The intellectual property (IP) profession is at an inflection point. Patent application volumes have grown at a compound annual rate of 5.1% among the world’s top five patent offices, while application complexity has risen alongside. More claims, cited prior art, and cross-disciplinary inventions require practitioners to command ever-broader domains of knowledge. AI is emerging not as a replacement for professional judgement but as the infrastructure that enables that judgement to scale.
Productivity and prior art search
The clearest evidence of AI’s impact comes from patent office deployments. In a collaboration between CAS and Brazil’s National Institute of Industrial Property, an AI-driven prior art search workflow reduced examination times by up to 50%, with 77% of applications requiring less examiner search time and 29% requiring little or no additional search. The office reduced its backlog by 80% without adding staff. These results required scientist-curated training data, multiple specialised algorithms, and ongoing human validation, not off-the-shelf tools.
Prior art search is where AI is making its most measurable early impact. Traditional Boolean methods struggle with patent literature spanning more than 60 languages and containing chemical structures described inconsistently across publications.
AI systems combining semantic analysis, syntactic pattern recognition, knowledge graphs, and structural similarity scoring surface relevant documents that conventional searches miss. Natural language processing further allows practitioners to query in plain language rather than constructing complex Boolean strings, which lowers barriers for less specialised users and accelerates examiner onboarding.
The shifting role of IP professionals
For its 2026 IP Industry Outlook Research report, Questel surveyed more than 500 IP professionals globally and found that 73% believe AI will permanently transform their roles, up from 64% the prior year. The shift is already under way: 88% of respondents now spend up to half their time reviewing work produced by trainees, AI agents, or external suppliers, representing a fundamental move from expert creation to expert review.
Legal knowledge and strategic acumen remain the most valued skills. Still, expertise in IP search platforms and workflow coordination have become increasingly important as practitioners configure, supervise, and validate AI-assisted processes.
Data quality and governance
The quality of training data is as important as the sophistication of the model. A CAS study found that human-curated content could increase AI prediction accuracy by 30%. For complex technical domains such as chemistry, where substance attributes are often embedded in images or tables that most systems cannot parse, domain-specific curation is essential.
Governance concerns are equally pressing. Data security, confidentiality, and the risk of AI hallucinations rank among practitioners’ foremost worries, and rightly so: an undetected error in patent prosecution can invalidate a patent or expose a client to liability.
The emerging consensus favours a human-expert-in-the-loop model, where AI handles high-volume processing but practitioners retain supervisory authority at defined checkpoints. Robust internal policies governing tool authorisation, output validation, and data confidentiality are now essential elements of responsible IP practice.
Looking ahead
With 82% of IP professionals planning to increase AI use in 2026, adoption is accelerating across search, drafting, office action response, translation, and portfolio analytics. The professionals best positioned to benefit are those who neither dismiss AI as hype nor surrender judgement to its outputs but who deploy it thoughtfully on quality data and within well-governed workflows. The intelligence of the system is inseparable from the intelligence of the practitioners who direct it.
