Not subscribed? Sign up to get it in your inbox every week.
⚙ Hi {{first_name_tally|Operator}},
Let's say you get laid off tomorrow.
I don't mean "let's imagine a hypothetical." I mean actually picture it. The calendar invite from HR, the awkward Zoom, the "we've made the difficult decision" script.
By the end of day, your Slack is gone. Your email forwards to nowhere. Your calendar, the one with 30 hours of meetings per week, is empty.
Company relationships have an expiration date printed right on them: your last day.
Now here's the question:
Who do you call?
Not to vent. To actually figure out what's next. Who do you call?
If you're like most operators, your answer is... thin. Maybe an old boss. Maybe a founder you worked with three jobs ago. Maybe a recruiter who cold-emailed you last year.
That's the layoff test. And most operators fail it.
Not because they're bad at relationships. The opposite. Operators are great at building relationships…they just build all of them inside the company.
We’re building something new to make sure that doesn’t happen to you, and bringing back the talent network this week.
- Rameel
PRESENTED BY DIGITAL OPERATIONS INSTITUTE
Where Operations Meets Technology.
Finding people who can implement them is the #1 bottleneck in AI implementations.
That's why Digital Operations Institute launched the first certified network for AI implementation and digital operations providers.
No consultants pitching transformation decks. No agencies learning on your budget. Just advice based on real world case studies and introductions to vetted experts certified, vetted, and matched to your actual problem.
Every certified partner has multiple case studies verified by the DOI team.
After 100s of audits and AI roadmaps, Digital Operations Institute is ready to go whether you need implementation partners, full time talent in-house, or a second opinion on your quarterly plan.
The 1-1-1 Rule
Every AI expert says the same thing: “AI is like a great intern...”
But interns aren’t that useful because they don’t really know what’s going on. My first internship was literally monitoring the paper feed of a bulk scanner to make sure it didn’t jam. That was the highest value task they could trust an intern with at a bank.
I think AI is better treated like a contractor: it needs to start with straightforward work that doesn’t leave room for much interpretation. Which is why I use the same rule for both AI and humans when we’re building up a workflow.
The atomic unit of AI isn't the role. It's the Task. Just like humans.
The Unbundling Audit
Don't try to build an "AI Coworker" or Agent. It’s unreliable without tons of foundational support. Instead, take one role and run the Unbundling Audit:
Kill the Role: Stop thinking about the role, start thinking about what it would look like broken into 5 separate contractors.
List the Tasks: Write down every task that role does and look for the times when a human wouldn’t even realize they’re moving to a new task.
Apply 1-1-1: Build for just one Task.
Repeat
The 1-1-1 Rule isn’t for prompts, it defines the basic unit of work: a task to be done. It is a tool to break down work into automatable pieces.
1 Input (Good Clean Data)
1 Task (A Single Action)
1 Output (Same Every Time)
How It Works
1. The Input: Single Data Point
Stop feeding the model copy pastes of everything you’ve ever considered may be useful when working with your company (I’ve seen them hit 5+ pages). Feed it just what it needs to know for the task at hand.
Bad: "What do you think of this web page copy?"
Good: "Analyze this landing page for a B2B SaaS selling to digital marketing agencies using Eugene Schwartz’s Spectrum of Awareness framework from the classic copywriting book Breakthrough Advertising. "
You don’t need to give it a copy of the whole book (that would likely degrade your answer), you just need to give it the copy and call out the desired method of analysis.
2. The Task: One Verb
LLMs are super easy to run off course. Picking one Task maintains clarity and keeps it small enough to avoid drifting off course. These are all things an LLM will excel at out of the box.
The most common LLM tasks are data focused: Extract, Summarize, Analyze, Generate, Transform, Edit, Classify. 2 verbs? Probably 2 tasks.
3. The Output: Single Result
The output shouldn't be a conversation; it should be a payload.
Inside of APIs and automation tools, this is normally something that you need to switch on and define for the specific task. 99% of the time you’re just going to want a JSON array like the one below because it is the most common way to organize data when passing it between digital systems.
1-1-1 was designed for automation tools like Zapier or N8N workflows but it works just as well with Claude Skills, Replit, and even prompt libraries. Unbundle a role, select the highest value task that stands alone, and 1-1-1 it away. Rinse, repeat, then chain them together until you’re done.
Jobs, jobs, and more jobs
Ops roles are in higher demand than ever before since we rule the business processes that are now being automated AND 60% of you are looking to take advantage of that demand this year. Starting next week we’ll be featuring top ops roles every week again.
We’ve already got some amazing roles and candidates in the network like Aiden (not their real name, but a real COO or VP Ops):
15+ years building ops organizations from the ground up in aerospace, defense, and tech. . Deep experience standing up entire functions, the regulated, high-stakes kind where compliance isn't optional and not just managing them. CEOs they've worked for vouch directly. Looking for a senior Ops or Program lead role where they can run transformation on something that actually matters.
Searching for your next role? Fill this form out, and we’ll intro you to the best companies in the world
Hiring? just respond to this email! I bet we have a great fit even if Aiden isn’t it.
