Here’s an area where I am optimistic about AI (for now, at least).
So many companies flatten things to meet the needs of the person with 1) the most power, 2) the lowest appetite for detail/seeing real-world complexity, and/or 3) the least amount of time to actually work through details and nuance. For good (and less good) reasons, a CEO says, “This is too in the weeds. There’s no way we are doing this much!” That one statement will kick off one of a couple of things:
Investing huge amounts of time in flattening reality to get that report to the “right” level
Not investing that time and just winging it last minute. A reasonable survival mechanism.
Sometimes #1 is shouldered by lots of people in the org. And sometimes the burden is shouldered by just a few people. But it boils down to few/many people translating one version of reality to another, simplified, flattened version of reality. The recontextualization tax can be massive. So massive, in fact, that companies often slip into just winging it because no one in their right mind would want teams dedicating that much time to pretending things work one way in order to get “simple” overviews, “simple” cascades that “roll up perfectly,” etc.
If you’ve ever been in these high-level meetings, you understand that the desire for simplicity and “less” is most often a survival mechanism. It isn’t petty. It reflects a real cognitive load barrier. They are hoping to “manage by exception” instead of viewing everything.
Which brings me to AI.
An area AI excels in is doing the grunt work of recontextualization and exception hunting (provided, of course, it has the raw context and that context is reasonably up to date). Specifically, if you have teams working in highly effective, emergent, varied ways, but you really want to flatten that information more artfully into one format, AI is a good tool.
Say I have 10 stakeholders, and each stakeholder likes information “a certain way.” Pre-AI, I didn’t have many options. I could:
1. Try to persuade them all to look at information the same way
2. Make 10 versions of every presentation so that we could have effective meetings
This is why roadmapping tools almost never travel around orgs: you have so many stakeholders/audiences, and they each have different needs out of the roadmap, which means that it is far easier to just PPTX or Miro it up ad hoc every time.
Now, you can let a team work however they want to work and write a skill for each of those audience segments, tailored to how they understand product, think about roadmaps, care about roadmaps, etc. It doesn’t matter if you want to basically work in GitHub issues and Google Docs; you can pretend that you work in nice swimlane roadmaps with clear deliverables and an orderly flow. Or in a outcome-oriented “move the metric” kind of way. Or whatever fits your fancy, and your audiences way.
At the new job, we don’t want to force different clients to play by our rules. We also don’t want to hyper-customize Linear to become a super-abstracted, “respond to every client workflow” tool. Clients want things their way and they come first. We want things our way so we can make our clients awesome. So my first area of focus is figuring out how to put together skills that translate how we need to work to a language that is empowering for every client and doesn’t force them to use words/concepts that they don’t want to use.
AI is great at this.
I get all the high-end AI use cases, but a lot of the value is doing something you wanted to do forever but didn’t have enough hours in the day to make happen.
You don’t reinvent the wheel. Just do what you knew you should do, but couldn’t.
Totally agree with your analysis that the folks who had to do this recontextualisation no longer have to suffer through the gruntwork, which as you say means they no longer have to do it, or if they didn't do it before, they do it now
The bit I'd add (and which makes me excited about AI & the flattening of orgs) is that enterprises are full of information middlemen whose roles were always of very questionable value to begin with: It's not just recontextualisation as a content-reshaping exercise, it's also the physical work to pass messages from A to B.
e.g. I attend the leadership meeting where updates are cascaded from the exec teams, then my role is to write (or not write) powerpoints about it, then attend meetings to explain those things to others, who no doubt have their own powerpoints to write (or meeting notes to take, or plans to update, or whatever)
It was a big reason why I left PepsiCo despite being in an awesome team: I kept seeing people around (and above) me who were clearly smart and capable, but whose role had been reduced to that of a human USB stick moving information around via the very inefficient medium of meetings, email reminders, plus the drudgery of moving boxes around roadmap slides and gantt charts.
The automation of recontextualisation also removes the need for (some) of the most pointless meetings - because the cost to search for info + the confidence that it is accurate and up to date + the ability to comprehend it is going down.
If David Graeber were alive right now, I think he'd be surprised to find himself on the pro-AI (and implicitly pro-capitalist) side (or maybe I've drank too much of the kool aid and think so, lol)
The recontextualization tax never shows up on a budget line. It still eats weeks. Someone up the chain wants a clean picture, so a few people spend their real job translating messy team work into something that rolls up. AI can cheapen that grunt work. It doesn't kill the incentive to keep asking for a simpler story than the work can honestly tell.