Copyright (c) 2026 MindMesh Academy. All rights reserved. This content is proprietary and may not be reproduced or distributed without permission.
6.2. Quick Reference
Solution-type selection
| Need | Best fit |
|---|---|
| Fixed, auditable logic with identical results for identical inputs | Rule-based automation |
| Forecast, score, or classification from structured history | Predictive ML (custom on Amazon SageMaker AI, or a product) |
| Draft, summarize, converse, extract meaning from documents | Generative AI (Amazon Bedrock, or an application like Amazon Quick) |
| Answers grounded in current company documents, with citations | RAG (Amazon Bedrock Knowledge Bases) |
| Multi-step tasks that take actions across business systems | AI agent with permissions, limits, and escalation (Amazon Bedrock AgentCore) |
| Consistent specialized style, format, or task behavior after prompting falls short | Fine-tuning |
| Problem caused by process design, policy, or missing data | Not AI first: fix the process or data |
Generative AI adaptation ladder (cheapest first): prompt engineering → RAG (knowledge gap) → fine-tuning (behavior gap) → continued pre-training or custom model (only where the model is the differentiator).
AWS services and tools at a business level
| Item | What it is for | Pricing pattern |
|---|---|---|
| Amazon Quick | AI-assisted BI, research, and automation for business users | Seat-based (per user) |
| Amazon Bedrock | Build generative AI apps on foundation models; Knowledge Bases, Guardrails, Evaluations, AgentCore | Consumption-based (tokens); batch discounts; Provisioned Throughput (hourly capacity) |
| Amazon SageMaker AI | Build, train, deploy custom ML | Instance-based (compute hours); Savings Plans for steady use |
| AWS Marketplace | Buy third-party models, AI products, and data with AWS billing | Varies by product |
| AWS Pricing Calculator | Estimate costs before deployment | Free tool |
| AWS Cost Explorer | Analyze actual costs after deployment | Free tool |
| AWS CAF / CAF-AI | Organizational readiness across Business, People, Governance, Platform, Security, Operations | Framework |
| Shared responsibility model | AWS secures the cloud; customer owns data, access, configuration, use case, outputs | Model |
Strategy and value
- Build-buy-partner factors: budget, timelines, capabilities, vendor proposals, regulatory compliance. Buy commodity; build differentiation you can sustain; partner for speed or expertise.
- Prioritization criteria: business value, feasibility, sustainability, strategic alignment. Verdicts: scale, pause, terminate.
- Transition considerations: business continuity, cost implications, data readiness, performance impact.
- Measurement order: define KPIs → capture baseline → deploy → track leading indicators → report ROI with full cost.
- ROI = (total benefits − total costs) ÷ total costs; include data, integration, change management, governance, and operations costs.
- Durable advantage: proprietary data, data flywheels, workflow integration, organizational capability. Not: model access.
Governance and risk
- Responsible AI dimensions (AWS): fairness, explainability, privacy and security, safety, controllability, veracity and robustness, governance, transparency.
- Governance by design: start at use-case intake with an impact assessment and risk classification.
- Oversight scales with impact and reversibility: human-in-the-loop for high-impact decisions; guardrails plus escalation criteria everywhere.
- EU AI Act tiers: unacceptable (prohibited), high (strict obligations), limited (transparency), minimal (no specific obligations).
- NIST AI RMF functions: Govern, Map, Measure, Manage.
- ISO/IEC 42001: certifiable AI management system. ISO/IEC 23053: framework describing ML-based AI systems. ISO/IEC 22989: AI concepts and terminology.
- Bias can enter at every lifecycle stage and drift over time; monitor continuously.
- Hallucination mitigations: RAG grounding, citations, contextual grounding checks, evaluation sets, human review for high stakes.
Readiness and transformation
- Readiness dimensions: leadership alignment, data quality, cultural preparedness, technical infrastructure, governance frameworks.
- Capability gap categories: people, process, technology, governance.
- Shadow AI control: classify tools (approved, under evaluation, blocked), fast intake, sanctioned alternatives, enforcement.
- Communication topics: implementation timelines, outcome expectations, effects on roles.
- Workforce development: POC programs, hackathons, training programs, responsible AI training (continuous and role-based).
- Exam transformation phases: envision, experiment, launch, scale.
- CoE models: centralized → hub-and-spoke → federated as maturity grows.
Written byAlvin Varughese
Founder•20 professional certifications