GenAI Value-Realization Quick Start Program

Process Overview

Phase 1: Enablement (Week 1-4)

GenAI Platform Enablement: Highly collaborative effort focused on creating or strengthening the organization’s AI roadmap and enabling the Cloud and Data foundations required for your organization to become AI-enabled .

Task Platform Enablement:  Assessment, Cloud Platform Design, Setup and Provisioning Deliverables Initial GenAI governance framework document Development environments setup Configured cloud infrastructure GenAI platform enablement

Phase 2: Implementation (Week 2–10)

Development and Deployment of Use Case: Encora builds and deploys a high-value Generative AI use case to demonstrate AI’s value within the organization, setting the foundation for future use cases.

Task Development and Deployment: Design and develop use case, Data pipelines, Validation and Testing, and Model management. Deliverables Technical architecture document GenAI use case- developed, tested and deployed (for internal use only)

Phase 3: Optimization (Week 9-12)

Feedback and Red Team Strategy to Optimize Use Case: Encora takes additional steps to optimize and strengthen your Cloud and Data foundation, focusing on refining the use case, by incorporating user feedback to improve quality and reliability.

Task Deploy Red Team strategies to optimize the safety mechanisms. Review monitoring feedback and implement improvements to deliver better and higher-quality outputs. Deliverables Optimized GenAI use case and Cloud infrastructure Roadmap to scale adoption of additional use cases

Engagement Team Structure

Project Manager (1) - Manage project execution and liaise between the client and Encora. Cloud/DevOps Engineer (1) - Setup, configure, manage, and monitor Cloud infrastructure and associated services.
Gen AI Architect (1) - Lead the design, implementation strategy, and optimization of AI capabilities, ensuring seamless integration.
Gen AI Full-stack Developer (2) - Lead the development and integration of Generative AI capabilities and additional software components required to enable the new functionality. Gen AI Data Engineer (1) - Lead and execute data engineering strategies for Gen AI workloads.

Assumptions and Dependencies

Assumptions Technological Compatibility: The current testing and development environment is compatible with or can be adapted to integrate GenAI and associated technologies within the current technology landscape. Data Accessibility: Sufficient and relevant data is available and accessible to the engineering team so that they can use and identify the best approach to incorporating GenAI. Stakeholder Support: There is buy-in and support from the client stakeholders, including management and the development teams, for exploring and potentially adopting GenAI technologies. Resources Availability: All the personnel required during the assessment and platform enablement will be available for the planned activities Encora schedules.

Dependencies Technical Infrastructure: The assessment's viability relies on the existing technical infrastructure's ability to support GenAI technology integration, encompassing hardware, software, and network requirements. Change Management Processes: The successful implementation of GenAI technologies depends on effective change management processes thatfacilitate adaptation and adoption among the development team. Leadership and Management Support: Leadership and management support are crucial, requiring not just approval and budget allocation, but also active involvement in championing GenAI adoption throughout the organization. User Acceptance and Adoption: The success of GenAI integration relieson the teams’ willingness to adopt and adapt to new technologies. It is essential to address resistance, manage expectations, and demonstrate GenAI’s value and benefits to encourage acceptance