PRODUCTHEAD is a regular newsletter of product management goodness,
curated by Jock Busuttil.
4 minute product #
every PRODUCTHEAD edition is online for you to refer back to
tl;dr
With AI, shipping a product is not ‘done’, it’s the start of testing whether its behaviour is acceptable
Staff-facing products are about tasks, decisions and protection
Operating models are shifting because of AI; during the transition communication and collaboration need to change
hello
This week I’ve collected together a few recent articles that, in various ways, are describing how the concept of ‘done’ and the language of getting to that point is changing. Jeff Gothelf is looking at it from a practical perspective: shipping is no longer the defining moment when the AI thing you’re shipping is non-deterministic (or in other words, doesn’t consistently give the same output for the given input). Instead, it’s about measuring the quality of responses from the AI thing and defining at what the acceptable threshold is.
Scott Colfer also reminds us that putting the thing into live service is not being ‘done’ either. It’s when the users of that product or service have successfully completed their goal – for which your product or service may only be a stepping stone towards. Although Scott is considering the topic through the specific lens of product in UK digital government, the broader points he makes are equally applicable outside of that sector.
Lastly, Amy Mitchell makes some astute observations of the shift in communication and collaboration that’s occurring as organisations adapt their operating models in the wake of ubiquitous AI. It may also be the case at some organisations that senior leaders have invested heavily in adopting AI and are coming under increasing pressure to demonstrate return on their investment. Inevitably this is pushed down the chain to the product managers. Amy offers useful examples of the shifted signals you may now be observing and how to decode them into more constructive actions. With delivery (perceived to be) vastly accelerated, impatience for the end-results (or outcomes) is much more the norm.
As it seems to me, the changes the three authors are highlighting this week are broadly positive from a product perspective. They each return us closer to what product management was always meant to be about. AI tooling is minimising much of the busywork that was often mistaken for value. Lines of code or user stories shipped (all outputs) were always pointless metrics, however AI has rendered them meaningless when outputs can be churned out for comparatively minimal effort.
The value in the work was never primarily in defining and coordinating the process, or building and shipping the thing, rather it came from the desirable and valuable outcomes the resulting product or service enabled for the people using it. If AI is indirectly restoring everyone’s focus on user and business outcomes, then it’s difficult to argue against at least that aspect of it.
Speak to you soon,
Jock
what to think about this week
What “done” means when you’re shipping AI features
Ah, the good old days of sprint reviews. Engineering says they shipped [something]. All tests passed. No P0 bugs. The demo worked like a charm. “Works as designed” had been achieved once again. Sadly, we don’t have the luxury of this simplicity any more (if we ever did).
The definition of “done” has always been the problem. For AI, it has to be completely rewritten.
“Done” was the focus for building. “Calibrated” is the focus for learning.
[Jeff Gothelf]
Why It’s So Hard for Leaders to Prove the Value of Digital Work
How to establish the outcomes of staff-facing and internal products
If you’re a Digital leader and I asked you to prove the work of your Directorate was valuable – could you do it quickly and confidently?
Most digital work is not public-facing – it’s staff-facing
[Scott Colfer / Product in Service]
Pressure Before Clarity
A year ago, collaboration in product teams felt productive. It was common to explore adjacent ideas, loop in other functions early, and treat unfinished thinking as part of the process.
Recently, many product managers have started noticing a shift: conversations feel sharper, feedback arrives faster and more directly, and there is less patience for work that doesn’t sit on the critical path.
Caught straddling two different operating models
[Amy Mitchell / Product Management IRL]
recent posts
Startup to Scale-up Club Q&A – 14th April 2026
For Startup to Scale-Up Club‘s April Q&A session, we covered topics including:
Structuring analytics for user engagement
Defining an MVP in the AI era
When to use genAI and when to use machine learning (ML)
Product differentiation in a crowded market
Implementing reusable components and designs
Free advice for startup and scale-up founders
[I Manage Products]
We’re all addicted to AI, but it’s going to be okay
We seem to stuck in a contradiction in which we worry about AI’s effect on our critical thinking, while finding it equally hard to resist using. Why is that?
[I Manage Products]
Canary in the mine: AAA game developers are unionising
Product management has had its own fair share of problems over the last few years. Nevertheless, there are early warning signs from AAA game studios that there may be another storm brewing in tech for us to weather.
Union-busting just isn’t a good look
[I Manage Products]
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PRODUCTHEAD is a newsletter for product people of all varieties, and is lovingly crafted from erroneous copyright recordations.