Product Talk
JUNE 19, 2024
I’m disappointed to see the rise of generative AI tools that are designed to replace discovery with real humans. I’m a big fan of generative AI. I’ll then share how and where I think generative AI can help, and clearly identify what we should avoid. Everything we do in discovery is in service of that.
Product Innovation Educators
DECEMBER 16, 2024
Drawing from his 20+ years of technology experience and extensive research, Nishant shared insights about how these activities vary across different organizational contexts – from startups to enterprises, B2B to B2C, and Agile to Waterfall environments.
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Product Talk
DECEMBER 4, 2024
If it’s a backlog full of feature requests and bugs reported directly from customers, it’s already much more valuable than a backlog full of internal ‘ideas.’ If your backlog is full of feature requests and bugs reported directly from customers, it’s already much more valuable than a backlog full of internal ‘ideas.’
Roman Pichler
APRIL 7, 2025
Note that Ive decided not to state the names of the tools I found, partly as the AI landscape is changing rapidly and partly as you should research and select the tools that work best in your context rather than trusting my judgment. [2] 5] What about Product Roadmap Generation?
Speaker: Dr. Greg Loughnane and Chris Alexiuk
Technology professionals developing generative AI applications are finding that there are big leaps from POCs and MVPs to production-ready applications. However, during development – and even more so once deployed to production – best practices for operating and improving generative AI applications are less understood.
Product Talk
DECEMBER 18, 2024
This definition is a mouthful, so I like to visualize it. I’m going to walk through this visual quickly, and then Cecilie and I are going to dive into this in more depth. Using the Opportunity Solution Tree to Guide Discovery The visual at the center of this is called an opportunity solution tree. It’s that simple.
Product Innovation Educators
MARCH 3, 2025
How AI captures customer needs that human product managers miss Watch on YouTube TLDR In my recent conversation with Carmel Dibner from Applied Marketing Science, we explored how artificial intelligence is transforming Voice of the Customer (VOC) research for product teams. However, these early efforts faced significant limitations.
Speaker: Anindo Banerjea, CTO at Civio & Tony Karrer, CTO at Aggregage
When developing a Gen AI application, one of the most significant challenges is improving accuracy. This can be especially difficult when working with a large data corpus, and as the complexity of the task increases. The number of use cases/corner cases that the system is expected to handle essentially explodes.
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In the fast-moving manufacturing sector, delivering mission-critical data insights to empower your end users or customers can be a challenge. Traditional BI tools can be cumbersome and difficult to integrate - but it doesn't have to be this way.
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In the rapidly evolving healthcare industry, delivering data insights to end users or customers can be a significant challenge for product managers, product owners, and application team developers. But with Logi Symphony, these challenges become opportunities. But with Logi Symphony, these challenges become opportunities.
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Generative AI is upending the way product developers & end-users alike are interacting with data. Despite the potential of AI, many are left with questions about the future of product development: How will AI impact my business and contribute to its success?
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The complexity of financial data, the need for real-time insight, and the demand for user-friendly visualizations can seem daunting when it comes to analytics - but there is an easier way. Together, we can overcome these hurdles and empower your users with the data they need to drive success.
Speaker: Data Robot
Enterprise AI maturity has evolved dramatically over the past 5 years. Most enterprises have now experienced their first successes with predictive AI, but the pace and scale of impact have too often been underwhelming. Now generative AI has emerged and captivated the minds and imaginations of leaders and innovators everywhere.
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Organizations look to embedded analytics to provide greater self-service for users, introduce AI capabilities, offer better insight into data, and provide customizable dashboards that present data in a visually pleasing, easy-to-access format.
Advertiser: Data Robot
The buzz around generative AI shows no sign of abating in the foreseeable future. Ultimately, the market will demand an extensive ecosystem, and tools will need to streamline data and model utilization and management across multiple environments.
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