Giovanni Beggiato recently shared something that caught my attention: "Clients don’t care what software you use, they care that you can solve their problems." He even added a punchy reminder that hits a little too close to home for anyone who loves systems: "Most of the time a simply Apple Notes beats your: Readwise - Notion - Quizlet - Google Drive system."

I want to expand on that, because it is not just a productivity hot take. It is a go-to-market lesson for anyone building an AI automation agency (or selling any tech service, honestly). If you are trying to grow to consistent revenue, the fastest path is usually not more tooling. It is clearer problem definition, tighter delivery, and communication that makes the client feel progress.

The uncomfortable truth: clients buy outcomes

Let us make Giovanni's point concrete. When a client hires you, they are not buying:

Your favorite automation platform
Your prompt library
Your "agent" framework
Your Notion dashboard with 12 databases

They are buying a specific outcome that they can feel in their business:

Fewer manual hours
Faster lead response time
More qualified meetings booked
Cleaner data and reporting
Less churn and fewer support tickets

That is why the sentence "clients don’t care what software you use" is so useful. It forces you to speak the language of outcomes.

"Clients don’t care what software you use, they care that you can solve their problems." - Giovanni Beggiato

If you are pitching AI automation and you lead with tools, you are asking the client to do extra translation work. They have to map "Zapier + Make + Airtable" to "my team stops copying and pasting all day." Most clients will not do that mapping. They will either stall, price shop, or pick the vendor who makes the result feel obvious.

Why "Apple Notes" beats your stack (most of the time)

Giovanni's Apple Notes line is not anti-tech. It is anti-friction.

When you build a complex knowledge and delivery system too early, you create three problems:

1) You optimize for your comfort, not the client’s clarity

A beautiful internal system can still produce confusing client updates. Clients do not care that your backend is organized. They care that they know:

What is happening this week
What you need from them
What changed since last update
What results to expect next

A simple shared doc, a weekly email, or a short Loom often creates more trust than an elaborate portal.

2) You create hidden work that steals delivery time

Every extra tool adds setup, maintenance, and switching costs. That time comes out of the only thing that matters early on: shipping results.

If you have 10 hours this week, you can either:

Spend 3 hours refining your Notion template, or
Spend 3 hours tightening the workflow that saves the client 15 hours per week

Only one of those gets you renewals and referrals.

3) You increase failure points

AI automations fail in the real world for boring reasons: missing fields, inconsistent naming, permissions, edge cases, unclear handoffs. The more moving parts you add, the more places things can break.

Simple systems reduce breakage, and less breakage means more confidence. Confidence is what makes a client say yes to Phase 2.

A better framework: Problem first, tooling last

If you want a practical way to apply Giovanni's advice, here is a simple structure I use when evaluating any AI automation project.

H3: Step 1 - Write the problem in one sentence

Not "we need AI" but something like:

"Inbound leads wait more than 5 hours for a response, so conversion drops."
"Ops spends 12 hours per week manually reconciling invoices."
"Sales notes are inconsistent, so handoffs and forecasting suffer."

If you cannot write it in one sentence, you do not understand it yet.

H3: Step 2 - Define the success metric

Pick one primary metric for the first sprint:

Response time from lead submission to first reply
Hours saved per week
Error rate reduction
SLA adherence
Meetings booked per 100 leads

This becomes your north star. Tools are just the route.

H3: Step 3 - Map the current workflow (the "as-is")

You do not need a fancy diagram. Even Apple Notes is enough.

Document:

Trigger (what starts the process)
Inputs (where the data comes from)
Decisions (what rules people follow)
Outputs (what gets produced)
Owners (who does what)

H3: Step 4 - Remove steps before you automate

This is where many AI agencies lose. They automate a messy process and the mess becomes faster.

Ask:

Can we delete any step?
Can we standardize inputs?
Can we reduce approvals?

Automation amplifies whatever exists. Make sure you are amplifying the right thing.

H3: Step 5 - Choose the minimum viable toolchain

Only now should you decide tooling.

The right stack is the one that:

The client already uses, when possible
Is easy to hand off and maintain
Has clear failure alerts and logs
Supports the required integrations reliably

This is the heart of Giovanni's argument. Tool choice is downstream of problem clarity.

Examples: what "clients don’t care" looks like in real life

Here are three scenarios that show why outcomes beat tools.

Example 1: Lead response automation

Tool-first pitch: "We will set up an AI agent with a vector database and orchestrate it through X."

Outcome-first pitch: "We will cut your lead response time from 6 hours to under 5 minutes, with guardrails and human handoff for edge cases."

The second one makes the decision simple.

Example 2: Support ticket triage

Tool-first: "We will use an LLM to classify tickets."

Outcome-first: "We will reduce first-response time and route tickets to the right team automatically, so VIP customers stop waiting."

Again, the client buys the pain relief.

Example 3: Internal reporting

Tool-first: "We will build a dashboard in BI tool Y."

Outcome-first: "Every Monday, leadership gets a 1-page summary with the three numbers that drive decisions, pulled automatically and verified."

Apple Notes can literally hold that 1-page summary. The value is the decision-making speed.

The hidden advantage: simple delivery improves your marketing

This is also a content strategy lesson.

Giovanni's post went viral because it is specific, contrarian (in a helpful way), and speaks to a real behavior: builders over-invest in the stack and under-invest in solving problems.

When you apply this in your own LinkedIn content, you get stronger posts by doing the same:

State a truth your audience needs
Use simple language
Anchor it in a vivid example (Apple Notes vs the mega stack)
Make the takeaway actionable

That is why "LinkedIn content" that performs often sounds like advice you would give a friend, not a software brochure. Viral posts usually win because they reduce complexity and increase clarity.

A simple operating system for AI agencies

If you want something you can adopt immediately, here is a lightweight cadence that keeps you out of the tooling trap:

One doc per client: goals, metric, scope, links
One weekly update: what shipped, what is next, blockers
One backlog list: ranked by impact on the success metric
One place for decisions: notes from calls, agreements, constraints

That is it. If Apple Notes is where you start, great. If you later graduate to a fuller system, also great. Just do it after you have repeatable delivery.

"Most of the time a simply Apple Notes beats your: Readwise - Notion - Quizlet - Google Drive system." - Giovanni Beggiato

Closing: be the person who removes friction

The deeper message in Giovanni Beggiato's reminder is this: clients hire you to reduce uncertainty.

Tools can help, but they can also become a shield that lets you avoid the hard work of scoping, prioritizing, and delivering a measurable result. If you keep your focus on the client’s problem, the right tools become obvious, and your agency becomes easier to sell.

This blog post expands on a viral LinkedIn post by Giovanni Beggiato, I help founders scale to $10K/mo+ with their AI Automation agencies, from zero | Made $50k+ in 6 months with mine | Join other AI Agency owners in my Skool community (Link in the featured section). View the original LinkedIn post ->