AI Methodology and Framework
AI Methodology and Framework
Sigma’s AI methodologies combine industry research with our own best practices to guide organizations through AI adoption and continuous improvement
ARC: Align – Realize – Consolidate
Align
Before writing code, teams explicitly document architectural decisions, share understanding and identify design patterns. This step ensures everyone moves in the same direction.
Realize
Teams implement features with intention, applying architectural patterns and using AI tools with appropriate guardrails. AI amplifies patterns when intent is explicit and multiplies inconsistency when it’s implicit.
Consolidate
After implementation, teams validate the architecture, extract reusable patterns and prevent system drift. ARC complements Agile: Agile focuses on velocity and iteration; ARC ensures coherence and sustainability.
APEX: AI Program Execution Excellence
01
Assess
Evaluate current AI maturity—experimentation, pilot deployment, AI ways of working and readiness for scale—and identify gaps.
02
Plan
Set strategic objectives and choose AI technologies appropriate for each use case. Determine whether to use generative AI or traditional machine learning based on data availability, domain specificity and privacy concerns.
03
Execute
Launch pilots with clear metrics. Build AI‑native roles, train employees and embed AI into daily workflows.
04
Excel
Scale successful pilots across the organization. Invest in upskilling, impact measurement and change management to sustain improvements.
PRISM: Plan – Refine – Iterate –Scale – Monitor
Follow a manual added link
Plan
Identify a specific problem or opportunity, assemble data, define success metrics and establish responsible AI principles. Align stakeholders early.
Follow a manual added link
Refine
Use small‑scale experiments and user feedback to refine models and prompts. Employ domain expertise to correct inaccuracies and reduce bias.
Follow a manual added link
Iterate
AI capabilities evolve quickly; iterate often to incorporate new techniques, address changing requirements and adapt to user needs.
Follow a manual added link
Scale
When a solution is validated, integrate it into broader workflows. High performers scale AI across multiple use cases to maximize impact.
Follow a manual added link
Monitor
Continuously monitor outcomes, model performance and risks. Track metrics such as productivity, quality, customer satisfaction and regulatory compliance. Retrain models and update prompts as the environment changes.
Continuous Learning and Human Alignment
Latest Blog
Key Highlights: Growth-stage companies in SaaS, HealthTech, eCommerce, and hardware-adjacent sectors managing product data, bills of materials, and compliance requirements across spreadsheets and shared drives face
Read more
Key Highlights: As engineering organizations scale beyond 10 to 15 engineers, developer productivity stalls because teams spend too much time managing infrastructure and not enough time building products. Platform
Read more
Key Highlights: Founders and product leads routinely over-engineer their MVPs, delaying launch by months and burning budget on features that do not drive early traction, user validation, or investor confidence. A
Read moreConclusion
AI Technology Partners
OPENAI
AZURE AI
GOOGLE CLOUD AI
AWS BEDROCK
