AI Maturity — Leadership Dashboard
Illustrative dataThis report has four tabs — you're on Overview, the domain-level summary. From here, Components drills into all 24, People shows who to focus on, and Recommendations drafts the action plan.
Executive summary
Where your people stand
—
On the 1–10 AI Maturity scale. Actions = what people demonstrably do, from situational-judgement items. Self-perception = how they rate themselves. The distance — the Self-Perception–Actions gap — is the coaching signal.
How your team splits, by domain
Why this matters: averages hide trouble — this shows how your people actually split, so a healthy-looking number can’t mask an at-risk group.
Each bar shows how your people split across the four levels. The status on the right is the plain read; the line beneath is what it means for the business.
Where confidence outruns capability
Why this matters: capability is only half the risk — the dangerous gap is the person who’s confident and wrong. This is the only view that finds them.
right / red = blind spots — people rate themselves above what they do; the mistakes happen hereleft / green = hidden strength — people are better than they think; safe to lean on
Ready to act on this?
See the recommended actions and AI-drafted plans generated from these results.
Self-reflection alignment
How often self-perception matches demonstrated behaviour — a read on reflective accuracy across the org.
Component heatmap — all 24 (hover for the band split)
Each bar shows the share of people at each band (red→green). ▲ flags meaningful overconfidence (self-perception ≥ 1.0 above demonstrated).
So what — where to actWhere to act — the components most worth building, ranked by where growth will help most.
[5] Growth priorities that move the needle.
Attention flags — self-perception vs. behaviour
Who may need your attention
A supportive starting point for enablement conversations — a wider net, not a verdict. Worth a closer look, not a conclusion.
11 of 50 people surfaced (~22%)
3 Start here
8 Keep an eye
Coaching view · visible to the people leader for this group; gated by role & minimum-group permissions.
R. TanakaOperations Analyst
3.2 · Not yet
4.6→3.2 ▼
Foundational or belowDeclined ▼
▾ Learning
Auto-matched to this person's components & Awareness Summary result — served from your Learning Library (64 vetted resources, plus your own).
Focus: 5.2 Governance & policy-setting 3.3 Workflow integration 2.4 Knowing when not to use AI
Framework · Reference · 5.2 Governance & policy-setting
Course · ~5h · 3.3 Workflow integration
Course · ~6h · 2.4 Knowing when not to use AI
S. OkonkwoCompliance Officer
4.4 · Foundational
5.8→4.4 ▼
Foundational or belowDeclined ▼Confidence > evidence
▾ Learning
Auto-matched to this person's components & Awareness Summary result — served from your Learning Library (64 vetted resources, plus your own).
Focus: 4.5 Compliance & policy 4.2 Privacy & data protection 5.2 Governance & policy-setting
Framework · Reference · 4.5 Compliance & policy
Article · ~15 min · 4.2 Privacy & data protection
P. RomanoFinancial Advisor
4.6 · Foundational
6.0→4.6 ▼
Foundational or belowDeclined ▼Confidence > evidence
▾ Learning
Auto-matched to this person's components & Awareness Summary result — served from your Learning Library (64 vetted resources, plus your own).
Focus: 4.2 Privacy & data protection 4.5 Compliance & policy
Article · ~15 min · 4.2 Privacy & data protection
Framework · Reference · 4.5 Compliance & policy
K. LarssonOperations
3.6 · Foundational
3.8→3.6 ▼
Foundational or belowConfidence > evidence
▾ Learning
Auto-matched to this person's components & Awareness Summary result — served from your Learning Library (64 vetted resources, plus your own).
Focus: 3.1 Prompting & interaction 2.4 Knowing when not to use AI 5.3 Change & adoption management
Course · ~5h · 3.1 Prompting & interaction
Course · ~6h · 2.4 Knowing when not to use AI
Report · ~30 min · 5.3 Change & adoption management
M. DelgadoFinancial Advisor
4.2 · Foundational
4.0→4.2 ▲
Foundational or belowConfidence > evidence
▾ Learning
Auto-matched to this person's components & Awareness Summary result — served from your Learning Library (64 vetted resources, plus your own).
Focus: 4.2 Privacy & data protection 4.5 Compliance & policy 2.3 Verification & sourcing
Article · ~15 min · 4.2 Privacy & data protection
Framework · Reference · 4.5 Compliance & policy
Article · ~10 min · 2.3 Verification & sourcing
＋ Expand to see 6 others
J. PetrovClient Service Lead
4.8 · Foundational
5.0→4.8 ▼
Foundational or belowConfidence > evidence
▾ Learning
Auto-matched to this person's components & Awareness Summary result — served from your Learning Library (64 vetted resources, plus your own).
Focus: 4.2 Privacy & data protection 2.3 Verification & sourcing 2.1 Evaluating outputs
Article · ~15 min · 4.2 Privacy & data protection
Article · ~10 min · 2.3 Verification & sourcing
L. FournierMarketing Manager
5.0 · Foundational
5.2→5.0 ▼
Foundational or belowConfidence > evidence
▾ Learning
Auto-matched to this person's components & Awareness Summary result — served from your Learning Library (64 vetted resources, plus your own).
Focus: 4.4 IP, ownership & attribution 2.2 Bias & fairness awareness
Official guidance · ~25 min · 4.4 IP, ownership & attribution
Article · ~12 min · 2.2 Bias & fairness awareness
D. MwangiIT & Security
5.2 · Foundational
5.4→5.2 ▼
Foundational or below
▾ Learning
Auto-matched to this person's components & Awareness Summary result — served from your Learning Library (64 vetted resources, plus your own).
Focus: 4.3 Security & safe use 5.5 Measuring impact
Doc / list · Reference · 4.3 Security & safe use
Report · ~30 min · 5.5 Measuring impact
T. NilssonPeople / HR
5.4 · Foundational
5.5→5.4 ▼
Foundational or belowConfidence > evidence
▾ Learning
Auto-matched to this person's components & Awareness Summary result — served from your Learning Library (64 vetted resources, plus your own).
Focus: 4.1 Human agency & accountability 5.4 Delegating work to AI
Framework · Reference · 4.1 Human agency & accountability
Report · ~30 min · 5.4 Delegating work to AI
C. AbaraManaging Director
5.8 · Practitioner
5.6→5.8 ▲
Confidence > evidence
▾ Learning
Auto-matched to this person's components & Awareness Summary result — served from your Learning Library (64 vetted resources, plus your own).
Focus: 5.1 Identifying opportunities & ROI 5.2 Governance & policy-setting
Course · ~6h · 5.1 Identifying opportunities & ROI
Framework · Reference · 5.2 Governance & policy-setting
A. BianchiPortfolio Manager
6.4 · Practitioner
7.6→6.4 ▼
Declined ▼
▾ Learning
Auto-matched to this person's components & Awareness Summary result — served from your Learning Library (64 vetted resources, plus your own).
Focus: 5.2 Governance & policy-setting 2.3 Verification & sourcing
Framework · Reference · 5.2 Governance & policy-setting
Article · ~10 min · 2.3 Verification & sourcing
Flags: Foundational or below = current band at Foundational or below · Declined ▼ = dropped a band or ≥1.2 since last cycle · Confidence > evidence = self-rating ≥1.5 above demonstrated. These are people who may benefit from a conversation or some targeted learning — context always matters. Expand a person to see what 2gnoMe would suggest, matched to their specific areas.
Learning alignment — pair your strengths with your gaps
The asset-based view. People already demonstrating Leader-level skill (and self-aware about it), matched to colleagues working on the same area. Being named here is recognition — the cheapest, highest-trust learning you have.
Internal leaders
★ N. Castellano Portfolio Manager · 4.2, 4.3★ E. Sørensen Senior Analyst · 2.3, 2.1★ G. Haddad Operations Director · 5.2, 5.1★ M. Flores Financial Advisor · 3.1, 3.44.2 Privacy & data protection
★ N. Castellano Portfolio Manager
mentors →
M. Delgado, S. Okonkwo, J. Petrov, P. Romano
2.3 Verification & sourcing
★ E. Sørensen Senior Analyst
mentors →
M. Delgado, J. Petrov, A. Bianchi
5.2 Governance & policy-setting
★ G. Haddad Operations Director
mentors →
R. Tanaka, S. Okonkwo, A. Bianchi, C. Abara
4.3 Security & safe use
★ N. Castellano Portfolio Manager
mentors →
D. Mwangi
5.1 Identifying opportunities & ROI
★ G. Haddad Operations Director
mentors →
C. Abara
2.1 Evaluating outputs
★ E. Sørensen Senior Analyst
mentors →
J. Petrov
4.5 Compliance & policy
No one at Leader level internally → external training / outside expert
M. Delgado, S. Okonkwo, P. Romano would benefit
A person qualifies as an internal leader only when their demonstrated actions are top-band and aligned with their self-perception — never on confidence alone. Coaching view · gated by role & minimum-group permissions.
✨ AI-drafted · optional
Generate recommendations
Turn the patterns above — your priorities, the Self-Perception–Actions gaps, and the people who may need attention — into a draft AI enablement & governance plan, grounded in the exact numbers on this page. You can edit everything; 2gnoMe never lets AI score or label anyone.
Reading your results and drafting recommendations…
Action roadmap AI draft · editable
Now · 0–30 days
Contain the high-stakes risk
- Stand up an AI governance policy with a named owner (5.2)
- Mandatory data-handling & privacy training — no client data/PII in public tools (4.2, 4.3)
- Declare "do-not-use-AI" zones for sensitive decisions (2.4)
Next · 1–2 quarters
Build judgement & scale strength
- Make source-verification a norm; require citations (2.3)
- Map AI use to regulatory obligations (4.5)
- Package the productivity strength into a role-based use-case library (D3)
- Run an opportunity/ROI scan (5.1)
Ongoing
Make it stick
- Re-measure quarterly; watch the gap narrow (5.5)
- Champions & adoption plan (5.3)
- Progress leadership and the board can see
Staff announcement — draft email
AI draft · editableSubject: What we learned from our AI Maturity baseline — and what's next
Hi team,
Thank you to the 47 of you who completed the AI Maturity reflection. The short version of what we found and where we're headed:
The good news: we're already getting real value from AI — our strongest area is using it to do good work, faster.
Where we're focusing next: handling client data and meeting our compliance obligations when we use AI. This isn't about anyone being "behind" — it's about making sure our confidence matches our practice in the areas that matter most in our industry.
What to expect: short, role-relevant learning over the next few weeks, a clearer set of do's and don'ts for AI at Meridian, and a chance to revisit your reflection later this year.
Your individual results stay private to you — we only look at anonymized team patterns. Questions are welcome.
— Leadership
Manager talking points
AI draft · editable- Lead with the strength. Our people are productive with AI — this builds on that, it doesn't reset it.
- Frame the gap as opportunity, not deficiency. It's about confidence matching practice, especially with client data.
- Name the two priorities plainly: protect client data, and verify what AI produces before it goes out.
- Reassure on privacy. Individual results are private; this is growth support, not evaluation.
- One action this week: point your team to their matched learning, and check in with anyone flagged for support.
AI-use policy — starter draft
AI draft · editable- Client data & PII never goes into public/consumer AI tools. Use only approved, contracted tools.
- Verify and review. AI-assisted client materials must be source-checked and human-reviewed before they leave the firm.
- Approved-tools list. New tools go through a security review before use.
- Humans decide. Sensitive or regulated decisions are made by people; AI may assist but never decides.
- Accountability. Every team has a named person responsible for how AI is used.
- Review cadence. We re-measure AI maturity quarterly and update this policy against the gaps.
Drafted from this group\'s data on · grounded in the values above. Review and adjust before sharing — a starting point, not a directive. Re-runs only when you ask.
What observation adds
This baseline measured self-reflection — what your people say they'd do and how confident they are — and surfaced where confidence outruns self-assessed skill. To validate those gaps and act on them at scale, add the one thing self-report can't: observation.
Observation feedback loopManagers observe and rate against the same framework — turning self-reported gaps into confirmed ones.
Personalized learningEach person gets learning matched to their validated gaps, from your Library.
Coaching & feedbackStructured coaching cycles and feedback, tracked over time in one place.
Re-measure quarterlyWatch the gap close — progress leadership and the board can see.
Aggregated, anonymized view. Individual results stay private to each person; group views require a minimum size. No AI scores or labels anyone — the reflection stays theirs. · 2gnoMe · AI Maturity Framework · figures are illustrative composite data.