratedtospo=true&app=Word

Objective

To provide a comprehensive understanding of Generative AI’s impact on Product Management, particularly for software and digital products, this review aims to: analyze current GenAI use cases, explore its future potential and evolving role, and assess the associated challenges and considerations for effective implementation.

“This creates a gap between the creative and experimental potential of GenAI and its practical application in real-world PM scenarios.[HHO1] ’

We were looking for specific things to product management Ressaltr o desafio, cuidado Coisa fundacional, gererica e especifica Uma hipotese pode ser .. as coisas seram genericas quais a fronteiras do estudo em relacao a PM…

• mesmo que o que os product managers usam sejam coisas genericas, que servem para tudo, é importante dar uma visibilidade, hipotese Especificifc.

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 [HHO1]GOOD! This is what you want to explore!”

Core

Core Activities of a Product Manager: https://gemini.google.com/app/eb733869420f4ad6

what is the best research paper that defined product management responsibilities? Gemini

• Current situation: Lots of research analyzing Gen AI from a perspective of what the current tools… but what if i could bring a perspective, that untimely can help PM and people with interest in navigating the Gen AI?
• What does Generative AI provide? Content.
• Whether is new or review content: In simple terms… text, tables, charts, images, data..
• Product Managers deal with content all the time, need to produce.
• In complex terms, requirements, roadmap,
• Where does Product Management uses this type of contents? In some artifacts.. that can help.
• Untimely … on it’s activity… produce, review,
• What are the key factors influencing the effective integration and impact of GenAI on workflows? → It is less, because it will change quickly

So this SLR is up to show .. R2D2 Research: Gemini

DEFINE THE WHAT !!!!

reviewing and synthesizing user-generated content, such as product feedback and reviews. On the other hand, AI offers significant potential for streamlining workflows, enhancing market research, personalizing user experiences, and automating routine tasks, ultimately freeing up Product Managers for more strategic work.

• Not interested in marginal things.. improve tasks or productivity

Alex D. VP Product, Curative

The main thing that product leaders should be focused on right now is the complete transformation of discovery and delivery with AI tooling. While the product principles will remain, the rest is rapidly compressing on all stages of the process, timelines, and teams.

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Nao se limitar ao que existe agora, mas colocar um olhar, de que, com a evolucao da tecnologia o que sera?

Porque selecionei? Ponto de partida, comparar com o que ele faz hoje, entao pode ser valido.

New capability : User persona from Live visits

Points to think:

• Specialized Tools vs a LLM
• Culture aware product?
• Includes forming a Product Manager? Than includes francis more
• Tenho que olhar o que a pesquisa entregou, e nao o que ela falou

Value Proposition / Contribution

JAN - Weakest contribution

EVERYONE BELIEVES … everyone believes is true. KNOW ONE knows and you bring

This systematic literature review makes three key contributions:

• First, identity key uses of GenAI in Product Management
• Second, identify how the world of Product Management Changes because of GenAI - Other areas using gen AI will impact Product management. Since product manager is a orchestrator
• And finally, define a vision on how Product Management can be rethinked with GenAI https://gemini.google.com/app/726d0e865bcb0a62

First, identify key uses of GenAI in Product Management, based on existing studies along with the associated outcomes and challenges. Second, explore the broader transformative effects of GenAI on the product management role. And finally, define a vision on how Product Management can be rethought with GenAI.

1. What are the applications of generative AI in software product management? SRC: [2306.04605] Empowering Business Transformation: The Positive Impact and Ethical Considerations of Generative AI in Software Product Management — A Systematic Literature Review

Change a little bit.. if the key application are partially covered.. what would be the contribution??

RQ1: What is Generative AI? RQ2: How is Generative AI used in Software Engineering? RQ3: What are the benefits associated with using Generative AI in Software Engineering? RQ4: What are the risks associated with using Generative AI in Software Engineering? Src: Analysing the Role of Generative AI in Software Engineering - Results from an MLR | SpringerLink

RQ1 - Which product management activities have the highest potential to be addressed by GenAI?

RQ2 - How will advanced AI capabilities, enabled by the successful mitigation of current limitations, reshape the strategic focus, operational execution, and overall value proposition of product management activities?

RQ3 - From the evaluated use cases, what crucial challenges and considerations emerged for maximizing their potential and potentially improving preparation for future use cases?

Improved RQ2 https://gemini.google.com/app/61bfcea4407c52d8?hl=pt-BR

Key figures

Chart info: Now that’s matrix innovation : r/consulting https://gemini.google.com/app/e946d2020e49d44e

Interest

Would you be able to explore a bit more what the implications of GenAI on PM are?

Estamos interessados em entender o que GenAI: 1/ Complementar ou fazer melhor do que é feito 2/ Fazer coisas que ele poderia fazer mas não faz por impossibilidade de recursos 3/ Fazer coisas que simplementes ele não teria capacidade de fazer 4/ Fazer coisas que ele nao deveria fazer → Coisas que adiconam pouco valor relativo em relação a outras

• Would you be able to explore a bit more what the implications of GenAI on PM are?

GenAI can enhance current tasks, enable new ones previously hindered by resource constraints, achieve genuinely novel capabilities, and avoid low-value endeavors

My thinking is that the interaction between the GenAI agent and the human PM would be an issue to study? Also, what is the relationship to the rest of the organization? How can we use fast feedback loops with products deployed in the field to improve the performance of the AI agent?

WHAT I WOULD USE GEN AI FOR in Product? Idea state..

Text Polishing

Great insights here

https://gemini.google.com/app/d63d8585da6f82fc

Text harmonization

Here is the proofread text with all verbs in the simple present tense:

Generative AI → GenAI Artificial Intelligence → AI Product Management → PM Software Product Management → SPM Large Language Models → LLMs Avoid “will” as we don’t know. You can say “it has potential to”.

Qual a lista de palavras em ingles que sao usadas por generative ai mas que não sao comuns de serem utilizados na visa real, exemplo, delve.

https://www.perplexity.ai/search/3e92b04a-da5e-45f4-aa12-5cbbbc3d674c?login-source=sharedThreadLoginGate&login-new=false**

The Ultimate List of Common AI Words and Their Uses Discover the most common AI vocabulary words. | GPTZero

Conclusion

Concluion and abstract prompt: https://gemini.google.com/app/0513de42d729d386

Start the conclusion with repeating the objective of the study to then tell the reader how you accomplished this. What was the paper about? Why is this important? What did you find? (Helena)

Conclusion scheme:

• What was the paper about?
• Why is this important?
• What did you find?
  • Research Questions
  • Research Methodology
  • Future Looking
• Summary

Methodology

Other important aspect observed on the research methodology of the studies is a noticeable absence of methodological standardization in the reviewed studies; a common framework to facilitate the reproduction of experiments could simplify their re-execution. The monoculture of LLMs models creates a significant methodological weakness. The rationale for model selection, being largely based on convenience or “state-of-the-art” status at the time of writing, suggests that findings may be ephemeral. As new, more powerful models emerge, the conclusions of these early studies could quickly become outdated. This creates a hypothesis that research in this area will have a very short half-life of relevance in its initial phase. 

The field is building its foundational knowledge on a very narrow and potentially biased technological base. The capabilities, limitations, and inherent biases of the ChatGPT family of models are being implicitly baked into our understanding of how GenAI can serve product management. 

The focus on efficiency (time saved) over robust effectiveness metrics (impact on product-market fit, reduction of feature creep, user satisfaction) means the research is not yet measuring what truly matters for product success.

Future Looking

In seeking to anticipate future breakthroughs, this paper moves beyond mere documentation of the present state, actively laying the groundwork for future investigations and applications. This proactive approach aims to ensure the field is prepared to capitalize on the evolving capabilities of these powerful AI tools. Anticipating the evolution and challenges of these capabilities, and outlining necessary preparations, further equips practitioners with insights into both the present and the future – a crucial perspective given the rapid evolution of AI.

Textos Perdidos

Textos perdidos C:

Other important aspect observed on the research methodology of the studies is a noticeable absence of methodological standardization in the reviewed studies; a common framework to facilitate the reproduction of experiments could simplify their re-execution. The monoculture of LLMs models creates a significant methodological weakness. The rationale for model selection, being largely based on convenience or “state-of-the-art” status at the time of writing, suggests that findings may be ephemeral. As new, more powerful models emerge, the conclusions of these early studies could quickly become outdated. This creates a hypothesis that research in this area will have a very short half-life of relevance in its initial phase. 

The field is building its foundational knowledge on a very narrow and potentially biased technological base. The capabilities, limitations, and inherent biases of the ChatGPT family of models are being implicitly baked into our understanding of how GenAI can serve product management. 

The focus on efficiency (time saved) over robust effectiveness metrics (impact on product-market fit, reduction of feature creep, user satisfaction) means the research is not yet measuring what truly matters for product success.

Textos perdidos B:

Hypotheses were posited to explain the current situation, such as the possibility that the embedding of many generative AI capabilities within existing product management tools accounts for a significant portion of their current application.

Textos perdidos C:

“As conclusões que Podemos tirar dos estudos são de que as pesquisas disponíveis são experimentos, e não evidenciam uma maturidade na indústria. O fato de a tecnologia estar evoluindo em uma velocidade muito grande torna os estudos obsoletos muitos rápidos, uma vez que o avanço dos modelos pode significar uma completa superação das dificuldades encontras e das possibilidades de uso. Nota-se uma ausência de um padrão metodológico dos estudos, e um eventual modelo que possa facilitar a reprodução dos experimentos, pode facilitar a re-execução dos experimentos. O fato de terem muitas capacidades de generative AI embarcadas em ferramentas que são utilizadas para fazer gestão de produto podem representar uma grande parte do uso.”

https://gemini.google.com/app/7ae05c7e5cbc145b

Include info about early stage of the technology and even use cases All the claims of the papers, despite makes sense, are not fully prooved and can be used for generalization (more time saying suggests than generates)

One the challenges observed in doing research with a such fast-pacing theme, is that perspectives bring a “snapshop” related to the current capabilites. One author [x] maded a very thoughtly analsisis of tasks and if they could be assisted by ChatGPT with a explanation. But that explanation is made looking ChatGPT capabilities available at that time. With the perspectives of fast improvement and further innovations we are challenged to look how we should work with AI today, but also, how we will be working in the next year with the advancement’s perspective’s. 

Using a Systematic Literature Review to produce a view current specific PM use cases as a foundation point, a forward-thinking perspective that envisions the potential advancements of a future where today’s limitations are surpassed, operationalizing them in a stable and repeatable manner with managed risks, and integrating them seamlessly into existing workflows, and stated future vision for Generative AI Augmented Product Management.  

It is for sure does not cover everything, but it uses assuming these early-stage capabilities will evolve, overcome challenges and become the de-facto way do product managegement, it brings a reference to help shape and foresee the future, helping to take a look and prepation on how product managers will be working in the future and nurturing the path for new academic studies that will come.

Discussion

Discussion Section: The primary purpose of the Discussion is to interpret and explain the significance of your findings. It answers the question, “What do my results mean?” It should connect back to your introduction and research questions, explain how your results fit within the existing literature, and, importantly, discuss the implications of your work.

RQ1

Google Gemini - Discussion Topics

Note C

Focusing on outpus, and grouping product management outputs in the following groups: Product, Market and Customer.

Product related use cases, are the most promeint, as helping product managers and product team in producing/revieing content

Market related use cases seems very promissable, because by nature, it generates information that would be normally used as a input for strategy formulation, review, etc.

Efficiency, augmenting task with first versions or reviews.

Note B Customer Insight Capabilities - Summarization

Google Gemini

knowing its limitations to be context aware, for example, can be helpful to find ways to embed that into the interactions, or guidance that these are the perspectives that need to be evaluated, becoming more specific than the generic message that warms the output may be not accurate and need to be reviewed.

The first example provided was Voice of the Customer (VoC) analysis. Traditional VoC analysis can be manual, time-consuming, and struggle with the sheer volume and velocity of modern customer feedback, often leading to insights that are either too summarized to be deeply actionable or too delayed to be strategically relevant. If GenAI can reliably detect subtle patterns, emerging trends, or latent needs within VoC data, it can provide early warnings or identify untapped opportunities. This would elevate VoC from an operational reporting tool to a strategic asset that directly informs product decisions and helps preempt potential issues. 

The second example related to building User Personas and identifying behavioral tendencies, pain points, and other attributes from data-driven user analysis. 

The third example was related to improving questionnaires for customer interaction. 

The final example demonstrated how GenAI could enable the creation of interactive functional prototypes, making it possible to gain more insights when exposing them to customers.

Note A

From the thematic analysis, we were able to group the in themes those activities. 

Trying to see in a “capability” lens, the activities that have the most… 

Based on the sources, several product management activities show strong potential to be addressed or significantly enhanced by LLM. The reported outcomes and empirical findings suggest promise in areas related to understanding user needs, defining product features, and accelerating early-stage development tasks.

MECE principle (mutually exclusive and collectively exhaustive)

**

Prototyping (2)	Content Generation from Data– (4)	Content Analysis/Improvement – Plan (4)

• Product Planning, Vision?

• Capability Thought? 

• Not basic. Review of text. But include … 

• Customer?  

• Automatic for example, or supportive? 

• Hard … sensacao de ficar truncado.. Se eu tenho que ficar validando a toda hora.. Pode ser que nao valha a pena…

<<<<<<< HEAD Is this you speculating or is this what you found in the papers? Be clear on why the information originates! (Helena)

This SLR found experimental studies that primarily use ChatGPT or equivalent tools for product management activities, mainly related to product strategy and planning. Thinking about capabilities (pegar aqui). The findings indicate increased efficiency and capacity, and in some cases, improved quality compared to human performance. Furthermore, language and modal barriers are removed. GenAI also offers the possibility to enhance discussions, bring dynamism to activities, enrich processes, and potentially lead to faster outcomes. [HHO1] 

RQ2

Q2 Drafts - Google Docs

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Human Validation: A key aspect noted across the studies is the persistent need for human validation. This indicates that Generative AI (GenAI) outputs are often considered a starting point or supplement rather than a complete replacement for human expertise. The requirement of human validation was explicitly highlighted in two papers, as their systems were designed to assist humans.

[HHO1]There is quite limited information on this RQ compared to the next one. Are there more results on RQ1 that would be relevant?

======= Are these your reflections based on the papers you reviewed?

fdf43c9f1c106a0f7b41182deef17ee3c3661c81

RQ3

Discussion around potential overlap between underplored areas and RQ3 https://gemini.google.com/app/689d262dd04adba4

Product Provocative discussion? https://gemini.google.com/app/7fed0f3a45555b41

Viagem minha

LLM tem suas características, por exemplo, conseguir analisar grandes volumes de dados textuais. 

Esta pesquisa encontrou alguns estudos com alguns casos de uso. 

Considerando estes casos de uso, como eu consigo projetar uma direção no sentindo de apontar  

Looking to the three product management perspectives: Market, Customer, Product, a perspective based on the studies found have been formulated: 

A use case-based perspective was created to project capabilities toward what would be high potential. 

Customer: AI, exemplified by LLMs has potential to empower more perceptive and immediate data-driven approaches to customer interaction and provide a stronger foundation for shaping product direction and operational improvements. By overcoming traditional limitations in gathering meaningful user input, AI will facilitate continuous feedback loops through novel methods, enabling iterative refinement and a deeper grasp of user challenges. Furthermore, AI will transform how user understanding is synthesized and communicated, potentially offering dynamic and insightful representations that foster better-informed decisions and stronger alignment across stakeholders, ultimately leading to more customer-centric outcomes. This ability to deeply understand and anticipate user needs, currently exemplified through AI’s potential in analyzing user stories, can be extended to other product management artifacts. For instance, AI could analyze product principles to ensure they genuinely reflect customer values, scrutinize product requirements for their alignment with user needs, generate diverse use scenarios based on anticipated user behaviors, visualize user journeys through AI-powered mapping, and even contribute to the creation of storyboards that accurately depict user interactions. 

Market: As AI evolves, product managers gain unprecedented abilities to understand and leverage the competitive landscape within the market. By employing AI-powered tools, they can continuously monitor competitors, accessing real-time data on their product performance, pricing strategies, marketing campaigns, and even customer sentiment towards them. This constant flow of information allows for the identification of successful features and emerging trends adopted by competitors, serving as valuable inspiration for a product’s own roadmap. Furthermore, AI can analyze vast datasets of competitor activities to predict their future moves and potential market disruptions, enabling proactive strategic adjustments. The outcome is a more dynamic and informed approach to product strategy, leading to features that are not only innovative but also strategically positioned within the competitive market for optimal performance and differentiation. This AI-driven competitive analysis could inform the creation and refinement of various PM outputs, ensuring they are strategically differentiated.  

Product: Impacts foreseen from the studies in the rroduct” dimension is enhancing the quality and relevance of the offerings. AI’s ability to evaluate user story properties ensures a more robust and valuable backlog. By identifying well-written, critical questions, AI refines requirements elicitation, leading to a clearer understanding of user needs and a reduction in overlooked or incorrect requirements. The generation of user stories from initial requirements, coupled with AI-driven quality assessments and prioritization, results in a more focused and impactful product roadmap. Furthermore, AI transforms the design process by enabling the creation of more interactive and data-instrumented prototypes, significantly speeding up their development and allowing for earlier user studies with functional prototypes. AI also serves as a powerful inspiration for new feature ideas by analyzing user behavior tendencies, pain points, and attributes from existing data, ultimately leading to products that are more aligned with user needs and market demands. The example of AI evaluating user story properties can be seen as a microcosm of its potential across various product management artifacts. AI could similarly analyze product principles for internal consistency and market relevance, assess the completeness and clarity of product requirements, generate diverse use scenarios to test product assumptions, contribute to the visual coherence and user-centricity of storyboards, and identify key pain points and opportunities within journey maps. In essence, AI’s analytical and generative capabilities can enhance the creation, evaluation, and utilization of a wide spectrum of product management deliverables, ensuring they are more insightful, user-focused, and strategically sound.

Operations

https://gemini.google.com/app/0efd7649c9bd322b

Generative AI Augmented Product Management signifies … https://gemini.google.com/app/ec814ba2d7475b7e

Impact in society

Focus on the PM activities, not in the market impacts:

With the increase of abundance of customer data, has the change of creating the true connection with the customer, understanding it’s needs really needs, or its ability to understand may create products that Un purposeful products?

Will market perspective increase the level of competiveness to a certain level that creates higher market asymmetries and reduce the number of competitions? Will the number of product failures and the product life cycle be changes from current partners, whether direction is? (more long-lasting products or products that a launched and retired in faster cycles)

C. Underexplored Research Areas

Future Research Section (or “Limitations and Future Directions”): This section is more forward-looking. It answers the question, “What should be done next?” It explicitly identifies the boundaries of your study and proposes concrete questions or avenues that you or other researchers could pursue.

_Impact on Role Activities and Tasks Impact on Required Skills and Knowledge Impact on Role Relationships and Collaboration Impact on Role Performance and Effectiveness Impact on Role Satisfaction and Well-being

D. Threats to Validity

Poder ter tido pesquisas valiosas, que pelo título e abstract, não foi possível identificar. Assegurar que não estou usando palavras que somente GenAI usa

Internal Validity - Internal validity concerns the integrity of our research process and the potential for errors within the study itself.

• Selection Bias
• Researcher Judgment

External Validity - External validity relates to the generalizability of our findings beyond the specific context of this review.

Construct Validity - Construct validity addresses the potential for misinterpreting the concepts and data from the primary studies.

Temporal Validity - Temporal validity acknowledges that the findings are specific to the time frame of the review.

Can we draw conclusions based on a single study or should we aim to have it confirmed by multiple?

Definitions:

Threats to Internal Validity: Internal validity concerns the extent to which our review accurately reflects the true relationships within the existing literature.

Threats to External Validity: External validity refers to the generalizability of our findings to a broader context beyond the scope of this review.

Threats to Construct Validity: Construct validity addresses the extent to which our operationalizations accurately represent the underlying theoretical constructions being investigated.

Threats to Temporal Validity: Temporal validity concerns the extent to which the findings of this review remain accurate over time.

First input:

• Threats to internal validity: search strategy not include some relevant studies. Selecting only studies from four sources could lead to a selection bias. Quality assessments and other analysis relied on researches judgments.
• Threats to external validity: The focus on a certain keywords might limit the generability or even the amplitude of the findinds; much more other complimentary aspects are expected to be found.
• Threats to construct validity: Interpretation has a certain level of subjectivity and Proposed analysis were based on researches previous knowledge in the field
• Threats to temporal validity: are based on the existing body of literature at the time.

Results Others

C. Under explored Research Areas

Unexplored areas: There is a concept called MECE: Mutually Exclusive; Collectively Exhaustive. How do we create a sense of MECE for these topics? It reads a bit as a laundry list.

https://gemini.google.com/app/9db3d704ffb0b212

Please write a text for a section “Under explored Research Areas” based on the following input:

This systematic literature review came with a bottom-up approach, from the use cases mapping the capabilities and projecting then for the future, in other perspectives, that said, looking to the whole scope of product management can have some holes. Furthermore, based on this research identified this Under-explored Research Areas>

• Relationship Human vs AI in Product Management - Gain and Losses
• Models to operate AI withing product management - How to deal with a incredible increasing options of models and tools while doing the work - impact the work bringing innovation in the middle, like changing the turbines of the airplane in the air, without the risk of affecting negatively the travel objectives
• Integration of AI tools to seamless workflow - most of use cases where isolated, and combining

https://gemini.google.com/app/281f28bd1da3d131?hl=pt-BR

Appling the MECE concept:

• Inner
• Side
• Outer

Take some outputs, potentially leading   Help them deal with data, for example, a high volume of customer data.

Results

A. Descriptive Synthesis

https://notebooklm.google.com/notebook/a9f58940-0fa0-4985-8a8f-8ebc2a51d7cd

Notebook LLMS →

Bases on below statement, generate a challenge/difficulty GenAI proposal for each source, in a few words: It would be interesting to add more results/discussion around what exact challenge/difficulty GenAI is solving in each use case. For instance, using GenAI as a voice of customer (may) aim to address difficulties to reach out to customers/end-users. Such analysis would add depth to results and more focused discussion is using GenAI to solve a particular PM problem appropriate @review2

Refinement of the list and get the full potential https://gemini.google.com/app/b5e1f2ae6ce65d87

please help me understand the following comment about the attached document:

 For the results, it would be better to provide details for all the results to answer RQs when currently it is basically a set of bullet points. For example, it would be good to see what has been done and what is missing (based on each included paper) in the different aspects of PM activities that have potentials for GenAI.   https://gemini.google.com/app/6a2ded0d90d3f741

Positive impacts distribution - Make it a chart?

Is there any way to in some way visualise the findings, like in a graph. A figure or in a table? Just to help the reader see what you found in the papers.

quantitative_vs_qualitative - Google Docs - from notebookllm

B. Thematic Synthesis

write one phrase about Stablished Capabilities in Release Planning of this case write one phrase about Stablished Capabilities that can be used in Release Planning from the sources

To learn more, see Thematic Syntesis theory

Generative AI in Requirements Engineering: Automating and improving requirements elicitation, analysis and generation processes - GenAI was used to analyze product context and existing information to automatically identify well-written and critical questions for comprehensive requirements. It can also automatically generate initial user stories based on gathered requirements, assess the quality of these stories against established criteria, and provide suggestions for improvement. Furthermore, GenAI was used to inspire and suggest potential new feature ideas during the requirements elicitation process, thereby expanding the scope of consideration. Finally, it analyzes user log data to extract key insights into user behavior tendencies, pain points, and other relevant attributes, providing an important feedback loop from product usage. [NUM] 

Data-Driven Personas and User Analysis: Transforming static personas into dynamic ones using behavioral data. They can be used to generate user personas based on real-time application activity, overcoming challenges associated with obtaining direct user involvement and addressing information privacy concerns.  [NUM] 

AI-Design Integration and Prototyping: Tight coupling between AI and design workflows [NUM]  Voice of the Customer and Advanced Feedback Analysis - Overcoming limitations of traditional feedback analysis methods [NUM] 

Market Analysis: LLMs can analyze competition, as exemplified in the context of mobile apps. By processing app descriptions, user feedback in app stores, and potential functionalities, LLMs can identify popular fea tures, market trends, and unmet user needs. [NUM] Go beyond, faster.

Intro Suggested: https://gemini.google.com/app/3b4b2c38d31fcab9

Helena Feedbacks “Say something about how the themes you present were identified (from where were they derived and how)?” You could think about listing the themes according to their presence in the papers, I.e., topics that were mentioned win all papers are presented before topics that were mentioned in only a few papers. If a common pattern was not found how can this be a theme…? Is the suggestion about Design Science “just a speculation”? Be precise. How many papers? We know this. Was this said in the papers? In all papers? In only a few? (LLMs enable the processing of large volumes of data, which can be in multiple languages and modalities (text, voice, image, and video). As an overall comment, make sure to have each topic and theme read as if they are derived from the papers. Be precise on how many papers you found them in and how they are presented in these papers.

GEMINI ANALYSIS OF THE QUESTIONS BELOW, based on the descriptive https://gemini.google.com/app/e1edb2de4b04f8d1

What do these findings mean when put together? Why these models have been chooses? What does predominance of experiment means? Within Product Management, is there a trend of focus where it was studied? Things that are being done, of new thins? Does the positive impacts make sense?

What new insights, explanations, or hypotheses can be generated from combining these descriptive themes?

What overarching story or theory emerges from the collective evidence? Opportunity for

O ponto de estudos experimentais

Despite, More than half (55%) included evaluations of both efficiency and effectiveness

More i efficiency gains, some studies also noted faster delivery and potential cost reductions, especially when considering costs beyond those directly associated with product management staff. For evaluating effectiveness, various metrics were employed, including the generation of more perceptive and immediate insights, quality, accuracy, and relevancy.

O fato da predominancia tambem de um unico fornecedor, em geral, sem detalhamento do racional, e estudos com uma unica ferramenta reduzem a possibilidade de se entender se há diferença nos resultados, facilitando uma possivel decisão, que em alguns casos impacta em custo.

Themes to think about the question

-Q1 Product management activities

Methodology for Evaluation Capabilities Perspective Expanding Data-Driven Analysis Customer Perspective Market Perspective Product Requirements

Q2 - Challenges and Limitations

Summary, combination of notebook LLM and Gemini https://gemini.google.com/app/0904054fbcb3b0d6

• Human Validation

Q3 - Envision Future

here was no overlap between the use cases, but combining them in a capability perspective to give a higher-level sense of use as described in Table XX:  

Prototyping (2)	Content Generation from Data– (4)	Content Analysis/Improvement – Plan (4)

Manus output

Concluí a análise temática abrangente dos 10 artigos acadêmicos fornecidos. A análise identificou quatro temas principais emergentes na aplicação de IA generativa em engenharia de software e desenvolvimento de produtos:

1. IA Generativa na Engenharia de Requisitos - Automação e melhoria de processos de elicitação, análise e geração de requisitos
2. Personas e Análise de Usuários Orientada por Dados - Transformação de personas estáticas para dinâmicas usando dados comportamentais
3. Integração IA-Design e Prototipagem - Acoplamento estreito entre IA e workflows de design
4. Voice of Customer e Análise Avançada de Feedback - Superação de limitações de métodos tradicionais de análise de feedback

A análise revela padrões transversais importantes como hibridização metodológica, automação inteligente, integração sistêmica e validação empírica. As tendências emergentes incluem democratização da IA, colaboração humano-IA otimizada, personalização em escala e crescente demanda por transparência.

O relatório final de 47 páginas inclui metodologia detalhada, análise aprofundada de cada tema, discussão de padrões e implicações, identificação de limitações e recomendações para pesquisa e prática futura.

https://manus.im/app/k5ixCvktNVrKrYr91rpuCv

Should i consider the following dimensions?

Understanding and Defining Value Strategic Planning               Communication and Collaboration Execution and Delivery      Decision Making and Problem Solving Measurement and Analysis

Capabilities Perspective: Some papers introduce new capabilities, demonstrating tasks or activities that were not previously possible without Large Language Models (LLMs), such as real-time data integration and the generation of active people. In other instances, LLMs offer an alternative to manual processes or other existing techniques.

Additional Capabilities Worth to Highlight

Tentando destacar aspectos que preseupom coisas que não seria possível, isto é, expandiu-se a capacidade, duas capacidades apareceram:

Expanding Data-Driven Analysis: LLMs enable the processing of large volumes of data, which can be in multiple languages and modalities (text, voice, image, and video). This allows for the use of unconventional datasets, such as effortlessly analyzing all public posts about a competitor’s product.

Hardcore new files

Is it accurate to say the use case of this paper is “User Stories Quality Evaluation”?

use case: enhancing persona consistency for role-playing, to maintain a believable and consistent character throughout a conversation when they are acting in a specific role.

From a product management perspective, is it accurate to say that the core challenge this paper’s GenAI solves is enhancing persona consistency for role-playing, to maintain a believable and consistent character throughout a conversation when they are acting in a specific role?

From a product management perspective, what is the core challenge this paper’s GenAI solves in one sentence?

Customer

Capability

 Write a item of named “Customer” of a section “Results” subsection of a Thematic Synthesis section of an systematic literature review research paper based on the following inputs:

Customer Insight: Enhancing the extraction of Voice of the Customer (VoC). Its capability to handle large volumes of data in multiple languages leads to a more thorough and inclusive VoC analysis. 

Active Personas (APs): fictional users capable of generating contextual feedback through an interactive multi-modal interaction, such as text, voice, image, and video. APs enable development teams to gather feedback on their solutions through iterative internal experimentation.  User Personas from Live Activity: LLM-based user persona generation from live website visits. 

Generation of personas candidates.

Outcomes

https://gemini.google.com/app/e1a4d694a641aa71

Bridging the gap… getting information of user. Assess and process data that were not able before. More intense.

 Create a paragraph to represent an partial answer to “How does the future of AI-augmented product management look?” related to “Customer” dimension, based following inputs, with more emphasis to the outcomes, and not mentioning the use cases itself, being more agnostic:

• This breakthrough in VoC analysis suggests possibilities for more perceptive, immediate data-driven approaches in customer service, and lays a stronger groundwork for decisions in product evolution and process enhancement.
• Development teams use user feedback to derive requirement engineering artifacts, such as user scenarios, user stories, concept mindmaps, and user personas, to guide them in identifying and addressing a particular user problem. However, finding enough user participation to collect meaningful feedback is challenging, and less attention has been given to addressing this. In this paper, we propose Active Personas (AP), fictional users capable of generating contextual feedback through an interactive multi-modal interaction, such as text, voice, image, and video. AP enables development teams to gather feedback on their solutions through iterative internal experimentation and overcome the challenge of finding enough user participation to collect meaningful feedback
• Research on transparency tools for information privacy has largely focused on users’ perceptions of their usefulness—showing users prefer aggregated forms of data representation (e.g., interest segments) over hard-to-interpret raw data. However, users still do not understand what to learn from such interest segments. Representing a user’s digital footprint as a user persona may serve as an effective way to communicate such information. Large Language Models (LLMs) show promise in dynamically presenting information in a way that can change user perceptions of complex issues—including privacy, personal data disclosure and user persona generation.
• streamline the generation of personas, thereby enhancing the efficiency of UX researchers and providing inspiration for stakeholder discussions.

Considerations

• The paper concludes by urging further investigation to confirm these initial results and to explore the ethical aspects of employing such advanced natural language processing technologies.
•  indistinguishable from human-written personas, demonstrating similar quality and acceptance.

Market

Capability

 Write a item of named “Market” of a section “Results” subsection of a Thematic Synthesis section of an systematic literature review research paper based on the following inputs:

• Feature competitor analysis - Explore competitors’ apps to gather inspiration for new features using LLMs

Outcomes

 Create a paragraph to represent an partial answer to “How does the future of AI-augmented product management look?” related to “Market” dimension, based following inputs, with more emphasis to the outcomes, and not mentioning the use cases itself, being more agnostic:

• Explore competitors to gather inspiration for new features, access their performance.

Considerations

• Some recommended features are imaginary with unclear feasibility, which suggests the importance of a human-analyst in the elicitation loop.

Operations

Capability

 Write a item of named “Operations” of a section “Results” subsection of a Thematic Synthesis section of an systematic literature review research paper based on the following inputs:

• Use of LLMOps methodology for continuous monitoring and multi-agent approach for continuous improvement. Incorporating GenAI into product management in the manufacturing sector, facilitating cross-disciplinary knowledge sharing.

https://gemini.google.com/app/ed3d5113a3442d06

Outcomes

Create a paragraph to represent an partial answer to “How does the future of AI-augmented product management look?” related to “Operations” dimension, based following inputs, with more emphasis to the outcomes, and not mentioning the use cases itself, being more agnostic:

• facilitating cross-disciplinary knowledge sharing - continuous knowledge transfer and improvement through a knowledge flywheel.

Considerations

Product

Capability

Write a item of named “Product” of a section “Results” subsection of a Thematic Synthesis section of an systematic literature review research paper based on the following inputs:

• Leverage GenAI to identify well-written, critical questions. In our proposed Problem-Driven GenAI Requirements Elicitation Process, question formulation and requirements gathering are systematic, iterative, incremental, and interleaving
• Generate user stories from initial requirements, assess and improve their quality, and prioritize them using a selected technique
• Bring close to live interactive to feature validation using prompt-based prototyping into designing functional user interface (UI) mock-ups
• Requirements elicitation. LLMs can assist in this process by providing inspiration for new feature ideas.
• Users’ behavior tendencies, pain points, and other attributes are output solely from log data

Outcomes

Create a paragraph to represent an partial answer to “How does the future of AI-augmented product management look?” related to “Product” dimension, based following inputs, with more emphasis to the outcomes, and not mentioning the use cases itself, being more agnostic:

• Evaluation user story properties include structure, independence, value, testability, and grammar.
• identify well-written, critical questions, narrow down the infinite number of potential questions and helps address critical questions related to requirements elicitation, which is about understanding and addressing stakeholder needs. reduces the occurrence of omitted and wrong questions.
• generate user stories from initial requirements, assess and improve their quality, and prioritize them using a selected technique.
•  transform the design process making prototypes more interactive and data instrumentalized
• significantly speed up creating functional prototypes and enable user studies with functional prototypes earlier.
•  inspiration for new feature ideas.
• Users’ behavior tendencies, pain points, and other attributes from existing data

Considerations

• ChatGPT to write user stories does not guarantee high quality.
• ChatGPT can fail to recognize dependencies between user feedback and create structurally incorrect user stories. We see a need for students to be trained in effectively using this tool by carefully examining AI-assisted output and making revisions.
• Model selection - different models perform differently in effectiveness and efficiency
• The results suggest that, although some parameters still need improvement, fairly rated persona outcomes were attained.

Wrap

Outcomes

please review the following text, trying to extrapolate the case to other product management capabilities. For example, use story is mentioned, but here is should be noted as an example, that could be extended to other PM artifacts, like product principles, product requirements, use scenarios, storyboards, journey mapping (NOT USED, used proofread directly)

https://gemini.google.com/app/5f058b955c7a5f93

Considerations

Write a text to answer the following question “From the evaluated use cases, what crucial challenges and considerations emerged for maximizing their potential and potentially improving preparation for future use cases?” from the following input:

• The paper concludes by urging further investigation to confirm these initial results and to explore the ethical aspects of employing such advanced natural language processing technologies.
•  indistinguishable from human-written personas, demonstrating similar quality and acceptance.
• Some recommended features are imaginary with unclear feasibility, which suggests the importance of a human-analyst in the elicitation loop.
• Some recommended features are imaginary with unclear feasibility, which suggests the importance of a human-analyst in the elicitation loop.
• ChatGPT to write user stories does not guarantee high quality.
• ChatGPT can fail to recognize dependencies between user feedback and create structurally incorrect user stories. We see a need for students to be trained in effectively using this tool by carefully examining AI-assisted output and making revisions.
• Model selection - different models perform differently in effectiveness and efficiency
• The results suggest that, although some parameters still need improvement, fairly rated persona outcomes were attained.

Ideas?

Include a summary of benefits?

This facilitates ongoing operational improvements and fosters cross-disciplinary knowledge sharing, leading to enhanced efficiency and innovation.

IV. IMPLEMENTATION

A. Search Execution 

Exemplo - O que ficou muito complexo de incluir Why case studies…not only saying it being use, but a more formal approach.. How and how much.

Efficient and Effective Role Player: A Compact Knowledge-grounded Persona-based Dialogue Model Enhanced by LLM Distillation

https://gemini.google.com/app/a2b40e16a12b9d82

 Write a synthetic Search Execution subsection of a IMPLEMENTATION section of an systematic literature review research paper based on the following inputs:   A execução seguiu a metodologia definida na sessão III.B.1, iniciando a busca em cada uma das bases selecionadas com os keywords definidos.

Due to search engine limitations on the number of “AND/OR” combinations, some searches required breaking down the full query into multiple smaller queries, strategically exploiting the “OR” variants within each PICOC element until a permissible combination was found, but with no alteration of the query or terms, just adding an extra effort to combine results at the end.

To facilitate the automated execution of queries across these databases, a mechanism using a spreadsheet was developed. This spreadsheet contains the keywords, pre-constructed search strings (with necessary adjustments for the specific syntax of each search engine), and generated links for each database. This approach addresses limitations on the number of AND/OR combinations and significantly increases research productivity. Furthermore, it minimizes errors that could arise from the repetitive and mechanical process of manually replicating searches across multiple databases.

With these things solved, the results are;

• A busca na base SpringerLink, gerou X resultados.
• A busca na base IEEE Xplore, gerou X resultados.
• A busca na base ScienceDirect, gerou X resultados.
• A busca na base ACM Digital Library, gerou X resultados.
• Somente para fins de referência quantitativa, fica o registro que no google scholar houveram X resultados totais.

RESUTS

B. Selection of the Relevant Papers

PRISMA 2020 flow diagram — PRISMA statement PRISMA Flow Diagram

SUM of springer	SUM of acm	SUM of science	SUM of ieee
26	192	22	2

• SpringerLink: 26 results 
• IEEE Xplore: 2 results 
• ScienceDirect: 22 results 
• ACM Digital Library: 192 results 

https://gemini.google.com/app/1d3c7f80b82d11c2

• Cada combinação busca realizada teve seu número total de resultados registrado na planilha 

• Cada resultado então era inspecionado verificando os critérios de inclusão e exclusão.  

• Os artigos que atendiam os critérios eram então armazenados em uma lista, com marcação sobre a combinação de palavras-chave usada e o database. Em alguns casos o atendimento dos critérios I6 e I7 não era conclusivo somente pelo título e pela descrição resumida. Neste caso, optou-se por incluir, e garantir o atendimento a estes critérios mais adianto. 

• Ao final de que todas as listas foram montadas, abriu-se o detalhe para obter mais informações, como lendo a descrição completa do artigo suas referências e qualquer outra informação adicional. Novamente, optamos pelo conservado, se ainda se houve dúvida, optamos por incluir estes itens para leitura detalhada do paper. 

Na última etapa, que envolveu a leitura do paper em si, esclarecendo qualquer dúvida que tenha ficado em relação aos critérios e também aplicando o quality accessment. Os passos descritos, assim como as informaç1ões quantitativas de cada etapa estao sumarizadas em um PRIMA FLOW Diagrama.

METHODOLOGY

Feedback - Helena Fri, May 16, 9:48 AM Well done on shortening the research method section. I appreciate what I see and I have only very minor comments:

• Make sure to provide a reference for the frameworks/guidelines you use (e.g., PRISMA)
• Similarly, maybe there are references that can support your data extraction, analysis and synthesis? It would look even better if you helped the reader understand that what you did was to follow a common and well-established process
• There are a few formatting “mistakes” where I think you could improve the lay-out of the paper (to avoid e.g., large spaces with no content)

Product Management is such a practice that something you find research about it, even not having the research declared as product management, as pointed in a SLR  

We structure the search, considering three aspects role, practice and artifact in other to find  

To maximize comprehensiveness, keywords were curated into three blocks. Each block contained variations combined with “OR” logic. The full search query was constructed by combining these segmented blocks using “AND” logic and parentheses.

Keywords

see Keywords Master List

Hans-bernt product strategy, roadmap, release plan, requirements document.

Software Product Management search challenge, based on the SLR https://gemini.google.com/app/eba4e0764070b01a

Notebook LLM: https://notebooklm.google.com/notebook/f6f77edd-05aa-4263-a663-757aa22fef80

Variants:

Role

• “product manager” or “product managers”

Practice

“Product Management”

Strategic Management “Market Analysis” “Product Analysis”

Product Strategy “Product Strategy” “User Experience” “Product Vision” “Product Positioning” “Delivery Model” “Service Strategy” “Ecosystem Management” “Sourcing” “Pricing” “Financial Management” “Performance Management” “Risk Management” “New product development” “Business Model” “Product performance” “Product usage”

Product Planning “Product Planning” “Customer Insight” “Life Cycle Management” OR “Life-cycle” OR “Lifecycle Management” “Roadmapping” “Release planning” “Requirements management” “Requirements elicitation”

Artifact

Strategic Management & Business Aspects: Product Strategy “Product Definition” “Product Scope”

• Customer Insight:

  • “Customer insight ""
  • “Customer Journey”
  • “Customer feedback”
  • “Voice of the Customer”
  • “Persona”
  • “User Behavior”

• Requirements Management:

  • “Issue tracking”
  • “Wiki”
  • “\n”Knowledge base” OR “knowledge-base” OR “knowledgebase""
  • “Modeling”
  • “Prototyping”
  • “UX design”
  • “User Story”
  • “Requirements specification”

• Product Planning & Roadmapping:

  • “Roadmap”
  • “Backlog”
  • “Release Plan”

• Collaboration & Communication: (not)

  • Communication tools
  • Conferencing tools (e.g., Skype, GoToMeeting)
  • Messengers
  • Shared repositories (e.g., Dropbox)
  • Collaboration tools

PICOC

Descontinued

Population → Who? Intervention → What or How? Comparison → Compared to what? Outcome → What are you trying to accomplish / improve? Context → In what kind of organization / circumstances?

History of versions:

C - To create better products through deeper market understanding and more effective strategies.

P: Product Managers
I: AI enabled PM activities
C: Non-Enabled PM activities
O: Knowledge about where is used, can be used, benefits of being used and risks
C: Product Managers Activities

Database Selection

See: https://docs.google.com/presentation/d/1Ohs-8o5vC4Biel__t3NaGV-XVrN5yPZ_pWXQtOLMlek/edit?slide=id.g329097f7612_0_120#slide=id.g329097f7612_0_120

References

[PDF] Procedures for performing systematic reviews

B Kitchenham - Keele, UK, Keele University, 2004 - Citeseer

[PDF] Standard quality assessment criteria for evaluating primary research papers from a variety of fields

LM Kmet, LS Cook, RC Lee - 2004 - era.library.ualberta.ca

Perceived Value of UX in Organizations: A Systematic Literature Review | SpringerLink Local PDF: Preprint_PerceivedValueofUXinOrganizations-ASystematicLiteratureReview.pdf Peguei um pouco de cross reference aqui…

Evaluation of sources

Francis seems to be related to education..

From [2306.04605] Empowering Business Transformation: The Positive Impact and Ethical Considerations of Generative AI in Software Product Management — A Systematic Literature Review

E. Data Extraction

https://gemini.google.com/app/159fc76c696d1f23

Data Categories: 

• Article/Paper metadata – Like title, author, keyworks, publisher 
• Research Origin – Tracking which keywork combintation resulted in that link 
• Use case identification – what PM tasks it is related to, and what outcome 
• Classification among related PM capabilities 
• Evidence of improving efficiency  
• Evidence of improving effectiveness
• If the research refers is about software products exclusively

F. Data Analysis and Synthesis

https://gemini.google.com/app/4d79462372c97808

 - Descriptive Analysis - Thematic analyst using Deductive coding - Rather than using a distilled taxonomy as coding, we opted to focus on foundational elements. This choice avoids pre-establishing limitations on what GenAI can truly be or change within product management. - Understanding and Defining Value -  - Strategic Planning - Communication and Collaboration - Execution and Delivery - Decision Making and Problem Solving - Measurement and Analysis 

• Tables and charts will be used to display the synthesis results

Methodology Options

PRISMA/PICOC

https://www.google.com/search?q=prisma+%28preferred+reporting+items+for+systematic+reviews+and+meta-analyses%29+guidelines&sca_esv=6cae8d09a1050544&sxsrf=AHTn8zpMpQ4Te1CmtPnhlMuRm9G-n3zKug%3A1743445338179&ei=Wt3qZ-LPCpzT1sQPiMCkyQw&oq=PRISMA+%28Preferred+Reporting+Items+for+Systematic+Reviews+and+Meta-Analyses%29+&gs_lp=Egxnd3Mtd2l6LXNlcnAiTFBSSVNNQSAoUHJlZmVycmVkIFJlcG9ydGluZyBJdGVtcyBmb3IgU3lzdGVtYXRpYyBSZXZpZXdzIGFuZCBNZXRhLUFuYWx5c2VzKSAqAggAMggQABiABBjLATIIEAAYgAQYywEyCBAAGIAEGMsBMgYQABgWGB4yBhAAGBYYHjIGEAAYFhgeMgYQABgWGB4yBhAAGBYYHjIGEAAYFhgeMgYQABgWGB5I3BlQAFgAcAB4AZABAJgBfKABfKoBAzAuMbgBAcgBAPgBAvgBAZgCAaACiAGYAwCSBwMwLjGgB9MM&sclient=gws-wiz-serp#vhid=8-fkFzviIecM9M&vssid=_dt3qZ-vBMZnY1sQPkJSusAM_66

BACKGROUND

AI/PM/SPM Product Management Practices/ AI & SPM

Helena: A way to structure the background section could be to first introduce what PM is about to then go into how AI is currently used → Review Helper: https://gemini.google.com/app/e3160f42dce0daf0?hl=pt-BR

Product Management

Software Product Management

AI, GenAI, LLMs, Agentic, GENAI

What is LLM and what are other Generative AI types beyond LLM from trusted academic source? https://gemini.google.com/app/97a2938853eb2f56

GenAI, LLMs, Agentic Paragraph: https://gemini.google.com/app/6438e5b9ce691cd0

https://notebooklm.google.com/notebook/4b33d471-9d97-4fc0-9e81-a64835d83cf5

Common Challenges and Limitations of Large Language Models

https://manus.im/app/TfBamTkk7fo73Lq0qgNYZ3

AI and Product Management

Group 1

Deep dive: Revolutionising product management with ChatGPT - Mind the Product Local PDF: Deep dive_ Revolutionising product management with ChatGPT - Mind the Product.pdf Local PDF: Deep dive_ Revolutionising product management with ChatGPT - Mind the Product - Table.pdf

Group 2

Notebook LLM: https://notebooklm.google.com/notebook/2758bbbd-c1b5-48d2-982c-ab2a5fd01db5

AI AND PRODUCT MANAGEMENT: A THEORETICAL OVERVIEW FROM IDEA TO MARKET | International Journal of Management & Entrepreneurship Research Local PDF: AI_AND_PRODUCT_MANAGEMENT_A_THEORETICAL_OVERVIEW_F.pdf

AI Product Management 2 Years In - Silicon Valley Product Group : Silicon Valley Product Group Local PDF: AI Product Management 2 Years In - Silicon Valley Product Group _ Silicon Valley Product Group.pdf

Artificial Intelligence in Product Management Local PDF: IJCTT-V72I6P112.pdf

[2412.17866] Artificial Intelligence, Scientific Discovery, and Product Innovation Local PDF: Artificial Intelligence, Scientific Discovery, and Innovation.pdf

Text Segements Recent rapid advances in GenAI have sparked significant interest in its applications for product management. Its content generation capabilities empower product managers to accelerate exploration and execution. For instance, GenAI enables the instant creation of mockups, UI elements, and animated demos, drastically shortening design cycles. This facilitates rapid ideation, validation, and iteration, significantly enhancing product discovery.

Context

What about including the categories that fall into how genAI can support Product Management?

Another common approach is to classify Generative AI applications based on their specific use cases across different industries and functions. Examples include:

• Content Creation: Generating text (articles, product descriptions), images, audio, video, and code.
• Customer Experience: Enhancing chatbots and virtual assistants, providing personalized recommendations, and offering agent assistance.
• Employee Productivity: Building employee assistants for information retrieval and summarization, automating report generation, and aiding in code development.
• Process Optimization: Automating document processing, augmenting data for training models, and optimizing supply chains.
• Product Development: Generating design prototypes and optimizing existing designs.
• Software Development: Assisting with planning, development, DevOps, quality assurance, and safety aspects of software creation.

https://gemini.google.com/app/7148577ad0dc467d

Research Question

Thinking about the feedbacks:

• Research question 1 asks about the use cases with the highest potential. However, from my understanding, the study does not actually explore this. Instead, it generally focuses on identifying use cases. @review1

• RQ1 could have been defined better. Despite the importance of the question, it is not particularly accurate to map “the frequency of emerged themes from use cases” to “the potential for GenAI”. Please further reason such connection and/or reshape the question. @review3

https://gemini.google.com/app/57c21b34e88db9de

Before PROFES:

·     RQ1 - Which PM activities have the highest potential to be addressed by GenAI?

·     RQ2 - From the evaluated use cases, what key challenges and considerations emerged for maximizing their potential and improving preparation for future use cases?

·     RQ3 - How will advanced AI capabilities, enabled by the successful mitigation of current limitations, reshape the strategic focus, operational execution, and overall value proposition of PM activities?

Introduction

How that helped build or evolve better product Any business decision vs product related decisions

Talk Points: 1/ Context 2/ The GAP 3/ The Contribution 4/ Broader field [Talk a little bit more about how it looks like to adopt GenAI in product management – detail more the GAP – Creative/Experimental - Distraction]

Reference: Artificial Intelligence (AI) has become a core feature of today’s real-world applications, making it a trending topic within the software engineering (SE) community. The rise in the availability of AI techniques encompasses the capability to make rapid, automated, impactful decisions and predictions, leading to the adoption of AI techniques in SE. With industry revolution 4.0, the role of software engineering has become critical for developing productive, efficient, and quality software. Thus, there is a major need for AI techniques to be applied to enhance and improve the critical activities within the software engineering phases. Software is developed through intelligent software engineering phases. This paper concerns a systematic mapping study that aimed to characterize the publication landscape of AI techniques in software engineering. Gaps are identified and discussed by mapping these AI techniques against the SE phases to which they contributed. Many systematic mapping review papers have been produced only for a specific AI technique or a specific SE phase or activity. Hence, to our best of knowledge within the last decade, there is no systematic mapping review that has fully explored the overall trends in AI techniques and their application to all SE phases. (REF) What do we know about software product management? - a systematic mapping study | IEEE Conference Publication | IEEE Xplore

Abstract

https://gemini.google.com/app/9184cbff11c2cdfc

Research Question

History of versions: Q1: In large enterprise software companies, fast-growing SaaS startups, and organizations undergoing rapid digital transformation, does the use of AI-enabled Positioning & Product Definition using Generative AI tools (compared to traditional methods) lead to the creation of better products through deeper market understanding and more effective strategies, as perceived and measured by product managers?

Q1 What generative AI tools and techniques are used for product outcomes within platform ecosystems in a business environment?

References/Inspirations

Mapping DevOps capabilities to the software life cycle: A systematic literature review - ScienceDirect

Final inspiration Impacts of generative artificial intelligence on the future of labor market: A systematic review - ScienceDirect

Perceived Value of UX in Organizations: A Systematic Literature Review | SpringerLink

Multivocal Analysing the Role of Generative AI in Software Engineering - Results from an MLR | SpringerLink

Inspiration about the keywords? What do we know about software product management? - a systematic mapping study | IEEE Conference Publication | IEEE Xplore Systematic Mapping: Artificial Intelligence Techniques in Software Engineering | IEEE Journals & Magazine | IEEE Xplore

Full article: Exploring the use, adoption, and ethics of generative artificial intelligence in the public relations and communication professions

A que eu gostoei da Helena

Local PDF: SEEA_SLR.pdf

1. Search Strategy

Population	”software ecosystem*” OR ”platform ecosystem*” OR ”digital ecosystem*” OR ”digital platform*” OR ”software platform*
Intervention	”quantitative” OR ”driven” OR ”analy*” OR ”machine learning” OR ”deep learning” OR ”neural” OR ”reinforcement” OR ”predict*” OR ”mining” OR ”metrics” OR ”continuous” OR ”adapt*” OR ”simulat*” OR ”network” OR ”graph” OR ”visual*” OR ”agent”
Outcome	”cluster*” OR ”assess*” OR ”impact” OR ”recommend*” OR ”decision” OR ”health” OR ”generativ*” OR ”requirement*” OR ”performance” OR ”outcome” OR ”improve” OR ”grow*” OR ”support” OR ”feedback”
Context	”business*” OR ”industry” OR ”market” OR ”customer” OR ”user”

**

R2D2

give this list of articles, could you suggest which ones: https://gemini.google.com/app/8d99fdf41af0c702

What is a PICOC? » CEBMa PICOC https://gemini.google.com/app/4089e302dd52ea6e