With Halloween right around the corner, the Toronto Product Management Association spearheaded the spooky topic of AI in a double-feature focused on the development of AI products and the application of AI in product management. The discussion panel drew a full house of professionals eager to learn more about the world of AI innovation.
Check out the video below for a full recording of the discussion.
Adam Haines, VP of Product at Blue J Legal
Sam Kawsarani, formerly VP of Product at RAILZ.ai
Moderator - Nahla Salem, Head of Product, Haven Energy
Adam is a product leader with two decades of experience delivering and growing products. He most recently launched Blue J’s generative AI solution for tax research, and learned a lot along the way. Here are the highlights:
· Beginner's mind: Roll up your sleeves and face challenges head on, be willing to continuously learn and adapt with the rapidly changing landscape of AI.
· Magnify product management competencies: Collaboration and communication are instrumental in aligning the multidisciplinary experts required to develop AI products.
· Evolve the product trio: Adapt the traditional product trio of design, product, and tech to include subject matter experts. This inclusion is critical for maintaining data quality and delivering valuable outputs.
· Aligning capability with business goals: Managing the tension that exists between the rapidly evolving technology, the capabilities that you're aiming to deliver to the market, and the goals of the organization to guide the product development.
· Data: Collaborating with subject matter experts to source high-quality, dynamic, and correct data to drive our AI products and ensure quality output.
· Multidisciplinary collaboration: Cultivating communication across multidisciplinary teams, and empowering teams to build context between themselves.
· Solve problems: Focus on addressing real issues and pain points. Effective product management involves engaging with users, concentrating on problem-solving, and assessing whether AI is a meaningful solution for both users and business objectives.
· Build trust: Establish trust with stakeholders by demonstrating the reliability of the technology. Ensure users can validate that the product effectively solves their problems and implement safeguards for secure and responsible technology deployment.
· Feedback loops: Identify opportunities for continuous improvement in algorithms based on user interactions with the product.
So what is the key? Take on a growth mindset, roll up your sleeves and dive in.
Sam is an experienced product leader who enjoys taking an entrepreneurial approach in product development. With a track record of countless successes, failures, and hard lessons, this is how he positions AI in his product practice:
AI can supercharge a product manager’s product practice by supporting the activities, but it cannot develop the skills. This is because AI relies on our changing activity. What is the difference between activities and skills?
· Activities are specific actions or tasks (ie. story writing, writing technical documents, planning a sprint, analyzing data from user discovery)
· Skills are the abilities and proficiencies we gain through experience (ie. leadership, communication, empathy)
· Market & user research: Collect, analyze, and predict future success based on historical data, or identify and build personas within a consumer database
· Idea management: Generate ideas for the roadmap, and discover pain points
· Technical specification: Generate templates to write user stories, technical specifications for APIs, build workflows to automate testing, define user acceptance criteria
· Product roadmaps: Identify key relationships between product development, company objectives, and metrics
· Prioritization: Identify stakeholder objectives and build prioritization suggestions
· Product development: Assist with various stages of product development (ie. UX copywriting, test case automation, SEO)
· Customer feedback collection: Personalize email campaigns, build automated and personalized flows to capture feedback
A tool! Embrace AI as an ally to help you create better products.
Through this thought-provoking discussion, we are left with a hopeful message: AI is not a mysterious force but a powerful addition to our toolbox. The possibilities are limitless, and the AI landscape is only starting to unfold. Thank you to our exceptional speakers and hilarious moderator for facilitating this engaging and valuable discussion with the community.
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With Halloween right around the corner, the Toronto Product Management Association spearheaded the spooky topic of AI in a double-feature focused on the development of AI products and the application of AI in product management. The discussion panel drew a full house of professionals eager to learn more about the world of AI innovation.
Check out the video below for a full recording of the discussion.
Adam Haines, VP of Product at Blue J Legal
Sam Kawsarani, formerly VP of Product at RAILZ.ai
Moderator - Nahla Salem, Head of Product, Haven Energy
Adam is a product leader with two decades of experience delivering and growing products. He most recently launched Blue J’s generative AI solution for tax research, and learned a lot along the way. Here are the highlights:
· Beginner's mind: Roll up your sleeves and face challenges head on, be willing to continuously learn and adapt with the rapidly changing landscape of AI.
· Magnify product management competencies: Collaboration and communication are instrumental in aligning the multidisciplinary experts required to develop AI products.
· Evolve the product trio: Adapt the traditional product trio of design, product, and tech to include subject matter experts. This inclusion is critical for maintaining data quality and delivering valuable outputs.
· Aligning capability with business goals: Managing the tension that exists between the rapidly evolving technology, the capabilities that you're aiming to deliver to the market, and the goals of the organization to guide the product development.
· Data: Collaborating with subject matter experts to source high-quality, dynamic, and correct data to drive our AI products and ensure quality output.
· Multidisciplinary collaboration: Cultivating communication across multidisciplinary teams, and empowering teams to build context between themselves.
· Solve problems: Focus on addressing real issues and pain points. Effective product management involves engaging with users, concentrating on problem-solving, and assessing whether AI is a meaningful solution for both users and business objectives.
· Build trust: Establish trust with stakeholders by demonstrating the reliability of the technology. Ensure users can validate that the product effectively solves their problems and implement safeguards for secure and responsible technology deployment.
· Feedback loops: Identify opportunities for continuous improvement in algorithms based on user interactions with the product.
So what is the key? Take on a growth mindset, roll up your sleeves and dive in.
Sam is an experienced product leader who enjoys taking an entrepreneurial approach in product development. With a track record of countless successes, failures, and hard lessons, this is how he positions AI in his product practice:
AI can supercharge a product manager’s product practice by supporting the activities, but it cannot develop the skills. This is because AI relies on our changing activity. What is the difference between activities and skills?
· Activities are specific actions or tasks (ie. story writing, writing technical documents, planning a sprint, analyzing data from user discovery)
· Skills are the abilities and proficiencies we gain through experience (ie. leadership, communication, empathy)
· Market & user research: Collect, analyze, and predict future success based on historical data, or identify and build personas within a consumer database
· Idea management: Generate ideas for the roadmap, and discover pain points
· Technical specification: Generate templates to write user stories, technical specifications for APIs, build workflows to automate testing, define user acceptance criteria
· Product roadmaps: Identify key relationships between product development, company objectives, and metrics
· Prioritization: Identify stakeholder objectives and build prioritization suggestions
· Product development: Assist with various stages of product development (ie. UX copywriting, test case automation, SEO)
· Customer feedback collection: Personalize email campaigns, build automated and personalized flows to capture feedback
A tool! Embrace AI as an ally to help you create better products.
Through this thought-provoking discussion, we are left with a hopeful message: AI is not a mysterious force but a powerful addition to our toolbox. The possibilities are limitless, and the AI landscape is only starting to unfold. Thank you to our exceptional speakers and hilarious moderator for facilitating this engaging and valuable discussion with the community.
Toronto Product Management Association (TPMA) is a community focused on development, growth and networking of Product professionals in the Greater Toronto Area.
