Based　on　the＇Technology-Organization-System＇　analysis　framework,　this　study　explores　how　to　reduce　the　application　rate　of　AI　while　enhancing　innovation　effectiveness　through　optimizing　human-machine　collaboration　during　the　process　of　industrial　intelligence.　The　research　found　that　by　selectively　deploying　AI　technologies　(such　as　retaining　manual　operations　in　low　technology　density　stages),　constructing　a　blockchain-enabled　skills　certification　system,　and　establishing　a＇reverse　Moore’s　Law＇　training　mechanism,　it　is　possible　to　reduce　AI　penetration　by　8%　while　increasing　innovation　output　by　22%.　The　study　proposes　a　governance　path　of＇government　guidance-enterprise　-social　collaboration,＇　including　policy　tools　such　as　implementing　an　AI　density　tax　system　and　building　hybrid　reality　training　bases.　Empirical　evidence　shows　that　this　scheme　can　improve　the　efficiency　of　resource　allocation　for　training　by　35%.　To　address　the　technical　lock-in　effects　and　organizational　inertia　during　the　implementation　process,　further　mechanisms　such　as　modular　transformation　interfaces　and　manual　veto　rights　have　been　designed.　This　study　provides　an　operational　solution　for　balancing　intelligent　transformation　and　human　capital　development,　particularly　suitable　for　high-end　manufacturing　sectors　with　a　technology　elasticity　coefficient　greater　than　0.7.　©　2025　Copyright　held　by　the　owner/author(s).