Page: Q2 2026 Strategy — Atlas Inc
Workspace: Atlas / Leadership
Last edited: April 2, 2026 by Sarah Lin (CEO)
Permissions: Leadership team only

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Where We Are

Q1 closed with $2.0M MRR, up from $1.0M at the start of Q4 2025. Headline growth is strong but most of it is driven by Enterprise expansion. SMB has flatlined and the leadership team needs to decide what to do about it before Q3.

Q2 Priorities

1. Hit $2.5M total MRR by end of Q2.
Success: total MRR ≥ $2,500,000 as of June 30. Reported weekly to leadership.

2. Land 4 new Enterprise logos.
Success: 4 net-new closed-won Enterprise contracts at minimum $50k ACV, by June 30.

3. Stabilize SMB.
Success: TBD. Working session with revenue ops scheduled for week of April 15 to define what "stabilize" means in measurable terms.

4. Expand Mid-Market motion.
Marc's team will run a focused playbook on Mid-Market — better SDR coverage, vertical-specific case studies. Success metric not yet set, will track Mid-Market net new MRR as proxy.

5. Improve onboarding speed.
Product team is launching a redesigned onboarding flow. We expect faster activation and lower 30-day churn. No specific target — observe first, set a goal next quarter.

6. Prepare Series B narrative.
CEO and CFO building the investor deck. Targeting late Q3 / early Q4 raise. Success defined as deck ready and pre-marketed to 5 lead investors by end of Q2.

Open Questions

- Do we hire a VP of SMB to own SMB stabilization, or fold SMB into Mid-Market under Marc? Decision deferred to leadership offsite May 22.
- Pricing review: is the $99 SMB monthly plan priced too low for the value delivered, or is the real issue activation rather than price? No owner assigned yet.
- Should we deprioritize the European expansion previously planned for Q3? Risk of acting: distracts from SMB stabilization. Risk of waiting: losing first-mover window.

Summary  
This chapter demonstrates how to programmatically extract and classify key information (priorities, success metrics, unresolved questions) from unstructured prose using prompt-driven data extraction techniques.  

General domain of usage  
Strategic planning analysis

A strategy doc is twelve pages of prose almost nobody fully reads. Inside, one or two sentences change everything — a priority shift, a metric redefinition, a stop-doing decision. Those sentences are easy to miss and easy to surface with the right extraction prompt.

In this lab, Claude reads the Q2 strategy doc and pulls out only the parts that change how you'd analyze anything else.

## Prose Fails Differently Than Numbers

| Input Type | Typical Failure |
|---|---|
| Numbers (spreadsheets) | Restating instead of interpreting |
| Prose (strategy docs) | Missing the line that matters |

For prose, the most useful extraction isn't a summary — it's a check: which priorities have measurable success criteria, and which don't? The ones without are where you're flying blind.

## The Document

The lab has Atlas Inc's `Q2 2026 Strategy` Notion page loaded. It contains the leadership team's stated priorities, success metrics, and open strategic questions for the quarter.

Some priorities have clean numeric targets. Others don't. Your job is to spot which is which.

>## Task
>
>Send Claude:
>
>><b>Pull the Q2 priorities out of our strategy doc.
>
>You should get back: each priority, the success metric defined for it (or "not defined" if the doc is silent), and any open strategic questions left unresolved.
>
>Then push it:
>
>><b>Which priorities don't have a clear success metric? That's where we're flying blind.
>
>><b>Of the open questions, which one blocks the most other decisions?
>
>Done when you've got a list of priorities you could hand to an analyst and they'd know what to measure — and which ones they can't yet.

A hands-on course for non-technical managers and operators who need to turn raw business data into decisions, not summaries. You'll learn how to frame analytical questions sharply, read spreadsheets and internal docs through AI, combine internal numbers with market context, and build your own analyst agent that produces decision-ready briefs end-to-end.

Introduction to business analytics through the lens of AI — why raw data doesn't make decisions, the three layers of analytical questions, and where an AI assistant actually fits in the workflow.



Train the framing reflex. Turn vague business questions into sharp ones, pick the metric that actually matters, and lock down time windows, segments, and comparisons before touching any data.



Hands-on labs with the three sources every operator hits: sales spreadsheets, strategy docs in Notion, and market context from the web. Single-tool labs first, then a combined investigation.



Move from running labs to building your own. Write the persona, wire up the tools, and create an agent that delivers weekly KPI briefs and pre-meeting decision packs — finishing with a capstone Quarterly Business Review.

