ClashWise
AI can draft a clash name or summarise a recorded decision. Whether that helps a project depends on the quality of the input and the effort needed to review the result.
Test the tasks you repeat each week, and keep project decisions with the people responsible for them.
What AI can help with
Naming clash groups
Changing "Clash847 — Pipe / Beam" into "L03 sprinkler main conflicts with W18x35 perimeter beam" makes a list easier to scan. AI can draft that description from the involved elements' metadata. The coordinator still needs to check that the level, element names and relationship are correct.
A small trial is enough to expose missing properties or inconsistent naming. Correct those inputs before generating titles across a large test.
Interpreting a coordination matrix
A matrix records the team's priorities, conditions and responsibilities. Wise can use it to suggest priority, status and contact for a clash. Review the recommendations, particularly where the source data is incomplete or several rules could apply.
The matrix gives the AI project context. It does not remove the need to decide how a particular clash affects the work.
Translating between disciplines
A concise description can help a reviewer outside the trade understand the issue. Translation can also help teams working in different languages, provided that element names, units and project terms retain their meaning.
Compare a sample with the source metadata and ask the receiving consultant to check terminology before adopting it across the project.
Where AI needs project judgment
Deciding which clashes matter
A suggested priority cannot account for information that was never supplied: installation sequence, fabrication lead times, access constraints or a subcontractor's next site visit. Keep those factors in the review and record the reason for exceptions.
Resolution suggestions
Moving a pipe or changing an opening is a design decision. Suggestions can give the team options to discuss, but the responsible designer must assess cost, access, compliance and effects on other systems. A generated answer does not verify a revised design.
Detecting clashes the geometry engine missed
For the ClashWise workflow, detection remains in Navisworks. If an expected clash is missing, check the source revision, test selections, clash type and tolerance before assuming the review tool can recover it.
What this means for your workflow
Use AI on a bounded task and measure the work that remains. For example, take one clash test and compare manual naming with generated titles after review and correction.
Record the time spent on each step, the number of corrections and whether the receiving consultant could use the result. Those observations are more useful than applying a general savings percentage to every project.
A small project with clear names may have little naming work to remove. A larger federation with inconsistent metadata may need input cleanup before automation helps.
What to ask vendors
Three useful trial questions are:
- "Show me a clash that was named yesterday, end-to-end." Inspect the input, generated name and any edits made before publication.
- "What happens when the AI gets the name wrong?" Check how to edit, reject or retry output. Ask separately whether feedback is used for training; do not assume that an edit trains a model.
- "Does it work on Navisworks 2024?" Confirm the exact Navisworks version used by your project against the vendor's current supported versions.
Where to start
Choose one task, keep the original results and review the output with someone who will use it. For ClashWise, start a 14-day trial and use a real clash test to compare naming, matrix recommendations and the resulting review record.
Tags: AI, BIM Coordination, Industry Trends, Automation
