AI STRATEGY

AI strategy built around workflows and measurable outcomes

Deployed Labs helps leadership teams decide which workflows are worth applying AI to and what it will take to put those systems into production. The strategy covers ownership, technical requirements, and the order in which the work should happen.

COVERAGE

What AI strategy covers

Outcome and scope

Identify the workflows worth pursuing and define what success should look like for each one.

Operating model

Define who owns the AI initiative and how responsibilities are divided across the teams involved in delivery.

Architecture direction

Map how the AI system will connect to existing data, applications, and tools.

Model approach

Choose the model approach based on the performance, cost, and latency requirements of the workflow.

Governance and risk

Set the access requirements, human approval points, and process for reviewing AI activity.

Roadmap

Prioritize what should be built first and what needs to happen before each workflow moves into production.

DELIVERABLES

Strategy deliverables

AI workflow portfolio and prioritization
Target architecture and integration plan
Recommended model approach
Evaluation plan and performance metrics
Governance model and review checkpoints
Delivery roadmap with milestones and acceptance criteria
Deployment plan aligned to reliability targets
Adoption plan and usage metrics

Where agentic AI fits

Agentic AI is useful when a workflow requires the system to complete several steps or take actions through other tools. Deployed Labs defines what the agent can do on its own, where a person needs to approve an action, and how those actions will be reviewed.

GET STARTED

Build an AI strategy your team can use

Tell us which workflows you are considering for AI and what work has already been done. Deployed Labs will propose a strategy scope based on where the project stands today.

Get in touch