Operations is the connective tissue of the business: campaigns launch correctly, creative gets approved, new hires know what they're doing on day one, and leadership has the numbers they need to make decisions.
Before AI, all of that ran on manual effort. The shift came when the team stopped using AI for one-off tasks and started building repeatable systems. AI handles the scaffolding so the team can focus on the work that actually needs a human: client relationships, coaching, and strategic decisions.
The use cases below represent a selection of highlights — not an exhaustive list of what the team has built.
Replaced ad-hoc onboarding with a structured, multi-track curriculum featuring an AI-powered hub with knowledge assessments, a resource library, and progress tracking for managers.
Creative ReviewA multi-layer AI review system that catches policy violations, image issues, and landing page problems before a human reviewer is involved. By the time a submission reaches human review, the routine checks are already done.
Metrics & ReportingReplaced manual data pulls and spreadsheet reporting with a real-time leadership dashboard covering team workload, project health, and operational performance.
Tooling & EnablementA centralized platform bringing together automation tools, dashboards, product updates, and team-built projects with tool discovery, usage analytics, and a feedback loop.
Workflow AutomationAutomated chat space creation for partner integration QA — when a Jira ticket is assigned, the system creates a space, adds stakeholders, surfaces context, and connects staging through production to post-launch in a single thread.
Quality AssuranceA human–LLM collaboration tool for partner QA. An Ops team member drives the test purchase while a browser extension records network requests, integration data, and screenshots — then AI validates everything against the checklist and policy handbook and generates the QA document.
Includes all use cases, prompts, and the full methodology