The Applied AI Framework™
A Structured Methodology for AI Delivery
The Applied AI Framework™ is the flagship offering of Data As A Product, LLC, designed to bridge the gap from experimentation to value in AI initiatives. Rooted in real-world practitioner experience, AAIF™ provides a structured, repeatable methodology for organizations to align AI strategies with business goals, validate opportunities, and deliver scalable solutions.
The Three Pillars of AAIF™
The framework advances through three sequential, yet iterative, pillars, each designed to ensure a consistent and repeatable process for AI implementation.
Envision
Strategy & Prioritization. This pillar aligns AI initiatives with business objectives. It involves hypothesis generation, risk and value assessment using the AAIF Impact Matrix, and resource planning. The output is a prioritized AI portfolio with defined bets and success metrics.
Explore
Discovery & Validation. This phase focuses on rapid testing and refinement. It includes cross-functional sprints for proofs-of-concept, data and technical validation, and scope adjustment. The output is a validated initiative with confirmed desirability and viability.
Execute
Delivery & ML/Ops Phase. This pillar emphasizes deployment and operationalization. It covers model productionization, monitoring, governance, and iteration. The output is a scalable AI solution with embedded ML/Ops, usage tracking, and ongoing optimization.
The AI Operating Model
The AI Operating Model forms the bedrock of AAIF™, defining cross-functional roles that ensure collaborative, sustainable AI adoption. These roles work interdependently to avoid silos and focus on value realization.
AI Leadership
Acts as the strategic navigator, setting the overall AI strategy, prioritizing initiatives using the proprietary AAIF Impact Matrix, and ensuring alignment with company objectives.
Data Scientist
Serves as the hypothesis researcher and technical designer, validating business value through data analysis, prototyping, and iterative testing.
Engineering Lead
Bridges engineering, ML/Ops, and data teams to assess feasibility. They handle technical viability, infrastructure, scalability, and maintenance for long-term reliability.
Product Manager
Owns outcomes, verifying desirability via stakeholder feedback, measuring business value, and managing go-to-market. Acts as the value guardian.
The 12-Week playbook
The 12- Week Playbook is a turnkey model for partners to deliver AAIF™ based services, ensuring consistent, high-value AI implementations across organizations.
Envision™ – Weeks 1-4
Kick off and workshops with the AI Leadership team to prioritize initiatives using the AAIF™ Impact Matrix and define the AI Operating Model roles and ways of working.
Explore™ – Weeks 4-8
Lead the Data Scientist, Engineering Lead and Product Manager in running validation sprints for 1-2 prioritized initiatives. Facilitate Impact Matrix readouts and approve production testing.
Execute™ – Weeks 8-12
The team deploys solutions, establishes ML/Ops, and measures initial impact.