How do you effectively train fund teams in AI without becoming too theoretical?
Effectively training fund teams in AI is not about imparting theoretical knowledge, but about transforming concrete work practices.
Relevant training always starts with the real situations encountered by teams. Investment professionals don't need a general discourse on AI, but an operational understanding: what the tool actually enables, its limitations, and the conditions under which it can be used without degrading the rigor of processes.
This means segmenting approaches. The needs of a partner, an analyst, an IR, compliance, middle office or ESG team are profoundly different. Effective training is therefore based on a common foundation (principles, risks, best practices), supplemented by targeted use cases: analyzing an investment memo, summarizing an Information Memorandum, exploring a data room, preparing for a committee, sector screening or managing a complex exchange with an LP.
The key is immediate applicability. Each module must enable action to be taken the very next day, with visible and measurable gains. This is what transforms acculturation into real adoption.
But training cannot be seen as a one-off event. Models evolve, tools change, uses become clearer and risks shift. An effective approach requires a long-term approach: initial awareness-raising, practical workshops by business line, feedback from peers, and ongoing support to adjust practices.
Finally, a point that is often underestimated: training in AI also means training in discernment. Knowing when to use the tool, when to be wary of it, and how to control its results is just as important as knowing how to use it.
The right system therefore combines teaching, practice and iteration. It is this logic that enables AI to be firmly anchored in a fund's processes, without falling into a theoretical approach disconnected from the field.
Relevant training always starts with the real situations encountered by teams. Investment professionals don't need a general discourse on AI, but an operational understanding: what the tool actually enables, its limitations, and the conditions under which it can be used without degrading the rigor of processes.
This means segmenting approaches. The needs of a partner, an analyst, an IR, compliance, middle office or ESG team are profoundly different. Effective training is therefore based on a common foundation (principles, risks, best practices), supplemented by targeted use cases: analyzing an investment memo, summarizing an Information Memorandum, exploring a data room, preparing for a committee, sector screening or managing a complex exchange with an LP.
The key is immediate applicability. Each module must enable action to be taken the very next day, with visible and measurable gains. This is what transforms acculturation into real adoption.
But training cannot be seen as a one-off event. Models evolve, tools change, uses become clearer and risks shift. An effective approach requires a long-term approach: initial awareness-raising, practical workshops by business line, feedback from peers, and ongoing support to adjust practices.
Finally, a point that is often underestimated: training in AI also means training in discernment. Knowing when to use the tool, when to be wary of it, and how to control its results is just as important as knowing how to use it.
The right system therefore combines teaching, practice and iteration. It is this logic that enables AI to be firmly anchored in a fund's processes, without falling into a theoretical approach disconnected from the field.