You probably didn’t expect this issue. I didn’t expect to write it either.

I’m breaking the usual bi-weekly cadence to share a story, and an announcement worth your attention.

The idea that survived a panic attack

Let’s travel back to April 2025.

I’m spending three weeks in Thailand on my own, trying to find some clarity, when a major storm breaks out, and I become fully convinced it is going to flood my bungalow and kill me.

I was shaking.

Not exactly the best moment to hop on my first real call with Diandra Escobar, someone I’d been following on LinkedIn for a while, commenting back and forth, never properly spoken to.

We stayed beyond the hour. It turned out to be the best thing I could do in that moment, because without knowing it, she stopped me from going down a spiral of googling past flood history in Thailand and saying my last prayer.

Between the storm anxiety and the feeling of being stuck at my job, I told her I had this idea for a newsletter that had been living rent-free in my head for a year.

I wanted to understand what was happening to marketing and GTM as AI entered the chat, past the daily hot takes, feature announcements, and the endless hype that was killing me.

I wanted to discover how people were using AI tools in practice, how their processes and roles had changed, and to counter the misleading advice spreading on social media.

I also wanted a reason to spend less time doom-scrolling on LinkedIn and more time talking to people who were building interesting things with AI.

But I knew I couldn’t do it alone.

Diandra told me she was running a LinkedIn ghostwriting agency she had been building under the radar for months, heads-down in client work. She recognised every frustration I had with the AI hype wave, and also regretted not taking email seriously earlier.

The vision clicked.

We clicked even more.

And suddenly, The Workflow became a concept with a timeline and a purpose. It finally moved out of my head and onto a Google Doc.

“Let’s fu****g do it.”

How the Workflow was born

What followed was a lot of iteration that nobody ever saw.

Diandra created the visual identity, which gave legs to the big idea behind The Workflow: that the human behind the tech is always the real driver of any outcome.

I drafted the first newsletter MVP.

We created a kanban board to keep the content ops on track. Week after week, we adjusted: tightened the format, improved the visuals, started lining up people from our network who were doing remarkable work, and figured out how to promote the newsletter without making it feel like promotion.

We discovered each other’s strengths by building it together.

In August, we created The Workflow’s Substack account and announced it on LinkedIn. 500 souls gave us their email before the first issue went live in October.

What most people don’t realize is that a good newsletter runs exactly like a product. It requires product development, R&D, sales, and constant promotion, most of which is invisible to anyone reading it.

Sixteen issues later, over 2,450 people trust us with their inbox. Every time I refresh the subscriber count, someone new has found their way to us.

⚠️ PSSSSSSSS: Want to be featured on The Workflow or know somebody we should absolutely interview?

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101 different time zones

Not even a month after the first issue went live, we got to hang out at the Semrush conference in Amsterdam. If you have spent enough time online, you know how this goes: you end up talking to some people more than people you see in real life, and in some cases, you get a cold shower once you meet them in person.

Not in this case. Diandra’s live version was even better than the one I got to see through a screen, and also much taller.

Since then, we’ve been running the whole thing across time zones, Zurich to Colombia, except Diandra switches continents roughly every other month, so we were almost always in different time zones regardless. Coordinating interviews, having calls at unreasonable hours, and making decisions asynchronously.

We overcame every hurdle because we always came back to the same principles: strong respect for each other and the focus on maintaining our friendship intact. Every win had a better taste when shared, and the hard moments never turned too sour.

Including the hardest one. A few weeks back, she told me she had decided to step back from The Workflow to focus on her other projects.

The sadness was there for both, but the gratitude was much stronger.

Finding a real friend on the internet is harder than building any workflow we have ever documented.

The next chapter of The Workflow

So now, I feel like I am standing in an empty flat that I get to furnish and arrange for myself. Very scary and quite exciting at the same time.

I want to do so many things!

I want to write more AI playbooks and find the people doing exceptional work with AI who are not posting templates on LinkedIn, the way it has been with Joni, Mina or Clay.

I want to collaborate more with writers from Substack to reach new audiences and open the door to recurring contributors. A few people have already asked.

And in August, I am starting a new chapter alongside it. I’ll be dedicating most of my time to finding out where AI and editorial quality can meet: how to build AI-native content workflows without losing your voice, your soul, and your standards in the process.

All of it will feed directly into future issues. I cannot wait to share the news with you.

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⚠️ Fair warning:

I am European. Do not expect much before the end of August. There are laws against unnecessary brain cell activity during a summer like this one.

It does not matter how many more people I interview. The thesis I had back in April 2025 has been validated over and over. If you think AI can replace marketing, it just means you never understood what marketing was in the first place.

What AI does is expose the gap between people who had a solid GTM strategy and the ones who just wrote about it on LinkedIn.

As long as The Workflow exists, it will help you stay on the right side of that gap.

Thank you for trusting me as your editor and anti-AI hype officer going forward.

Sara Stella ✌🏼

Here's the real problem with AI inside most companies. Everyone agrees AI adoption matters, but nobody owns it.

In bigger teams, it cuts across operations, support, sales, and data, so the initiative belongs to everyone and moves for no one. In smaller teams, everyone is already flat out doing their actual job, so using AI properly stays important but never urgent. Either way, strategic adoption stalls, and the cost of falling behind hides in the hours your team spends on work AI could be doing instead.

Sometimes the move is getting more output from the team you already have. Sometimes it's avoiding a hire you'd otherwise have to make. Either way, the goal is the same: remove the work that shouldn't need a person doing it by hand.

Today’s guest, Cam Trew, ex software engineer, now founder at Headcount, runs this exact diagnostic with his clients before a single AI tool gets bought. It’s called the Headcount Audit.

By the end of this issue, you'll have a playbook to find your most expensive repetitive workflow, match it to the right size of AI solution, and know the payback period before you spend a cent building it.

Let’s dig into it👇

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✅ What you’ll learn

• How to find the one workflow that is bleeding the most time and money, and turn it into a single benchmark number

• How to match that problem to the right size of AI solution, from a $20 Claude setup to a full custom build

• How to calculate the payback period and treat AI spend like any other investment decision

🧨 What triggered The Workflow

Most AI advice starts with the tools. Pick a platform, build the agent, wire up the automation. That’s starting from the end, and it’s the reason why most AI projects fail.

Cam kept walking into the same scene. Most teams know they should be using AI, so someone gets excited, picks a shiny tool, and three months later, nothing’s fundamentally changed.

No surprise. That’s what happens when nobody quantifies what the work actually costs, nobody watches how it really flows, and nobody matches the problem to a solution that fits. So the project floats on vibes until it sinks.

Cam flipped the order to make sure this wouldn’t happen again. Before any tool, you find the most expensive repetitive work, sit with the people doing it, and put a real number on it. Only then do you choose what to build.

The method goes back to the fundamentals. As Cam puts it, “a company can automate a million things, but it’s the twenty percent that gets you eighty percent of the results.”

The goal isn’t to buy tools and hope. It’s calculating the payback before you start your AI rollout.

🧑🏼‍💻

🛠️ How to build The Workflow

This is a diagnostic, so the “build” is the thinking you do before the building. It runs in six steps.

Step 1: Find the work worth automating

Pick one function to point the audit at. The one you are about to hire for, or the one eating the most time and money. Do not waste the audit on something that saves ten minutes a day. Target what actually pushes the business forward.

How you get there depends on how obvious it is:

• If it is already clear, trust what you see and go straight to the maths. You already know your support team is drowning in repetitive work, or your ops team spends half the day re-entering data between systems.

• If it is not obvious, get the team logging what they do every day for a week or two. It feels uncomfortable to ask and people get naturally wary of it, but you need the data.

• If your business is small (just you and one or two others), skip the logging. You already know where the repetitive work lives.

You're aiming for one number. Take what you pay the team per month, and roughly what share of their time goes on the repetitive work. That's what the work is costing you in total, and it's the input for the payback calculation later. Cam calls it the benchmark: the baseline you compare every future solution against.

Worked example: four people on a support team, each at $4,000 a month, is $16,000 a month. Around 60% of their time goes on phone data entry. That is $9,600 a month on one task. That block is the target.

By the end, you should be able to put this in one sentence: “This function costs this much, and a real chunk of it is time my team shouldn't be spending this way.”

Step 2: See it with your own eyes

Numbers tell you where the cost is, but what they don’t tell you is how the work flows or where it breaks. For that, you have to watch it happen.

As Cam says, “how a process is supposed to work and how people actually do it every day are two completely different things.” The gaps are what you’re hunting.

Every process is data coming in, getting handled, and data going out. Look at these two components:

• The main flow. The 80% that comes in one way and leaves another way, day after day. Email addresses enter CRM, customer calls are logged into a ticket system.

• The exceptions. The cases that need custom paths. An email comes in that needs a second team, then a phone call comes in chasing the same thing, and now it’s two channels, two teams, and someone has to stitch it together.

This matters because of one key rule: “AI doesn’t fail on the bulk of the work, it fails on the exceptions.” This is the biggest risk, so focus on this one.

During your observation, do three things.

• The price of an error. A mistake in a support ticket costs less than a mistake in a contract or a compliance form. The higher the cost of an error, the more a human needs to stay in the loop, and that shapes which solution you choose in step 3.

• What the people doing the work find truly painful. They’ll tell you if you ask directly. The goal is to remove the part of their job that grinds them down, not the judgment calls that actually need them.

• The go-to person. Every team has one, and every problem eventually lands on their desk. Find them early. They know where every edge case lives, they're your best source of truth during the observation, and they become your rollout champion in step 5.

  You’re not designing anything yet, just building the full picture.

Step 3: Match the solution to the work

This is the hardest part. The job is to match the problem you just mapped to the simplest solution that will move the needle. The philosophy: people get hung up on the latest tech and building armies of agents, but most of the time, the answer is just “a neat, small solution that joins two systems together and removes the painful work.”

Here are six possible solutions, from the simplest to the most complex. Which one feels more appropriate for your case and why?

Two things to weigh across all six.

• Your data. The question is not whether yours is messy, since modern AI systems can handle that. The question is whether AI can process your data at all. If it is locked in PDFs or scattered across systems that can’t communicate with each other…we have a problem to solve.

• The human in the loop. The higher the cost of a mistake, the more a human stays in the loop, and that alone rules some solutions out.

Worked example, continued: for the support team, the input is calls and emails (unpredictable, varied), the output is precise CRM records, and there’s a lot of judgment in the middle. That combination points to a thin-layer SaaS.

Here’s how it works: AI takes the unstructured input and pre-fills the fields. A human checks, edits, and submits. That submission hits the CRM via API. The human stays in the loop at the moment that matters most, which is exactly what gives you reliable output.

A plain Claude setup doesn’t work here because the team isn’t technical and needs a proper interface. No-code breaks down because the input is too variable and edge cases will bring the whole flow down. An agent is overkill and removes the human checkpoint. A full custom build is far too expensive for what is essentially a connecting layer.

Step 4: Run the numbers

Now it's time to put a price tag on your AI solution. Talk to development teams and agencies, or figure out if it makes sense to build it in-house. Whatever the route, you need a number to get this live, and you need someone you trust to build it.

Then run the calculation: build cost divided by monthly net saving equals months to pay it back.

Worked example, finished: the data entry costs $9,600 a month. AI halves it, saving $4,800. Build cost is $30,000, running cost $500 a month, so the net saving is $4,300. $30,000 divided by $4,300 is roughly seven months to pay it back. After that it's net saving every month.

Once you can see a number like that, the conversation changes. It's no longer a tech decision. It's a business investment decision.

Step 5: Make it stick

This is where most projects break. Never big-bang it. You don’t want the whole team adopting on day one and hitting issues you never caught.

Remember the person everyone already asks from step 2. They’re now your rollout champion. Build the solution with them and let their input shape the solution as well. They’ll be invested because they helped build it, and because it makes their job easier.

Then test it with them first. Let them use it every day until they’re happy with the output. Once they are, roll it out in stages

• One person for a week. They find things, you fix them.

• Then 50% of the team. See how it holds.

• If it’s still solid, 100%, with a fallback in case something breaks.

Within two to four weeks you’ll know if it works. “It works” means two things: the team uses it without falling back to the old way, and the output is at least as good as before. If either isn’t true, don’t expand.

When you scale from one person to 50%, your champion onboards the new people. They’ll teach it better than any documentation, which is the whole reason you built it with them in the first place.

Step 6: Make the call

You now have the four things you need: the highest-leverage workflow, its monthly cost, the right AI solution, and the payback period. That's enough to make a confident decision.

👉 Cam walks you through the Headcount Audit, with worked examples and templates, in his course here.

🤖 Tools powering the Workflow

The Headcount Audit is tool-agnostic. The stack is whatever sits at the right rung of the six-step solution ladder from step 3, and Cam’s advice is to go no higher than the problem actually requires.

• Claude / ChatGPT (+ Cowork) for individual and small-team work, roughly $20 a month, and live in an afternoon.

• n8n, Make, Zapier for connecting two systems with AI in the middle.

• Thin-layer SaaS when you need a human checkpoint you cannot skip.

• OpenClaw or Hermes when, and only when, the work is unpredictable enough to need an agent.

Stop focusing on the tools and start finding the simplest solution to solve your problem.

👉 Cam's full method, with templates and worked examples, is in his course.

🎢 Highs, lows and Workflow warnings

This is a method, not a product, so the trade-offs live in how you run it.

✅ What shines

• It kills the “I don’t know” answer. The audit exists to replace a shrug with a number. You walk into the room able to say what your AI implementation will cost and when the build will pay back.

• It defaults to small. The six-rung ladder is built to push back on shiny AI object syndrome. “Most of the time, the answer is a neat, small solution that joins two systems together,” says Cam. That bias against complexity is the most valuable thing here.

❌ What doesn’t

• It is only as good as your time data. If the team fudges the logging in Step 1, or you eyeball the percentages, the benchmark is soft, and the payback maths will inherit that softness.

• The hard part stays hard. Choosing the right solution in step 3 is still a difficult call. The framework helps narrow it down, but it won't make the decision for you. Get the shape wrong and you can overbuild a connector or underbuild something that needed a human in the loop.

⚠️ Workflow warning

• Watch the agent itch. The most expensive mistake in this process is reaching for rung five when rung two would do. Agents are harder to control, cost more to run, and most problems don’t need one. As Cam puts it, “don’t reach for this unless you actually need it.” The full custom build carries the same warning, only louder.

✨ The Goldflow

The bottleneck in most companies is ownership. AI implementations stall because the initiative belongs to everyone and therefore no one feels accountable for moving it forward. This audit works because it forces someone specific to own a specific number.

That number does more work than you might think. Once a workflow has a monthly cost and a months-to-recoup figure attached to it, the conversation stops being an opinion. “Should we invest in AI?” is an unanswerable question. “Is seven months a good return on this?” isn’t. Operators know how to answer the second question. The audit just makes sure they’re finally being asked it.

Sara Stella & Diandra ✌🏼

🧾 Know someone about to drop $50k on an "AI transformation" with no payback math? Forward this before they sign anything.

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😢 Can’t survive two weeks without us?

Catch up on past Workflows and steal the playbooks you missed.

🧠 The Claude Code brain for AI-native marketing teams

🦞 The OpenClaw Research Workflow to catch viral news before LinkedIn does

😴 The Content Repurposing Workflow that runs while you sleep

The 2026 Claude Code hype script goes like this: a newborn AI bro discovers Claude Code, automates one task, and concludes they can now replace their entire marketing team.

Spoiler: Building an army of skills and automating your content creation when you’re flying solo is a cool flex, but…

What does it actually take to roll out a Claude Code marketing brain across an 18-person team, when you’re the one who has to build it, sell it internally, and keep it running?

This week’s guest, Joni Helminen, is the CMO of Finnish recruitment company Barona. Joni doesn’t post much on LinkedIn, but when his post about recreating his marketing brain in Claude Code broke our feeds, we knew we had to get him on The Workflow before the next AI bro got to him first.

Over the last few months, Joni has spent his evenings perfecting the art of writing markdown files. The Marcoms Operating System (Marketing OS) was born from his streak: a centralized GitHub and Claude Code system of markdown files, skills, tools, and MCPs now powering his team’s operations.

Today, we bring you behind the curtains of Joni’s journey building his team’s AI marketing brain, and we will answer the question 99% of content on Claude Code misses: what does it actually take to make this work in a real business context, with different people, different stakeholders, and company policies?

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✅ What you’ll learn

• How to build a marketing AI operating system your entire team can use

• How to use the four-layer framework to feed the system the right context

• How to roll out the system across your team with the right governance and usage permissions in place

• How to keep the system evolving so it gets smarter with every output your team produces

🧨 What triggered The Workflow

No marketing leader wakes up one day thinking, “Yay, I want to spend hours building a Marketing OS with Claude Code.” On the other hand, most teams keep Frankensteining their way through AI systems. Everyone runs their own tools, their own context, their own version of the brand voice.

On top of that, there’s usually a very different AI literacy level across people on the team. Some build custom skills in Claude Code. Others still use it as an advanced research tool.

“If people use AI at a very basic level, they end up producing worse outcomes than if they weren’t using it at all,” Joni told us.

The chaos was spreading across other functions too. Devs were building their own tools. Salespeople were building their own creations. If marketing kept doing the same, the brand positioning and tone of voice would die a little more with every prompt.

To course correct, Joni stopped thinking about AI as a tool every team member had to adopt and started thinking about it as infrastructure. His job, as a marketing leader, was to build it.

🧑🏼‍💻

🛠️ How to build The Workflow

Read more

Newsjacking is the fastest way to grow on LinkedIn right now. You take something that just happened in your industry, add your take before anyone else does, and you borrow the audience the news already has.

The problem has never been writing the post. The problem is finding the right story at the right time, fast enough to matter. By the time a story trends on LinkedIn, it already broke on X days or even weeks ago.

So I built something that fixes the bottleneck. An AI agent that lives on a server, runs 24/7, scrapes the web for me, and lands ready-to-edit drafts in my Slack before I open my laptop.

Even on the days we don't end up posting, we open Slack and are already caught up. The agent doubles as our morning industry brief.

Today I’m walking you through how to build it. Plus two simpler versions if you want the same outcome without spinning up a server.

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✅ What you’ll learn

• Why newsjacking is one of the growth formats carrying LinkedIn right now

• How to deploy an autonomous AI agent on Hostinger in 15 minutes

• How to wire it to Slack or Telegram so it feels like a team member

• How to prompt it to surface real stories and draft real posts

• Two simpler backup versions if the agent route feels like too much

🧨 What triggered The Workflow

One of Distinctiva’s clients covered the story about ChatGPT pulling directly from Google search and what that meant for SEO strategy. 208,000 impressions. 293 comments. 234 reposts.

That post worked because we were early. No one else had talked about it (yet).

The problem with timing is that it doesn’t scale. You can’t sit on X and Google News for an hour every morning scanning for the right story across multiple clients. You’ll always be late, or you’ll burn your best thinking time on research.

So I kept experimenting. First with Claude Cowork. Then with n8n. Both worked, both had trade-offs.

Cowork runs on a schedule, but your laptop has to be on. It also researches through Google, so if a story breaks on X first you can wait a couple days for it to get indexed. n8n scrapes X live and runs without you, but it’s more setup: API keys, the X scraping config, the workflow nodes.

Then I tried OpenClaw and something clicked. I gave it the task once and it ran itself on a server. Same research, zero babysitting.

🗞️🔥

🛠️ How to build The Workflow

Read more

That’s when we had the first call that led us to build this newsletter.

We started The Workflow because the AI hype was killing us.

Every week, a new model launch. Every day, a new tool promising to replace marketers, designers, humanity. Every hour, a new LinkedIn post telling you that if you weren’t vibecoding your own SaaS, you were already behind.

Our first issue declared war on AI FOMO: this newsletter is for people who want to use AI to work smarter, not for the sake of using AI.

May 2026.

The noise has 70X-ed.

Claude Opus 4.6. Claude Sonnet 4.6. Claude Code. Gemini 3.1 Pro. Grok 4.20. GPT-5.4. Llama 4. Claude Opus 4.7. Claude Design. Claude Cowork. ChatGPT 5-5.

Product releases are faster than anyone can possibly adopt them. Social media is drowning in AI slop. For the first time, there is more supply than demand, both for products and for content.

So what did we do to keep this newsletter from disappearing into the content slop?

We promised you workflows that were easy enough to implement right after reading but challenging enough to give you new ideas, without ever omitting where AI falls short.

13 issues later, almost 2,000 people trust us with their inbox. Some even paid for a subscription without us asking.

Here’s everything we’ve shared so far:

🔥 The AI Website Audit Workflow roasting homepages in minutes

📚 The Marketing Report Workflow to slash production time by 50%

💡 The LinkedIn Content Workflow for scaling thought leadership

🗣️ The Voice of Customer Workflow to power your content strategy

🤝 The Buyer Validation Workflow to align Marketing and Sales

🤖 The BOFU Workflow for killer product-led content

🧮 The AI Lead Scoring Workflow to 2x free-to-paid conversions

🏅 The Brand Positioning Workflow for the AI era

💛 The Harmony Workflow to scale evergreen content

🤖 The Brand Mention Workflow to fix what LLMs say about you

🚀 The AI Workflow scaling 1600+ comparison pages with Clay

🤘 The Claude Code Workflow that enforces your brand style guide

😴 The Content Repurposing Workflow that runs while you sleep

These issues are not built to give you FOMO, and they’re not written by people who screenshotted n8n nodes to rack up likes on social. They’re built to help you think and work better. That’s the only promise we’ll always keep.

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What’s next 🔮

We’re taking a short break to sharpen our newsletter format.

When we come back in June: more solo issues, where we share the workflows running behind Sara’s advisory work, behind Diandra’s agency, and behind our own content systems. More guests too: CMOs, builders, and tons of smart people doing the lord’s work without getting the visibility they deserve.

The Workflow is free for now. Considering the time we both put into it, we’re exploring ways to keep it sustainable, through the right sponsors and, eventually, a paid tier. Some readers have already chosen to pay before we made it mandatory, and those are the people who will lock in the best price we’ll ever offer.

Dig into the archive while we’re gone. The beauty of strategy over hype is that no matter how fast models change, the logic behind these workflows holds.

We’ll be back in June with new workflows worth stealing.

Sara Stella & Diandra✌🏼

Behind every “consistent creator” is either a team, a burnout waiting to happen, or a system nobody’s sharing. For the past few years, the default advice has been “repurpose your content,” which is technically correct but practically useless without the infrastructure to back it up.

This week’s guest is Brandon Smithwrick, a content marketing director turned full-time creator who built his audience across roles at Ralph Lauren, Squarespace, and Kickstarter before stepping out on his own in August 2025. Brandon writes Content to Commas, a weekly newsletter with 8K subscribers, has 55K+ followers on LinkedIn, and holds brand partnerships with Canva, Zapier, and HubSpot. The system he shares today turns a single newsletter issue into a full week of platform-specific content across all six platforms he’s on, automatically, and without the output sounding like it was written by a robot who just discovered em dashes.

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✅ What you’ll learn

• How to use AI and automation to build a content repurposing workflow

• How to turn long-form content into platform-optimized posts for 6+ channels

• How to set up a never-ending content pipeline that keeps running while you’re asleep

🧨 What triggered The Workflow

This workflow exists to solve a pain every creator and content manager knows all too well: the need to stop working at 3am.

For months, Brandon was running his day job as Director of Content & Creative at Kickstarter while writing a newsletter and posting across six platforms. The content was good. The pace was *BONKERS*. “I felt like I had three full-time jobs,” he said. “And I wanted to still be very intentional about my content.”

Brandon knew that copy-pasting a LinkedIn post into Threads, or turning a newsletter section into a LinkedIn post without rewriting it, was what he calls attention leakage. You “repurpose” content, but it falls flat because it was clearly made for a different channel. He needed a way to take one piece of deep, long-form writing and turn it into many, without losing the platform-specific voice that made each worth consuming.

🤯

🛠️ How to build The Workflow

Read more

Welcome back to The Workflow, where we think AI hype is a new kind of yellow fever.

Unless you live under a rock, you must have seen the Claude Code posts. “Marketers who aren’t using this will be obsolete by Q3.” “I automated my whole life, and I’ve never felt more alive.” We figured we owed it to you to check what’s noise and what’s real.

So we called Tim Metz, Director of Marketing and Innovation at Animalz, one of the most respected B2B SaaS content marketing agencies in the space. Tim has a basic coding background and the specific kind of brain that, once it discovered Claude Code, immediately started automating everything in sight: content workflows, LinkedIn post generation, AEO audits, and yes, at some point, his own sleep tracking. The terminal is now his natural habitat.

His workflow for this issue fixes a problem every content team has and rarely admits to: your brand style guide exists in a Notion doc, gets mentioned during onboarding, and enforces absolutely nothing in practice.

Tim’s Claude Code workflow reads your published articles, extracts your actual editorial patterns, turns them into a working brand style guide, and then runs eight parallel agents to check any new article against it, line by line. He built the whole thing in about 20 to 30 minutes, which means by the time you finish this issue, you could have one running too.

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✅ What you’ll learn

• What Claude Code actually is and how it works

• How to generate a brand style guide from your existing published content, without manual work

• How to run eight parallel AI agents to copy-edit any article against your brand style guide

• How to set up Claude Code even if the word “terminal” makes you cry

🧨 What triggered The Workflow

At Animalz, content goes through a meticulous editorial process: writer to editor to copy editor, with each step significantly impacting quality and stylistic nuance. For the agency’s own marketing, the same rules apply… in theory. Client work always takes front row, and Tim runs internal marketing largely solo, which means when a newsletter issue goes out on a Thursday and a blog post needs to be ready by “yesterday”, his editor is often staffed on external projects.

“Unfortunately, it’s happened that I published an article on the Animalz blog without spending enough time on the style editing review,” he told us. Anyone who has ever been a team of one knows exactly how that happens.

The second problem: because some articles skipped the process, the brand style guide had drifted. Certain rules were respected in some articles, but ignored in others. Nobody had a reliable picture of what the actual house style even was anymore. Brand style guide maintenance is the kind of task that always gets added to the list but never makes it to the top.

Tim had tried solving the problem with AI before. ChatGPT, earlier Claude models, various prompting approaches. They would catch some things and miss others. Inconsistently. Unpredictably. The problem was much bigger than the model: one context window, one pass, too much context and too many assets to hold at once.

Claude Code changed the constraint. And that changed the game for internal marketing at Animalz.

🛠️ How to build The Workflow

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This week’s guest is Matteo Tittarelli, founder & solopreneur at Genesys Growth, a B2B SaaS product marketing and content consultant whose AI workflows regularly make us stop scrolling. If you’ve seen him document his work on LinkedIn, you’ll recognize him as a true GTM engineer in action.

Matteo is a solo operator, which usually means capacity is the ceiling. With AI, that ceiling stretched to the max, allowing him to scale his output without scaling headcount.

In 2025, that leverage allowed him to handle up to 6 clients per month on his own, while keeping a (mostly) normal workweek without turning his calendar into a sleep-deprivation experiment.

Today, we scrutinize one of his wildest and most successful AI experiments so far. Scaling competitor and category comparison pages (1600+, to be exact), killing manual work while keeping the quality bar high.

Impossible? You’ll see.

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✅ What you’ll learn

• How to scale 1600+ competitor pages without sacrificing quality

• How to scale SEO content without producing AI slop

• How to use Clay for programmatic SEO

🧨 What triggered The Workflow

Comparison pages became a recurring client request for Matteo. SaaS in crowded markets with thousands of products and features need content that explains why they’re different (and better). There are so many ways to approach this: comparing tools, feature categories. Then adjacent categories. Before long, you’re staring at hundreds of pages.

Doing this manually wasn’t an option if he wanted to keep his sanity. Pricing changes. Reviews age. Feature claims drift. At scale, errors spread fast.

So the GTM engineer re-engineered the process.

He built a workflow that pulls together live market data, reviews, and pricing, anchored by a structured positioning library. The result is a scalable comparison-page engine that stays fresh, doesn’t become a maintenance nightmare, and drove a 38% increase in organic impressions just two weeks after the first pages went live.

🛠️ How to build The Workflow

To build this workflow, start by defining what matters in your ICP’s buying decision. Only then use AI to handle the grunt work needed to scale. You don’t just want to create pages, you want to express how you see the market.

Here’s how the pieces connect, from the positioning that drives every comparison to a scalable hub of product marketing pages.

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LLMs care less and less about what brands say about themselves. How others describe them carries way more weight.

Oh, the irony. Teams keep shipping fresh content on autopilot while the internet keeps repeating a brand narrative from three positioning pivots ago. This quietly kills online visibility because, unlike SEO, AI search gives you no noindex button.

Today’s guest is only in his mid-20s, but he understands how online search works better than most people ever will. Usman Akram, Discoverability Lead at influencer marketing platform Modash, built a workflow to help brands take back control of their narrative in AI search. It audits how the internet understands your brand, highlights gaps against your intended positioning, and tells you exactly what to fix first.

Less “publish and pray.” More “make the web speak with your brand voice.”

Let’s get to work 👇🏼

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✅ What you’ll learn

• How to build an AI workflow to improve your brand positioning in AI search

• How to find third-party brand mentions across the web and compare them with your intended positioning

• How to prioritize which positioning mismatches to fix to improve your AI visibility

🧨 What triggered The Workflow

In a world where no one can say with 100% certainty how to show up in AI search (despite everyone having a hot take), Usman had a clear edge.

When he built this workflow, he was working at a well-known SEO agency in the middle of its shift into AEO. He was advising and managing dozens of client accounts, from fast-growing startups to brands with millions of existing mentions across the web.

That put him in a privileged position: deep SEO fundamentals, a ton of curiosity about how LLMs work, and enough real-world data to test his hypotheses instead of pontificating on LinkedIn.

One pattern kept showing up. Companies would evolve their positioning and expect a few fresh brand mentions to magically shape how AI talks about them.

“That’s when it clicked,” Usman told us. “LLMs care about recency, but they care even more about consensus. Adding X new mentions a month doesn’t change anything if the rest of the internet already spreads a different story.”

That’s when he flipped the approach. “Dear brand, before creating anything new, what if you fixed what already exists? What if you identified where LLMs get your brand wrong and corrected the highest-impact mentions first?”

Now he had to build something to make that possible. But not a content engine as he’d use for traditional SEO.

More like a brand guardian.

🛠️ How to build The Workflow

This workflow is essentially an internet-wide positioning audit. You feed it a brand name and a domain, and it will define

• how you describe yourself today

• scrape how the rest of the web describes you

• surface where those two stories don’t match, and

• tell you which mismatches you should fix ASAP.

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In our last issue, we unpacked Harmony, the sophisticated editorial engine powering Search Engine Land, a full-blown n8n orchestra built by tech specialists and serving an entire editorial team. Today we are sticking with n8n, but our guest brings a workflow that is definitely lower-maintenance.

Aldo Gnocchi, Program Director and Lecturer at Swiss University FHNW and Owner of the Senior Digital Expert Collective Gnocchi & Friends, has been experimenting on the internet since you had to pay by the minute and pray your new hobby didn’t bankrupt you. Today, he still gets the same spark in his eyes when he talks about what AI makes possible.

One of the biggest shifts for him has been cutting out the tasks that eat time but add little value. Creating website and homepage audits from scratch was right at the top of that list. His solution is a surprisingly simple n8n workflow that scrapes any webpage, analyzes it with an AI agent, and spits out a structured audit ready for a touch of human expertise.

It’s fast. It’s rough around the edges. And it saves him from the kind of repetitive work that makes even the most seasoned marketers question their career choices.

Let’s get into it 👇🏼

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✅ What you’ll learn

• How to build an AI website audit workflow [complex and simple version]

• How to prompt AI to audit any webpage like a seasoned CRO expert

• Where human review fits in so your audit doesn’t turn into AI slop soup

🧨 What triggered The Workflow

In theory, auditing websites should be a strategic exercise. In reality, it often feels like dental work: necessary, repetitive, and quite painful.

Aldo has been doing website and conversion audits for years, repeating the same steps, spotting the same patterns, and spending hours on work that felt far more mechanical than creative.

Same steps, same checklist, different website.

→ Is the headline clear or trying to say five things at once?
→ Is the call to action specific enough?
→ Are the visuals supporting the message or just filling space?
→ Does the structure match the typical customer journey?
→ Is anything on the page actually building trust?

After years of doing website audits, Aldo could feel the creativity draining out of the process.

“That is why I wanted to automate it,” he told us. “It is interesting work, but it is repetitive.”

So he did what a marketer facing a lightbulb moment eventually does: he opened n8n at 7 a.m. and built an agent-powered workflow to do the first 80% of the audit for him.

The whole thing started as a fun experiment… until he realised he never wanted to go back to doing things the old way.

🛠️ How to build The Workflow

Aldo’s automation is a six-step agent-powered workflow that runs end-to-end inside n8n.

You enter a URL, hit submit, and the system scrapes the homepage, analyses it with a Conversion Rate Optimization (CRO) agent, formats the output, turns it into a client-ready PDF, and stores everything in your Google Drive. We focus on a homepage audit here, but the process is the same whether you are auditing a landing page or an entire website. You simply adjust the prompts.

Here’s how the whole thing works:

Step 1: Collect the URL via a simple n8n form

Everything starts with a lightweight n8n form where you paste the URL you want to audit. The moment you hit submit, the entire workflow fires automatically. Anyone on Aldo’s team can now run an audit without touching the backend or configuring anything. Paste, submit, done.

Step 2: Scrape the homepage

Once the form fires, n8n sends the homepage URL to a scraper that pulls the full HTML and turns it into clean text. This gives the agent a proper snapshot of the page and avoids the hallucinations and errors that often happen when you feed it copy-pasted fragments.

The output from this step becomes the data packet passed into the CRO agent, which we will now refer to as the “homepage roaster.” Aldo did not call it that, but he really should.

Step 3: Roast the homepage

This is where the workflow turns into a real audit engine. The scraped text flows into Aldo’s homepage roaster agent, powered by a structured prompt shaped by years of hands-on CRO teardown experience.

Grab Aldo’s homepage roaster agent prompt here 🤖

What should you consider when auditing a homepage?

• Clarity of messaging

• Relevance for the target audience

• Value proposition and its strength

• Call to action quality and specificity

• Trust and credibility signals

• Accessibility and usability

• Visuals and design

• Mobile optimization

This is Aldo’s go-to list. Think of it as a flexible audit backbone you can adjust to any website or business model.

To keep audits consistent and comparable across sites, he uses a scoring system from 1 to 10 for each factor, which then adds up to an overall score. Within each category, the agent evaluates specific sections of the page and produces actionable recommendations tied to the areas that need improvement.

To run the agent, Aldo uses OpenRouter, an API hub that gives him access to any AI model through a single API into n8n, making experimentation across models a breeze.

For this workflow, Aldo picked Gemini Flash. Not because it’s the most advanced model, but because it’s fast and cheap per token. “A CRO audit doesn’t need creative genius; it needs consistency, structure, and speed.” Using an advanced reasoning model here would be “an overkill that just burns money.”

Grab Aldo’s homepage roaster agent prompt here 🤖

Even if n8n is not your thing, you can plug this prompt into any LLM or adapt it to whatever AI tool you already use.

Right after this step, Aldo uses a small formatting agent to turn the output into clean markdown so the HTML node can process it. It is a quick workaround that saves him from writing custom code. A simple custom node could do the same job for free, which is why Aldo sees this solution as functional but a bit of an overkill.

Step 4: Convert the audit into HTML

Once the markdown is ready, n8n passes it into a standard conversion node that turns the structured text into HTML. This is the point where the audit stops being a block of text and becomes a structured document with headings, lists, and a clear hierarchy.

HTML is the ideal intermediate format because it keeps the structure consistent and prevents formatting errors that often appear when exporting directly from an LLM. Aldo reviews the output here, while it is still editable, before the next step where n8n renders the HTML as a clean, client-ready PDF.

Steps 5 & 6: Generate the PDF and store it in Google Drive

After the HTML is created, n8n passes it to a PDF generator node that renders the audit into a clean, shareable document. Right after that, a separate Google Drive node uploads the PDF to the folder Aldo uses for all his audits.

The audit is proofread, formatted, exported, and filed away. Time to run the next one 🙂‍↔️

Lightweight alternative: Audit webpages using Manus

Unlike Aldo, not everyone enjoys the thrill of tinkering with n8n at 7 a.m. That is why he also showed us the alternative every marketer can use, no n8n skills required.

Good news for the non-techies out there. A tool like Manus lets you generate slide-ready audits without touching a single node. You create a knowledge bank with your brand guidelines, your TOV, and any previous audits or frameworks you want the model to follow, and it will generate a full presentation.

Compared to the n8n version, you get fewer customisation options and a higher price tag, but the process is much faster and the output comes with far better design out of the box. Use it to audit single webpages or full websites; the power is yours.

We have also included a shorter prompt you can use for Manus 🤖

🤖 Tools powering the Workflow

If you already use n8n or plan to build more automations, this workflow is a great place to start. If not, getting n8n just for this one use case is overkill.

🎢 Highs, lows and Workflow warnings

This is a shockingly simple workflow running on an overly complex platform. Most days it behaves like a well-trained assistant. On others, it reminds you that n8n is a playground where things can break just because you looked at a node the wrong way.

✅ What shines

• A repeatable, scalable, and fast homepage audit workflow

  This workflow turns a 2/3-hour homepage teardown into a structured audit that is ready in the time it takes to refill a coffee. The scoring system makes the process objective and consistent across websites, and the recommendations read like the work of a seasoned CRO consultant.

• Human expertise is injected only where it matters.

  Instead of spending hours structuring tables or copy-pasting audit templates, Aldo simply reviews the HTML output before it is converted to a PDF.

• Getting started with n8n

  If you want to break the ice with n8n, this workflow is the ideal place to begin. It is short, modular, and relatively easy to adjust. Plus, you will learn the basics of scraping, agent calls, formatting, and file export (yay!).

❌ What doesn’t shine

• n8n requires a steep learning curve

  Even though Aldo has been experimenting with hundreds of AI tools over the years, he openly admits that n8n is a hard nut to crack. “Whenever you see an error warning, it’s super hard to figure out what the problem is. Did you choose the wrong output or the wrong authentication? Debugging can drive you crazy.”

• Editing HTML output is a pain

  The workflow goes straight from HTML to PDF, which means the HTML node is where Aldo fact-checks and tweaks the agent’s output.

⚠️ Workflow warning

To solve the pain of editing output in the HTML node, we tried to vibe-code a Google Docs node on the fly during the interview (are we cool now?) using n8n’s AI assistant. It went nowhere. With n8n, the moment you start tweaking nodes, you feel the steep learning curve. Complex workflows or custom nodes often require JavaScript or Python, and while you can eventually hack your way through with ChatGPT, Claude, Gemini, or the n8n community, it still takes time to get right.

This isn’t a warning against non-technical builders. It is a reminder that behind each shiny workflow there are plenty of unsexy bits nobody mentions, like setting up API infrastructure, configuring HTTP requests, debugging your vibecoding attempts, and more.

✨ The Goldflow

“AI has freed up a lot of my time, but that’s nothing compared to how much it’s feeding my curiosity.” For Aldo, every workflow and every experiment becomes a doorway to ten more ideas he feels the urge to explore immediately. “Today I am a movie director with Sora… and tomorrow who knows? This is SO exciting for me!” You could genuinely hear the spark in his voice.

The more possibilities open up, the faster we all start tinkering with things that are fun, pointless, and suddenly: two hours. Gone. This is the paradox of our AI era.

Building workflows is only one skill. Knowing when to close the laptop is the other.

But only for now. In two weeks, you will be back here stealing the next roaring workflow we cooked up for you.

Sara Stella & Diandra ✌🏼

Can’t survive two weeks without us? Catch up on past Workflows and steal the playbooks you missed.

🚀 The LinkedIn Content Workflow for scaling thought leadership

🗣️ The Voice of Customer Workflow to power your content strategy

🤝🏼 The Buyer Validation Workflow to align Marketing and Sales

🔥 Help us roast more webpages. No violence involved.

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A few months ago, Search Engine Land (SEL) did something most publishers and creators avoid: they told the world they were using AI to write a section of their content…and the internet exploded.

Some accused them of killing journalism, others of secretly training models on contributors’ content. A few applauded the transparency.

Behind the storm was an editorial experiment led by Kyle Morley, our guest today. He heads Acquisitions at Semrush and Sales and Marketing at Third Door Media, the publisher of SEL and MarTech (both now part of Semrush. Yes, it’s all connected!).

Their goal was to test how far AI could go in speeding up the production of their evergreen content section without sacrificing the quality and tone their readers love.

To solve that challenge, they built Harmony, a hybrid workflow where AI drafts and humans refine, cutting production time from weeks to days.

This issue gives you a rare look at how a global brand scales content with AI and the bigger questions that come with it.

How much should you scale?

How do you keep quality intact?

How do you communicate AI usage transparently?
How should you adjust your current content ops and team?

…and many more.

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✅ What you’ll learn

• What an AI-assisted editorial process really looks like inside a top publication

• How humans and AI share the work across the content pipeline

• SEO writing guidelines that will help you nail that first draft with AI

🧨 What triggered The Workflow

SEL has been publishing trusted SEO and PPC insights since 2006. They cover daily industry news and analysis, trends, expert columns, and the in-depth evergreen guides marketers keep bookmarked for years.

That scope is both a blessing and a curse.
News needs constant coverage and sharp opinions, not something you hand over to AI.
Meanwhile, each long-form guide took three to four weeks to go from outline to publish, bouncing between strategists, writers, and editors. With more than 1M marketers reading every month, the team couldn’t keep up with demand.

Kyle and his team were facing a tricky crossroads.
On one hand, hiring more writers would have boosted output but risked undermining consistency in tone, structure, and quality. Each new writer meant more rounds of edits and reviews, stretching an already slow four-week cycle even further. Instead of solving the problem, scaling the writing team would have only scaled the bottleneck.

On the other hand, keeping the status quo wasn’t an option. Demand for evergreen SEO guides was growing faster than their ability to produce them.

So they turned to AI to solve the dilemma. Instead of expanding the writing pool, they re-engineered the process around editors, the subject-matter experts closest to the audience.

For this type of evergreen content, AI proved surprisingly good at research and first drafts, while editors keep full control over structure and accuracy, adding the expertise and perspective robots can’t fake. The result is the hybrid workflow we’re unpacking today: part curation, part original insight, without reinventing the wheel each time.

And here’s the part we truly love: the efficiency gains went straight back to the humans. Cutting back on external writers freed up budget to increase editors’ salaries, hire real experts, and turn great writers into even better editors.

🛠️ How to build The Workflow

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This week’s guest, Sarah Adam, Head of Influencer Marketing at Wix, spends her days building long-term partnerships with hundreds of creators across every social platform you can imagine. Which is why the rise of AI influencers and “AI twins” freaks her out (same for us, by the way). “It’s hard to relate to someone who’s made of pixels and looks like a model,” she told us. “Influence only works when it feels human.”

The workflow Sarah shares today is her quiet rebellion in the name of authenticity. It uses AI to find the right creators faster, not to replace them.

If influencer marketing has a place in your 2026 strategy, you’ll want to steal this one.

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✅ What you’ll learn

• How to use AI to speed up influencer discovery without killing authenticity

• How to extract keywords for influencer discovery from product documentation

• How to define ideal influencer profiles for several products and different ICPs

🧨 What triggered The Workflow

There’s no such thing as a one-size-fits-all influencer marketing approach at Wix. With several brands in their portfolio targeting very different user groups, each campaign needs its own formula. Some speak to professional designers building sites at scale with Wix Studio, others to individuals or small businesses building apps with Base 44, Wix’s recently acquired AI app builder for non-techies. The first group looks for depth, tutorials, and design inspiration. The second values snackable content, simplicity, and creators who show that tech can be accessible to anyone.

TLDR; Each audience needs its own story, tone, and influencer type to make the product click.

The prerequisite for winning influencer campaigns is crystal-clear product marketing. Sarah starts with a detailed deck outlining features, positioning, and target users. From there, she has three main jobs: defining influencer profiles that each audience will resonate with, finding those influencers, and making the deals happen.

To identify the right cleators, Sarah starts by clustering keywords related to the product and the audience’s topics of interest.

Before she created the Influencer discovery workflow, she had to chase the SEO team for help (spoiler: not their top priority) or spend hours buried in decks and demos, manually extracting keywords and hoping they might unlock the right creators.

Today, she just drops the product marketing deck into ChatGPT and instantly builds hyper-relevant influencer profiles based on topical keyword clusters.

🤯

🛠️ How to build The Workflow

It all starts with the product marketing deck, the single source of truth that defines features, benefits, and ICPs. From there, the workflow runs through three simple prompts: one to extract keywords, one to map them to ICPs, and one to reverse-engineer influencer profiles for each audience.

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From creating a marketing report to generating blog article briefs, we’ve shared quite a few content workflows, but never one that pokes holes in your content before the CFO does.

Andrei Zinkevich, co-founder of ABM consultancy Fullfunnel.io, has seen 50 shades of sales and marketing misalignment throughout his career. The usual story: Marketing cranks out content on a streak, and Sales won’t touch a single piece (sometimes they even pretend it doesn’t exist).

The reason is that most content for high-ACV businesses is disconnected from real customer pains, recycled from the top ten Google results, and created in isolation without input from subject matter experts. Marketing is too far removed from customers and AI has only made the problem worse. “No sales rep will ever send anything that makes them look like an amateur to a prospect. As simple as that.”

To stop the bleeding, Andrei built what he calls a synthetic buyer panel, an AI-powered feedback loop that reviews and challenges your content from the perspective of your actual target buyer.

His workflow mirrors his ABM approach: define ICPs based on closed-won deals, segment buying committees into personas, and create content that speaks to buyers’ pain points and supports long sales cycles. Not another Frankenstein PDF bloated with gut-feeling edits and marketing fluff, you heard that right.

🥜 Let’s crack it open.

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✅ What you’ll learn

• How to review your content through your buyer personas’ eyes using AI

• How to make every asset reflect your ICP’s real pain points and priorities

• How to rebuild Sales–Marketing alignment with content that actually moves deals

🧨 What triggered The Workflow

Most B2B marketing teams create content to please algorithms instead of helping buyers, or hide whitepapers behind forms to chase MQLs. This outdated playbook became the industry standard, with few marketers actually measuring whether their content drives pipeline or helps close deals faster.

Despite popular belief, adding more AI tools on top only makes the problem worse. The real solution is a reality check from the market itself. Andrei’s workflow uses AI to bring the buyer’s voice back into the process, not bury it deeper in keyword soup.

👇🏼 If you think it sounds too good to be true, wait till you see the step-by-step.

🛠️ How to build The Workflow

Andrei’s Buyer Validation Workflow is the grown-up version of “let’s see what the market thinks.”

The panelists are AI-simulated buyers trained on real ICP data. The setup couldn’t be simpler: one Claude project, no automations (and zero shame in admitting that).

What follows is the playbook (prompts included!) you can use to build a validation engine around your own buyer personas.

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In fact, this issue wasn’t even planned.

But starting the year without saying thank you didn’t feel right.

By the end of 2025, 1100 of you trusted us with your email addresses. Inbox trust is sacred real estate these days. We couldn’t just… disappear into January pretending that didn’t happen.

So this is us. Two humans behind keyboards, writing to say: THANK YOU.

How the Workflow started workflowing

The Workflow started as a survival mechanism to cope with the AI hype and somehow turned into a newsletter with 1100 marketers, growth leads, founders, and CMOs across the US and EU loving it. People we truly admire ask us to be interviewed and brands reach out to ask if we’re accepting sponsorships

…which is wild at best with just 6 issues live (!)

The first couple of interviews helped us refine the newsletter format. That’s where having friends with deep AI knowledge (and a lot of patience!) really helped.

Just like people who build products to solve pains they once experienced, we literally built our own space to learn AI from and with our community, because everything else felt like a race to stack more nodes in n8n for no particular reason.

Everywhere we looked, AI was being presented as either magic or menace. Endless tool lists. Loud promises. Perfect outputs, with zero context for the work behind them. What was missing were the receipts, the “but what’s the goal?”s, and the messy middle between ideas and results.

We wanted to document that messy middle to show how marketers and operators (like you and us) use AI and automation when deadlines, stakeholders, and imperfect data are part of the game.

This attitude shapes (and will continue to shape!) everything we publish, for as long as this newsletter exists.

🌍 Also: geography did not stop this

One more thing we love about this project.

Diandra lives in Colombia.
Sara Stella lives in Switzerland.

Different continents, same allergy to AI hype, LinkedIn gurus, and marketing fluff. We’ve grown closer than many people who meet daily, simply because we share the same worldview and…hate the same things, probably?

One of the highlights this year was finally meeting in person at Semrush Spotlight and realizing three amazing things:

✅ Yes, we both have legs (phew)
✅ Yes, we both exist (double phew)
✅ Yes, the other one is even better in person (triple love)

What we’ve workflowed so far

In 2025, we stuck to our bi-weekly shipping cadence and published 6 issues.
Okay, there was also a pilot Manifesto issue that stated our intentions with this newsletter, but that doesn’t count.

From simple prompt chains to workflows that made us rethink what’s possible, we’ve debunked the myth that AI needs to be overly complicated to be useful.

If you want a head start on your New Year’s resolutions, this is the perfect moment to catch up on the issues already live and maybe start building something small when you need an excuse to avoid your relatives.

📚 The Marketing Report Workflow by Sara Stella Lattanzio

🧮 The Lead Scoring Workflow by Vukasin Vukosavljevic

🏅 The Brand Positioning Workflow by Clay Ostrom

📝 The BOFU Article Workflow by Lashay Lewis

📞 The Call-to-LinkedIn Content Workflow by Grant Hushek

🗣️ The Voice of Customer Workflow by Mina Mesbahi

Expect many more non-AI gurus next year. How many of those do you recognize?

❓What type of workflow should we cover next?

🎁 The important stuff: your gift

Only ~20% of readers actually click on newsletter links. That’s a shame, considering we share templates or prompts in every single issue of The Workflow.

These prompts are essential if you want to replicate one of our workflows, but they’re just as useful if you want to solve one specific task with your LLM of choice (ChatGPT, Perplexity, Claude, Gemini… we don’t discriminate).

Ready to unwrap the paper? 3,2,1…

Enter the Prompt Vault: the best prompts from all our issues so far.

• 15 prompts

• Ready to Ctrl + C / Ctrl + V straight into your LLM of choice
  

→ Get the Prompt Vault Here! 🎁

👩🏼‍🍳 What’s cooking

We’re currently on a small hiatus (which we happily broke with this issue).
Read: Sara’s stuffing her face with excellent Italian food and Nano Banana-ing family pics, while Diandra is forcing her whole family to take 20 supplements a day and explaining the ROI of magnesium glycinate at dinner.

The first 2026 issue of The Workflow drops on Jan 8th, where we’ll tackle one of the hardest problems out there: simplifying Sales and Marketing alignment.

Enjoy the break. AI still can’t do it for you.

Thanks for your early support. It means a LOT. ❤️

Sara Stella & Diandra ✌🏼

🙏🏼 P.S. If our newsletter was useful to you this year, consider forwarding this to someone who’d appreciate fewer manual tasks in 2026.

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In our last issue, our guest Grant combined multiple LLMs, automation, and human review to transform his endless Zoom calls into a daily pipeline of LinkedIn posts that sound exactly like him.

This week, we’re flipping the script. Same raw material, transcripts, but a completely different use case.

Sara Stella’s friend Mina Mesbahi, founder of boutique content consultancy Digichapter, built a CustomGPT (baptism name: Customer Echo 🫨) that digs through sales call transcripts (discovery, pitches, demos etc.) with Swiss precision, extracting customer insights to fuel a winning content strategy.

Time to trade clickbait hacks for what your buyers actually care about. You ready?

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✅ What you’ll learn

• How to build and train a CustomGPT to extract customer pains, goals, and language directly from sales call transcripts

• How to automatically organize emerging insights in a centralized knowledge hub for better analysis

• How to ground your content strategy in what buyers actually say

🧨 What triggered The Workflow

For B2B marketers, pitches, demos, and customer call transcripts are goldmines that mostly go untouched. The old problem was access: marketing rarely sat in on sales calls, and relying on someone else to share insights was a losing bet. Today, the problem is the opposite: endless recordings with no system to turn them into repeatable, actionable insight.

Mina spotted this gap again and again: “Teams have a ton of insights in their sales conversations, but it’s either not being operationalized or they just don’t have access to it.”

Her remedy was to build a CustomGPT that pipes transcript insights into Airtable, surfacing customer pains, goals, and phrasing at scale.

🌟If you’re already sitting on this kind of gold, how long can you afford to leave it buried?

🛠️ How to build The Workflow

Mina’s Customer Echo works like a sharp-eyed virtual analyst, combing through transcripts and tagging every quote by pain, goal, trigger, or objection. Once that engine is in place, the rest clicks together: capture the calls with Fathom, run transcripts through the GPT, drop structured quotes into Airtable, and watch themes roll up into insights that actually steer your content strategy. What follows is the playbook, complete with GPT instructions and Airtable views — so you can mirror the system, giving your transcripts a second life.

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Last time, we cracked the code on writing high-converting product-led SEO articles with the ultimate BOFU workflow.

This week, we’re shifting gears across the funnel (and across platforms) to the one channel that’s non-negotiable in B2B: LinkedIn.

If you’ve ever thought, “I should post that,” during a client call and then never did, this one’s for you. Whether you’re a founder juggling back-to-back Zooms, a marketer ghostwriting for execs, or anyone sitting on piles of unused transcripts, we’ve got a system. Calls become spicy takes, spicy takes become drafts, and drafts land neatly in your calendar.

Posting daily on LinkedIn suddenly stops feeling like your second full-time job.

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✅ What you’ll learn

• How to automate LinkedIn content creation from Zoom call transcripts

• How to extract and filter post-worthy topics with ChatGPT

• How to turn founder insights into spicy, opinionated LinkedIn takes with Claude

• How to build a human-in-the-loop workflow in Slack for authentic content

🧨 What triggered The Workflow

Creating consistent LinkedIn content is where most founders stall. Grant Hushek, founder of AI and automation agency Grantbot, was no exception. He lives on Zoom, not in Docs, and the blank page kept winning. “I really needed a draft. I HATE staring at a blank page and creating content from day to day,” he told us. RIP consistent posting, even though the raw material to power his LinkedIn presence was trapped in his many calls.

So, he built Call-to-Content to handle a few repetitive jobs: scan every Fathom transcript, pull out the quotable bits, and hand him ‘spicy takes’ to approve in Slack. It’s built for founder-led LinkedIn content, but works just as well for any exec, consultant, or team sitting on a mountain of transcripts. Sounds like AI scale and efficiency, minus the slop.

👇🏼 If you think it sounds too good to be true, wait till you see the step-by-step.

🛠️ How to build The Workflow

We’re starting to notice a clear pattern: the best workflows come from solving a personal pain first, then scaling, never from duct-taping 50 AI tools together. Grant’s calls were overflowing with content-worthy insights, but none of it was making it to LinkedIn. “I built this to keep up my LinkedIn game, and then founder friends started asking for it. That’s when I realized others might even pay for it,” he told us.

That’s how his four-step workflow was born: capture & filter, humanize, generate content, schedule.

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Today’s guest is Lashay Lewis, aka the queen of bottom-of-funnel (BOFU) content. Two and a half years ago, she built the BOFU framework she wished she’d had back when she was grinding at a content agency.

That Notion-based dashboard morphed into Authority Plug, her specialized bottom-of-funnel content advisory for high-growth SaaS teams. As the AI boom hit, she saw an opportunity to scale client operations by building her own AI-powered platform to run her consultancy end-to-end.

Unfortunately, we can’t hand over her platform (off the market, sorry). But we can hand you this: a dead-simple starter kit for BOFU content that performs—even if you’re stuck with messy docs and one faithful LLM tab.

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✅ What you’ll learn

• How to build a BOFU content strategy with AI

• How to gather information to create high-quality product-led content

• How to run BOFU keyword research for Google + LLMs

• How to create product-led content briefs that convert

🧨 What triggered The Workflow

Creating bottom-of-funnel content that actually converts is harder than most marketers want to admit. You’re writing for buyers who are already weighing options, not scrolling for generic advice. They want proof, facts, specifics, and reasons to choose you over the competition.

“This is how most SaaS companies approach BOFU content: skim a few product facts, research some keywords, write something ‘optimized’, hit publish… then wonder why it doesn’t convert.” Lashay told us.

She has a different approach: her frameworks combine product messaging, sales data, and customer intelligence. When AI entered the picture, she layered automation on top to minimize the busywork.

“At some point I realized I wasn’t losing deals to other consultants. I was losing them to content tools”, she admitted. The systemization that made her work so effective also blurred the line between consultancy and SaaS.

That tension pushed her to rethink the balance between bot and machine. The result is her new consultancy BOFU.ai, which combines the expertise of a senior content specialist and the scalability of AI.

👇🏼 At the core is the proprietary workflow we’re about to break down step by step.

The impact: Interview prep to first draft took half the time compared to our usual pace. Admin work dropped by 70%. And we finally stopped chasing transcripts and started shaping insights.

Want to replicate this workflow or tweak it for your own use case? Keep reading.

🛠️ How to build The Workflow

Lashay runs her consultancy on a custom platform. You don’t need that. What you do need are the four core steps behind her BOFU strategy.

🎁 Each step includes a signature prompt you can drop into your favorite LLM to guide research and content. In Step 4, you’ll also get a ready-to-use BOFU brief template, the same structure Lashay now runs inside her platform.

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Whether you work in marketing, sales, or run your own thing, each issue delivers a real-world workflow from a business pro that you can replicate as you read.

Last time, HeyReach’s CMO showed us the behind-the-scenes of an AI lead scoring Workflow that doubled their free-to-paid conversions. This week, we’re jumping from something extremely measurable like lead scoring to something typically impossible to define: brand positioning. Spoiler alert: this week's guest built a workflow to make it more tangible (without breaking the magic).

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✅ What you’ll learn

• How to build a complete brand positioning workflow with AI

• How to measure and quantify “brand” as much as possible

• How to run competitive brand analysis, fast

• How to create a reliable scoring system to analyze your brand

• How to craft resonant messaging around your key differentiators

🧨 What triggered The Workflow

Our guest, Clay Ostrom, founder of brand strategy agency Map & Fire, worked in the past with giants like Google, Sony, and Mattel and has spent the last 10 years helping everyone from scrappy startups to SMBs stand out in crowded markets.

Thanks to the AI boom, that job just got a whole lot harder.

“I started noticing how businesses, especially in SaaS, aren’t just up against two or three rivals anymore. They’re up against 10–15, each chipping away at market share.”, Clay told us.

The good old “gut feel” approach to positioning can’t keep up. Agencies like his feel it even more: every new client requires starting from zero on competitive analysis and messaging, often at high volume and under brutal timelines.

“It’s the classic ‘Here are 20 competitors and 50 internal docs to review for our kickoff tomorrow’ kind of thing. We need better tools. As competition levels go up, we have to fight fire with fire.”

His answer? A workflow that turns fuzzy positioning into something measurable and actionable, helping you quickly analyze dozens of brands, understand their superpowers, and position against them effectively.

Clay even built his own tool, SmokeLadder, to automate and scale the process. But here’s the best part: he’s sharing a slimmed down version of that workflow that you can run with any of your favorite LLMs (probably ChatGPT, Claude, or Perplexity, we’re guessing).

Want to replicate this workflow or tweak it for your own use case? Keep reading.

🛠️  How to build The Workflow

This Workflow is your playbook for defending (and winning) your brand budget.

It takes one of the most abstract, “fluffy” aspects of marketing — positioning — and makes it concrete, structured, and even measurable. Follow these steps and you’ll have hard evidence to back up your brand decisions, instead of hand-waving through strategy decks. If more SaaS companies did this, we’d all spend less time squinting at homepages wondering, “But what does this tool actually do?”

Clay has gifted us some simplified prompts for each step listed below (thank you, Sir!), so you can grab a coffee, fire up your favorite LLM, and start running your first AI-powered positioning sprint today. If you want to go further, SmokeLadder (his platform) pulls these steps into one streamlined workspace, provides deeper consistent scoring, and layers in benchmark data to help you see exactly where your brand stands.

💘Get all the prompts for steps 1–5 of the brand positioning Workflow and run them with whatever LLM you love 💘

1. Nail your brand fundamentals

Before you can position against competitors, you need a crystal-clear definition of your own brand’s foundation. This step is all about answering: Who do we serve? Where do we play? What needs are we solving?

To define your brand, you need absolute clarity on these points: 

• Target customer – the exact audience you’re trying to reach

• Target category – the market space you want to own

• Offering definition – what you actually provide

• Core benefits – the value your product or service delivers

💬 Clay doesn't beat around the bush: “If you can’t articulate this internally in plain language, it’s impossible to differentiate externally.”

For demonstration purposes, we'll use ChatGPT to analyze and build Tella's positioning, a screen recording and editing tool:

2. Analyze the brand category

Now it's time to understand the ecosystem you’re stepping into. This step is all about answering: “Who else plays in our space? How are they framing themselves? Where do we have room to stand out?”

You'll need to map out:

• Category alignment – the primary category your audience would place you in

• Alternative categories – other spaces you could credibly compete in

• Key competitors – the main players to benchmark against

• Challenger brands – emerging or niche players making interesting moves

🗺 By now, you should have a clear map of the players that matter most; the ones you need to contrast with if you want to stand out.

And that’s just the enrichment layer. With a detailed profile built out (including ICP match, firmographics, behavior, and job titles), each lead is now ready for scoring, assignment to a responsible team member, and for a suitable next step.

3. Score your brand (and your competitors)

This is where positioning gets tangible. Instead of playing the “We’re the best!” card, you will turn differentiation into numbers.

Clay’s philosophy: “If you can quantify it, even if it’s subjective, it’s a lot easier to find points of improvement.”

Score your brand and your competitors across these dimensions:

• Value delivered – how much your offer satisfies customer needs

• Differentiation – how clearly you stand apart in the market

• Category fit – how well you align with the space you’re competing in

🧮 The scores aren’t set in stone; they’re a reliable compass for spotting strengths, weaknesses, and areas to refine.

4. Lean into your brand differentiators

With scores and comparisons in hand, it’s time to figure out where you can stand out the most. This step breaks your brand into:

• Advantage points – the unique value you deliver that others can’t match

• Table stakes points – things you must have just to stay in the game

• Weakness points – areas where competitors currently outshine you

• Where to double down – the strengths that can set you apart even more

🤑 The goal here is simple: maximize separation from the competition by leaning into your most unique points of value. As Clay puts it, “The magic isn’t just spotting where you’re strong; it’s deciding where that strength will actually win you customers.”

5. Craft resonating brand messaging

Wild how many people think this is the first (and often the only) step of brand positioning. In reality, it’s the last mile. By the time you get here, you’ve already mapped your category, identified competitors, scored your positioning, and pinpointed your unique advantages. Now, you’re turning all that evidence into words.

In this stage, you’ll craft:

• Headline concepts – The core statement that captures your brand’s promise.

• Subheadline concepts – A concise expansion that explains how you deliver that promise.

• Customer pain points – The problems your audience feels most acutely.

• Brand benefits – The tangible value you deliver to solve those pains.

Clay notes, “You’re no longer starting from a blank page; you’re now starting from hard evidence.” That’s why your messaging will land stronger, read sharper, and connect faster.

⚠️ Note: While AI tools can help you brainstorm messaging ideas and angles, we still recommend having a human copywriter shape the final product because nuance, tone, and emotional resonance are what make the words stick.

🤖 Tools powering the Workflow

Sara made fun of her pretty basic tech stack for executing the Marketing Report Workflow, but this tech stack is next-level minimalist. All you really need is your LLM of choice (ChatGPT, Claude, Perplexity… pick your poison).

Alternatively, if you want an all-in-one option that lets you plug in URLs and get structured scores, visualized outputs, and strategic insights without juggling 15 spreadsheet tabs, that’s where SmokeLadder comes in. That's it.

We love workflows like this because they show how tech can accelerate an evergreen strategic project, making it faster, scalable, and easier, without pretending the tech is the strategy. Positioning still depends on your thinking and product, not on how many tools you stack.

⚠️The size of your tool list says nothing about how good of a marketer you are (although many people on LinkedIn clearly disagree).

🎢 Highs, lows and Workflow warnings

The magic of this workflow is how straightforward and repeatable it is. Once you’ve run it a few times, it becomes second nature. The best part is how it turns a notoriously fuzzy process into a clear, objective sequence you can run for any brand (or multiple brands at once, if you're an agency) without drowning in open tabs or spreadsheet hell.

Clay points out that having structured scores for positioning “pulls the soul of your brand out of your head and gets it on paper” — a huge advantage when conversations drift into subjective debates. Instead of arguing over gut feel, you’ve got data.

That said, here are the most common brand positioning mistakes to avoid:

• Garbage in, garbage out. If your inputs (competitor sites, messaging, product info) aren’t accurate or up to date, your scores will reflect that. Clay stresses the importance of sanity-checking sources before running the analysis.

• Don’t skip the foundation work. Many people want to jump straight to crafting messaging, but without clear answers to “Who do we serve?” and “What category do we play in?”, you’re polishing fluff.

• AI ≠ copywriter. Even with sharp insights and competitor breakdowns, the final messaging still needs a human brain (and tone). Think of this workflow as your research muscle, not your brand voice.

✨ The Goldflow 

AI is fast. Too fast. But the beauty of this workflow is that it forces you to slow down where it matters. Positioning isn’t a “set it and forget it” document you dig up once a year; it’s a living strategy that needs regular stress-testing. AI just makes it easier to run that test so it doesn’t feel like a dreaded maintenance chore.

Clay’s biggest takeaway is to stack prompts and break the process into pieces. Don’t feed an LLM the whole brand universe and expect gold to spill out. Run the competitive scan. Validate your sources. Score your position. Then step back and think. That’s the part AI can’t do for you (and the part that makes all the difference).

It’s tempting to chase the perfect headline straight out of the gate, but the real win here is having a repeatable, data-backed lens on your market. When you know exactly where you stand, every creative choice stops being a gamble and starts being a strategy.

Maybe you’ll even join the top 3% of B2B companies where visitors instantly “get” what you do.

See you at the next Workflow,
Sara Stella & Diandra

💡Know someone working on their positioning?

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🌯 It’s a major wrap

This was the third edition of The Workflow, a bi-weekly newsletter by Sara Lattanzio & Diandra Escobar, featuring one real workflow that moved the needle for someone even smarter than the system they built.

We could get used to this: collecting battle-tested workflows from ridiculously sharp people and passing them on like contraband. Next issue’s already cooking.

💌 Loved this issue and want to show us a little kindness?

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This week’s issue is special: after our launch edition, it’s our first-ever guest feature, and we'd never start with just anyone. We sat down with Vukasin Vukosavljevic, CMO at HeyReach, a Balkan SaaS that hit $8M ARR in just 26 months (and a good friend of ours), to break down how to build an AI-powered lead scoring system that can run your entire go-to-market (GTM) engine.

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✅ What you’ll learn

• How to score leads automatically using data enrichment and Clay tables

• How to route leads to CS, Sales, or Marketing instantly

• The stack powering The Workflow that doubled free-to-paid conversions

• How to build a system that evolves with your business

🧨 What triggered the Workflow

Lead scoring is one of those things every GTM team knows they should systematize. But most don’t. Why? Because building an automated, scalable lead scoring engine that’s accurate, flexible, and doesn’t collapse under the weight of conflicting opinions on what makes a qualified lead has always felt like a pipe dream.

Until now. At HeyReach, a LinkedIn outreach platform pulling in 2K+ free trial signups per month, Vuk and his team couldn’t afford to waste time chasing the wrong leads, or even worse, missing out on the right ones.

So they created a custom-built, automated lead scoring system that qualifies inbound leads using AI and routes them across sales, growth, marketing, and CS teams. It blends human logic, agentic AI, outbound campaigns, and a good ol’ human touch, exactly where it makes a difference.

Want to replicate this workflow or tweak it for your own use case? Keep reading.

🛠️  How to build the Workflow step-by-step

This lead scoring system has become the operational brain of HeyReach’s GTM machine. It’s smart, scalable, and something you should seriously consider, especially if you’re juggling high inbound volume from multiple sources like LinkedIn, YouTube, email, organic traffic, and referrals (key acquisition channels for HeyReach).

The impact: Conversion from free trial to paid jumped from 9% to 17%+. Salespeople now spend their time following up only with highly qualified leads. Marketing is crushing acquisition targets…and finally getting those high-fives from Sales. Lovely.

This exact workflow isn’t plug-and-play, but the logic is. Here's how it works.

1. Align on your ICP (and how to best serve them)

“We shouldn’t care about how many signups we get. We should care about how many qualified signups we get.” That insight was the real trigger for the workflow. Vuk and HeyReach’s CRO, Ilija Stojkovski, were frustrated by two things: a lack of clarity on which free trial sign-ups were actually ideal customers, and a relatively low conversion rate at the time (~9%) from trial to paid. 

“I wanted to know: Are our acquisition channels bringing in the right people? Are we spending time where it counts?” Vuk asked himself.

The first step is to align internally on customer tiers and their importance to the business. Without that, any system or tool will only cause even more problems.

“You can’t score leads if you don’t know what a good one looks like.” Vuk and the team mapped their ideal customer profile into tiers:

🤝🏻 Tier 1–2: High-fit, routed to CS or Sales for human onboarding

📩 Tier 3: Medium-fit, pushed to Marketing and self-serve flows

🚫 Disqualified: Not the right audience (e.g., ecomm agencies using non-LinkedIn channels)

Only now it’s the time to get nerdy with tools.

2. Enrich inbound leads using AI agents

At HeyReach, enrichment begins the second someone starts a free trial. The team collects only a business email at signup; no friction, no long forms. From there, automation takes over.

🧲 Step 1: Trigger

A new contact is added to HubSpot (their CRM). That’s the only input the system needs to get started.

🧠 Step 2: Enrichment

• Clay orchestrates a full enrichment sequence:

• Apollo fetches company and contact info 

• The system pulls website and LinkedIn data to understand the lead’s business model and ICP, assessing whether it would benefit from using HeyReach.

• If the trial signup is part of a team, only Admins and Power Users get enriched and contacted. All other team members are filtered out to avoid spamming the entire org (and to save enrichment credits).

• Custom prompts in Clay generate clean, properly formatted job titles to use for HeyReach’s outbound email campaigns (more on that later). 

All of this is handled in a single, monster Clay table (plus connected sub-tables), which acts as the central nervous system for scoring and routing leads.

And that’s just the enrichment layer. With a detailed profile built out (including ICP match, firmographics, behavior, and job titles), each lead is now ready for scoring, assignment to a responsible team member, and for a suitable next step.

3. Score and qualify leads (and act upon them!)

Up next: a maze of Clay columns (even Vuk admits some look like Chinese) handles scoring and lead routing automatically.

The system evaluates every free trial lead across multiple dimensions—company size, industry, ICP match, team composition, LinkedIn presence, and past tool usage. Each data point feeds into the logic that determines where the lead belongs and what action to take next, based on the four tiers defined in Step 1 (Tiers 1-3 and “Disqualified”).

This qualification is fully automated through several deeply connected Clay tables, each customized for Sales, CS, or Marketing. Teams focus only on the KPIs and signals that matter to them. All enriched and (dis)qualified contacts are synced back to HubSpot, which acts as a backend tracker while Clay remains the daily operational hub.

“This is the first company I’ve ever worked for that has their CRM under control. I don’t even need to log into HubSpot. The information lives in Clay anyway.”

Man, did he make us jealous with this statement.

4. Activate leads with targeted outreach

Once every lead is enriched, scored, and bucketed, it’s time to act. For Tier 1 and Tier 2 leads (the high-fit ones), the system triggers personalized follow-ups: assigning a CS owner in HubSpot and launching a multichannel outreach sequence using Smartlead for email and HeyReach for LinkedIn (so much for dogfooding!).

“It’s not just about qualification. It’s about knowing exactly what to do with each signup, without having to log into five tools to figure it out.”

Here’s a sample cold email template they use. Now you’ll see where all that ICP enrichment data they enrich inbound leads with really shines:

Hi [NAME],[CS NAME] here, your dedicated Account Manager. Think of me as Robin to your Batman. 💪

I’ve done some research and noticed your team is targeting [your lead’s ICP job titles].

If you’re up for it, I’d love to give you a personal walkthrough of [your tool] and help you build high-converting LinkedIn campaigns.

We could cover things like:

• Building GTM playbooks

• Sourcing leads from anywhere, capturing social signals, and enriching with any data points you need

• Connecting [your tool] with your GTM stack [specific tools the lead uses]

Want me to share some LinkedIn strategies that always work?

Happy hunting, [CS NAME]

On LinkedIn, each campaign branches based on whether a contact is already connected or not:

• 💬 If already connected → Send a message, engage with a recent post, and follow up if needed.

• 📬 If not connected → Send a connection request. If they accept, follow up with a message. If not, trigger an InMail.

The HeyReach API makes this seamless: campaign senders are dynamically assigned based on the lead’s HubSpot owner (if one exists), so they get messages from a familiar face. One campaign, multiple senders, all automated.

Vuk shared some results from a recent outbound campaign following this strategy:

🧑🏻‍💻 LinkedIn: ~47% connection rate and ~20% reply rate

📧 Email: ~12.5% reply rate (with zero optimization for opens or clicks)

Where can we sign to get that??

5. Keep refining

This thing doesn’t just run. It evolves. What started as a single lead scoring system quickly turned into a full-blown GTM intelligence engine. The team keeps layering new workflows on top: acquisition reports, action-based ICP intelligence, campaign tailoring, and routing to human-led or self-serve onboarding. “Next up, my dream would be to have a fully automated system for personalized onboarding, tailor-made retention routing, and what I call my Mount Everest: fully automated churn prevention.”

🤖 Tools powering the Workflow

Not the leanest stack, admittedly, but the results more than make up for it.

Clay is the brain, running enrichment, scoring, and routing logic across Sales, CS, and Marketing. Apollo plugs in verified emails and company data. HubSpot acts as the CRM backbone where enriched records and ICP tags are stored. Smartlead takes over for automated cold email outreach and retargeting, while HeyReach powers LinkedIn campaigns synced directly with HubSpot owners through Clay’s tiering logic.

Together, they form a GTM engine that qualifies and activates leads without the team juggling five dashboards.

📌 Disclaimer: you’ll probably need a tech-savvy friend (Vuk works with several automation magicians) to replicate something this intricate.

🌟 The good news? You can swap in your preferred tools and still build a similar system. Vuk even rebuilt the whole thing in Make, because apparently managing all of this wasn’t enough. So, whether you’re using Clay or Make, Smartlead or Instantly, HubSpot or Pipedrive, this workflow adapts if you’re willing to go deep.

🎢 Highs, lows and Workflow warnings

Like every workflow we feature, this one has its sweet spots and its breaking points.

✅ What shines
Clarity unlocked: “You’re not blind anymore,” as Vuk puts it. The team stopped guessing and started knowing — what channels convert, which leads deserve attention, and why certain ICPs churn.

Conversion doubled: Within a quarter of launch, free-to-paid rates jumped from 9% to 17%.

One source of truth: Sales, CS, and Marketing now work from the same enriched data without tool-hopping, conflicting reports, or duplicate effort.

More bandwidth: With manual lead qualification reduced, the team spends time on GTM strategy instead of admin bottlenecks.

❌ What doesn’t
Maintenance load: Prompts, syncing across tools, and refining ICP logic demand constant iteration.

Complexity tax: Scaling the logic required collaboration across departments and plenty of “techie sparring partners.”

⚠️ Workflow warning
The system solves qualification and alignment, but it won’t erase every CMO headache. As Vuk joked, “Some days I still feel like I’m crying over product marketing.”

✨ The Goldflow 

We close every interview with the same question: "What did building this workflow teach you about AI and automation on a macro level?"

Vuk didn’t hesitate. "My delegation skills are becoming crazier. Now I can delegate stuff to AI agents. I don't need to do it anymore on my own."

He’s not just talking about task automation, but REAL delegation. To agents, logic flows, and systems that just do the thing without human intervention.

And because Vuk’s the kind of person who loves solving problems that used to keep him up at night, seeing them finally melt away feels like magic. "It sounds fluffy, but it gives me that feeling of: Okay, now we can actually do the things I used to see only in movies."

Because the entire point of building systems like this is one: remove the mental load of hamster-wheel admin work and chaos.

Because yeah, you might still cry over product marketing, but at least now your workflows cry with you. And they file the right lead under "Tier 1" while doing it.

See you in two weeks,

Sara Stella & Diandra

😱 Know someone struggling to set up a lead qualification system?

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🌯 It’s a major wrap

This was the first-ever guest edition of The Workflow, a bi-weekly newsletter by Sara Stella Lattanzio & Diandra Escobar, featuring one real workflow that moved the needle for someone even smarter than the system they built.

If you missed the first edition (where Sara Stella built a full AI-powered editorial engine while yelling “AI isn’t the strategy” from the rooftops), go catch up. It might include traces of desk research that doesn’t melt your brain, outreach that doesn’t suck, and transcripts that file themselves.

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Whether you work in marketing, sales, or run your own thing, each issue delivers a real-world workflow from a business pro that you can replicate as you read. 

This is our very first issue, and for the special occasion, we kept things in-house.
I let Diandra interview me. You’re getting a behind-the-scenes look at the exact workflow I used to create a qualitative marketing research study using AI tools like ChatGPT, Zapier, and Fathom.

Starting from the next issue, you’ll meet our real guests: operators, marketers, and builders we actually look up to — the ones creating workflows worth stealing.

Let’s kick this off, shall we?

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✅ What you’ll learn

• How to plan and create B2B research reports

• How to recruit and reach out to study participants

• How to automate a huge chunk of admin work

• How to use ChatGPT to cluster quotes and extract themes

• How to slash overall research report production time with AI

🧨 What triggered The Workflow

Research reports are one of the top B2B content formats, but an ABSOLUTE nightmare to produce. You typically face two bad options: outsource it to an agency charging five figures for a glorified PDF, or keep it in-house and watch it drain your team and block the content pipeline for months.

Over the years, across different roles, I’ve been refining a better way. And I finally put it to the test for a recent project.

My team and I set out to create a qualitative industry report to explore key industry trends as a core piece of our quarterly content strategy. The starting point: ~40 expert interviews to run, a tight timeline, and a lean crew (leadership, writer, designer, and me as project lead).

None of us had the time to waste on admin hell, so we built a workflow that turned every interview into structured, shareable data with minimal friction. No more chasing transcripts, no more chaos folders. One teammate put it best: “It feels like a CRM for thoughts.”

The impact: Interview prep to first draft took half the time compared to our usual pace. Admin work dropped by 70%. And we finally stopped chasing transcripts and started shaping insights.

Want to replicate this workflow or tweak it for your own use case? Keep reading.

🛠️ How to build the Workflow step-by-step

I shout it from the rooftops in every single LinkedIn post of mine: AI and automation are NOT the strategy. And they’re definitely not a shortcut to avoid thinking.

I used them to kill the admin work so my team could focus on insights, not logistics. A crucial move when you don’t have a 50-person marketing department.

Replicate my workflow right away using whatever tool alternatives you’ve got:

1. Set the strategy, the old-school way

Before touching any AI tools, you’ll want to start the old-school way with an interactive team workshop in Miro to align on your goals, define your audience, and shape the narrative your report will tell. For us, the outcomes were clear: understand key industry trends through expert interviews, build relationships in the space, and position the brand as a thought leader in a relatively new vertical.

Once your point of view is locked in, design the study, define a study participant ICP, and craft a questionnaire that reflects your hypotheses and that can be easily adjusted to different interview profiles according to their specific focus.

2. Collect insights: deep REsearch

Next, kick off your desk research to map the landscape. But skip the classic 40-tabs-open chaos. Instead, use ChatGPT to synthesize background reports, surface market trends, and scan existing thought leadership. You’ll get a clear view of what’s already out there and where your report can actually add something new, without wasting hours in browser hell.

3. Find experts to interview

To recruit your study participants, take a hybrid approach. Start with warm intros from your network. No surprise here: they convert like crazy (we hit a 70% yes rate). But since that doesn’t scale, layer in cold outreach: build a Sales Navigator list, enrich it with Clay, and automate outreach via a tool like Dripify. We got a 40% “yes” on that. Not bad.

Send every expert a skimmable, well-designed one-pager that explains the study and include a Calendly link to book their interview slot. No endless email ping-pong.

4. Prep and personalize interviews

To avoid prep stress, send guests a personalized interview questionnaire five days before the call, tailored to their background and expertise. Ask unique questions if you want unique answers. Most interviewees will fall into 2–4 categories, so building a few tailored question sets already goes a long way. Want to take it up a notch? Build an automation that scrapes LinkedIn data and customizes each questionnaire accordingly (story for another newsletter issue).

And don’t forget to activate reminders in your scheduling tool because chasing confirmations or dealing with reschedulings manually should be a hard pass.

5. Record and transcribe the call

We ran all interviews on Google Meet, with Fathom handling real-time recording and transcription. Choose a tool that doesn’t just capture words, but actually highlights key takeaways so even the longest interviews stay scannable, killing the need to rewatch or hunt for quotes later.

6. Automation magic: post-call distribution

Set up a Zapier automation to save an insane amount of time. Use finalized interview transcripts as a trigger to automatically:

→ Update a Google Sheet with the interviewer’s name, summary, and transcript link.

→ Send a Slack notification so your writer can jump in right away.

→ Add the full transcript to a rolling Google Doc, where every interview lives in one place.

No clicking around. No chasing links. Just clean, centralized data—a marketer’s and writer’s dream!

7. Cluster insights with AI

Once you’ve gathered your interview transcripts, use ChatGPT to analyze qualitative data: identify themes, group expert quotes by topic, and summarize takeaways. This approach transforms hours of expert interviews into actionable research insights.

🧠 Pro tip: Create a dedicated project in ChatGPT where you can upload all your project docs, briefings, tone of voice, and branding guidelines in one place. It’ll save you hours in back-and-forth.

Here’s a prompt you can use (and I REALLY recommend stacking one request after another for best results):

I’ve uploaded multiple documents containing full-length interviews and project notes for a research report. Please use all of them to:

1. Identify up to 7 recurring themes and insights across the interviews.
   

2. Group key quotes under each theme (attribute by interviewee full name).
   

3. Summarize 3–5 key takeaways for each theme in clear, insightful bullets.
   

4. Write a concise executive summary that captures the big-picture insights from the research (max 6–8 sentences).

Please refer to the Brand guidelines document I’ve attached for writing style and TOV guidance. Let me know if you have any remarks, complaints or doubts before you get started.

8. Final steps: build the story and get feedback

With themes and clusters mapped, review the distilled document with everyone who needs a say. Refine the framing, narrative, and pick standout quotes. This tight feedback loop keeps the focus on the story, not the spreadsheet. From there, your writer can dive into the first draft and kick off the review cycle.

🤖 Tools powering the Workflow

My tool stack doesn’t win any awards for complexity or exclusivity (I’ll survive), but it dramatically cut the effort needed to ship this report. Here are a few more details about the tools I used, but feel free to swap in your preferred alternatives.

Fathom is the heart of the workflow: it transcribes interviews in real time, tags key insights, and surfaces actually usable quotes. But its value doesn’t stop at writing support. Each finalized transcript triggers a series of Zaps updating the interview tracker, pinging the project Slack channel, and dropping the transcript into a rolling Google Doc. “Where’s the transcript?” and “Did this interview already happen?” became questions of the past.

Clay and Dripify powered the cold outreach engine, helping us zero in on best-fit study participants with minimal manual effort. And once the transcripts started piling up, ChatGPT made sense of the chaos, clustering quotes, surfacing themes, and extracting takeaways fast.

And then there’s the “boring” but reliable foundation: Google Suite. Docs for transcripts, Sheets for lists and the interview tracker, and Meet for video calls.

🎢 Highs, lows and Workflow warnings

The biggest wins of implementing this workflow for me were saved time and the spotless, scalable organization of files, usually a magnet for human error. Down the line, our writer could start from curated, structured material instead of raw chaos. That alone saved at least six hours per week in admin work and cut our concept-to-draft cycle time in half.

Everyone always knew where the project stood, in real time. That made it easy to spot and fix bottlenecks (like a slow interview booking rate) before they snowballed. Because yes, it’s critical to save time on tasks that destroy value but it’s just as important to protect time for the ones that create it. I strongly recommend a weekly standup to align, unblock, and gather input from the team. It catches what async updates miss and helps keep motivation up when the project starts dragging.

Of course, tech isn’t a silver bullet. Fathom missed a few recordings entirely. It was active during the call, but the transcript never appeared and I only realized it after the guest had already signed off. If you want to avoid awkward follow-ups asking for notes, turn on Google’s built-in transcription feature as a backup. Just in case.

Another hiccup? Zapier’s task cap. We hit it midway through the project while I was on vacation (classic!), and the entire workflow just froze. Lesson learned: check your task meter early and understand what your setup consumes. The easiest solution? Turn on pay-per-task billing. Zapier will charge you per extra task once you max out your plan, so you never have to worry about it again.

✨ The Goldflow

Sue me: I haven’t tried all the other LLMs before deciding to use ChatGPT for this one. Not Claude, not Perplexity. Same goes for most of the tools in this stack. I just worked with what I had and what I knew best. I only start hunting for new tools when I hit a real cap… not to optimize something from 97% to 99%.

That’s the real tea. ☕️

It might go against the grain of how most people approach tooling today. There’s almost a stigma if you haven’t tested every AI tool under the sun, compared every feature, and stacked up 50 subscriptions like digital Pokémon. But here’s the truth: most companies already have enterprise tools in place, so you’re not starting from zero. And unless you’re solving the same complex, niche use case on repeat, you probably don’t need to build a rocket-ship tech stack.

See you at the next Workflow,

Sara Stella

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🌯 It’s a major wrap

This was the first edition of The Workflow, a bi-weekly newsletter by Sara Stella Lattanzio & Diandra Escobar, featuring one real workflow that moved the needle for someone even smarter than the system they built.

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