After working with over 50 business owners as their Fractional CFO, I noticed how some consistently broke through their growth ceilings, while others remained stuck. 

The difference? The ability to understand and ACT on the numbers.

The biggest shocker here is that most businesses still rely on a basic P&L and some messy sheets to track their performance and plan ahead. We're putting a stop to that. With our best-in-class financial models and forecasts, we don't just put our clients back in control, we provid the direct actions to take to run your business without the constant guesswork

Joey Wit

Exactly 5 months out from my first Iron Man.

Ramping up now to 15+ hour training weeks. Schedule and nutrition dialled in. Weekly training session with my coach.

Definitely underestimated this beast. But life isn't fun without big goals.

Joey de Wit

Founder of AgenCFO - ROI-obsessed CFO services for agencies and eCom | 50+ clients served | Building own portfolio along the way | Ironman prep 7’26

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Onboarded a new client this week doing $130K/mo.

Absolute killers who scaled their agency super quickly to this point.

But on the backend they still operated across multiple entities and had no complete view of their numbers.

Seeing this with a lot guys from outside the US. Multiple entities across countries are a pain to deal with.

The problems this creates:

• Margins can drop 10-15% and you won't notice until it's too late
• Near impossible to exit the business with no clean structure
• Required to spend hours tracking & reviewing numbers

Plus big issue now is that no AI is useful with bad data.

We're now consolidating everything into one management view.

Automating it all to get near real-time margins and metrics like LTV, churn, CAC, and client profitability.

These guys will scale to $300K/mo while holding 50%+ margins.

With a proper view you don't have to guess anymore. Or waste time.

Lot of power in seeing the truth.

We've worked with 50+ agencies.

Most pay themselves far too little, or way too much.

If you want to make your agency work (and get rich doing so), you need to pay yourself right.

This template tells you exactly how. Comment "Comp" and I'll DM it (no sign-up).

Main reason why your agency's profits aren't growing:

Your hiring trigger is broken.

See all the time how agencies add new hires at the same rate as they get clients.

Team is always busy, and "bad" clients are always taking too much time.

The answer isn't adding more headcount when revenue doesn't support it yet.

Each team member and pod must be justified, or something is broken.

First step is knowing your client profit & team capacity.

Fix this & you fix your profit.

The agencies winning in 2026 won't be signing the most clients.

They'll be keeping AND scaling them.

Most still don't realise how much extra $ you'd make by improving retention by a few %.

This area is getting me so excited, as now with AI you can genuinely do sooo much more to nail this.

It's also the answer to your rising CAC problems and roller coaster revenue months.

You won't survive long if you're just churning & burning through them (think churn rates above in 10% - 15% range).

Goal is to get all our clients now to 5% (we're at sub 3% ourselves).

Becoming the partner they can't afford to lose.

LTV is the name of the game.

AI can now get your agency to 7/8 figures with 80% gross margins

May not last forever, but first movers are winning big.

But only if you approach it right.

I'm now seeing lots of agencies spend tens of thousands to:

• Save an hour a week on reporting
• Create summaries of call transcripts
• Help with creative analysis (and just have more insights)

All are great. 

But efficiency ≠ more cash.

To get more CASH you actually have to:

1. Get your team to cover more clients
2. Lower your team cost per client
3. Charge more for the same thing
4. Sign a bunch more clients

All can be ENABLED by AI. But AI won't actually do it for you.

If you actually want to hit 80% margins and get more incremental profit, you need to fundamentally change your business model.

That requires knowing in detail where you are actually at.

Capacity, churn rates, LTV, CAC, client level profitability etc. 

Once you know where you're at, you can find out which areas to really focus on.

Not to make them 10% more efficient, but to completely transform them.

Will require hard conversations and real planning.

Doing work faster is just the start. Don't stop there.

This is what we do it all for!

Jason West 🇬🇧🇦🇪 is going places (best agency in the fashion space by a mile).

Here for the ride 🤝

Founder & MD @ Fabrik | We build the systems, stories, and storefronts that power fashion’s next global names

View Jason West 🇬🇧🇦🇪’s profile

Working with a fractional CFO has been transformative for me as an agency owner.


For the longest time, we educated our clients on unit economics, financial literacy, margins, capacity etc. 

We told prospects they needed to have all of this in place otherwise we couldn’t work with them.

And truthfully, we didn’t have this fully in place ourselves. 

Practice what you preach as they say.

At the start of January we began working with Joey de Wit and Elyse de Wit.

Since coming in, along with Jasper Martens  we’ve implemented and identified 

1. Performance driven team bonus structures
2. Competitive company wide salary scales 
3. Precise client profitability 
4. Target Gross Profit Margins
5. Target Revenues 
6. Service pricing that meets the above
7. A full financial dashboard for clarity.

The entire team is now working toward a single goal, with clear visibility into how they can influence profitability for our clients, and therefor for us. 

The more profitable growth they generate our clients, the more they earn, and the more the agency earns in return. 

Clear outcomes, clear levers, and now clear paths for increased earning potential for staff.

We’re now geared up better than ever, to service our clients in line with their own objectives. 

Happier clients, happier staff, happier Jason.

The UAE has been subject to a lot of talk this week, for me it’s head down and push forward. 

LFG 💪🏽

This lazy accounting setup is probably costing you $100K+ every year.

It's also the reason why your P&L swings 50%+ every month.

→ Cash Basis accounting

This only tracks money when it enters or leaves your bank account.

So if you pay some team members late one month, you will now record $0 cost for them in month 1, and 2x the cost in month 2.

Your margins look insane in Month 1, and suck in Month 2, when they're actually stable.

So now you hold off on hiring in month 1 since your P&L tanked.

Or you paused your ads or delayed investments cuz of it.

Then you panic hire in month 2 cuz here you look way more profitable again.

You can't spend hours every month figuring out what's actually happening.

We rebuilt a $200K/mo agency onto accrual (tracking the transactions in the actual months).

P&L swings dropped from 50% to under 10%.

First time they could actually see what was happening in their business.

Your generalist accountant won't raise this. Can DM me if you want a quick check of your P&L.

This is why most agencies never scale from $100K to $500K/mo.

→ Churn Ceiling.

Meaning: as you keep growing, your sales volume isn't strong enough to offset your churn.

Early on with 10 - 15 clients, losing one per month isn't a problem.

You sign 2 new clients and you're still growing. You easily get this from your inbound or referrals.

But at 50 - 100 clients, the math won't math anymore.

Even at a 5% - 10% churn rate, you're losing 5 - 10 clients every month.

Your "sales system" that got you to $100K/mo won't be enough to offset your natural churn.

This is why you plateau aka hit your Churn Ceiling.

Breaking through this requires dialling in your marketing investments.

Few agencies actually spend enough on sales or know how this impacts their margins.

We always models this out in detail. Growth target, churn rate, current margins, cash position. Want to know exactly what you need to invest in sales and how long your business can fund it.

That's how you scale through that $100K/mo - $200K/mo range.

Can DM me if you want to find out what this would look like for your agency.

Founder @ Drilldown | Custom-designed business dashboards for B2B SaaS that deliver insights in one click, eliminating the need for complex spreadsheets in just 60 days.

We help B2B SaaS teams build an owned data system - so metrics align across the organization and growth decisions are based on evidence.

This might resonate:

As B2B SaaS companies scale, it becomes harder to understand on where growth comes from and what to do next.

Most teams already have dashboards, data integrations, and the technical ability to build new ones. 

Yet they still struggle to agree on what metrics mean, explain drivers of revenue, or produce reliable forecasts.

What you need is one set of accurate, explainable numbers that your team can rely on.

Our work focuses on the data system underneath dashboards: the definitions, structure, and logic that determine whether analytics can support decision-making at scale.

If you’re trying to make decisions based on data but the numbers don’t line up, this explains why: https://thomasvarekamp.substack.com/p/you-have-dashboards-why-are-decisions

Thomas Varekamp

I used to love Power BI. 

Now I hate it.

Not because it’s a bad tool. But because of how most teams use it.

Power BI lets you build dashboards.

But to build dashboards, you need to model the data first. And that’s the trap.

Most people do datamodelling inside the BI tool. That’s a huge mistake.

Why?

Because as your business grows, your data grows too.

What starts as a simple report becomes a spiderweb of logic.

- impossible to trace.
- brutal to maintain.
- easy to break.

Here’s what to do instead:

1. build a proper data warehouse.
2. manage your single source of truth there.
3. use Power BI only as a presentation layer.

A good warehouse gives you:

- version control → roll back mistakes.
- traceability → find and fix issues fast.
- quality checks → know the data is right. 

When you separate logic from visuals:

- people trust the numbers.
- teams make faster, better decisions.
- you don’t have to rebuild everything later.

It’s cleaner, smarter, cheaper.

Don’t duct-tape your data stack.

Build it right from the start.

Drilldown | a data science team in your pocket for B2B SaaS.

I saw a horror story on Reddit that highlights the biggest AI risk in 2026 (and beyond)

A company let an AI data agent answer leadership's business questions for three months.

The output was complete fabrication:

- Sales targetted territories based on fake revenue.
- CFO presented decks filled with incorrect metrics.
- Managers made decisions based on hallucinations.

If you plug AI directly into your data without providing sufficient context, it will simply guess the answer. 

It will sound confident, but it will likely be completely wrong.

The missing piece here is the foundational work; data modeling + metadata + governance that explicitly tells the AI:

"This is how we calculate Net Revenue."
"This is the definition of a qualified lead."
"This is how we measure churn."

It might not be the most exciting part of data management, but as we rely on AI to do more of the heavy lifting, it is absolutely essential.

Business intelligence is being commoditized way faster than anyone realizes.

Workflows that used to require a team of 5 are now being handled by 1 marketer using Claude Code.

Here is exactly where BI is heading in the next 12 months:

1. 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗮𝗻𝗱 𝗱𝗿𝗮𝗴-𝗮𝗻𝗱-𝗱𝗿𝗼𝗽 𝘁𝗼𝗼𝗹𝘀 𝗮𝗿𝗲 𝗮 𝗱𝗲𝗮𝗱 𝗲𝗻𝗱.

→ using code and letting AI handle it is much faster, cheaper, and robust. 

Learning Power BI now? You're in for a big surprise.

2. 𝗦𝗽𝗿𝗲𝗮𝗱𝘀𝗵𝗲𝗲𝘁𝘀 𝗮𝗿𝗲 𝗼𝗻 𝘁𝗵𝗲𝗶𝗿 𝘄𝗮𝘆 𝗼𝘂𝘁 

→ no one will manually update cells and write formulas in Excel. Just hook your data to AI and ask for the insights.

3. 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝟵𝟵% 𝗮𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 

→ AI will do the heavy lifting. Humans will transition from "analysis" to "validation", only checking outputs.

4. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗴𝗲𝘁𝘀 𝗼𝘂𝘁𝘀𝗼𝘂𝗿𝗰𝗲𝗱 𝘁𝗼 𝗔𝗜 

→ machine learning is code-heavy. AI writes code perfectly. Humans will just set the framework. AI does the rest.

5. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝗶𝘀 𝗯𝗲𝗰𝗼𝗺𝗶𝗻𝗴 𝗮𝗻 "𝗲𝗮𝘀𝘆" 𝗷𝗼𝗯.

→ AI can now auto-generate connectors, map schemas across sources, and self-fix broken pipelines in real-time. 

So, where is this going?

I think we will see companies either:

1. hire a single data person & upskill employees.
2. have external agencies handle it.

(or both)

Just as computers replaced most mathematicians, AI is doing the exact same thing to BI.

Only this time 1000% quicker.

If you disagree, you simply aren't paying attention to what's happening right now. 

I'm shocked how fast things are progressing.

Data teams will look completely different in 2 years.

The standard data analysis workflow used to work like this:

1. you have a question.
2. you ask the analyst.
3. you wait a week.
4. you get a report.

And then you figure out if you actually got what you wanted.

Now?
1. you ask the question.
2. you get the answer in seconds.

Think of this:

Marketing can ask “what is our best channel?”
…without opening 20 dashboards.

The CEO can ask “where to focus next quarter?”
…without going through slide decks.

Sales can ask “which deals need attention?”
…without digging through the CRM.

Everyone will be able to use business insights, instantly. 

So what will data teams do? 

Their primary value no longer comes from structuring and analyzing data. 

It will come from:
→ building foundations AI can understand.
→ helping others formulate questions.
→ translating insights into actions.

The titles will have to catch up with the work.

Expandi hit $10M ARR bootstrapped. Here’s the data system we built to help them grow faster.

From the outside, Expandi was an unstoppable force:

→ 0 to $7M ARR in under 3 years.
→ fully bootstrapped.

But as they scaled, internal problems were growing.

- marketing couldn't measure LTV. 
- finance struggled to reconcile $ vs. € ARR. 
- teams burning hours updating spreadsheets.

We partnered with them to install The SaaS Intelligence System.

The execution plan:

𝟭. 𝗣𝗿𝗶𝗼𝗿𝗶𝘁𝘇𝗲

We identified growth blockers and defined clear goals for every department. 

No vanity metrics.

𝟮. 𝗕𝘂𝗶𝗹𝗱

We implemented complex ARR logic (New, Expansion, Churn) into a centralized warehouse they own.

𝟯. 𝗔𝗱𝗼𝗽𝘁

We put the whole team (Marketing, Customer Success and Finance) on shared metrics. 

Before: no realtime insights.
After: trusted metrics for decision-making.

Swipe to see how we built their data system.

Drilldown | Helping B2B SaaS leaders regain control by turning data into direction.

How we built Expandi's data system

We are entering the era of the 1-person data team.

I spent the past week testing AI data agents. It made me to rethink everything:

- data science.
- data analysis.
- data engineering.
- analytics engineering.

AI can now execute all of it.

Which makes one thing very clear: most data tools with buttons are going to fizzle out.

Why pay for a rigid analytics platform when an AI can write, refactor, and maintain the underlying code directly?

My predictions:

1. 𝗖𝗼𝗱𝗲-𝗵𝗲𝗮𝘃𝘆 𝘀𝘁𝗮𝗰𝗸𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗱𝗼𝗺𝗶𝗻𝗮𝗻𝘁.

AI makes raw code the fastest, cheapest, and most robust way to build data systems. 

Writing and maintaining SQL, Python, and infrastructure is no longer the bottleneck.

2. 𝗗𝗮𝘁𝗮 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗰𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝘆.

Moving data from A → B is a solved problem. 

Just give the API docs to an AI agent and ask to build the pipeline. 99% of the time it will one-shot it.

(try Claude Code if you don't believe me)

3. 𝗝𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝘄𝗶𝗹𝗹 (𝘀𝘁𝗶𝗹𝗹) 𝗯𝗲 𝗸𝗶𝗻𝗴.

AI can write the code, but it doesn't know your complete business context. 

Deciding what to model, why it exists, and how it should be interpreted remains a human responsibility.

The next 12 months are going to be VERY interesting.

4 deals in pipeline. 2 calls. 

A 7-day update:

I spent a chunk of the week experimenting with Claude Code, and holy shit I'm impressed.

A single person can now easily fulfill all the roles of a data team.

Data work will look very different in 12 months. 

𝗢𝗙𝗙𝗘𝗥 𝗨𝗣𝗗𝗔𝗧𝗘:

Due to these new insights, I'm testing out new things we can do for our clients.

Technical obstacles are pretty much a thing of the past.

My idea is to put more focus on advanced data science work.

Meaning: building custom prediction models (churn prediction, ARR forecasting, etc.)

This used to take ages to build & validate, but not anymore. 

𝗪𝗘𝗘𝗞𝗟𝗬 𝗦𝗧𝗔𝗧𝗦:

Deals:

- 4 potential deals in pipeline.

Calls (7 days):

- 1 call with a previous client.
- 1 call with a new lead.

Substack (7 days):

- 24 new views.
- 1 new sub.

(will publish new article soon)

Linkedin (7 days):

- 40,259 new impressions.
- 74 new followers.

Let's see if we can improve this week 👊

The data analyst role we know won’t exist in 3 years.

And that’s a good thing.

For years, analysts were valued for one output: dashboards.

Pretty charts, nice filters and providing a surface-level view of the business.

That is ending.

Agentic AI is changing how analysts work completely. 

They’re not just dashboard builders anymore. They’re end-to-end solution developers.

That means:

1. defining business logic up front.
2. modeling data to drive strategy.
3. delivering insights without 5 handoffs.

And yes, way fewer data people will be needed.

But the ones who remain?

They’ll have more trust, and more impact.

The analyst isn’t just the “report person” anymore.

They’re becoming orchestrators behind the insights.

In 2-3 years, all data teams will become AI teams. 

Here's why:

Data teams today spend countless hours in meetings explaining dashboards and writing SQL. 

Most of this work doesn't add real value.

AI can do this faster, better, and without human bias.

In the next few years, analysts will stop spending time on dashboards and answering questions.

They will be maintaining infrastructure for AI:

- designing data models.
- adding context to data.

Here's how that works:

1. business software generates data.
2. data warehouse ensures high quality data. 
3. AI pulls insights and executes tasks.

Data will be the most valuable resource in every organization. 

This will be the source of truth for AI.

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