Maximizing Product Management Efficiency with ChatGPT and Generative AI
Maximizing Product Management Efficiency with ChatGPT and Generative AI
Explore innovative AI strategies for product management. Gain insights on leveraging AI for planning, executing tasks, and analyzing feedback, boosting your productivity as a product manager.
Ciao,
Radical Curiosity continues to grow: we are up to over 870 subscribers with an email open rate of over 50%. Thank you. I consider this achievement very flattering. I hope the fun I have exploring curiously and writing each week translates into useful and interesting content for all of you to read.
This week, I’m sharing a collection of intriguing content instead of writing a short essay. Specifically, I focus on a lecture from Product School featuring Sam Stevens, CEO and co-founder of Catalyst AI. In this lecture, Sam explores the use of generative AI to enhance the productivity of product managers.
Table of Contents
Maximizing Product Management Efficiency: Insights from Samantha Stevens’ AI-Driven Strategies
7 Open Innovation Trends in 2024 by Paolo Borella
How Revenue Leaders at Box, Calendly, and Lattice Scaled From $0 to $100M+ and Beyond - SaaStr
Maximizing Product Management Efficiency: Insights from Samantha Stevens’ AI-Driven Strategies
Sam Stevens, an AI Executive in Residence at Product School, has a distinguished career trajectory. She began her journey as a product manager at American Express before transitioning to Tinder as a Director of Products. She led several teams in this role, focusing on growth, revenue, and new product development. After four years in the online dating industry, Sam joined Google. There, she contributed to the Google Assistant team for a year, working with early iterations of large language models, followed by a stint at YouTube. Subsequently, she co-founded Catalyst AI, an AI-driven project management assistant.
During her webinar with Product School, Sam shared insights on leveraging AI to boost personal productivity. Like myself, Sam utilizes a premium subscription of ChatGPT. While numerous specialized generative AI tools are available for task optimization, ChatGPT is sufficiently versatile and capable for most tasks.
Sam suggests using large language models (LLMs) in three key areas: planning (employing AI as a collaborative tool in product strategy), execution (enhancing daily productivity), and measurement (analyzing user feedback).
Plan: Use AI as a thought partner in product strategy
Sam’s process for harnessing AI to generate high-quality, useful ideas involves a blend of AI capabilities and human judgment:
Brainstorming with AI: Initially, use a Large Language Model (LLM) to generate new ideas related to your project, such as decision-making on a feature or product strategy for the year.
Providing Examples: To guide the AI, provide specific examples. This could include options you’ve already considered or discarded. The more context and information you give, the better the AI’s output.
Applying Human Judgment: As ideas flow from the AI, use your expertise to sift through them. Not all ideas will resonate, but some may inspire you. Your understanding of your product and market will help you identify which ideas are practical and align with your strategy.
Iterative Process: Interacting with an LLM is an iterative process. It’s unlikely to be perfect on the first attempt, so treat your ChatGPT interactions as a refinement cycle. Adjust your prompts based on previous responses to narrow down to more targeted ideas.
Providing Direction: Encourage further exploration if the AI starts exploring a particularly promising direction. This might involve focusing more on a specific target audience or business objectives.
Finding the Spark: Expect to encounter many subpar ideas; the goal is to find the ‘spark’—that one brilliant idea or a few inspiring concepts. It’s about searching for that ‘needle in the haystack’ that stands out.
After generating various ideas using AI, Sam recommends a four-step process to evaluate these ideas by weighing their pros and cons:
Providing Relevant Content: Initially, input all relevant data and insights into the AI tool. The more comprehensive and detailed the information, the better the AI can generate meaningful options.
Brainstorming and Comparing: Ask the AI to list all possible actions based on the provided information. This helps cast a wide net, uncover potential overlooked choices, and compare AI-generated options with those already considered.
Requesting Pros and Cons: Sam prefers asking the AI to create a structured table weighing each option’s pros and cons. This helps in understanding the implications and potential outcomes of each choice.
Review and Decision: Review the AI-generated list and integrate these insights with personal knowledge and judgment. This combined approach aids in making the most informed decision that aligns with one’s strategy and goals.
Finally, Sam suggests using AI as a tool to find blind spots. She also provides some interesting details on how to do it:
Detail the current situation,
Critique your strategy,
Evaluate AI feedback,
Incorporate new insights,
Prepare for pushbacks.
Execute: Optimize your day
As product managers, balancing long-term initiatives and immediate tasks is a common challenge. We all rely heavily on our shared calendars to manage meetings, timebox activities, and set expectations. Sam suggests that AI can help synthesize information to formulate a plan. To facilitate this, she has developed a prompt template that helps break down big goals into manageable tasks and assists in daily planning.
The process starts telling AI what to focus on and guiding it in the right direction. Then, Sam details the specific task, ensuring that any need for clarification or additional information is addressed. This step is crucial because sometimes AI tools can prematurely jump into solution mode without full context, leading to suboptimal outcomes. By having the AI first ask clarifying questions, it gathers the necessary information to break down large goals into actionable tasks effectively:
Once this initial interaction is complete, the process becomes a back-and-forth dialogue. I provide clarifying information, which the AI uses to refine the breakdown of my goals into practical, tactical tasks. This leads to the second step, which involves planning and optimizing my day.
In this step, Sam gives the AI a preamble containing details like energy levels throughout the day, how long specific tasks like sending emails take, and the goals she is aiming to achieve. The AI then uses this information to create an optimized daily schedule.
Measure: Analyze user feedback
Leveraging generative AI for user research, competitive analysis, and feedback analysis is highly efficient in enhancing our work as product managers. Typically, resources for such tasks are limited because most of the budget to create software-based products is allocated to engineering.
There are many specialized apps for analyzing user feedback, but if you don’t want to pay for another SaaS tool for your team, you can use chatGPT. Here is the process Sam suggests, including prompts.
Firstly, prepare your data by inputting it into a spreadsheet. This could be YouTube comments, app store reviews, survey responses, or Slack messages. Clean the data by removing spam, translating or filtering out non-English comments, and deleting irrelevant entries like random characters or spam links. Ensure your columns are clearly labeled for the LLM to understand, then download the spreadsheet. Quality data is key.
Next, upload your table or CSV to ChatGPT, assuming you have an advanced subscription with the data analysis and code interpreter functions.
Use a detailed prompt with clear instructions to run the analysis and identify themes. Here are some tips:
Ensure the system comprehensively reads all comments rather than just a sample.
Avoid overly broad categorizations; refine categories to be more specific.
Apply the principles of mutual exclusivity and collective exhaustiveness in category creation.
Use your judgment to verify the accuracy of categories.
Use a few-shot example to sort each comment into the most relevant category. Include a few rows of data in your prompt, showing the comment and its appropriate category. This helps the system understand what constitutes correct categorization.
After processing, always review the output for accuracy. While a few miscategorizations are acceptable, the level of accuracy depends on your use case.
I’ve also begun using this method primarily for analyzing Reddit conversations, reviews of competing products, and discussions in LinkedIn groups. It is a game-changer!
You can watch the full webinar on YouTube: Webinar: How PMs Use AI to 10X Their Productivity by Product School EiR, Samantha Stevens.
7 Open Innovation Trends in 2024 by Paolo Borella
On my usual quest for news and intriguing updates on LinkedIn, I share a summary of seven key trends in open innovation, as curated by my friend Paolo Borella. The full article on Boro blog: Trends in Open Innovation in 2024.
2023 marked 20 years since the term “open innovation” was coined by Henry Chesbrough at the University of California, Berkeley. During this time, the industry has grown and evolved, going through the usual phases of fast growth, big hype, recalibration, etc.
Open innovation is reaching maturity - so it’s a great time to look at the industry trends for 2024:
Shift from the ‘why’ to the focus on tangible results
Open Innovation verticals have crystalized: the big move from generic to industry-specific innovation
Education and change management are still important innovation drivers
Ecosystem engagement gains momentum
Corporate innovation models multiply
Smaller players are catching up
Industry complexity creates space for Open Innovation experts
Paolo Borella
How Revenue Leaders at Box, Calendly, and Lattice Scaled From $0 to $100M+ and Beyond - SaaStr
ChatGPT generated this summary as part of my experiments on automating content discovery and extracting valuable information from YouTube videos and podcasts. I’m open to suggestions for improving how transcripts are summarized.
In the dynamic world of tech, where innovation and revenue growth are pivotal, three seasoned Chief Revenue Officers (CROs) from notable companies shared their insights and experiences at a recent panel discussion. Dini Mehta, Kate Ahlering, and Mark Wayland, CROs from Lattice, Calendly, and Box, respectively, provided a deep dive into their strategies for scaling revenue and navigating the challenges unique to their companies.
Dini Mehta’s journey with Lattice, a people management platform, revolved around identifying and addressing a softening in new business growth. Upon joining Lattice at a time when it generated about $3 million in revenue, Mara faced the challenge of a team struggling to meet quotas despite high activity levels. His solution was a decisive shift in sales strategy - setting a minimum deal size, pausing the product-led growth (PLG) motion, and focusing on an Ideal Customer Profile (ICP). This bold move, though initially met with skepticism, resulted in a healthier sales pipeline and a significant increase in productivity.
Kate Ahlering’s experience at Calendly, a scheduling automation company, highlights the evolution from a product-led to an enterprise-focused approach. When Calendly decided to venture into the enterprise market, Ahlering emphasized the need for a comprehensive transformation across the organization, not just in sales. This included developing vertical solutions tailored to specific user groups and recruiting a diverse skill set across various departments, like marketing and customer success, to support this new direction. Ahlering’s approach illustrates the intricate process of successfully aligning a company’s product, people, and processes to cater to a larger, more complex customer base.
Mark Wayland’s tenure at Box, a cloud content management and file-sharing service, came with its unique challenges. When he joined, Box was grappling with a churn problem, which, upon investigation, turned out to be rooted in product issues. Wayland’s approach was methodical and data-driven, involving deep dives into customer feedback and market trends. The solution entailed not just sales adjustments but also product enhancements, acquisitions, and a revamped messaging strategy. His experience underscores the importance of holistic solutions encompassing multiple facets of the business, from product development to customer retention strategies.
These narratives from Metha, Ahlering, and Wayland provide invaluable lessons for any tech company aiming for sustainable growth. Their experiences stress the importance of agility in strategy, the need for a holistic approach to problem-solving, and the value of deeply understanding market needs and customer behavior. Their stories are a testament to the fact that in the fast-paced world of technology, adaptability, and strategic vision are key to driving revenue and achieving long-term success.
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Nicola