AI Readiness Snapshot

Strategy & Ownership

1. Who owns AI at your firm today?

• — A partner or ops leader owns it informally, alongside everything else
• — No one — tools get adopted ad hoc and we find out later
• — A named owner with explicit responsibility and time allocated to run it
• — It comes up in leadership meetings, but nobody owns it

2. An advisor comes back from a conference excited about a new AI tool. What happens next?

• — They sign up; the firm finds out when it shows up in a meeting
• — It goes through a written evaluation: vendor data policy, an approval decision, and an entry in our tool register
• — We'd kick it around informally — no defined criteria
• — We have rough criteria (compliance gut-check, cost) but nothing written

Workflow Sequencing

3. After a client meeting, how do the notes become action?

• — Standardized end-to-end: templated notes, tasks auto-routed with owners and SLAs, workflows triggered
• — An AI notetaker generates notes, but tasks get created manually — if at all
• — Notes sync to the CRM and tasks get created, but owners and deadlines are inconsistent
• — Handwritten or typed notes; follow-ups happen from memory

4. Which best describes AI use across your team today?

• — Several people use AI daily — each in their own way
• — We've standardized one or two workflows (e.g., meeting notes) firm-wide
• — One or two people experiment on their own
• — Multiple defined AI workflows with documented steps and owners

Tool Fit

5. How were the AI tools you use today chosen?

• — Someone liked a demo, or a free trial just… stuck
• — We compared options against our needs, but didn't document the decision
• — Evaluated against specific workflows and vendor data policies, with the decision documented
• — Peer or study-group recommendation; no formal vetting

6. Do you know what each AI vendor does with the data you put in?

• — Data policies reviewed and on file for every AI tool in use
• — Honestly, we've never looked
• — We assume it's fine because the tools are built for advisors
• — We've reviewed it for some tools, not all

Governance Evidence

7. An SEC examiner asks for your AI policy tomorrow morning. What do you hand them?

• — Some notes and emails — nothing assembled
• — The policy, the approved-tool register, and our review procedures — a binder, not a scramble
• — A draft exists, but it's not finalized and nobody's attested to it
• — We'd be writing it that night

8. When AI drafts something client-facing (an email, a letter, a summary), what happens before it goes out?

• — There's a review rule, but we don't retain evidence it happened
• — People "usually" review their own drafts; there's no rule
• — Nothing defined — it goes out
• — Required human review, with a retained record of AI-assisted output

Measurement

9. Do you know what your key workflows cost today — in time, before AI?

• — No idea; we've never measured
• — Measured baselines exist (e.g., minutes per meeting on prep/notes/follow-up)
• — Rough estimates for a few workflows
• — Gut feel only

10. How do you know whether an AI tool is paying for itself?

• — Defined metrics tracked against a baseline, reviewed on a schedule
• — We look at usage stats occasionally
• — We don't — we just keep paying
• — Anecdotes: it "feels faster"

Adoption

11. What share of your team uses the firm's AI tools in their daily work?

• — A few early adopters — under a quarter of the team
• — Almost nobody — one enthusiast at most
• — Most of the team, consistently, with training behind it
• — Around half, unevenly

12. If your most AI-fluent person left tomorrow, what happens to AI at your firm?

• — Workflows keep running but improvement stalls
• — Nothing breaks: it's documented, and anyone can run the playbooks
• — Big setback — others only dabble
• — It basically stops

Plus 3 quick context questions about your firm (advisor count, weekly meeting volume, tools in use) — not scored, used only to estimate your recoverable capacity.