AI Project Process
Data maturity for getting data ready for AI is the ability to consistently and efficiently provide clean, high-quality data to AI models in a timely manner. This requires having the right processes, tools, and infrastructure in place to manage and govern data.
Our agile delivery methodology includes a 9 month timeline that consists of 2-3 week sprints and 3 – 3 month epics.
Epic 1 (3 months)
AI prototype with measurable results on a key business metricSprints:
- Sprint 1: Develop and test initial AI model
- Sprint 2: Integrate AI model with existing systems and processes
- Sprint 3: Deploy AI prototype and collect data on business impact
Epic 2 (3 months)
Production-ready AI solution with measurable results on a key business metricSprints:
- Sprint 1: Optimize AI model for performance and scalability
- Sprint 2: Develop and test production deployment plan
- Sprint 3: Deploy AI solution to production environment
Epic 3 (3 months)
Mature AI offering with measurable results on a key business metric and a clear roadmap for future developmentSprints:
- Sprint 1: Develop and implement monitoring and logging system
- Sprint 2: Develop and implement training and support documentation
- Sprint 3: Develop and implement feedback loop to improve AI model over time
This agile delivery methodology is designed to help organizations quickly develop and deploy AI solutions that deliver measurable results. By breaking down the development process into small, manageable sprints, organizations can reduce risk and increase flexibility. Additionally, by focusing on deliverables at each epic, organizations can ensure that they are making progress towards their overall goals.
How we execute
- Establish clear goals and objectives for each epic: This will help the team to stay focused and ensure that they are developing an AI solution that meets the needs of the business.
- Create a detailed sprint plan for each sprint: This will help the team to stay on track and avoid scope creep.
- Use a variety of tools and techniques to track progress and identify potential risks: This will help the team to make adjustments as needed.
- Encourage collaboration and communication between team members: This will help to ensure that everyone is aligned on the goals and objectives of the project.
- Be flexible and adaptable: The AI landscape is constantly evolving, so it is important to be able to adjust the plan as needed.