Plan

Objective

Establish a strong foundation by defining clear business objectives and identifying actionable use cases for AI.

Pruning Down to Workloads for AI

Focus on the most impactful and feasible workloads to maximize ROI.

Assessing Organizational AI Readiness

Evaluate existing infrastructure, workforce capabilities, and data maturity.

Identify Funding Sources

Secure budget and stakeholder buy-in to support AI initiatives.

Discover

Objective

Gain a deep understanding of business needs and prepare data and infrastructure for AI solutions.

Business & Data Understanding

Collaborate with stakeholders to gather insights and define key metrics.

Preparing Data and Infrastructure

Ensure data quality, accessibility, and readiness for AI applications.

User Stories

Develop detailed user scenarios to guide solution development. 

Solution Architecture and Design

Create a scalable and secure architecture tailored to organizational needs.

Autogenerate BRD with Artifacts

Utilize previous steps to produce a Business Requirements Document (BRD) for streamlined implementation.

Research AI Models

Evaluate off-the-shelf solutions, customize existing models, or develop bespoke AI models as needed.

Build

Objective

Develop, test, and refine AI models to ensure they meet defined objectives.

Model Building

Leverage advanced techniques to create robust AI models.

Data Extraction and Pre-Processing

Clean, organize, and transform data for optimal model performance.

Conduct Proofs of Concept (PoC) or Pilots

Validate model effectiveness with small-scale implementations.

Reflection and Model Updates

Incorporate feedback and refine models to improve performance and accuracy.

Integrate

Objective

Seamlessly integrate AI models into enterprise systems and promote them to production.

Model Integration

Ensure compatibility with existing enterprise systems and workflows.

Transition and Promotion to Production

Deploy AI models in a live environment with full operational support.

Scale/Learn

Objective

Expand AI solutions across the organization and ensure continuous improvement.

Scaling Models for Multiple Business Units and Divisions

Adapt and deploy AI solutions to various parts of the organization.

Monitor for Bias, Model Drift, and Ethics Adherence 

Regularly evaluate models to ensure fairness, accuracy, and compliance with ethical standards.

Quarterly Evaluation and Check-Ins

Update models to reflect new data and insights.

Update Knowledge Sources

Enhance textual knowledge bases and integrate them into Retrieval-Augmented Generation (RAG) pipelines for ongoing AI learning and application.