Retained Virtual Chief AI Officer
The Virtual Chief AI Officer Role
A virtual Chief AI Officer is a named person retained monthly, who decides which AI work to do, in what order, on which platform, and at what running cost. The role covers responsibilities that job postings often split across three or four specialist hires. A vCAIO sits above a vCIO; the two overlap only on the tenant. The vCAIO decides what gets built; building the systems is separate work quoted separately.
The engagement starts with a two-to-three-week inventory and produces the use-case register and a first build recommendation. The engagement includes a standing monthly session and a quarterly written review. The engagement runs largely remotely and covers Savannah, the Lowcountry and the wider Southeast.
Deliverables and Governance
Deliverables include a use-case register, a sequenced roadmap, a platform recommendation, an issue and risk register, an acceptable-use policy, an evaluation set with run results, a monthly spend report, and a vendor model-retirement calendar. The role keeps a use-case register of every candidate, including rejected candidates and the reason for each rejection.
Governance includes an acceptable-use policy, a route for staff to propose use cases, risk tiering, and a written list of decisions that keep a human in the loop.
Platform Choice and Cost
The platform decision covers Microsoft Foundry or AWS Bedrock, region, and deployment type, metered or provisioned. AI cost is modelled per request, and the monthly spend is reported against the agreed model.
Azure AI Foundry is now named Microsoft Foundry, and the Assistants API was replaced by the Responses API. Azure moved model quota from per-resource to a shared subscription pool on 7 May 2026.
Evaluation and Technical Reliability
An evaluation set of real questions with known-correct answers is used to test results. Vendor-published studies are checked; a pre-registered trial of 85 officers and 755 reports found no statistically significant reduction in report-writing time from a vendor AI tool.
Document chunking settings change retrieval quality, index cost and response speed; one worked example of a 200-page book produces between 172 and 13,361 chunks. Changing the embedding model requires re-encoding the whole corpus. A step that succeeds 95% of the time is 77% reliable over five steps and 60% over ten. An agent can act as itself or on behalf of the user asking, which determines what it can reach.
Model Retirement
Most deployed models carry an 18-month retirement date; Anthropic, DeepSeek, Fireworks and Mistral models carry 12 months. Model retirement notice is 60 days, and no extensions are given. Moving to a successor model requires re-establishing accuracy on the firm's own work, using the existing evaluation set.
Contact CloudCentric
CloudCentric is a managed IT provider in Mount Pleasant, South Carolina, serving businesses across Charleston and the Lowcountry. We support professional firms, owners, and operators of 20 to 200 people on Microsoft 365 or AWS. Call (844) 422-7000 to discuss retaining a virtual Chief AI Officer.