The first stumbling block was that CYC wasn't openly available. Their research group was very insular, and they were very protective of their IP, hoping to pay for their work through licensing deals and industry- or academic collaborations that could funnel money their way.
They had a subset called "OpenCYC" though, which they released more publicly in the hope of drawing more attention. I tried using that, but soon got frustrated with the software. The representation was in a CYC-specific language called "CycL" and the inference engine was CYC-specific as well and based on a weird description logic specifically invented for CYC. So you couldn't just hook up a first-order theorem prover or anything like that. And "description logic" is a polite term for what their software did. It seemed mostly designed as a workaround to the fact that open-ended inferencing of the kind they spoke of to motivate their work would have depended way too frequently on factoids of common sense knowledge that were missing from the knowledge base. I got frustrated with that software very quickly and eventually gave up.
This was a period of AI-winter, and people doing AI were very afraid to even use the term "AI" to describe what they were doing. People were instead saying they were doing "pattern processing with images" or "audio signal processing" or "natural language processing" or "automated theorem proving" or whatever. Any mention of "AI" made you look naive. But Lenat's group called their stuff "AI" and stuck to their guns, even at a time when that seemed a bit politically inept.
From what I gathered through hearsay, CYC were also doing things like taking a grant from the defense department, and suddenly a major proportion of the facts in the ontology were about military helicopters. But they still kept beating the drum about how they were codifying "common sense" knowledge, and, if only they could get enough "common sense" knowledge in there, they would break through a resistance level at some point, where they could have the AI program itself, i.e. use the existing facts to derive more facts by reading and understanding plain text.
Description logic isn't just found in Cyc, either; Semantic Web standards are based on it, for similar reasons - it's key to making general inference computationally tractable.