6/26
Situation
Leadership pressure to adopt AI outpaced evidence, risking budget on integrations across ops, marketing, finance, and IT that simpler approaches would solve better.
Task
Evaluate four AI use cases without a shared framework to determine where AI delivers real ROI.
Actions
I built a consistent evaluation lens (using tried-and-true “effort vs. impact”) and ran each use case through it with the rigor the decision required, not vendor messaging.
In the end, presented client leadership (President, COO, Head of IT) with recommendations giving defensible, differentiated answer instead of a blanket “adopt AI” or “don’t”
For the client’s leading storefront-in-a-box platform, I dug into RFM segmentation, Admin API MCP scoping, and the CCPA/GDPR exposure of connecting an AI agent to live customer PII, surfacing scope-limiting as the practical guardrail.
RECOMMENDATION: a qualified “maybe” with privacy guardrails
For the client’s cloud ERP, I weighed scripted automation against LLM/MCP approaches and landed on a Crawl-Walk-Run sequencing rationale instead of defaulting to “AI-first.”
RECOMMENDATION: a scripted-foundation-first path
For the client’s cross-retailer analytics platform, I traced the actual integration constraints (no public API, SFTP-only) to their root cause and concluded Claude Enterprise wasn’t viable as currently configured, then scoped what a custom build would actually cost.
RECOMMENDATION: an explicit no-go on the platform that saved the organization from sinking effort into infrastructure that didn’t exist yet
For org-wide adoption, I flagged a token-economics risk that’s easy to miss: model selection discipline, not just the AI/no-AI decision, determines real-world ROI.
RECOMMENDATION: immediate adoption for org-wide knowledge work using Claude Enterprise
Result
Given a re-forecast, they’ll see cost savings of 45% over the next year.