Closing the Trust Gap: Engineering Dependable AI for Patent Practice by Anthony Brennand

Closing the Trust Gap: Engineering Dependable AI for Patent Practice by Anthony Brennand, Head of Innovation at RWS, United Kingdom.

In the IP Tech & Innovation Services Annual, Anthony Brennand, Head of Innovation at RWS, United Kingdom, explores how the patent profession can bridge the trust gap surrounding artificial intelligence. Rather than viewing AI as a replacement for human expertise, he argues that dependable, domain-specific systems built around precision, governance, and security will define the future of patent practice.

AI Adoption Is Becoming More Strategic

AI adoption across the IP profession is accelerating, but organisations are implementing it carefully. RWS research involving 312 IP professionals across 33 markets found that 92% intend to explore AI applications, while many have already begun integrating AI into selected workflows. The findings suggest that patent professionals are moving beyond experimentation towards practical, targeted adoption.

Enhancing Rather Than Replacing Professionals

The research indicates that AI is expected to automate repetitive administrative tasks while enhancing analytical and drafting activities, leaving strategic judgement firmly in the hands of experienced patent professionals. This balanced approach reflects the industry's continued emphasis on precision, reliability, and accountability.

Patent Drafting Requires Purpose-Built AI

Patent drafting remains one of the most demanding applications for generative AI. Brennand explains that while AI can organise disclosures, identify inconsistencies, and generate structured first drafts, professional review remains essential to ensure legal accuracy, strategic scope, and jurisdictional compliance.

Trust Comes From Engineering, Not Hype

Rather than relying solely on powerful language models, dependable AI systems require robust architecture, terminology management, validation processes, governance, and secure integration with IP management systems. Brennand emphasises that confidentiality, auditability, and predictable performance are fundamental requirements for AI operating within patent environments.

Infrastructure Will Shape AI Success

The article highlights that AI performs best when supported by modern, well-integrated technology infrastructure. Organisations with connected IP management systems are better positioned to benefit from AI-assisted drafting, portfolio analytics, and workflow automation than those relying on fragmented legacy platforms.

The Future of Patent Practice

As AI becomes more deeply embedded within patent workflows, competitive advantage will increasingly depend on disciplined implementation rather than rapid adoption alone. Organisations that combine dependable AI with strong governance, secure infrastructure, and professional expertise will be best positioned to improve efficiency while maintaining the standards expected within intellectual property practice.

Conclusion

As Anthony Brennand explains, the future of AI in patent practice will be built on trust. By combining domain-specific intelligence, disciplined engineering, and human expertise, organisations can deploy AI that strengthens quality, efficiency, and confidence across the patent lifecycle.

Read the full article in the inaugural edition of the IP Tech & Innovation Services Annual to discover how dependable AI is helping patent professionals improve drafting quality, strengthen trust, and integrate intelligent technology into modern IP workflows.

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