Welcome back, Jane
OpenAI announces GPT-5 enterprise pricing
Major shift in enterprise licensing model signals market maturation...
View in Landscape →How Stripe built their AI fraud detection
18-month journey from prototype to production...
Read in Practices →Landscape
What is everyone doing with Generative AI?
Claude 3.5 achieves near-human performance on complex reasoning benchmarks
Implications for enterprise document processing and analysis workflows...
AWS announces 40% price reduction on Bedrock inference
Significant cost reduction changes build vs. buy calculus for many enterprises...
Survey: 78% of enterprises report AI skills gap in engineering teams
Training and hiring challenges persist despite increased investment...
Practices
Is anyone succeeding? Patterns & case studies.
Start with RAG, not fine-tuning
Organizations seeing faster time-to-value with retrieval-augmented generation before investing in custom models.
Dedicated AI platform teams
Cross-functional teams owning AI infrastructure enable faster adoption across business units.
AI-Powered Fraud Detection at Scale
How Stripe reduced fraud losses by 40% using custom ML models.
GPT-4 for Personalized Language Learning
Inside Duolingo's rapid integration of LLMs into their core product.
Strategy
What are the risks and opportunities?
Function
What these systems can do — capabilities, limitations, trajectory.
Multi-modal capabilities enabling new use cases
Vision + language models opening document processing, visual QA, and media analysis applications.
Reasoning improvements in latest models
Step-change in complex task performance enables higher-value automation.
Hallucination in high-stakes applications
Factual accuracy remains problematic for legal, medical, and financial use cases.
Model capability plateau concerns
Uncertainty about continued improvement trajectory affects long-term planning.
Application
How to structure and build — architecture, integration, patterns.
RAG architecture maturity
Established patterns for building reliable retrieval-augmented generation systems.
Function calling standardization
Tool use APIs reducing integration complexity significantly.
Vendor lock-in concerns
Proprietary APIs and model-specific implementations creating switching costs.
Prompt fragility
Small prompt changes can cause large output variations. Testing is hard.
Systems
Infrastructure to run them — compute, data, tooling, operations.
Inference cost reduction
40% price drops from major providers making more use cases viable.
Open-weight models viable
Llama 3, Mistral competitive with proprietary for many use cases.
Cost management at scale
API costs can spiral quickly without proper monitoring and optimization.
Data security in cloud AI
Sensitive data through third-party APIs raises compliance concerns.
People & Process
Teams, roles, skills, governance, and organizational change.
Productivity multiplier
20-40% productivity gains for knowledge workers with AI assistance.
Democratized AI access
Non-technical users can leverage AI through natural language interfaces.
Skills gap widening
78% report difficulty hiring AI talent. Competition with tech giants.
Governance vacuum
Many organizations lack clear policies for AI use, approval, and oversight.
Benchmark
How are we doing? Assess and compare.
Current Assessment
Last updated: Dec 20, 2024
How effectively are you leveraging current model capabilities?
How well do you manage and improve AI output quality?
Hire AI Platform Team
5-person team owning AI infrastructure
Implement LLM Gateway
Centralized API management
RAG Standardization
Standard patterns and tooling
Experts
Where do you get help? Connect with specialists.
Sarah Kim
Principal Analyst, AI Infrastructure
15+ years building ML systems at scale. Previously led AI platform at Netflix.
Michael Johnson
Senior Analyst, Enterprise AI
Rachel Lee
Analyst, AI Governance
David Park
Senior Analyst, MLOps
Research
Deep dive into the evidence database.
Showing results for "RAG implementations"
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
Lewis et al., 2020 — Foundational paper on RAG architecture...
Building Production RAG: Lessons from 50 Implementations
LangChain case study compilation — Common patterns and pitfalls...
Enterprise RAG at Fortune 500 Financial Services
Anonymized interview — Implementation challenges at scale...
Reports
Browse and download report artifacts.
GenAI Adoption Report Q4 2024
Comprehensive analysis of enterprise adoption patterns, spending trends, and emerging best practices.
Enterprise AI Infrastructure 2024
Systems requirements, vendor landscape, TCO analysis
Published Oct 15, 2024
AI Team Building Playbook
Roles, structures, and scaling strategies
Published Aug 1, 2024
LLM Selection Guide 2024
Comparative analysis of enterprise LLM options
Published Jun 15, 2024