Welcome back, Jane
Here's what's happening with GenAI in your industry.
Landscape
What is everyone doing with Generative AI?
Sentiment Snapshot
View details →Spending by Category
View details →Latest Signals
View all →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...
Adoption by Industry
Adoption by Use Case
Adoption Maturity Distribution
| Maturity Level | Description | % of Enterprises | Trend |
|---|---|---|---|
| Level 1: Exploring | Investigating use cases, running POCs | 22% | ↓ 5% |
| Level 2: Experimenting | Multiple pilots, measuring outcomes | 28% | → 0% |
| Level 3: Implementing | Production deployments, governance in place | 27% | ↑ 8% |
| Level 4: Scaling | Multiple production use cases, platform approach | 18% | ↑ 6% |
| Level 5: Transforming | AI-first strategy, competitive advantage | 5% | ↑ 2% |
Sentiment by Role
Top Concerns
Spending by Category
Spending by Industry
2025 Budget Expectations
Top Vendors by Market Share
| Vendor | Category | Market Share | YoY Change | Satisfaction |
|---|---|---|---|---|
| OpenAI | Model Provider | 34% | ↑ 8% | 4.2/5 |
| AWS Bedrock | Platform | 28% | ↑ 12% | 4.0/5 |
| Google Cloud AI | Platform | 22% | ↑ 5% | 3.9/5 |
| Anthropic | Model Provider | 18% | ↑ 15% | 4.4/5 |
| Microsoft Azure AI | Platform | 26% | ↑ 4% | 4.1/5 |
Emerging Vendors to Watch
Selection Criteria
Claude 3.5 achieves near-human performance on complex reasoning benchmarks
New Sonnet model shows 40% improvement on MATH and 25% on HumanEval.
AWS announces 40% price reduction on Bedrock inference
Significant cost reduction for Claude and Llama models on Bedrock.
Survey: 78% of enterprises report AI skills gap
Training and hiring challenges persist. Prompt engineering most in-demand.
RAG adoption reaches 45% among Fortune 500
Retrieval-augmented generation becoming standard for enterprise knowledge.
GPT-4 Vision adoption doubles in enterprise
Multi-modal capabilities enabling document processing and visual QA.
NVIDIA H200 availability improving
GPU shortage easing. Lead times down from 52 weeks to 16 weeks.
Practices
Is anyone succeeding? Patterns & case studies from the field.
Start with RAG, not fine-tuning
StrongOrganizations seeing faster time-to-value with retrieval-augmented generation. RAG provides 80% of value with 20% of effort.
Prompt management as code
StrongVersion-controlled prompt templates with A/B testing. Teams report 35% improvement in output quality.
Dedicated AI platform teams
EmergingCross-functional teams owning AI infrastructure enable faster adoption. Reduces time-to-deployment by 60%.
Human-in-the-loop validation
StrongCritical for high-stakes applications. Start with 100% review, systematically reduce as confidence increases.
LLM gateway pattern
EmergingCentralized API gateway for all LLM calls enables observability, cost tracking, and model switching.
Structured output schemas
StrongJSON schema constraints dramatically improve reliability. 85% reduction in parsing errors with function calling.
🚫 Boiling the ocean
High RiskTrying to transform everything at once. Successful organizations start with 2-3 focused use cases.
• 10+ simultaneous POCs • No clear success metrics • Scattered team focus
Prioritize ruthlessly. Pick highest-value, lowest-complexity use cases first.
🚫 Shadow AI proliferation
High RiskUngoverned tool adoption across teams. Creates security risks and compliance gaps.
• Employees using personal ChatGPT • No central AI inventory • IT unaware of AI usage
Implement approved tools, clear policies, and regular audits.
🚫 Ignoring data quality
High RiskGarbage in, hallucinations out. RAG systems fail with inconsistent, outdated data.
• Inconsistent answer quality • Contradictory responses • Users don't trust outputs
Invest in data cleaning, establish data ownership, implement quality metrics.
⚠️ Over-engineering early
Medium RiskBuilding complex ML pipelines before validating the use case. Start simple, add complexity when proven necessary.
• Custom training before trying APIs • Building infra before MVP • Months without user feedback
Start with API-first approach. Prove value before building.
AI-Powered Fraud Detection at Scale
Reduced fraud losses by 40% using custom ML models.
GPT-4 for Personalized Learning
Integrated LLMs into core product within 6 months, 2x engagement.
AI-First Customer Support
Scaled support to 2M+ merchants with 70% automation rate.
GPT-4 for Wealth Management
16,000+ advisors using AI assistant for research and insights.
AI Customer Service Revolution
AI handles 2/3 of customer chats, equivalent to 700 agents.
Notion AI Product Integration
Embedded AI writing and summarization, 30% of users active.
Why This Section Exists
Not everything in AI is working as promised. This section captures contrarian views, failed experiments, and honest assessments that balance the hype.
⚡ Overhyped Capabilities
"AI will replace developers"
Reality: Productivity boost of 20-40%, but senior oversight still essential.
"Plug-and-play AI solutions"
Reality: Even "simple" implementations require 2-3 months of customization.
"Fine-tuning solves everything"
Reality: Most enterprises get better results from RAG + prompt engineering.
📉 Documented Failures
Enterprise Chatbot Abandonment
45% of enterprise chatbot projects abandoned within 12 months.
Autonomous Agent Limitations
Multi-step autonomous agents unreliable for production. Error rates compound.
Cost Overruns at Scale
67% of enterprises report AI costs 2-3x initial estimates when scaling.
Strategy
What are the risks and opportunities across your strategic axes?
By Strategic Axis
Top Opportunities
View all →Multi-modal capabilities enabling new use cases
Vision + language models opening document processing, visual QA, and media analysis applications.
Cost reduction in inference
40% price drops making previously uneconomical use cases viable.
Top Risks
View all →Hallucination in high-stakes applications
Factual accuracy remains problematic for legal, medical, and financial use cases.
Vendor lock-in concerns
Proprietary APIs and model-specific implementations creating switching costs.
Function
What these systems can do — capabilities, limitations, trajectory
Opportunities (7)
Multi-modal capabilities
Vision + language models opening document processing, visual QA, and media analysis.
Reasoning improvements
Step-change in complex task performance enables higher-value automation.
Longer context windows
200K+ token contexts enabling analysis of full documents without chunking.
Risks (4)
Hallucination in high-stakes apps
Factual accuracy remains problematic for legal, medical, and financial use cases.
Model capability plateau
Uncertainty about continued improvement trajectory affects long-term planning.
Reasoning vs. retrieval confusion
Models sometimes fabricate rather than admit knowledge gaps.
Application
How to structure and build — architecture, integration, patterns
Opportunities (6)
RAG maturity
Established patterns for building reliable retrieval-augmented generation systems.
Agent frameworks emerging
LangChain, AutoGPT patterns enabling more autonomous workflows.
Function calling standardization
Tool use APIs reducing integration complexity significantly.
Risks (5)
Vendor lock-in
Proprietary APIs and model-specific implementations creating switching costs.
Prompt fragility
Small prompt changes can cause large output variations. Testing is hard.
Integration complexity
LLM-based systems harder to test and debug than traditional software.
Systems
Infrastructure to run them — compute, data, tooling, operations
Opportunities (5)
Inference cost reduction
40% price drops from major providers making more use cases viable.
GPU availability improving
Supply constraints easing, lead times down from 52 to 16 weeks.
Open-weight models viable
Llama 3, Mistral competitive with proprietary for many use cases.
Risks (6)
Cost management at scale
API costs can spiral quickly without proper monitoring and optimization.
Data security in cloud AI
Sensitive data flowing through third-party APIs raises compliance concerns.
Observability gaps
Traditional monitoring doesn't capture LLM-specific issues like drift.
People & Process
Teams, roles, skills, governance, and organizational change
Opportunities (5)
Productivity multiplier
20-40% productivity gains for knowledge workers with AI assistance.
New role emergence
Prompt engineers, AI product managers, ML platform engineers in demand.
Democratized AI access
Non-technical users can leverage AI through natural language interfaces.
Risks (3)
Skills gap widening
78% report difficulty hiring AI talent. Competition with tech giants.
Change resistance
Workforce concerns about job displacement creating adoption friction.
Governance vacuum
Many organizations lack clear policies for AI use, approval, and oversight.
Benchmark
How are we doing? Assess your maturity and compare with peers.
Your Position vs. Financial Services Peers
| Axis | Your Score | Industry Avg | Top Quartile | Gap to Top |
|---|---|---|---|---|
| Function | 3.5 | 3.1 | 4.2 | -0.7 |
| Application | 2.8 | 3.0 | 4.0 | -1.2 |
| Systems | 3.0 | 2.9 | 3.8 | -0.8 |
| People & Process | 3.4 | 2.7 | 3.9 | -0.5 |
| Overall | 3.2 | 2.9 | 4.0 | -0.8 |
Percentile Distribution
Peer Comparison
Based on 127 financial services organizations
Experts
Where do you get help? Connect with our network of specialists.
Upcoming Sessions
| Session | Expert | Date & Time | Topic | Spots | |
|---|---|---|---|---|---|
| AI Infrastructure Deep Dive | Sarah Kim | Thu, Jan 9 · 2:00 PM EST | Systems | 8/20 | |
| RAG Best Practices Q&A | David Park | Fri, Jan 10 · 11:00 AM EST | Application | 12/20 | |
| LLM Evaluation Strategies | James Chen | Mon, Jan 13 · 3:00 PM EST | Function | 5/20 | |
| AI Governance Framework | Rachel Lee | Wed, Jan 15 · 10:00 AM EST | People | 15/20 | |
| Cost Optimization Workshop | Sarah Kim | Thu, Jan 16 · 2:00 PM EST | Systems | 3/20 | |
| Enterprise AI Strategy | Amy Torres | Fri, Jan 17 · 1:00 PM EST | Function | 10/20 |
Guides & Frameworks
Enterprise AI Readiness Assessment
Comprehensive framework for evaluating organizational AI maturity
RAG Implementation Playbook
Step-by-step guide to building production RAG systems
AI Governance Template Pack
Policies, procedures, and checklists for AI governance
Tools & Calculators
LLM Cost Calculator
Estimate and compare costs across providers
ROI Modeling Spreadsheet
Model the business case for AI investments
Use Case Prioritization Matrix
Score and rank AI use cases by value and feasibility
Research
Deep dive into the evidence database.
Results
Showing 24 results for "RAG implementations"| Title | Type | Axes | Confidence | Date |
|---|---|---|---|---|
| Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks Lewis et al., 2020 — Foundational paper on RAG architecture |
Academic | Application | High | 2020 |
| Building Production RAG: Lessons from 50 Implementations LangChain case study compilation |
Industry | Application Systems | Medium | 2024 |
| Enterprise RAG at Fortune 500 Financial Services Anonymized interview — Implementation challenges at scale |
Interview | Application | High | 2024 |
| Vector Database Selection for RAG Systems Comparative analysis of Pinecone, Weaviate, Milvus, and pgvector |
Industry | Systems | High | 2024 |
Reports
Browse and download report artifacts.
| Report | Progress | Last Read | Time Spent | |
|---|---|---|---|---|
| GenAI Adoption Report Q4 2024 | 100% | Dec 22, 2024 | 2h 15m | |
| Enterprise AI Infrastructure 2024 | 45% | Dec 20, 2024 | 48m | |
| AI Team Building Playbook | 100% | Nov 15, 2024 | 1h 30m | |
| LLM Selection Guide 2024 | 100% | Oct 28, 2024 | 2h 45m | |
| AI ROI Measurement Guide | 72% | Oct 15, 2024 | 55m |