L1 is human-in-the-loop chat. Person prompts, edits, decides. No durable business memory, no internal data unless pasted, no execution beyond the turn. The starting line for roughly 80% of middle-market companies, and the place most stall.
L1 is per-employee uplift on tasks already being done. The economic value is 10-30% productivity on a per-seat basis. Real. But no process is redesigned, no workflow captured, no institutional knowledge accumulated. The "AI ROI" stories that fill quarterly board decks largely live here, and they hit a ceiling fast.
Underneath L1 sits the data foundation: warehouses, ingestion, governance. No agent orchestration exists at L1 by category. Data orchestration (Airflow, Dagster, Prefect) is the ancestor, necessary, but not the same animal.
| Function | In practice | Signal |
|---|---|---|
| Engineering | 80%+ Cursor / Claude Code adoption, the single highest-ROI L1 move | +15–25% PR throughput |
| Finance | Claude / ChatGPT drafts commentary, variance and the deck, numbers still from Excel | Flash P&L 10d → 3d |
| Marketing | Brand-voice drafting; the editor still ships the final copy | −40–60% draft time |
| Sales / RevOps | Power BI or Looker is the system of record; AI doesn't touch it yet | One governed dashboard |
| Legal | AI flags deviations from the playbook | Lawyer makes the call |
At L1 a person still runs every step; AI just makes one of them faster. Real uplift, but a faster headcount, not a cheaper function. What changes at L3 is who does the work.
Same workflow, different question: who does the work. L1 makes the person faster; L3 takes the workflow off their plate.
Middle-market calibrated. BOD doesn't hedge.
| Category | BOD Pick | Why | Pricing signal |
|---|---|---|---|
| General chat | CLClaude (Team/Enterprise) | Best long context (1M), strongest writing, portable Skills | ~$25-30/seat/mo |
| Chat (M365 shops) | MSMicrosoft 365 Copilot | Lives where work lives; Graph permissions inherited | $30/seat/mo Ent |
| Chat (alt) | GPChatGPT Enterprise | Broadest ecosystem; Workspace Agents | $20-25 Biz, Ent negotiated |
| Coding | CCClaude Code & Cursor | 78% SWE-bench; MCP-native; lowest tokens/task | $20-125/seat/mo |
| Research | PXPerplexity Enterprise | Best cited research; replaces ad-hoc googling | $20-40/seat/mo |
| Data SoT |  Databricks (BOD default) · Snowflake | Databricks default; Snowflake for analytics-first / SQL-only | Consumption-based |
| Ingestion |  Fivetran | Lowest time-to-first-row | Per MAR tier |
| Transform |  dbt Cloud | SQL-first, engineering-rigor modeling | Per developer seat |
| Governance | UCUnity Catalog / Horizon / Atlan | Native first; Atlan when multi-platform | Bundled / quoted |
| Observability | MCMonte Carlo | BOD primary at L1+ for data observability | Tiered enterprise |
L1 is the substrate. Treat it as serious infrastructure work, not as "we bought Copilot, we're good." A clean L1 unlocks every tier above it. A dirty L1 means every agent above it lies confidently.
The single highest-impact L1 move. PR throughput +15-25%. Reduces dependency on hiring against a backlog. Sets up an L3 graduation when the org is ready to put coding agents in CI.
Close-cycle commentary, variance drafting, board narrative, copilot-assisted. CFO draft time down materially. Doesn't redesign the close; speeds the human running it.
Content drafting time -40-60%. Editor still ships. Sets up the L2 graduation to brand-grounded retrieval and the L3 graduation to autonomous draft-to-publish.
Deviations from playbook flagged; lawyer makes the call. The L2 graduation is a full retrieval index over the contract library; the L3 graduation is the deal-desk feed.
The unglamorous L1 win. Identity resolution on leads. One system-of-record for revenue. Nobody automates a number they can't agree on.
Headcount, plan, gap: clean and grounded. The L2 graduation is recruiting Q&A over the policy library; the L3 graduation is the recruiting agent.
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