AI Sprint
Fast track to AI
Validate your AI vision in just 4 weeks
The AI Sprint is an intensive 4 week sprint style workshop that transforms the organization’s top 2-3 use cases into rapid proof-of-value demonstrations. This accelerated 4-week approach establishes enough evidence for business impact while building momentum for broader AI adoption.
Balanced Portfolio includes
- At least one immediate impact use case
- ne strategic use case with scaling potential
Potential Impact Score based on
- Measurable outcomes
- Data accessibility
- Data hygiene & readiness
Business readiness
- Executive sponsorship
- Cross-functional team
- Data Governance structure
Kickoff & Preparation
- Align on business objectives and formalize 2-3 business use case
- Formalize success criteria and metrics
- Establish sprint team roles and responsibilities
Environment set up and data discovery
- Set up technical environment: secure necessary access and permissions
- Validates key assumptions from the readiness assessment
- Assess data quality, identify gaps, and develop remediation strategy
Solution Design
- Define modeling approach and methodology
- Design preliminary solution architecture
- Get stakeholder buy-in: Articulate what a successful solution will specifically achieve
Model Development
- Deploy pre-trained models and configure for use case specifics
- Develop initial code infrastructure and pipelines
- Create feature engineering framework
Testing & Validation
- Run initial model tests against sample data
- Validate model outputs against business expectations
- Document performance baselines
Feedback Integration
- Identify unexpected results and performance issues
- Share progress & key insights with stakeholders
- Adjust development priorities based on early findings
Enhanced Development
- Refine models based on Week 2 feedback
- Optimize performance for critical use case requirements
- Implement data preprocessing improvements
Business Integration
- Develop compressed workflows to mimic at-scale live experience
- Test early results with business stakeholders
- Align technical capabilities with business processes
Performance Evaluation
- Conduct technical validations within existing infrastructure
- Measure against success criteria
- Share progress & key insights with executive sponsors
Solution Finalization
- Complete final iterative refinements
- Address remaining technical debt
- Finalize documentation and knowledge transfer materials
Business Case Validation
- Simulate outcome and ROI scenarios
- Calculate investments required for production scaling
- Develop implementation roadmap and timeline
Delivery & Next Steps
- Final testing and validation
- Present findings and demonstration to stakeholders
- Define path to production and scaling strategy
Rapid Learning
Gain AI knowledge quickly
Tangible Outcomes
Achieve measurable results and business impact
Risk Mitigation
Experiment in a safe environment with minimal risk
