Quartz 4

00 PhD Proposal

8 min read

Proposal

Field of Study → SPM, AI Topic → AI Impact in Software Management Practices - With the perspective of AI being able to transform several functions, the focus of the research is to understand how AI transforms core product management functions of strategic product definition and planning, encompassing market analysis, product strategy, and life cycle management to define and deliver successful products.

? Should it be more narrow ? Selecting either Product Strategy OR Product Planning? If i have to choose, would be Product Strategy.

Following ISPMA’s taxonomy, items would be focused on:

  • Strategic Management: Market Analysis, Product Analysis
  • Product Strategy → Positioning & Product Definition, Delivery Model & Service Strategy, Performance & Risk Management,
  • Product Planning → Customer Insight, Product Life Cycle Management, Product Requirements Engineering

Macro Process

SLR Paper? → Case Studies → Specific Publications → Consolidated Publication (PhD Defense) Knowledge Base Updates, Core Research

Phase 1: Foundation - Systematic Literature Review (SLR) ?

  1. SLR Paper Publication: Write and submit a peer-reviewed paper detailing the SLR process and findings.

Phase 2: Core Research & Case Studies

  1. Research Framework Development: Based on the SLR, develop a conceptual or theoretical framework that guides the subsequent case studies. This framework should address the identified research gaps.
  2. Case Study Selection: Select specific case studies that will provide empirical data to test and refine the research framework. Consider factors like data availability, accessibility, and relevance to the research questions.
  3. Case Study Design and Data Collection: Design the data collection methods for each case study (e.g., interviews, surveys, document analysis, experiments). Collect data rigorously and ethically.
  4. Case Study Analysis: Analyze the data collected from each case study, drawing connections to the research framework. Identify key findings and insights.
  5. Knowledge Base Updates: As insights emerge from the case studies, update and refine the core research framework and knowledge base. This is a crucial iterative step. Document these updates clearly, explaining how the case studies have shaped the research. This might involve revising hypotheses, adapting methodologies, or even shifting the research focus slightly.
  6. Iterative Refinement: The findings from one case study should inform the design and data collection of subsequent case studies. This iterative process allows for continuous learning and refinement of the research. The knowledge base is constantly being updated and improved.

Phase 3: Consolidation and Dissemination

  1. Consolidated Publication (PhD Thesis): Synthesize the findings from the SLR and the case studies into a comprehensive PhD thesis. This document should:
    • Clearly present the research question(s) and the theoretical framework.
    • Detail the methodology used, including the SLR process and the case study design.
    • Present the findings from each case study and how they contribute to the overall research.
    • Articulate the updated and refined knowledge base.
    • Discuss the implications of the research, its limitations, and directions for future work.
  2. PhD Defense: Defend the thesis in front of an examination committee. This is the culmination of the PhD process.
  3. Further Publications: Beyond the thesis, publish individual case study findings or specific aspects of the research in peer-reviewed journals and conferences. This disseminates the research to a wider audience.

Publications:

  • SLR Publication on AI Impact in Software Management Practices

  • Case Studies

    • The main core is identifying opportunities based on what the organization wants to achieve, identifying potential AI tools and techniques, applying them, observing how it works and iterating (desirable to bring benchmark data - the gain is enabledment, productivity, etc.)
    • Case Study 1 - XXX
    • Case Study 2 - XXX

  • … + case studies

    Example Paper Ideia #1 - The company, a startup, has identified its major business challenge as growth (Business Problem). Through interviews, we’ve discovered they have a good product, reference clients, and a healthy sales pipeline. However, their product implementation process is lengthy and requires significant effort, hindering their scaling efforts and creating opportunities for competitors (Product Problem). Since AI has a high estimated potential impact on software product management (SPM) practices, how can leveraging AI within these product management activities address this challenge? The relevant PM activities span the Product Strategy level, including areas like Positioning & Product Definition, Delivery Model & Service Strategy, and even Ecosystem Management.

    Checklist: GAP:

    https://gemini.google.com/app/4ec14b64687c8536

    Example Paper Outline #2 - Example of a potential outline for a paper with a well-established organization I have in mind: The organization has been going thru a profound digital transformation of several and their current “wave” is how the upend their product management practices. They are undergoing a major chances on how they conceive the product.. define the experience. Their usual practices were their PMs defining the experiences, and now they realized they need more outside perspective, than they use to be. This could be related to “Customer Insight” and “product requirements engineering” activities. So, they could learn from the best practice, apply, and then get the results from IT. But, what could AI change is this scenario? Could they already start some type of AI-enabled customer insight? Or saving some resources of customer insight by using some type Synthetic Generated Data, in order to get of optimal effort evolving customer?

  • What’s the common ground of the case studies ?

    Running these cases studies we aim to product a knowledge base on:

      - Specific AI techniques (e.g., machine learning, NLP, process mining) applicable to PM activities.
  - Develop/Extend/Complement a conceptual framework for integrating AI into PM processes
  - Evaluate the potential benefits of AI-driven PM in terms of reduced implementation time, improved client onboarding, and increased revenue.

Core Research → Potential Outcomes (PhD Publication - Defense)

From the knowledge base created using the case studies, there will be opportunities to product the following research outcomes:

  • Frameworks and models: Developing new frameworks and models for integrating AI into software product management processes.
  • Tools and techniques: Creating new AI-powered tools and techniques to support product managers in their decision-making.
  • Best practices: Identifying best practices for using AI in software product management to maximize its benefits and mitigate its risks.
  • Guidelines and recommendations: Providing guidelines and recommendations for companies looking to adopt AI in their product management practices.

R2D2

Macro Process

https://gemini.google.com/app/358bbc8a72ace74a

Theme Review

what do you think about the theme for PhD - AI Impact in Software Management Practices - Study How AI is transforming software product management and help companies leverage AI to develop better products..

This is an interesting and timely theme for a PhD! Here’s a breakdown of why it’s a strong choice and some potential areas to explore:

Strengths of the Theme:

  • Relevance: AI is rapidly changing the software development landscape. Understanding its impact on product management is crucial for companies to stay competitive.
  • Impactful Research: Your research could provide practical guidance for companies on how to leverage AI effectively in their software product management practices.
  • Interdisciplinary Nature: The theme combines elements of computer science, software engineering, and business management, offering a rich area for exploration.
  • Potential for Innovation: You could investigate new AI-powered tools and techniques for software product management, potentially leading to significant advancements in the field.

Potential Areas to Explore:

  • AI for Requirements Engineering: How can AI be used to gather, analyze, and prioritize user requirements? Can AI help identify unmet needs and generate innovative product ideas?
  • AI for Product Roadmapping: Can AI assist in creating and managing product roadmaps? How can AI be used to predict market trends and prioritize features based on their potential impact?
  • AI for Agile Development: How can AI enhance agile development practices? Can AI help with task estimation, sprint planning, and risk management?
  • AI for Product Analytics: How can AI be used to analyze product usage data and identify areas for improvement? Can AI help personalize user experiences and optimize product features?
  • Ethical Considerations: What are the ethical implications of using AI in software product management? How can bias in AI algorithms be addressed? How can fairness and transparency be ensured?

To make your PhD even stronger, consider:

  • Focusing on a specific industry or type of software: This will allow you to delve deeper into the unique challenges and opportunities in that area.
  • Combining qualitative and quantitative research methods: This will provide a more comprehensive understanding of the impact of AI on software product management.
  • Collaborating with industry partners: This will give you access to real-world data and insights, and increase the practical relevance of your research.

Overall, this is a promising theme for a PhD with the potential to make a significant contribution to the field of software engineering and product management. Good luck with your research!

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