AI Readiness Assessment  

• Assess existing infrastructure, data maturity, and capability.
• Pinpoint strategic gaps and areas for improvement.
• Point to important opportunities for AI deployment.

AI Roadmap Development  

• Sync AI strategy with business objectives.
• Define phased deployment plans.
• Establish measurable KPIs and milestones of success.

Use Case Identification and Prioritization  

• Identify AI use cases within functions.
• Prioritize on impact, feasibility, and ROI.
• Carry out Proof of Concepts (PoCs) for verification.

AI Model Development and Deployment  

• Develop personalized AI models with ML, NLP, and CV.
• Train/test models for performance and stability.
• Facilitate scalable deployment across environments. 

Data Strategy and Management  

• Construct robust data governance structures.
• Fuse heterogeneous data sources for single-platform analytics.
• Draw insights through next-gen analytics.

AI Technology Stack and Infrastructure  

• Provide recommendations on suitable tools and platforms.
• Develop cloud/on-prem/hybrid AI infrastructures.
• Guarantee performance, scalability, and security.

AI Talent and Skill Development  

• Evaluate organizational AI competencies.
• Provide training programs and workshops.
• Consult on recruitment and organizational structure for AI success.

Change Management and Adoption  

• Work with stakeholders for adoption assistance.
• Develop communication and change plans.
• Ensure seamless embedding into operations.

Ethics and AI Governance  

• Establish ethical guidelines for AI development.
• Ensure compliance and regulatory alignment.
• Keep a watchful eye on AI impact and fairness at all times.

Performance Monitoring and Optimization  

• Establish AI performance measures.
• Optimize models and processes on a continuous basis.
• Scale and evolve AI with business changes.