Weeknotes 446
I did:
- Wrote up last week’s workshop with the Delivery Manager’s community which focused on reporting and conflict resolution.
- Talked about vibe, and using it for workshops as a way of setting shared expectations. My go-to vibe is ‘rocket dog’, a happy jack russell riding a rocket. To me, it means we’re going to do this at pace, jump around a bit, and stay positive.
- Started planning a workshop to design team responsibilities, mandate level, interactions, etc.
- Met my new line manager. Was good to talk through the product vision and possible routes for getting there over the next few years.
- Did some more market analysis and theory of change work on a new opportunity, and lined up getting buy-in from senior stakeholders.
- Talked about how Profession and Community complement each other, with Profession providing the top-down ‘what’ and the Community creating the bottom-up ‘how’.
- Got my ticket for ProductCon next week.
The numbers
Tasks completed: 37
Minutes in meetings: 825
I read/listened:
The Impact of AI on Product Management: A Systematic Review and Future Trends
The integration of AI has greatly increased its functionality in product management across innovation and market development from the initial concept to the actual market share. AI tools have helped product managers to improve traditional processes as it is an advanced tool which can analyze a large dataset, identify the patterns and facilitates to generate efficient strategies. AI product managers are essential in driving the identification of business problems best solved with AI, defining the overall strategic plan, and guaranteeing that AI is implemented ethically, safely, and with transparency and reliability. The paper provides an idea of what is an AI product manager, how does AI influences more traditional marketing models (B2B and B2C). These advancements accentuate how AI holds promise for constant enhancement and sustaining competitiveness in a rapidly changing market environment.
The challenges of studying in the ‘platformised’ university
University life is now increasingly mediated by digital platforms. Joe Noteboom’s research looks at the everyday realities of studying through platforms, and how students’ dependence on these technologies can lead to a number of problems.
Remote-first team interactions
The tragedy of the anticommons
The ‘tragedy of the anticommons‘ occurs when a resource has many owners, all of whom have the ability to exclude others from using it, leading to the under-utilization of that resource. I wonder if/how this happens in organisations?
I thought about:
Looking back
Thought about how hard it is to see progress in the moment and how it only makes sense when we look back. Maybe this is why product histories are so important for helping teams see how far they’ve come.
AI for product managers
Few things going around this week about AI and product so I thought I’d try to get some of my thoughts down.
I see three interconnected layers of AI.
Starting at the bottom is what’s going on with AI even if you do nothing. This is about having a way of dealing with any emerging tech. It includes horizon-scanning, market analysis, trends, behaviours, etc. It also includes how AI effects you and your users, because it will.
The middle layer is using AI in how we do our jobs, which for now probably just means using Gen AI and Machine Learning, but which will probably include Agent AI in the near future. Understanding how to use AI is like understanding how to use a mobile phone or email. It’s another tool we’ll use to do our work regardless of whether we use it well (I’m looking at you, email). So, we might as well apply some of that critical thinking and figure out how to use it well (when is it worth it for the environmental impact, for example).
The top, and most interesting layer, is how we might use AI in the products we build. I’d suggest we won’t do this very well if we aren’t doing the other two layers because we won’t have wide and deep contextual knowledge about how people are using AI more generally. This layer of AI also includes machine learning, image recognition, data analysis and decision-making, and all the other kinds of AI that is used in products without the user necessarily knowing. But they are still part of the product so product managers need to understand how they fit.