28 people coached. AI workflows built around real PE work.

The challenge

The firm operates across 15 offices with more than 500 employees. AI experience varied across roles and teams. People needed help applying it to reports, presentations, spreadsheets, inboxes and information retrieval. Generic prompts missed the context, standards and judgement their work required.

What we did

We delivered 21 one-hour coaching sessions over three days. The 28 participants worked across investment, finance and operations, from associates to C-suite. Each session started with 10–15 minutes on the participant’s role, recurring tasks and priorities. We chose a useful workflow with each participant, built it together using available files and team-specific context, then tested the output. Coaching covered model choice, clear instructions, token-efficient best practices and human review. Participants also learned workflow automation and ways to collaborate as a team. Useful work became prompts, templates and skills participants could reuse.

Where AI improved the work

The coaching covered the investment lifecycle and the teams supporting it. Participants built and tested the components below. Potential time back is shown for each workflow.

• Deal origination and fundraising (2–3 hours per person, per week)

  Combined investor databases and news research into a prospecting skill that produced two timely leads during a session. Built and tested personalised outreach drafts, and designed a shared workflow for relationship notes, deal intelligence and follow-ups.

• Investment screening and due diligence (3 hours per person, per week)

  Turned a senior investor’s review criteria into an installed skill and tested it on an investment document. Credit teams created a reusable committee-deck skill and a method for extracting and reconciling evidence from large diligence packs.

• Portfolio oversight (1–1.5 hours per person, per week)

  Built and tested a company-specific skill that surfaced portfolio context and prioritised P&L changes, strategic questions and blocked decisions. This gave an executive a way to return to a workstream and see what needed attention.

• Financial planning and reporting (1 hour per person, per week)

  Built an actual-versus-budget dashboard with variance highlights and comments from a focused data export. Created a source-based budget Q&A structure, and tested a controlled approach to updating financial statements from Excel.

• Credit fund operations (3 hours per person, per week)

  Built a committee-paper summary skill for operations and tested it on a second pack. Tested research across email, Teams and documents to reconstruct transaction status, with FX mapping and secure deployment identified as follow-up work.

• Compliance and board reporting (30 minutes per person, per week)

  Created a reusable board-material request skill covering deadlines, replies and chasers. Defined policy-tracker and valuation-review workflows, keeping compliance decisions with people and making the required checks and access permissions explicit.

What changed

In 20 of the 21 sessions, the work progressed from an AI conversation towards a reusable system. Participants created working prototypes, prompts, skills and templates they could continue testing and share with colleagues. Human review was built into the approach in 19 sessions. In 17 sessions, available files were enough to make progress before live integrations were in place. Where prototypes needed lasting data storage or connected tools, the sessions clarified the next build step.