Why this matters now
If you work in financial services, you have already noticed the shift. Competitors are moving faster on deals. Research teams are producing analysis at a pace that was not possible two years ago.
The reason is AI — specifically, the current generation of large language models that can read, analyse, and produce work at a level that is genuinely useful for knowledge work.
How would you describe your firm's current AI adoption?
The uncomfortable reality
Most firms are not getting value from AI yet. According to Deloitte's 2026 State of AI report, only around one in three companies that have adopted AI describe it as "deeply transformative." The rest are experimenting, piloting, or have stalled.
Financial services has the highest concentration of "Frontier Firms" — organisations embedded across workflows seeing 3x higher ROI. But most firms fall into a middle category: they have AI tools, but no systematic approach.
The result? The firm is paying for AI but not capturing the value.
What do you think is the primary reason firms fail to capture value from AI?
The numbers that matter
| Metric | Value | Source |
|---|---|---|
| Firms achieving tangible AI value | ~26% | Deloitte 2026 |
| Frontier firms' ROI advantage | 3x | Deloitte 2026 |
| PE/VC firms using AI in investment decisions | 95% | V7 Labs |
| AI-driven due diligence time reduction | Up to 70% | BDO/Brightwave |
| Talent readiness score | 20% (lowest dimension) | Deloitte 2026 |
That last number is the most important one for you personally. Talent readiness is the scarcest resource — scarcer than the technology itself.
The agentic AI shift
The current wave — "agentic AI" — is a fundamental shift from AI as a chatbot to AI as a worker.
An AI agent can:
- Plan a multi-step task (not just answer a question)
- Use tools to interact with systems (databases, documents, APIs)
- Execute work autonomously while keeping a human in the loop
This is the difference between asking "what should I look for in a DD report?" and having AI actually read the 200-page prospectus, extract the key terms, flag the risks, and draft a summary memo.
An associate asks: 'Isn't AI just a chatbot?' How would you correct them?
AI in your vertical
Hedge Funds — Research at scale (NBIM: 213,000 hours saved), factor analysis, portfolio risk monitoring.
Investment Banking — Pitch book generation, deal screening, document review.
Private Equity — DD acceleration (up to 70% time reduction), portfolio monitoring, DDQ automation.
Venture Capital — Deal sourcing (82% of firms use AI), market mapping, founder assessment.
Which use case would deliver the most immediate value at your firm?
The skills gap as career opportunity
With talent readiness at just 20%, AI-literate professionals are extraordinarily scarce. This is not about learning to code. It is about understanding what AI can do, how to direct it, and how to build the case for it at your firm.
The firms pulling ahead are not the ones with the best tools. They are the ones where senior people understood AI well enough to direct its adoption intelligently.
That is what this course gives you.
Module 1 — Final Assessment
01What is the primary barrier to enterprise AI adoption?
02What does 'agentic AI' refer to?
03What distinguishes 'Frontier Firms' from companies that have AI tools but see limited returns?
04AI adoption in PE/VC investment decisions is now nearly universal. What does this imply for firms that have not started?