Six Checks for AI Readiness in Your Operations
Check workflow documentation, data ownership, exceptions, measurement, decision rights, and access controls before introducing AI into a business process.
Read the guideSecure Company Data Before You Deploy AI
Securing company data before AI deployment matters more than speed. A practical framework for mid-market businesses to adopt AI without leaking data.
Field NotesWhy AI Tools Go Unused and What to Check
Investigate low adoption by checking workflow fit, duplicate work, output quality, training, and ownership before renewing or expanding an AI tool.
BriefingMeasuring AI ROI Through Cycle Time and Capacity
Measure automation using cycle time, capacity, correction rates, and full operating costs. Distinguish time recovered from cash savings.
GuideAI Agents and Automation: How to Choose for Your Workflow
Compare rules-based automation and AI agents, including reliability, permissions, costs, and human review. Choose an approach for each step of your workflow.
BriefingPlanning an AI Pilot That Can Reach Production
Plan an AI pilot with representative tests, a production owner, integration requirements, costs, and a clear decision at the end.
GuideHow to Map a Workflow Before Automating It
Follow real cases to map steps, owners, systems, decisions, and exceptions. Use the map to choose what to automate and what to change first.
GuideWhat to Check Before Implementing AI
Review process stability, data access, ownership, and measurement before implementing AI. Identify dependencies and plan the next steps for a specific workflow.
Guide7 Questions to Answer Before You Implement AI
Check the problem, success measure, process, data, ownership, error handling, and full cost before committing to an AI implementation.
BriefingBuild, Buy, or Configure AI: A Practical Comparison
Compare buying, configuring, and building AI tools using workflow fit, data access, integration costs, and long-term ownership.
GuideCommon Reasons AI Projects Stall and How to Address Them
Review the objectives, data, ownership, adoption, and measurement gaps that can stall an AI project, with practical checks before implementation.
BriefingWhat an AI Opportunity Assessment Includes
Understand the findings and deliverables of an AI Opportunity Assessment, from workflow problems and data dependencies to priorities and cost assumptions.
Field NotesWhen to Standardize a Process Before Automating It
Identify unnecessary variation before building automation. Agree on the standard process, preserve legitimate exceptions, and check whether software is still needed.