AI research
Decision-ready AI research for practical teams.
Decision-ready AI research on governance, RAG, agents, model strategy, evaluation, privacy, and enterprise AI adoption.
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3Active months
AI Risk Classification Framework for 2026
A practical framework for classifying AI use cases by data sensitivity, decision impact, automation authority, user exposure, reversibility, oversight, and regulatory concern.
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July 2026
3 notes
AI Risk Classification Framework for 2026
A practical framework for classifying AI use cases by data sensitivity, decision impact, automation authority, user exposure, reversibility, oversight, and regulatory concern.
AI Content Refresh Quality Framework for 2026
A practical framework for improving older AI articles with stronger examples, clearer structure, useful FAQs, internal links, and reader-focused updates.
AI Browser Agent Permission Framework for 2026
A practical framework for managing AI browser agent permissions, page access, action approval, logs, sensitive data exposure, and safe browser workflow design.
June 2026
30 notes
AI Meeting Intelligence Quality Framework for 2026
A practical framework for evaluating AI meeting summaries, action items, speaker accuracy, customer commitments, follow-up quality, and meeting record reliability.
AI Tool Consolidation Framework for 2026
A practical framework for reducing AI tool overlap, comparing workflow value, reviewing costs, managing risk, and deciding which AI tools to keep, merge, restrict, or retire.
AI Incident Response Playbook for 2026
A practical playbook for detecting, containing, investigating, fixing, and learning from AI incidents involving harmful outputs, data exposure, prompt injection, retrieval failures, and agent workflow errors.
AI Model Routing Governance for 2026
A research framework for governing AI model routing across cost, quality, privacy, latency, fallback rules, and risk-sensitive workflows.
Private AI Knowledge Base Design for 2026
A practical research framework for designing private AI knowledge bases with access controls, source quality, retrieval rules, citations, freshness, and review workflows.
AI Copilot Adoption Scorecard for 2026
A practical scorecard for measuring AI copilot adoption across usage, quality, risk, cost, workflow fit, user confidence, and business value.
LLM Evaluation Datasets for Enterprise AI in 2026
A practical research note on building enterprise LLM evaluation datasets using real tasks, edge cases, scoring rubrics, reviewer feedback, and workflow-specific quality metrics.
AI Agent Permission Design Framework for 2026
A practical research framework for designing AI agent permissions across tools, data, approvals, logs, escalation paths, and workflow boundaries.
AI Agent Control Roadmap Framework for 2026
A practical enterprise roadmap for increasing AI agent autonomy alongside identity, permissions, approvals, monitoring, execution limits, and recovery controls.
Shadow AI Risk Assessment Framework for 2026
A practical enterprise framework for discovering shadow AI, scoring data and workflow risk, prioritizing response, and moving useful work into approved AI services.
AI Cost Control Framework for 2026
An enterprise AI FinOps framework for attributing, forecasting, governing, and optimizing model, RAG, agent, license, infrastructure, and review costs by workflow value.
AI Agent Governance Metrics for 2026
An enterprise measurement framework for evaluating AI agent safety, reliability, autonomy, human oversight, cost, compliance, and business value in production.
AI Workflow Auditability Framework
A practical enterprise framework for tracing AI-assisted decisions and actions through prompts, evidence, sources, approvals, tool calls, memory, outputs, and corrections.
Context Engineering Evaluation Framework for AI Teams
A practical framework for testing whether prompts, retrieval, memory, tool results, metadata, and enterprise knowledge give AI systems the right context at the right time.
Vector Databases and RAG in 2026: Smart Retrieval Architecture Guide
A practical RAG and vector database guide for teams building AI search, internal knowledge assistants, support copilots, and retrieval-backed LLM applications.
Prompt Engineering: Advanced Techniques and Patterns for 2026
Master advanced prompt engineering techniques including chain-of-thought, few-shot learning, role-based prompting, and optimization patterns for complex AI tasks.
Multimodal AI Adoption Trends in 2026
An analysis of how text, image, audio, video, and screen-aware AI tools are changing practical adoption across teams.
LLM Fine-Tuning Best Practices for 2026: When and How to Adapt Models
Comprehensive guide to fine-tuning large language models, including cost-benefit analysis, techniques, tools, and practical implementation patterns for teams.
AI Agent Readiness Framework for 2026
An enterprise readiness assessment for deciding whether AI agent workflows have the process, data, identity, controls, operations, and workforce support needed for production.
AI Search Reliability in 2026: What Teams Need to Know Before They Trust It
An in-depth analysis of AI search accuracy, hallucination risk, citation quality, and what reliability actually means for research and business workflows.
AI Model Pricing and Cost at Scale: A 2026 Framework for Teams
A practical enterprise analysis of AI model pricing, production cost drivers, routing, hosted versus self-hosted economics, and unit costs at scale.
AI Tool Privacy and Enterprise Data Handling
A practical enterprise analysis of how AI tools handle prompts, files, memory, RAG context, customer data, retention, permissions, and agent actions.
Open vs Closed AI Models in 2026: Which Strategy Wins for Teams?
An in-depth analysis of the tradeoffs between open and closed AI models across cost, control, performance, privacy, and long-term business risk.
Project Glasswing: Using AI to Secure the World's Critical Software
An in-depth look at Anthropic's Project Glasswing initiative, which leverages Claude Mythos Preview to identify zero-day vulnerabilities in critical infrastructure before adversaries can exploit them.
AI Governance Operating Model for 2026
A practical framework for building an AI governance operating model with ownership, intake, risk classification, review gates, policy enforcement, monitoring, and continuous improvement.
Enterprise RAG Governance Framework for 2026
A practical framework for governing enterprise RAG systems across source selection, permissions, chunking, freshness, citations, evaluation, feedback loops, and audit logging.
Small Language Models and Edge AI in 2026
A research note on small language models, edge deployment, privacy, latency, and when smaller AI systems are better than frontier models.
Synthetic Data for AI Testing in 2026
How enterprise AI teams use synthetic data to test RAG systems, agents, edge cases, safety controls, and workflow quality without exposing production records.
AI Agent Monitoring and Observability in 2026
A research note on monitoring AI agents, tracking tool use, reviewing failures, and building observability into automated workflows.
Human-in-the-Loop AI Review Patterns for 2026
A practical research note on where human review still matters in AI workflows and how teams can design review patterns without slowing everything down.
May 2026
31 notes
AI Trust Metrics for Leaders and Teams
A research note on measuring trust in AI systems through reliability, transparency, control, user confidence, and business outcomes.
Enterprise AI Roadmap Planning for 2026
A research note on planning AI initiatives across tools, workflows, governance, budget, training, and measurable business value.
AI Output Quality Assurance for Business Workflows
A practical framework for scoring, reviewing, escalating, and improving AI-generated outputs before they affect customers, decisions, documents, operations, or published content.
AI Vendor Due Diligence Checklist for 2026
A practical checklist for evaluating AI vendors before adoption across privacy, security, retention, training usage, compliance, pricing, reliability, support, integrations, and exit risk.
Agent Observability Basics for AI Operations
A research note on monitoring AI agents through traces, logs, tool calls, outcomes, failures, and escalation patterns.
AI Assistant Memory Governance
A practical research note on governing AI assistant memory, personalization, retention, deletion, user control, privacy, enterprise boundaries, and responsible rollout.
Open Model Risk Assessment for Product Teams
A research note on evaluating open models for privacy, licensing, safety, quality, support, and deployment control.
Data Retention Choices for AI Tools
A practical research note on data retention choices for ai tools, with decision criteria, rollout patterns, risks, metrics, and next steps for teams evaluating AI in 2026.
AI Change Management Patterns for Adoption
A research note on adoption patterns that help teams introduce AI tools with training, feedback, ownership, and measurable outcomes.
AI Evaluation Metrics for Enterprise Teams in 2026
A practical framework for measuring AI systems across accuracy, grounding, citations, safety, reliability, latency, cost, user acceptance, and business outcomes.
Role-Based AI Access Controls for Enterprise Adoption
A research note on using role-based access controls to manage who can use AI tools, models, data sources, and integrations.
AI Workflow Automation Governance for 2026
A practical framework for governing AI workflow automation across permissions, approvals, exceptions, audit trails, rollback plans, ownership, and operational risk.
Knowledge Base Readiness for AI Assistants
A practical framework for preparing accurate, structured, current, permission-safe knowledge bases for AI assistants and retrieval workflows.
AI Product Analytics Metrics That Actually Matter
A research note on measuring AI product usage, quality, latency, cost, review load, retention, and task success.
Private AI Deployment Tradeoffs for Enterprise Teams
A research note on private AI deployment choices, including security, cost, latency, model quality, and operational complexity.
AI Meeting Intelligence Governance for Teams
A research note on meeting transcripts, summaries, action items, consent, retention, and knowledge reuse controls.
Prompt Library Maintenance for Repeatable AI Work
A practical research note on prompt library maintenance for repeatable ai work, with decision criteria, rollout patterns, risks, metrics, and next steps for teams evaluating AI in 2026.
AI Tool Vendor Risk Scoring for Buyers
A research note on scoring AI vendors by privacy, security, reliability, pricing, integrations, support, and governance controls.
Multimodal Review Workflows for Images, Video, and Documents
A research note on reviewing multimodal AI outputs across text, images, video, documents, and brand-sensitive content.
Vector Database Cost Management for RAG Teams
A research note on vector database cost drivers, indexing choices, storage growth, retrieval design, and operational controls.
Small Team AI Governance Without Heavy Process
A research note on lightweight AI governance for startups and small teams that need clarity without enterprise bureaucracy.
AI Browser Workflow Risk and Permission Design
A research note on browser-based AI assistant risks, permissions, page context, data exposure, and workflow controls.
Synthetic Test Sets for AI Tool Evaluation
A research note on using synthetic test sets to compare AI tools, check regressions, and evaluate quality before rollout.
AI Data Classification for Prompts and Context
A practical framework for classifying prompts, uploads, RAG sources, memory, tool outputs, and connected enterprise data before they enter AI systems.
AI Cost Allocation Models for Growing Teams
An enterprise framework for allocating AI model, platform, RAG, agent, license, and shared-service costs through direct attribution, showback, chargeback, and hybrid funding.
Human Review Queues for AI Outputs
A research note on review queues, approval paths, and quality gates for AI-generated work in business workflows.
AI Agent Handoff Patterns for Human-Controlled Workflows
A research note on designing AI agent handoffs so automation can pause, escalate, and transfer work to humans safely.
RAG Source Quality Scoring for Reliable AI Answers
A research note on how source quality scoring can improve retrieval augmented generation and reduce weak or unsupported AI answers.
AI Model Routing Architectures for Cost and Quality
A research note on model routing patterns that send tasks to different AI models based on cost, risk, latency, and quality needs.
Enterprise Prompt Governance: Why Shared Rules Matter
A research note on prompt governance, reusable prompt libraries, sensitive data rules, and quality review for enterprise AI adoption.
AI Workflow Evaluation Framework for Practical Teams
A practical framework for deciding whether an AI workflow is reliable, measurable, supportable, and ready to move from pilot to production.