About this session

Investor relations is the most labour-intensive function in most private capital firms. Every LP wants their own cut of the data, on their own timetable. Quarterly reporting takes weeks of senior time and produces nothing proprietary. The ad-hoc query asking for a look-through exposure figure or a restated IRR arrives without warning and takes three people to answer. This is the work AI is now doing inside IR teams.

In 45 minutes we cover what those teams have built: capital call and distribution notices drafted and checked at a fraction of the effort, quarterly letters assembled from source data, and LP queries answered from a system that knows what has already been disclosed. We also cover what has not worked, and the questions that stop IR teams: how you keep a model from inventing a number, how you hold one version of the truth across bespoke reporting formats, what you tell an LP who asks whether their data has been near an AI model, and where human sign-off stays absolute.

Agenda

• Why IR came first. How bespoke reporting and LP service expectations built a workload no headcount plan can fix.

• The workflows these teams automated. Onboarding, capital calls and notices, quarterly reporting, and ad-hoc LP queries.

• The answer library. Why the highest-return AI project in IR is usually the approved, source-linked record a model draws on.

• Accuracy, disclosure and control. Preventing fabricated figures, keeping an audit trail, handling LP questions about AI use, and what a human must approve.

• Where these teams are in twelve months. Faster cycles, fewer errors, and senior IR time back on relationships.

• Live Q&A