219 East LLC

AI Strategy

Where AI fits, and what it should change first

Strategy work at 219 East LLC answers three questions for a specific business: where AI belongs, what success will be measured against, and which architecture keeps the cost and the control where the business needs them. The positions below come from Duane Grey's published writing, and each one links to the piece behind it.

Start a conversation Read the insights

How the strategy work is framed

Four positions shape every engagement. They come from field research, published studies, and builds the practice has shipped.

Discovery

Finding where AI fits is the hard part

Companies with identical tools find very different numbers of places to apply them, and a structured discovery process is what separates them. Candidate processes are weighed across several factors at once, and the work favors automating a full process chain over speeding up a few steps in the middle.

Value

Success is defined before any code

An AI project is still a project. Each one is tied to risk avoidance, cost reduction, or revenue expansion, with the specific number that will show whether it worked. Gains come where AI fills a real capability gap; where a competent process already exists, the same technology can lower results.

Architecture

Architecture decides cost and ownership

Model spend is an architecture question with tiered models, routing, and a gateway underneath it. The same choices decide what the business controls and what it rents across learning, continuity, data, measurement, and orchestration, and how easily it can move later.

Readiness

Production has requirements a pilot skips

Data that drives accurate dashboards can still produce unreliable agents. Customer-facing AI needs identity checks, authoritative reads, abstention thresholds, guardrails, escalation, observability, and replayable evaluation. For regulated data, smaller models running on premises are often the right fit.

What a strategy engagement covers

  1. DiscoveryGoals, current processes, constraints, and the data each candidate use depends on.
  2. Fit and priorityCandidate uses ranked against each other, with the reasons a use waits for later written down.
  3. Success definitionOne measurable outcome per initiative, agreed in writing before the build begins.
  4. Architecture and ownershipModel tiers, hosting, data boundaries, and which parts of the stack the business owns.
  5. Security and governanceTrust boundaries, oversight, and audit requirements, aligned with the security practice.
  6. The path to productionA roadmap that ends in a working system, delivered through implementation when the client wants it built.

Published strategy writing

Each piece is public and dated at duanegrey.com.

Where AI fits

Value and cost

Architecture and ownership

Production readiness

Quick takes

Start with the business problem

Strategy conversations start from the problem the business needs solved. Scoping, deliverables, and price are agreed before work begins.

Start a conversation