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How Chiefs of Staff can help teams diagnose what to automate (and actually build it)

Welcome back, aspiring and current Chiefs of Staff! 👋

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🤖 How Can Chiefs of Staff Help Their Teams Decide What to Automate with AI?

This week’s newsletter is guest authored by Sarah Sheikh, the Chief of Staff at Loop Financial. She is an operational leader with over 15 years experience in fintech, banking and higher education in Canada, the USA, and the UK, working across teams to drive growth, streamline operations, and in scaling businesses.

Loop Financial is a financial technology company built for Canadian companies that need the same financial tools as the largest global enterprises. Loop provides access to world-class FX rates, real multi-currency accounts, and corporate credit solutions – all within one integrated platform.

When most people think about automation, they think about tools such as Zapier, Retool, AI agents, internal dashboards… the list goes on. But in my experience, the real challenge around automation isn’t a tooling problem. It’s a diagnosis problem 🩺

Teams don’t struggle because they can’t automate. They struggle because they don’t know what’s actually worth automating in the first place.

And as Chiefs of Staff, this is where we have disproportionate leverage; not by building everything ourselves, but by helping teams see their own inefficiencies clearly enough to fix them.

🤖 A note on terminology: when I say “LLMs” (Large Language Models), I mean whatever AI tool your organization approves and uses: ChatGPT, Claude, Gemini, Copilot, etc. The point isn’t the specific tool. It’s using AI as a strategic thought partner during the diagnosis phase, then building the actual solution in whatever tool fits the job.

🔎 Step 1: Teach Teams to Spot Friction (Not Just Work Harder)

Most manual work hides in plain sight. It shows up as:

Copying and pasting the same information every week
Chasing people for updates before meetings
Jumping between tools to piece together a simple answer
Acting as the “go-between” for systems that don’t talk to each other

⚠️ The problem is, teams normalize this and say things such as “we’ve always done it this way.” They don’t flag it as broken and they just keep doing the manual task.

One of the most valuable things I’ve started doing is pushing teams to ask:

Where are you repeating yourself?
Where are you relying on memory for something the business depends on?
Where are you the bottleneck in a process?

✅ If a task is repetitive, time-sensitive, and slightly painful, it’s more than likely a candidate for automation.

Your job as a Chief of Staff isn’t to spot everything yourself. It’s to teach others how to see it 🔎

👥 Step 2: Make Automation a Team Sport (Not an Engineering Request)

One of the biggest shifts we made was moving away from the idea that automation = engineering work. That is no longer the case with the amount of advancement we’ve seen in tooling that is available to everyone.

For example, at Loop, we ran a growth hackathon internally and opened it up to everyone, and especially to non-engineers, which turned out to be a real success. People didn’t just participate, they built things that were immediately useful, such as:

✨ A sequencing flow that scrapes the internet for lead information and sends personalized outreach

✨ An internal tool that reviews customer product usage and flags upsell opportunities

These weren’t side projects. They were solving real business problems.

💡 The takeaway was simple: The barrier is no longer technical skill. It’s whether people feel empowered to try.

LLMs have fundamentally changed the game here. You no longer need to write production-level code to build something valuable. You just need to know how to break down a problem and iterate.

🧠 Step 3: Shift the Focus from “Tools” to “Thinking”

There’s a lot of noise right now around which tools to use or whether to use them at all for fear that you will “AI” yourself out of a job. In reality, the people who get the most out of LLMs aren’t the ones using the “best” tools. They’re the ones asking better questions and iterating faster.

✅ What I’ve found works:

Start messy and definitely don’t wait for a perfect spec; just getting started is the key
Break the problem into smaller steps
Treat the LLM like a collaborator, not a one-shot answer machine
Keep refining until it’s useful

🗣️ This is a mindset shift. “Prompting” isn’t a technical skill but rather a structured way of thinking.

And once teams understand that, they move much faster.

👉 Step 4: What This Looks Like in Practice

To make this real, here are a few things I’ve built myself and enabled across the team:

🗓 A compliance calendar that automatically reminds teams when key reports are due and removing the need to rely on memory or last-minute scrambling

🗃️ Retrieve historical documents and attachments from multiple inboxes dating back to 2018; something that would have taken hours (if not days) manually

📊 A lead qualification tool for the sales team to quickly assess whether a prospect fits our ICP, reducing time spent on low-quality leads

📃 Trend reports for LinkedIn to showcase our thought leadership positioning specifically targeted to our ICP

🤔 None of these required engineering and I built them on my own in between meetings. They came from identifying a friction point and asking: “What can I do to improve this current way of working?”

🔗 Step 5: Embed the Capability Where the Work Happens

An effective decision I made recently was moving a Business Operations team member to work closely with Operations specifically on automating their manual processes.

🎯 BizOps profiles are uniquely suited for automating processes because:

They understand the business context
They’re comfortable across tools and functions
They can translate problems into workable solutions

Instead of centralizing all automation efforts, we embedded the capability directly within the team.

✅ The result:

Faster iteration
More relevant solutions
Less dependency on a single “owner” of automation

💪 The win wasn’t the tools. It was putting the capability where the work actually happens.

🧑‍💼 Step 6: Your Role as a Chief of Staff

At the end of the day, this isn’t about becoming the most technical person in the room, but rather raising the baseline across the organization. I am a very strong believer in leading by example.

As a Chief of Staff, your role is to:

🩺 Help teams diagnose where they’re wasting time

😌 Create environments where people feel safe to experiment and build

💡 Encourage a mindset of working smarter, not just harder

❌ Reinforce that iteration is part of the process, not a sign of failure

This can feel daunting at first, but the reality is that with LLMs today, most of this is entirely doable.

❓ The most important thing is to start asking the right questions, and keep iterating.

💭 Pulling It All Together

Automation is no longer a technical advantage. It’s an operational one. The companies that move fastest won’t be the ones with the most engineers. They’ll be the ones where everyone knows how to identify inefficiencies and do something about them.

As Chiefs of Staff, we’re in a unique position to drive that shift. Not by building everything ourselves but by turning teams into builders.

And that’s where the real leverage is.

Thanks for reading! This post is public so feel free to share it.

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