Nobody starts a project hoping to fall in love with a traceability matrix. Most of us meet it later, when an auditor asks how a control maps to a test, or when a late change request blows through three modules and nobody can say what else broke.
I have spent more hours than I want to admit in giant spreadsheets: requirement IDs in column A, design references somewhere in the middle, test cases that may or may not still exist, and a "status" column that was optimistic last Tuesday. The matrix was supposed to prove coverage. Sometimes it mostly proved that we were busy formatting cells.
AI does not make compliance optional. It can make the first pass of linking less miserable. The review still has to be honest.
A traceability matrix is a map of connections. Common ones:
The point is not paperwork for its own sake. The point is to answer boring, important questions: if we change this rule, what do we retest? If this control is mandatory, where is the evidence we covered it? If this feature got cut, which requirements actually left the release?
Given a structured requirements list and a structured test list (or process inventory), a model can propose candidate links. That is useful when you have dozens or hundreds of items and a human would miss obvious matches through fatigue alone.
What I typically ask for:
I want the model to mark uncertainty. A confident wrong link is worse than an empty cell. Empty cells at least admit the gap.
Review is the job. A few habits that tend to keep me honest:
When requirements live near process knowledge, retrieval helps. Teams that keep policies and procedures findable (the same idea behind RAG for company knowledge) often have cleaner source text for linking than teams working from five conflicting slide decks.
Some matrices exist for regulators, risk, or internal audit. Some exist so the delivery team does not ship blind. The best ones serve both without becoming a second product nobody maintains.
For compliance, I care about completeness of mandatory controls, clear IDs, and evidence paths. For delivery, I care about change impact and regression focus. AI can propose both views from the same underlying links if the source data is decent. If the source data is a mess, you get a polished mess. Usually that is a requirements hygiene problem, not a model problem, and it is squarely BA territory. It is also why the skills in business analysis for AI work matter: structure and accountability, not just generation.
Instead of a heroic matrix rebuild every three months, try a thinner rhythm:
If your team still needs a simple place to track the work that sits around those requirements, a lightweight board such as the free project management tool can hold the delivery tasks while the matrix stays focused on links and coverage.
I will not let a model "sign off" coverage. I will not hide gaps with generated prose. I will not invent test cases in the matrix that do not exist in the test suite just to make a percentage look better.
Those moves create the kind of confidence that cracks in production or in audit. The value of AI here is proposing, clustering, and spotting orphans. The value of the BA is deciding which links are true, which gaps are real, and which risks leadership needs to hear before go-live.
Spreadsheet fatigue is real. So is the cost of flying without a map. Use the tools to draft the map. Walk the terrain yourself before you tell anyone you have coverage.
I help teams build traceability that people will maintain, not a one-time spreadsheet for a gate review. That can include:
Reach out for a quick chat on how I can help at Suganth@AruviConsultancyServices.com