Claudifying program operations at HIP

By Nina Friedrich🔸, High Impact Professionals @ 2026-08-20T08:57 (+22)

Summary

 

At High Impact Professionals the most valuable things we do can't (yet) be automated: supporting individual people through high-impact career decisions, making the right introductions, the human conversations that actually change someone's path. The program operations around those take a lot of time and can mostly be automated. THerefore, we are teaching Claude Code to run the recurring operational scaffolding, so our time goes to the parts only we can do, and so we can support far more people.

 

This post covers how we think about what to automate, the five steps we follow, and what it looks like in practice. We use Claude (specifically Claude Code); other coding agents are available and most of this should transfer.

Part 1: How we think about automating

Three reasons to automate

Not everything is worth automating. We found three reasons why automating something can make sense, and luckily they are not mutually exclusive!

  1. Speed. The obvious one: some tasks get much faster. If you do something every month and it takes you 10x as long to automate it, you are already gaining time by month 11. Giving an organisation recruiter access to our Talent Directory used to be really annoying. It never took more than ~10 minutes, but it involved task switching, several systems, and remembering various edge cases. Now it is done at the click of a button (two buttons in some cases). That means organisations get access faster, at the point where they most need it: when they are hiring.
  2. Evening out the workload. If many things need doing at roughly the same time, spending time early (when the workload is somewhat lower) to automate some of them means your peaks are lower overall. Our program rounds are exactly like this: cohort matching (a lot of thought goes into this, so it used to take us two days to get right), Google Drive and calendar set-up, welcome emails, and a good deal more are all needed on the same two days. Every task automated in a quieter week makes the peak week more manageable.
  3. Reliability and quality. Automation does the boring parts the same way every time. We have a very low drop-out rate in our Impact Accelerator Program (1.5% in 2025). But when someone does drop out, it involves updates to 15 internal systems. By hand, we never managed to remember all of them (there is a list, but with the program evolving it is often slightly out of date). Now we do ~95% of them with the click of a button.

Those three are all about automating tasks you already do. There is a fourth reason to use tools like Claude Code: enabling things that were never possible manually, but since this is not the focus of this post, here's just one example: 

Five steps to claudify a process

The process we follow, learned by doing. We have started calling it "claudifying": automating a task by teaching Claude to do it.

  1. Set a rough North Star. Roughly where do you want to go? It does not need to be precise, and (see step 5) it might change as you learn more about the process. This is also a good moment to ask whether your goal falls under any of the three reasons above.
  2. Start with a small version. Automate one concrete instance of the task, one step of the process, one example you understand well. Do not start with the whole system and every exception at once, mainly because you learn about many of the exceptions as you go.
  3. Document, especially for tasks you want to repeat. Make sure Claude knows what to do next time, otherwise it has a tendency to rewrite things from scratch. We store the instructions where the task lives: tickets in our task tracker carry the how-to, tagged so Claude knows to execute them directly next round.
  4. Build out from the example. Generalise once the small version works. It turns out there are far more paths for handling organisation applications to the Talent Directory than I would have expected: a new organisation name could be a genuinely new organisation, or a typo, or a different spelling, or a rename of an existing one. We add these as we find them (I hope we have them all now!).
  5. Set your actual North Star. Only now do you know enough to see where this is really going.

An example of the North Star shifting

When we started claudifying the program operations of our Impact Accelerator Program, the initial North Star was simply "automate any relevant tasks". As we worked through them, we realised we could turn it into a system. Along the way we got an overview artifact, a colour-coded map showing how automated each step of a program round is. For the record, we are at 11% of the ~350 tasks being mostly automated and climbing, working through them as and when they naturally become due.

More importantly, the actual North Star became clearer: a system that at the start of each week goes through that week's tasks and lists

We confirm, and Claude does its share on the relevant days. We did not have this in mind when we started, and that is exactly the point of steps 1 and 5.

Working principles we converged on

A word of warning: know your tasks

Saying "Claude, please automate our program ops" with no further context would have been a very limited success, to say the least. Instead, we have a template of ~350 tasks involved in running one round of the Impact Accelerator Program (which might be linked to participants and facilitators finding the experience rather well organised 😉). Marketing and outreach, vetting, building the material for every participant, coordinating facilitators, participant comms, M&E and much more, each with its due date. Automating these one by one, and ideally several at once, is far more achievable than trying to remember what is involved in the first place.

If you do not have that list, building it is the first automation-adjacent thing worth doing, and it probably pays off even if you never automate anything.

Part 2: What this looks like in practice

A selection, grouped by the three reasons above.

Speed

Evening out the workload

Reliability and quality

Caveats

Our time now goes to the work that creates the impact: supporting people, making connections, changing paths. The automation is also a good foundation to scale from: onboarding a new team member is far simpler, and it is a solid baseline for building new programs on.

We would love comments, both from organisations doing similar things and from anyone considering starting.


Josh Davidoff @ 2026-08-29T14:43 (+1)

This is an exceptional and inspiring org-wide approach to AI-enabled automation. Kudos!

Nina Friedrich🔸 @ 2026-09-04T07:52 (+1)

Thank you, that's very kind of you. I hope it's useful for other orgs - we've benefitted ourselves from hearing how AI is used at orgs in the past!

Alex Novikau @ 2026-08-20T18:49 (+1)

This was interesting to read, especially the point about the marginal cost of automating additional tasks falling as the underlying infrastructure gets better. One thing I was wondering about is how the maintenance side is developing as you add more automations.

At 11% you can probably still understand each automation fairly well, but if the aim is to automate a much larger share of program operations, some of the work presumably shifts from doing the task itself to keeping a growing set of automations working as forms, Drive structures, naming conventions, staff roles etc. change. I don't know if you are already measuring this, but something like human minutes per run or per program round, including review, exceptions and repairs, might be useful alongside the percentage of tasks automated. It could help distinguish automations that really reduce operational load from ones that mostly move that load somewhere less visible.

Nina Friedrich🔸 @ 2026-09-04T08:07 (+1)

This is an interesting point, and no, we're not measuring that exactly. The main measure we use is how much we get done with the same resources: either more output for the same time (like the referrals example, roughly 3-4x for about the same time spent), or being able to take on additional work at all (like the Talent Directory Automated Matching, we have something exciting coming up at HIP that's a mix of both).

On maintenance: if this is about changing underlying systems (tracking new data, therefore needing a new integration), that would be an addition to the manual work also. And when those systems change, that already broke the manual version. The drop-out example in the post touches 15 systems, and we did have a written list, which was often slightly out of date because the program kept evolving. What's different now is that Claude can flag when there's something else we might want to link, because it's already made the same change somewhere similar, rather than us trying to remember system no 16.

Alex Novikau @ 2026-09-04T09:46 (+1)

You are right, measuring AI maintenance burden without accounting for maintenance that had to be done regardless of AI deployment would not get you anywhere.

Interesting you are have Claude identifying opportunities to simplify/reuse/streamline, this could actually mean the per system maintenance burden will go down the more systems you connect. Pretty much the opposite of what I was thinking. Thank you.