JE Dunn is the third-largest semiconductor fab GC in America — but when buyers ask AI for recommendations, the company barely exists.
A 700-question simulation across seven procurement stakeholders reveals a structural visibility gap that's costing JE Dunn opportunities before anyone picks up the phone.
57.6% of LLM responses contain confidentiality friction language. Only 8 unique third-party sources cite JE Dunn, versus 15+ for Turner. The AI information layer is structurally biased against JE Dunn.
See the dataEach stakeholder on the buying committee encounters a different version of the gap. Procurement sees Turner in 42% of responses. The Owner's Rep faces 48% hedging language. Risk counsel barely encounters competitors at all.
Meet the committeeJE Dunn wins just 1 of 18 competitive visibility metrics against Turner Construction. The scorecard reveals systematic disadvantages across source authority, digital presence, and AI-specific findability.
View the scorecardEvery year, the visibility deficit could cost JE Dunn over $1 billion in semiconductor construction revenue — the conservative gap between fair market share and realistic capture with current AI visibility.
See the modelThree pillars — Signal Amplification, Technical Credential Deepening, and AI Findability — deployed across three phases over 12 months. Starting with actions JE Dunn controls entirely.
See the planTurner is investing in AI visibility. Skanska is entering the semiconductor market. LLMs reinforce their own biases. Every quarter unaddressed widens the gap — and the revenue at risk grows.
Understand urgencyA rigorous simulation of real-world AI-mediated buying behavior, designed to surface exactly what JE Dunn's target buyers see when they ask AI for help.
We constructed seven detailed buyer personas mirroring a real semiconductor fab procurement committee — from the CFO evaluating financial risk to the Owner's Representative validating references. Each persona was given 100 questions calibrated to their specific funnel stage and decision criteria, then processed through leading LLM platforms from the perspective of a neutral market analyst.
Every response was classified across six analytical dimensions: competitive positioning, source authority hierarchy, confidentiality friction, engagement signals, stakeholder-specific patterns, and trusted voice identification. The result is the most comprehensive picture available of how AI systems represent JE Dunn to the buyers who matter most.