Deep, practical guides on putting AI agents to work across the deal lifecycle. Start with the pillar, then go deep on the stage you care about.
A practical map of where AI actually earns its keep in deal work, stage by stage, and the one thing that decides whether a firm gets value from it or not.
What AI does in a data room today, where it still needs a human, and the part almost every tool misses: diligence against your firm's own precedent, not just the documents in front of it.
A generic model can write a slick memo it cannot cite. The version worth having is drafted from your firm's own deal record, with every number traced back to source, so it holds up in the room.
The term the whole category hangs on, defined plainly. What a firm ontology is, how it differs from a CRM and from search over your files, and why it is the thing that makes AI actually useful to a firm.
Bought databases still win for finding companies you don't know exist. The edge in the companies you already know lives in your firm's own relationship graph, not in another list everyone else also bought.
Dashboards show you last quarter. Agents built on a structured record tell you what changed this week, before the board pack goes out.
A plain look at the AI agents and copilots analysts actually use, what each is good at, and the two structural limits that separate a copilot from a firm brain.
Corporate development software splits into two jobs: finding companies you don't know about, and screening the ones you do against what your company already owns.
AI transcription is safe for most investment firms to adopt, if you get four things right: consent, privilege, vendor security, and retention. Here is the framework, the rules that actually apply, and the checklist to run any tool through.