Patent strategy relied on subjective manual judgment. AI now standardizes analysis, cutting research cycles from months to days.
One of the world's largest printing companies, employing over 37,000 people globally with a diverse portfolio spanning smart communication, healthcare, and electronics.
Critical expertise was trapped in analog documents and the minds of senior employees, while manual processes for patent research and IT governance...
“We drove adoption by making usage visible. Each team experimented, shared learnings, and iterated. That momentum created a scalable impact.”
Diversified manufacturer of printing, packaging, and electronic components.
AI research and deployment company developing generative models and tools.
Dai Nippon Printing (DNP)'s Enterprise workflow automation is part of this use case:
Related implementations across industries and use cases
Scouring foreign technical sources took months. AI now translates and analyzes materials in minutes.
Evaluating one investment took 2 hours. Custom GPTs cut reviews to 5 minutes, reducing the division's workload by 70%.
Comparing quotes and sorting inquiries manually bottlenecked staff. Now, AI handles both while custom agents proactively flag supply risks.
Scouring foreign technical sources took months. AI now translates and analyzes materials in minutes.
Evaluating one investment took 2 hours. Custom GPTs cut reviews to 5 minutes, reducing the division's workload by 70%.
Manual 12-day checks for EU deforestation rules created risk. Now, an AI traces the supply chain in 3 minutes, ensuring full compliance.
Minor updates required 3.5-hour re-recordings. Now, teams use AI avatars to regenerate training videos from slides in 30 minutes.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.
Patent strategy relied on subjective manual judgment. AI now standardizes analysis, cutting research cycles from months to days.
One of the world's largest printing companies, employing over 37,000 people globally with a diverse portfolio spanning smart communication, healthcare, and electronics.
Critical expertise was trapped in analog documents and the minds of senior employees, while manual processes for patent research and IT governance...
“We drove adoption by making usage visible. Each team experimented, shared learnings, and iterated. That momentum created a scalable impact.”
Diversified manufacturer of printing, packaging, and electronic components.
AI research and deployment company developing generative models and tools.
Dai Nippon Printing (DNP)'s Enterprise workflow automation is part of this use case:
Related implementations across industries and use cases
Scouring foreign technical sources took months. AI now translates and analyzes materials in minutes.
Evaluating one investment took 2 hours. Custom GPTs cut reviews to 5 minutes, reducing the division's workload by 70%.
Comparing quotes and sorting inquiries manually bottlenecked staff. Now, AI handles both while custom agents proactively flag supply risks.
Scouring foreign technical sources took months. AI now translates and analyzes materials in minutes.
Evaluating one investment took 2 hours. Custom GPTs cut reviews to 5 minutes, reducing the division's workload by 70%.
Manual 12-day checks for EU deforestation rules created risk. Now, an AI traces the supply chain in 3 minutes, ensuring full compliance.
Minor updates required 3.5-hour re-recordings. Now, teams use AI avatars to regenerate training videos from slides in 30 minutes.
Querying Wikidata required specialized syntax, locking out most AI systems. Vector search now lets LLMs navigate 100M+ entities in plain language.
On-premise systems, dispersed and brittle, bottlenecked every release. AI agents now run routine dev steps — hours cut to minutes.