Investment decisions
Identify the right AI opportunity, establish an inspectable baseline, and build a value case finance can challenge.
Insights
Practical research for leaders responsible for enterprise AI.
Research and field notes on AI automation, agentic systems, ROI, governance, and the operating work required to move from pilot to production. Written for decision-makers who need useful judgment rather than vendor theater.
Decision frameworks, operating models, and production lessons grounded in how enterprise systems actually work.
Featured analysis
Research note 01 · Aug 2, 2026 · 10 min read
Executive lens
Use this analysis to challenge the scope, controls, ownership, and evidence behind an AI initiative before the organization commits to scale.
Agentic AI
Agentic AI in Operations: Where It Works and Where It Fails
A practical guide to using bounded AI agents for multi-step operational work without giving up control, traceability, or recovery.
Read the analysisInsight library
Browse by operating question.
Each note is organized around a decision: where to invest, how to measure value, what must be controlled, and what changes when AI enters a live workflow.
Showing 5 of 5 library articles, plus 1 featured analysis.
0203040506
AI ROI11 min read
How to Measure Enterprise AI ROI: A Defensible Framework
A practical framework for connecting AI automation to time, cost, throughput, and risk using evidence finance can inspect.
AI ROI8 min read
Why Enterprise AI Pilots Fail Before Production
The gap between a promising pilot and a reliable production system is usually ownership, controls, measurement, and operating discipline.
HR Automation12 min read
HR Automation: Six Workflows to Prioritize First
A practical sequence for automating onboarding, policy support, offboarding, document handling, people analytics, and compliance evidence.
Operations9 min read
From AI Pilot to Production: The Operating Gap
A pilot proves technical possibility. Production requires ownership, controls, monitoring, recovery, and a service model around the system.
AI Governance10 min read
AI Governance for Automation: Controls That Preserve Delivery Speed
Practical governance for AI automation using human review, permissions, evaluation, monitoring, drift detection, and decision evidence.
Editorial focus
The questions leadership teams should ask earlier.
The library is organized around decisions that determine whether AI becomes an operating capability or remains a collection of experiments.
Production controls
Design human review, permissions, evaluation, monitoring, recovery, and evidence around automation and agents.
Operating practice
Move from pilot activity to systems with ownership, service expectations, and a disciplined improvement cadence.
Ready when you are
Request a free AI auditStop running AI experiments. Start running AI.
In a focused AI audit, we identify where AI can create measurable value, where current initiatives are leaking value, and the clearest production-ready next step.
Focused scope · No sales deck · Clear next step