Prepare — Strategize for Your AI Initiative
Timeframe: 2-4 weeks
Action: The Prepare phase is dedicated to strategizing for successful AI adoption. During this stage, your team will engage in collaborative sessions aimed at identifying practical AI applications, assessing their potential impact on your business, and formulating a roadmap to achieve your AI goals. Utilizing AI readiness diagnostics, critical considerations for successful AI integration will be identified.
Outcome: By the end of this phase, you will have developed a strategic plan for initiating AI adoption, including defining goals, assessing readiness, and establishing necessary frameworks.
Steps:
- Conduct an AI readiness assessment to evaluate organizational preparedness.
- Identify and prioritize business objectives that AI can address.
- Define feasibility and sustainability goals for AI implementation.
- Establish initial policies and guidelines for AI deployment.
Proof — Develop and Implement Your First AI Project
Timeframe: 8-12 weeks
Action: The Proof phase focuses on developing and implementing your inaugural AI project. It involves an iterative development process where your team collaborates with AI experts to build and deploy a prototype AI solution. This phase includes setting up the necessary technological infrastructure and ensuring seamless integration with existing systems.
Outcome: At the end of this phase, you will have successfully developed and demonstrated the feasibility of an AI solution within your organization.
Steps:
- Select a foundational AI model suitable for your project.
- Configure and deploy the AI solution on a cloud platform.
- Prepare and pre-process data for training and inference.
- Optimize and validate the AI model's performance.
- Develop an application powered by the AI model to showcase its functionality.
Productize — Launch and Scale Your AI Solution
Timeframe: 12-24 weeks
Action: The Productize phase focuses on refining and launching your AI solution for broader deployment. It involves scaling up the AI solution, integrating it into operational processes, and ensuring it meets business objectives. This phase also includes implementing governance frameworks, optimizing performance, and establishing metrics for continuous improvement.
Outcome: By the end of this phase, your AI solution will be fully operational, delivering tangible business value and aligned with organizational goals.
Steps:
- Implement DataOps, MLOps, and AI governance practices.
- Establish mechanisms for monitoring and managing AI solution performance.
- Define processes for incident management and response.
- Refine the AI model to enhance cost efficiency and scalability.
- Continuously optimize the AI solution through ongoing training and improvement cycles.