AI Delivery Framework and Idea Lifecycle

Standardizing how AI ideas get vetted, prioritized, and measured

The Challenge

A Fortune 500 manufacturer had AI ideas coming from all over the company. Different teams wanted to build different things. The problem was there was no standard way to vet them, prioritize them, or measure what worked. Different teams were reinventing process. Leadership had no clear view of the pipeline. Good ideas could get lost. Bad ideas could get resources. There was no way to know which AI products actually delivered value.

They needed a repeatable way to go from idea to deployed solution, with numbers to back it up.

The Approach

We created standard operating procedures (SOPs) for the key stages of AI delivery: architecture, testing, incident management, and go-live. That gave teams a shared playbook instead of everyone making it up as they went.

Next we defined a KPI (key performance indicator) and measurement plan tailored to AI products. What does success look like? How do we track it? Now they could see which AI products actually delivered value instead of hoping.

Finally we built an automated workflow for the idea lifecycle. Intake, validation, governance review, prioritization, tracking. All in one place with a live dashboard so leadership could see what was in the pipeline and where things stood. No more ideas in spreadsheets or stuck in email.

• AI delivery SOPs. Architecture, testing, incident management, go-live procedures
• KPI and measurement framework. Data-driven tracking of AI product performance against business goals
• Automated idea lifecycle workflow. Intake, validation, governance, prioritization, tracking with real-time visibility

The Results

Technologies Used

Standard operating procedure documentation, KPI and measurement frameworks, automated workflow system for idea management. The workflow and dashboard were built on the client's existing automation platform to fit their environment.