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.
38Research notes
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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
25 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.
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.
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.
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.
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
10 notes
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.
AI Assistant Memory Governance
A practical research note on governing AI assistant memory, personalization, retention, deletion, user control, privacy, enterprise boundaries, and responsible rollout.
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 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.
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 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.
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.