This article introduces a new model for risk management that is led by business analysts (BAs). This model builds upon traditional frameworks by incorporating user-centered design principles, predictive analytics, and continuous stakeholder feedback. Its purpose is to address the limitations of traditional frameworks, providing a broader lens that incorporates business objectives, evolving user requirements, and compliance shifts.  The approach driven by business architecture improves risk identification and prioritization by employing a criteria set that takes a multi-factory perspective and frameworks that allow iterative scenario planning. 

AI can generate requirements in seconds—but BAs know that’s not the same as getting a solution adopted, funded, and delivered without surprises. This article speaks directly to business analysts who feel the ground shifting: it pinpoints the hidden failure points behind “well-defined” initiatives, shows why clarity and alignment are becoming harder (not easier), and highlights where BA judgment still makes the difference. If you’re wondering how to stay indispensable in an AI-accelerated world, this is a practical reframing of where your value really lands.

This article shows business analysts, systems analysts, and product managers how to build “trust into the UI” by writing practical provenance requirements for AI-enabled features. It introduces a simple Provenance Requirements Template that turns vague goals like “show sources” into testable product behavior: when to display citations (ideally tied to specific claims), how to handle conflicting sources with a clear tie-breaker, how to define freshness SLAs by claim type and what to do when data is stale, and how to support confidence/uncertainty, “what changed,” and audit exports. The takeaway is a repeatable way to specify “why should I believe this?” so answers come with receipts, stay current, and can be verified or audited when needed.

In tech teams, the word “just” (“just add a field,” “just change a label,” “just add an exception”) is a warning sign—not because people are wrong to ask, but because they’re only seeing the visible slice of the work. This article introduces the “Just Tax” framework to make hidden costs visible: Data, Decision, Dependency, Documentation, Deployment, and Diplomacy taxes. Through three quick BA-centric mini-scenarios, it shows how “small” changes become requirements debt when definitions, approvals, downstream systems, testing, and stakeholder expectations aren’t accounted for. It closes with practical, copy-paste lines BAs can use to keep momentum while turning “just” into a clear tradeoff.

This article shows BAs, systems analysts, and product managers how to turn vague AI “safety” statements into clear, testable requirements. It introduces a simple artifact called a Guardrails Catalog—a reusable list of Allowed / Not Allowed rules that define boundaries for AI features (forbidden actions, restricted data, safe defaults, and what the system must do instead). The core technique is writing each guardrail like acceptance criteria: specify the trigger, the prohibited outcome, the required safe behavior, the exact refusal wording the user should see, and a straightforward validation step. The article includes practical guardrail patterns and examples (e.g., no irreversible actions without confirmation, redact sensitive identifiers, refuse unauthorized requests, don’t guess when ambiguous, don’t invent sources) plus a short list of common pitfalls to avoid. A separate downloadable template is linked for teams to copy/paste and use immediately.