AI Application Development
Enterprise AI Implementation Guide
A comprehensive guide to implementing AI in enterprise environments. Covers strategy, architecture, vendor selection, and production deployment.
1
Defining the Right AI Use Case
- How to identify high-ROI AI opportunities in your business
- Differentiating between automation, AI assistance, and AI-native processes
- Prioritisation framework: impact vs complexity matrix
- Common mistakes: starting with technology instead of the problem
2
Architecture Foundations
- RAG (Retrieval-Augmented Generation) vs fine-tuning: when to use each
- Choosing the right foundation model: Claude, GPT-4o, Gemini, Llama
- Vector database selection: Pinecone, Weaviate, pgvector comparison
- Designing for latency, cost, and accuracy trade-offs
3
Data Readiness & Knowledge Bases
- Assessing your data estate for AI readiness
- Document ingestion pipelines: chunking, embedding, and indexing strategies
- Handling structured, semi-structured, and unstructured data
- Data governance and PII handling in AI systems
4
Vendor & Partner Selection
- Evaluating AI development partners: 10 questions to ask
- Build vs buy decision framework for enterprise AI components
- Avoiding vendor lock-in while leveraging managed AI services
- Red flags in AI vendor proposals
5
Production Deployment & Operations
- Infrastructure requirements: compute, storage, networking for AI workloads
- CI/CD for AI systems: testing, versioning, and rollback strategies
- Monitoring AI systems in production: hallucination detection, latency, cost
- Change management: driving adoption across enterprise teams
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