---
id: "claim-l2-roi"
type: "claim"
source_timestamps: ["00:02:29", "00:11:07"]
tags: ["roi", "business-strategy"]
related: ["concept-three-levels-ai", "action-move-to-l2"]
confidence: "high"
testable: true
speakers: ["Jake Van Clief"]
---
# The Jump to Level 2 AI Use Yields Highest ROI

## Claim

Based on consulting work with enterprise companies, [[entity-jake-van-clief]] claims that moving an organization from Level 1 (ad-hoc copy/paste into chatbots) to Level 2 (structured prompts and verified outputs) of [[concept-three-levels-ai]] delivers the **highest ROI** of any AI adoption step.

See [[quote-l2-roi]] for the punchy framing.

## Reasoning

- **L3 has higher absolute impact** but requires significant engineering investment (distributed systems, observability, orchestration).
- **L1→L2 is comparatively cheap**: build shared prompt libraries, brand-tone guides, and basic markdown 'skills'.
- The ratio of (gain in quality + consistency) to (effort) is maximized at this transition.

## How To Act On It

See [[action-move-to-l2]] and [[action-codify-voice]].

## Confidence: **high** (per source) — validation says:

- **Well-aligned with practitioner consensus**. Standardizing prompts/patterns before building automation is widely recommended (Microsoft's adoption guidance, prompt-library/playbook literature, vendor playbooks).
- **Empirical ROI quantification is scarce**. The 'highest ROI' framing is consultant insight, not a controlled finding.
- **Counter-case**: organizations with large repetitive workloads and strong engineering teams may extract outsized ROI by jumping directly into a narrow L3 deployment.

## Testability

**Yes, with caveats**. ROI must be operationalized (output quality scores, cycle time, error rate) and measured pre/post intervention across a comparable cohort. This is hard but feasible.