46 episodes

PM Hub Podcast Series features Product Leaders and Tech Entrepreneurs around the globe.

Product Manager Hub (PM Hub‪)‬ Cyrus Shirazian

    • Technology
    • 5.0 • 6 Ratings

PM Hub Podcast Series features Product Leaders and Tech Entrepreneurs around the globe.

    The Agile Data Science Playbook: Quarterly Sprints to ML Success featuring Lauren Creedon

    The Agile Data Science Playbook: Quarterly Sprints to ML Success featuring Lauren Creedon

    Summary







    Summary







    In this conversation, Cyrus and Lauren discuss the intersection of Agile and data science, specifically focusing on the challenges of shipping AI-enabled products quickly. They emphasize the importance of democratizing AI within organizations and the need for product managers to understand AI and ML concepts. They also discuss the prioritization of AI ML feature sets per quarter and the balance between quick wins and long-term strategic initiatives. Lauren shares her recommendations for getting buy-in and support from leadership, including listening, scenario planning, and making informed decisions.















    Takeaways









    Democratizing AI within organizations is crucial for enabling more people to understand and work with AI and ML.







    Product managers should prioritize AI ML feature sets based on business goals and market expectations.







    Balancing quick wins and long-term strategic initiatives is important for delivering outcomes and driving growth.







    Getting buy-in and support from leadership requires listening, scenario planning, and making informed decisions.







    Understanding the constraints and goals of different teams and stakeholders is essential for successful product management in the AI ML space.









    Chapters







    00:00 Introduction and Background







    03:25 Challenges of Delivering Business Value Quickly







    06:52 Democratizing AI within Organizations







    11:05 Scoping AI/ML Feature Sets for Revenue Outcomes







    14:12 Staying Up-to-Date with New Technologies







    27:40 Incorporating AI into Product Strategies







    28:54 Aligning Organizational Expectations and Goals







    30:09 Understanding Constraints and Goals







    33:10 Planning and Execution







    36:04 Balancing Quick Wins and Long-Term Strategic Initiatives







    40:17 Gaining Buy-In from Leadership







    43:10 Democratizing Knowledge about AI and ML















    Keywords







    Agile, data science, intersection, challenges, shipping, AI-enabled products, democratizing AI, product managers, prioritization, feature sets, quick wins, long-term strategic initiatives, buy-in, leadership

    • 44 min
    AI Integration in Established Product Ecosystems with Geert Timmermans

    AI Integration in Established Product Ecosystems with Geert Timmermans

    Summary







    Geert Timmermans, CPTO at StoryTech, shares his background and experience in integrating AI into product development. He emphasizes the importance of bridging the gap between engineering and product teams to maximize the value of AI. The challenges organizations face when integrating AI include the need for the right skillset, avoiding gimmicks, and focusing on the value AI brings to customers. Timmermans suggests a strong focus on product discovery and continuous discovery to ensure AI is integrated effectively. He also highlights the importance of giving engineers the freedom to experiment and collaborate with the product team. AI should be seen as an enabler and an opportunity to enhance and augment human capabilities, rather than a threat or replacement. It can assist in various industries, such as healthcare and marketing, by speeding up processes and improving quality. The adoption of AI requires a mindset shift and a willingness to upskill. It is important to build AI architecture in a way that allows for flexibility and the ability to plug in different models and suppliers. Data readiness is a challenge for many organizations, and a phased approach to AI implementation can help overcome this by starting small and gradually scaling up.















    Takeaways









    Bridging the gap between engineering and product teams is crucial for successful AI integration.







    Product discovery and continuous discovery are essential for effective AI integration.







    Avoid gimmicks and focus on the value AI brings to customers.







    Give engineers the freedom to experiment and collaborate with the product team. AI should be seen as an enabler and an opportunity to enhance and augment human capabilities.







    AI can assist in various industries by speeding up processes and improving quality.







    The adoption of AI requires a mindset shift and a willingness to upskill.







    Building AI architecture with flexibility and the ability to plug in different models and suppliers is important.







    Data readiness is a challenge, and a phased approach to AI implementation can help overcome this.









    Chapters







    00:00 Geert's Background and Role as CPTO







    07:06 Challenges of Integrating AI into Established Product Ecosystems







    10:08 The Importance of Collaboration between Engineering and Product Teams







    12:27 Product Discovery and Continuous Exploration for AI Integration







    14:23 AI as a Foundational Aspect of Product Development







    14:49 Introduction and Product Discovery







    19:20 Collaboration between AI and Software Development Teams







    36:09 The Phased Approach to AI Integration







    40:39 The Challenges and Realities of AI







    48:10 Data Quality and IP Protection







    51:47 AI as an Enabler, Not a Threat















    Keywords







    Geert Timmermans, CPTO, StoryTech, background, integrating AI, product development, engineering, product teams, skillset, value, challenges, product discovery, continuous discovery, engineers, collaboration, AI, enabler, opportunity, enhance, augment, assist, healthcare, marketing, adoption, upskill, architecture, flexibility, data readiness, phased approach

    • 49 min
    The Language of Innovation: Navigating the NLP Revolution with Ivan Lee, CEO of Datasaur

    The Language of Innovation: Navigating the NLP Revolution with Ivan Lee, CEO of Datasaur

    Summary







    In this conversation, Cyrus and Ivan discuss various topics related to NLP (Natural Language Processing) and its impact on AI. They cover Ivan's background in AI and NLP, pivotal moments in his career, the current state of the NLP industry, best practices for data collection and NLP-powered products, the challenges of scaling LLM-POCs (Large Language Models Proof of Concepts) into production, and the ethical considerations of NLP. They also touch on the future of NLP and AI, including the potential for AI agents and the role of NLP in unlocking human creativity.







    Takeaways









    NLP is revolutionizing AI by enabling machines to understand and process human language.







    Data collection and the design and build of NLP-powered products require careful consideration and alignment with business metrics.







    Labeling data for NLP models can be time-consuming and expensive, and automation tools can help save time and money.







    Ensuring consistency and accuracy in NLP models is crucial, especially when dealing with multiple correct answers and user intent.







    The future of NLP and AI holds exciting developments, such as multimodal language understanding and unlocking human creativity.







    Ethical considerations are essential in the application of NLP, and measures must be taken to protect user privacy and ensure fairness.







    Integrating NLP into products and services requires a positive and forward-thinking mindset, embracing the potential of NLP to enhance user experiences and drive innovation.









    Chapters







    00:00 The Current State of NLP Industry







    00:15 Pivotal Moments in Ivan's Career







    03:24 Advancements in NLP and LLMs







    14:27 Data Labeling and Saving Time and Money







    17:54 Impact of Lawsuits and Real-Time Use Cases on User Experience







    18:51 Future-Proofing Products and Fine-Tuning Models







    19:52 Standardization and Automation in Model Development







    21:19 Scaling LLM-POCs into Production Environments







    23:03 Complexity of Multiple Truths and User Intent in NLP







    24:20 Best Practices for Labeling and Model Training







    27:01 Case Study: Impact of DataSaur's NLP Technology on the Legal Industry







    28:55 Ensuring Consistency and Accuracy in Model Output







    34:14 Ethical Considerations in NLP and AI







    39:04 Exciting Developments in NLP and AI







    45:18 Advice for Integrating NLP into Products and Services

    • 47 min
    Ethical Considerations and the Future of AI in Healthcare with Arnon Santos

    Ethical Considerations and the Future of AI in Healthcare with Arnon Santos

    Summary







    In this conversation, Arnon discusses the ethical concerns surrounding AI in healthcare, including privacy and data protection, explainability and liability, and the balance between regulations and innovation. He also explores the role of clinicians in the adoption of AI, the importance of informed consent and patient education, and the need for multidisciplinary discussions to navigate the ethical challenges. Arnon envisions the widespread adoption of personalized medicine integrated with telemedicine as the most significant change in healthcare as a result of AI in the next decade.







    Takeaways









    Privacy and data protection are paramount in AI healthcare, and regulations like HIPAA and GDPR need to be adaptable to the evolving AI landscape.







    Explainability and liability are crucial in building trust and ensuring that AI is used as a tool to enhance human decision-making, not replace it.







    Clinicians play a vital role in ensuring the ethical use of AI in medicine and should engage in multidisciplinary discussions with AI experts, ethicists, and legal professionals.







    Patient education and informed consent are essential to empower patients and ensure they have a say in how their data is handled.







    The future of healthcare will likely involve the widespread adoption of personalized medicine integrated with telemedicine, breaking down geographic and economic barriers to high-quality care.









    Chapters







    00:00 Introduction and Background







    03:01 Ethical Concerns in Healthcare AI







    06:04 Privacy and Data Protection







    07:25 Explainability and Liability







    08:28 Balancing Regulations and Innovation







    10:40 Using Synthetic Data







    11:58 Addressing Bias in Healthcare AI







    19:21 The Role of Clinicians in AI Healthcare







    25:03 Informed Consent and Patient Education







    30:18 Educating Healthcare Institutions and Regulatory Bodies







    31:44 The Evolving Role of Clinicians







    35:37 Regulations and the Future of Healthcare AI







    40:05The Future of Personalized Medicine and Telemedicine

    • 41 min
    UX for AI Products with TikTok’s Global Head of Monetisation Integrity Services, Simon O’Regan

    UX for AI Products with TikTok’s Global Head of Monetisation Integrity Services, Simon O’Regan

    In this episode with Simon O'Regan, you will learn:







    What is UX for AI products and why does it matterHow does UX for AI products change depending on the type of productSimon's overall strategy for UX part of AI Products















    Intro music by Peter Boros of The Nameless Citizens

    • 44 min
    Stakeholder Management for AI Products with Consultant, coach, keynote speaker, and author, Paul Ortchanian

    Stakeholder Management for AI Products with Consultant, coach, keynote speaker, and author, Paul Ortchanian

    In this episode with Paul Ortchanian, you will learn:







    How do you figure out the right stakeholders for an AI product that you’re working onWhat are some unique challenges for stakeholder mgmt for AI products?Paul's overall strategy for managing stakeholders for AI products?















    Intro music by Peter Boros of The Nameless Citizens

    • 41 min

Customer Reviews

5.0 out of 5
6 Ratings

6 Ratings

benry ,

A true product passion project

Cyrus puts his all into this project and it shows. With a great range of guests, products, leaders and doers, and thinkers and thought provokers you’re sure to find something here to help your product journey.

kimp900 ,

Love it!

Love how Cyrus talks about topics that are in-time with what's happeining in the Product and Tech industry. Highly recommended!

Lui878 ,

Fantastic show!

I love how this podcast is set up; very relatable topics with notable experts. keep it up!

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