llms.txt Explained: What It Is, What It Isn’t, and How to Write One
llms.txt is a proposed convention, not a standard, and no major engine confirms reading it. Here is an accurate account of what it does and how to write one.
Attended vs Unattended RPA: Choosing the Right Deployment Model for Your Process
The attended versus unattended RPA decision is one of the most consequential choices in an automation program, and one of the most frequently made for the wrong reasons. This post provides a structured decision framework — including a process assessment matrix, licensing cost analysis, and change management guidance — for operations leaders evaluating RPA deployment models. Getting this decision right is the difference between automation that scales and automation that stalls.
UiPath vs Power Automate vs Automation Anywhere: A Mid-Market Comparison for 2026
Choosing between UiPath, Power Automate, and Automation Anywhere is one of the most consequential technology decisions a mid-market operations leader will make in 2026 — and most organizations get the selection criteria wrong. This post provides a structured comparison across total cost of ownership, AI integration depth, governance tooling, Microsoft 365 integration, citizen developer capability, and vendor support at mid-market scale, including a weighted decision matrix to guide the final call.
Prompt Engineering for Business Users: Seven Templates That Drive Measurable Output
Most business users who dismiss generative AI as “not ready” are actually encountering a prompt quality problem, not a model capability problem. This post provides seven structured prompt templates — covering executive briefings, stakeholder communications, data commentary, process documentation, and decision memos — designed for senior professionals at mid-market companies who need consistent, near-final output from tools like Microsoft Copilot. Each template includes the four-part structure, a worked example, and a plain-language explanation of why each element matters.
AI in Finance Operations: The Six Use Cases That Are Production-Ready Today
AI automation in finance operations is past the pilot stage — but the maturity and prerequisites vary significantly across use cases. This post covers the six finance AI applications that are in production at mid-market companies today, with an honest assessment of typical ROI, deployment timelines, and what has to be true before the technology can deliver.
When Not to Use AI: A Decision Framework for Operations Leaders
AI automation creates real value in the right processes — and real liability in the wrong ones. This post identifies the four categories of operations work where AI consistently underperforms: exception-heavy processes, low-volume tasks, compliance-sensitive decisions, and trust-critical customer interactions. It includes a practical decision framework — the AI Fit Audit — for operations leaders who need a rigorous method for evaluating automation candidates before committing budget.
How to Build an AI Readiness Assessment Before You Buy Anything
Most mid-market AI deployments fail not because of bad technology, but because organizations buy before they diagnose. This post provides a scored, four-dimension AI readiness framework — covering data maturity, process standardization, governance, and change capacity — to help operations and technology leaders identify and fix critical gaps before committing vendor spend.
Agentic AI vs Copilot AI: What Mid-Market Companies Actually Need in 2026
The 2026 AI market has bifurcated into Copilot AI — which augments individual knowledge workers — and Agentic AI, which autonomously executes multi-step processes across systems. Most mid-market companies are buying the wrong one for their actual problems. This post provides a practical decision matrix, cost comparison, and sequencing framework for operations leaders and CFOs making this investment decision.
Document AI in Practice: What Invoice Extraction Gets Wrong
Key takeaways Off-the-shelf models plateau at 85–90% extraction accuracy; the remaining gap is structural, not a model maturity problem Edge cases concentrate on non-standard layouts, multi-page invoices, and handwritten annotations — these need targeted post-processing rules, not more training data alone human-in-the-loop review queues built without confidence thresholds become bottlenecks that kill ROI faster than…
Why I Built Synapse: The Story Behind Our AI Project Management Platform
The short answer I built Synapse because I kept watching brilliant project managers waste hours every week as human copy-paste machines. Synapse is StrategyPeeps’ AI project management platform, built on the Microsoft 365 stack companies already own — SharePoint, Power Automate, Power BI and AI. It watches your project data, spots risks, and builds reports…
