When I joined Ascend, it was an incredibly successful company. $20m ARR, 650+ clients, virtually no churn, and a product that people genuinely loved. But the company completely lacked operational and organisational structure. I ran a Listen & Learn tour — structured conversations with nearly every senior leader over two weeks — and the same four gaps surfaced repeatedly:
Why EOS
I chose to solve this with a framework called EOS — the Entrepreneurial Operating System. Other frameworks like OKRs focus primarily on goal-setting: define an objective, attach measurable key results, review quarterly. That's useful, but our problems went beyond goals. We needed a single system that addressed organisation, metrics, documentation, and priorities together. EOS covers all four in one interconnected framework.
Each component maps directly to one of the problems above:
The full system cascades from permanent company identity down to weekly decisions, each layer getting more specific and more frequently updated:
Why this is the perfect context layer for AI
Most companies treat AI as something you bolt onto individual tasks — summarise this document, draft this email, generate this report. That works for isolated tasks. But the moment you want AI to do anything that requires understanding your company — what you're trying to achieve, what was decided last week, what metrics are off track — it needs context.
The bottleneck isn't AI capability. It's that the context doesn't exist in any structured, consistent form. EOS solves this as a side effect of running the company well. Every document it produces — the V/TO, the scorecard, the decision log — is structured by design and updated on a predictable cadence. This is exactly what AI tools need to operate effectively.
In practice, every Claude Code session starts with the V/TO for ICP definitions and brand voice, current Rocks for quarterly priorities, and scorecard targets for what "on track" means. Automations reference the scorecard directly — an alert fires when a metric misses its target, no human interpretation needed. When the L10 resolves an issue, that decision is logged in Notion with reasoning, and any AI session touching that area references it directly.
The conventional thinking is that structured processes slow you down. With AI, the opposite is true. Structure is what makes speed possible, because it gives every tool the context to operate without constant hand-holding.
The weekly loop
The L10 meeting is where EOS and AI close the loop most concretely. Here's how one actual week works:
Wednesday: A Make.com automation sends the L10 Notion document to Slack. Each leader fills in their wins, headlines, and issues before the meeting — so we don't spend meeting time on status updates.
Monday: The L10 runs. 90 minutes, fixed structure: wins, scorecard review, Rock check-in, headlines, to-do review, then 60 minutes solving the biggest issues. Fireflies transcribes the entire meeting live.
Post-meeting: The Fireflies transcript is processed. Decisions are logged to the Notion decision log automatically. Action items become Asana tasks. Nothing falls through the cracks.
Following Monday: The next L10 reviews last week's to-dos, checks the scorecard, and assesses Rock progress. The loop closes.
Before this, someone had to write up meeting notes by hand, chase people for follow-ups, and hope that decisions made in conversation actually got executed. Now the operating rhythm generates structured data as a byproduct — and that data feeds directly into every AI tool we use.
Where we are now
Two quarters in. L10s have run every week since October 2025. Rocks evolved from Q4 (implement EOS, identify growth channels, secure partnerships) to Q1 (fix the search bottleneck with Guru, slash concierge overhead by 50%, scale new growth channels to $100k gross profit). The scorecard tracks revenue, growth metrics, concierge SLAs, sprint completion, and runway — reviewed every Monday.
Every AI tool at Ascend plugs into the same context layer. Growth automations reference the ICP and brand guidelines from the V/TO. Guru references client preferences documented through the same system. CRM workflows use scorecard targets to define thresholds. The context compounds — every quarter of structured documentation makes every AI session more effective.
If you're considering EOS, the standard advice is about accountability and focus. That's real. But if you're also building with AI, the structured documentation it produces might be the most valuable part of the whole system.