arqmetrica
Dimension 03 · arqmetrica AI Maturity Index

People & capability

The literacy, hiring posture, training cadence, and role evolution of the workforce in relation to AI.

Weighted at 17% of the overall score

What we ask

The four questions in this dimension, with the four ordinal options and their fixed scores. Identical wording to what you will see in the assessment.

PPL-01

1. What proportion of your workforce has had structured AI literacy training in the last 12 months?

Structured = at least a half-day session with measurable learning outcomes, not a town hall slide.

OptionResponseScore
a
Over 70% — including non-technical roles
100
b
30–70%
67
c
Under 30% — mostly tech and product teams
33
d
No structured programme
0
PPL-02

2. Do you have at least one person with formal data science or ML engineering qualifications on the team?

In-house, fractional, or strategic partner all count — but they must be embedded in delivery, not on a one-off contract.

OptionResponseScore
a
Yes — multiple, in different functions
100
b
One in-house plus a strategic external partner
67
c
External consultancies only, ad-hoc
33
d
No specialist capability
0
PPL-03

3. How are job descriptions and performance reviews adapting to AI-augmented work?

Are you formally rewriting role expectations as AI becomes part of the workflow, or just hoping people figure it out?

OptionResponseScore
a
Roles formally re-scoped; AI fluency in performance criteria
100
b
Some roles updated; case-by-case
67
c
Discussed at HR/leadership level, not yet implemented
33
d
No change to roles or evaluation
0
PPL-04

4. When an AI tool produces a wrong or biased output, who is empowered to escalate and stop its use?

Look for explicit authority — "any employee can flag and pause" beats "raise a ticket and wait for IT".

OptionResponseScore
a
Any employee; documented escalation path with SLA
100
b
Only managers and above; documented path
67
c
Informal — depends on who you know
33
d
No clear path
0

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