Legal AI Academic Client Solutions Manager
Thomson ReutersRemotePosted 7 April 2026
Job Description
Please note this is a fixed term contract for 12 months
Legal AI Academic Client Solutions Manager will play a pivotal role in driving the adoption of Thomson Reuters AI products among law students, trainees, and early-career lawyers by collaborating closely with faculties and course leaders or designers. This individual will be responsible for building strong and scalable relationships with law schools, professional training providers, and student bodies to ensure that AI tools are effectively embedded into both learning and assessment processes. By fostering these partnerships, the manager will help create a pipeline of AI-native future lawyers who will advocate for and demand these innovative tools in their professional practice. Acting as a crucial bridge, the Legal AI Academic Client Solutions Manager will connect training, product, marketing, sales, and the broader academic and legal training ecosystem to support the integration and success of AI solutions in legal education.
About The Role
As Legal AI Academic Client Solutions Manager, you will be responsible for:
Market development & partnerships
• Identify, prioritize, and secure partnerships with priority law schools, bar/vocational training providers, and leading student societies
• Negotiate and manage pilot programs, institutional licenses, and curriculum integrations.
• Maintain a map of key decision‑makers (deans, program directors, careers offices, student org leaders) and own those relationships.
Student engagement & adoption
• Design and execute campaigns to drive active usage among students (events, workshops, competitions, bootcamps, clinics). This might include developing and running ambassador or AI champion programs in target institutions.
• Set and track student adoption and engagement targets (sign‑ups, MAUs, depth of usage).
Education & enablement
• Work with the Training team and other internal experts to create training content tailored to students and early‑career lawyers (CPD‑style sessions, webinars, on‑demand modules).
• Collaborate with academics to integrate AI tools into coursework, moot courts, clinics, and skills modules
• Ensure messaging and materials are aligned with ethical/legal practice expectations (confidentiality, bias, supervision, etc).
• Regularly publish student-relevant content on social media and track engagement.
Feedback loop into product & GTM
• Capture structured feedback from students and academics and channel it into product improvements.
• Share insights on trends in legal education and AI adoption to inform roadmap and commercial strategy.
Reporting & governance
• Own KPIs for the education/early‑career segment (e.g. # institutions onboarded, # active student users, activation/utilization rates, conversion to paid relationships).
• Produce regular reporting for leadership on performance, learnings and proposed next moves.
• Ensure compliance with relevant data protection, academic regulations and professional standards when running initiatives.
About You
To be considered for the role of Legal AI Academic Client Solutions Manager, you will have:
• Experience in legal education and preferably law firm practice, innovation or knowledge
• Strong relationship‑building skills across senior academics, administrators and student leaders.
• Comfortable running workshops and presenting to groups (students, faculty, professional audiences).
• Good understanding of the legal profession's structure (law schools, firms, vocational training, regulators)
• Enthusiasm for, experience of, or a willingness to learn, about legal technology
•Be o rganized, articulate, reliable and personable
#LI-IG1
What’s in it For You?
Hybrid Work Model: We’ve adopted a flexible hybrid working environment (2-3 days a week in the office depending on the role) for our office-based roles while delivering a seamless experience that is digitally and physically connected.
Flexibility & Work-Life Balance: Flex My Way is a set ... (truncated, view full listing at source)
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