Weeknotes 526
I did:
Creating the conditions
Lots of conversations around the topic of creating the conditions for good work this week. It’s hard because it’s never clear where the problems are and we don’t have the language to describe what needs to be done. But it’s worth it. Also did this stuff…
- Interviews for a content design role in one of our service teams. I’m always the hardest scorer.
- Presented the work I’ve been doing on using AI for product strategy to the other product managers.
- Wrote numerous documents as I continue my one-man mission to move from an oral culture to a written one. They included an onboarding guide, context for a show and tell, and ideas for a product strategy.
- Launched automated reminder emails with the hypothesis that timeliness matters.
- Set up a co-writing workshop with product managers to help us develop a consistent tone of voice for how we talk about our products.
- Set up more of my personal AI OS, including helping with planning at the start of the week, writing reports at the end and looking for gaps in my product documentation.
This week’s numbers
I haven’t looked at my dashboard in a while, so here’s some numbers:
- Completed 52 tasks across 9 projects this week.
- Spoke to 44 people.
- Spent 905 minutes in meetings.
- The number of minutes I’ve spent in meetings since I started tracking my time went over 75,000 this week. That’s over 130 days straight.
I read:
Continuous discovery
Wonderful explanation of why continuous discovery matters. “Perhaps the greatest value of continuous discovery is that it creates a culture of ongoing learning. Rather than treating research as a phase that ends when a service launches, it becomes an integral part of managing and improving the service throughout its lifecycle.” Yes, yes, yes.
(Thanks Benjy Stanton for the link)
The future isn’t multidisciplinary
Jack Strachan writes about how teams and professions work together (in both senses of the word, “work”). He covers a lot, including different ways of organising, and how, “boundaries around that craft can be permeable enough for people to contribute past them, while still knowing where their expertise ends and when somebody with deeper knowledge needs to come in”.
Increasingly, I think dynamic reteaming is the best way to organise teams in complex and resource-constrained environments to tackle urgent, intractable problems.
(Thanks James Green for the link)
AI and Education
MIT’s AI and Education report is really interesting (if you’re into AI and education, that is). One of parts that grabbed my attention was, “Leaning into learning means creating a new “social contract” between teachers and students. All of us who teach at MIT will need to be prepared to help students understand both that the process of education is necessarily a productive struggle, and that the most important product of their education is not a GPA or a diploma but themselves: their personal growth and intellectual maturity and the development of their own imagination, insight, and judgment.” Outside of the education sector, it’s plausible that AI will go further and completely change our relationship with information and knowledge.
I thought:
Simple, complicated and complex work
I’ve been thinking about how we might group different types of work to provide a pattern for organising around it, and the Cynefin Framework offers something very useful:
- Simple – There is a clear, linear link between cause and effect, where the same actions consistently yield the same outcomes, a connection that is widely accepted and undisputed. Things like password resets are this type of work. Once you’ve figured it out once, you can repeat it.
- Complicated – There is a clear, linear cause‑and‑effect relationship, though multiple solutions may exist and choosing the right one depends on expert advice. Most issues are this type or work. Every issue is different and can only be figured out by getting the right people together. I reckon there are efficiencies to be gained from not treating simple work as complicated.
- Complex – Cause and effect are unclear, so solutions only emerge through action. Small, safe‑to‑fail experiments reveal possibilities and gradually make the space more manageable. This is where most product work fits.
There’s probably an ideal volume ratio between these types of work where simple is high volume and complex is low volume.
From oral culture to writing culture
Oral cultures, where information, knowledge and decisions live in conversations and create no tangible record suffer from a lot of context loss. Only those in the room get the full context, and every communication step away from that original conversation losses more and more nuance.
When organisations have a writing culture, and information is more available to everyone asynchronously, any context contained in the writing is maintained.
As AI becomes more pervasive in the workplace whether we have conversations and let AI do the writing, or we do the writing which AI uses as a source, will affect. Personally, I’d rather be telling AI what I think rather than AI telling me what others think.
Roles, responsibilities and relationships
Roles and responsibilities are fine but they are always miss the important stuff; relationships.
Roles and responsibilities treat individuals as isolated, contained, and fixed. The assumption is that, once written down, someone’s responsibility stays the same. And, like cogs in a machine, if everyone sticks to their responsibilities then the machine will keep working. It’s an idea for bygone age.
Introducing relationships into the mix recognises that the connections between those individuals matter. Someone’s responsibilities change depending on who they are working with. Their role changes over time as they learn more. Building in change makes things more robust.