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07-Opportunities and solution
07-Opportunities and solution
Introduction
Introduction
After final definition of the solution from the four different perspectives, you should provide the following:
Detailed architecture capabilities
Different alternatives
Selection criteria
Best-fit selection per your criteria
The consolidation of this practice is the ADR: Architecture decisions record, which encapsulate all decisions together.
Activities
Activities
Discussing alternatives to the proposed solutions, for the enterprise architecture.
At this stage, We can have different opportunities to provide per each area, which the power of the enterprise architect, as he act as the consultant to provide and confirm the optimum solution that fit with organization objectives.
Per Business Architecture:
Make or buy strategy (software/component/Platform/Cloud/HW)
Best fit Marketing Strategy
Process improvement
Per Data Architecture
Best-fit data architecture model (Relational/Non-Relational)
Best-Fit analytics tools
Per Application Architecture
Best-Fit standards (CMMI/SAFe)
Best-Fit Software engineering tool
Best-Fit approaches (API first/Micro-Services)
Best-Fit technology Stack (.Net/Java/Python)
Best-Fit Data Analysis Tool
Per Technology Architecture
Infrastructure (Primary and DR)
Security Model
Network Model
Generally:
Architecture principles
Supportive policies
AI Strategy As Example
AI Strategy As Example
1. AI Strategy
1. AI Strategy
An effective AI strategy defines how artificial intelligence aligns with an organization’s mission, business goals, and digital transformation roadmap. It focuses on identifying high-impact areas where AI can deliver measurable value — such as automating repetitive tasks, optimizing decision-making, and enhancing customer experience. The strategy should integrate governance, data readiness, talent development, and ethical use of AI. It must ensure scalability through cloud-native architecture, promote explainable and responsible AI, and be aligned with enterprise architecture principles for interoperability and resilience.
2. Recommended ADR (Architectural Decision Record)
2. Recommended ADR (Architectural Decision Record)
A recommended AI ADR documents key design decisions that influence how AI solutions are built and governed. Example:
Context: Need to integrate AI-driven prediction models with enterprise applications.
Decision: Adopt microservices-based architecture with model endpoints exposed via REST APIs.
Rationale: Enables scalability, modular deployment, and independent lifecycle management of models.
Alternatives Considered: Monolithic integration (rejected for low agility).
Consequences: Requires centralized model registry and monitoring pipeline.
ADR documentation ensures traceability, standardization, and alignment with enterprise AI governance.
3. Sample AI Use Cases
3. Sample AI Use Cases
Predictive Maintenance: AI models analyze sensor data to forecast equipment failure and reduce downtime.
Customer Sentiment Analysis: NLP models assess customer feedback to improve product experience.
Fraud Detection: ML algorithms detect abnormal transaction patterns in real time.
Intelligent Document Processing: OCR and NLP extract structured data from invoices or contracts.
Demand Forecasting: AI models predict future demand to optimize supply chain and inventory levels.
AI Ops: Autonomous monitoring systems detect, diagnose, and self-heal IT incidents proactively.
Tools
Tools
Enterprise Architect
Archimate
Dr. Ghoniem Lawaty
Tech Evangelist
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