Prompt Volume: How to Find What People Actually Ask AI About Your Category
Nobody publishes AI prompt volume. Here is a free, systematic method for harvesting the real questions in your category, plus honest caveats about what it proves.
Topical Authority in the AI Era: How Engines Decide Who Is Credible
AI engines assess credibility per topic, not per domain. Here are the five signal groups they read and how to build authority narrow enough to actually win.
Project Portfolio Management: How to Prioritize When Everything Is Priority One
When every project is priority one, scoring models alone won’t save you — they ignore resource constraints, dependency sequencing, and the organizational politics of deprioritizing a project the CEO owns. This post lays out a complete portfolio prioritization framework for mid-market operations and strategy leaders, from strategic alignment weighting to the mechanics of running a portfolio review cadence that actually changes decisions.
Why AI Cites Reddit, Wikipedia and Forums More Often Than Your Site
AI engines favour forums and Wikipedia for structural reasons, not conspiracy. Here is the mechanism behind it and which question types your site can realistically win.
AI Visibility Audit: A Step-by-Step Checklist You Can Run Yourself
A five-section AI visibility audit checklist covering access, extraction, attribution, current citation rates and the competitive landscape you can run internally.
Hybrid Project Management: When Agile Meets Waterfall in Real Enterprise Programmes
Hybrid project management works when it is designed deliberately — not when it happens by accident. This post lays out a practical framework for assigning agile or waterfall methodology to each workstream based on risk profile, designing the integration cadence between streams, and building a reporting layer that gives senior stakeholders confidence without disrupting delivery team autonomy. Aimed at operations directors, VPs of IT, and CFOs at mid-market organizations running complex enterprise programmes.
The Content Structures Large Language Models Extract Most Reliably
Which content structures large language models extract most reliably, which ones consistently fail, and the self-containment test that catches most problems.
How to Track AI Visibility Over Time Without Enterprise Software
A manual protocol for tracking AI visibility monthly: four core metrics, a frozen testing protocol, a workable spreadsheet and light automation options.
Building a Single Source of Truth: The Architecture Decisions That Actually Matter
When the CFO and VP of Operations present different revenue numbers in the same executive meeting, the instinct is to buy a data platform. The real problem is almost always architectural: undefined system-of-record decisions, absent data contracts, and governance that was supposed to come after the build. This post covers the architecture decisions — medallion vs. ODS vs. DWH, centralized vs. federated ownership, minimum viable MDM, and data contracts — that determine whether a Single Source of Truth initiative actually produces consistent reporting or just consolidates the confusion into one expensive tool.
Brand Mention vs URL Citation: Two Very Different AI Visibility Metrics
Brand mentions and URL citations have different causes and different fixes. How to compute each, read the gap between them, and decide which to prioritise.
