PURPOSE_LABEL

DATA_EVIDENCE_LABEL

WORKFLOW_LABEL

CONTROL_LABEL

ROLES_JUDGEMENT_LABEL

BAU_LABEL

BINDING_CONSTRAINT_LABEL

Pensions Administration AI Readiness Check

CONST_1

CONST_2

CONST_3

CONST_4

CONST_5

LABEL_EXPLORING

LABEL_DEVELOPING

LABEL_CONTROLLED

LABEL_EMBEDDED

LABEL_ASSURED

Q1_SCORE

Q2_SCORE

Q3_SCORE

Q4_SCORE

Q5_SCORE

Q6_SCORE

Q7_SCORE

Q8_SCORE

Q9_SCORE

Q10_SCORE

Q11_SCORE

Q12_SCORE

PURPOSE_SCORE

DATA_EVIDENCE_SCORE

WORKFLOW_SCORE

CONTROL_SCORE

ROLES_JUDGEMENT_SCORE

BAU_SCORE

AI_CHOICE_CODE

CONSEQUENCE_CODE

HUMAN_CHECK_CODE

LIVE_BAU_CODE

CONFIDENCE_CODE

RESULT_CODE

RESULT_LABEL

BINDING_CONSTRAINT_CODE

RESULT_RECONSIDER

RESULT_NOT_READY

RESULT_PILOT

RESULT_LIVE

RESULT_ASSURED

CONSTRAINT_PURPOSE

CONSTRAINT_DATA

CONSTRAINT_WORKFLOW

CONSTRAINT_CONTROL

CONSTRAINT_ROLES

CONSTRAINT_BAU

CONSTRAINT_HUMAN_CHECK

CONSTRAINT_CONFIDENCE

CONST_0

CONST_6

CONST_7

CONST_8

Purpose score

Data & Evidence score

Workflow score

Control score

Roles & Judgement score

BAU score

It helps someone prepare or organise their work.

It informs someone who then makes a decision.

It contributes to something communicated to a member.

It contributes to a calculation, payment, entitlement or member decision.

Is AI the right tool?

A — Not yet. We haven't seriously compared it with fixing the process, data or existing technology.

B — Partly. We've considered alternatives, but it isn't yet clear that AI is better.

C — Yes. We can explain what AI adds that a simpler solution cannot.

Is AI really the best tool for this?

What if it's wrong?

Limited — The mistake would mainly affect internal work and should be straightforward to identify and correct.

Material — The mistake could affect administration or member communication, but there is a separate control before the outcome becomes final.

Significant — The mistake could directly or materially affect a benefit, payment, member match, right, materially important communication, regulatory obligation or sensitive member data.

If the AI gets this wrong and nobody catches it immediately, what could happen?

Strong — Someone independently verifies the important facts or outcome against an authoritative source.

Partial — Someone reviews the AI output, but may still rely on it to frame the answer or identify what needs checking.

Weak — Someone mainly reads the output and decides whether it looks reasonable.

No human check — The output can move on without a person reviewing it.

And what does the human check actually involve?

1 — Exploring: We're trying AI because it looks useful.

2 — Developing: We have an intended benefit, but it isn't yet specific or consistently measured.

3 — Controlled: We have defined the operational or member outcome this use is meant to improve.

4 — Embedded: We routinely measure whether that outcome is actually improving.

5 — Assured: We can evidence the benefit and stop or change the use when it no longer delivers.

1. What problem is the AI actually solving?

1 — Exploring: We haven't really challenged it. The use has mainly grown from what the technology can do.

2 — Developing: We've considered alternatives, but haven't established that AI is better.

3 — Controlled: We've tested whether fixing the process, data or existing technology would solve the problem better.

4 — Embedded: We repeat that challenge as the use develops rather than assuming AI remains the answer.

5 — Assured: We have evidence that this use of AI adds worthwhile benefit and would stop using it if a safer or simpler solution became better.

2. Have you tested whether AI is still the right answer?

Data & Evidence

1 — Exploring: People use what's available and make their own judgement about whether it is reliable.

2 — Developing: We know about important gaps or weaknesses, but still rely on local knowledge and workarounds.

3 — Controlled: We know which data and source material this use relies on, where it came from, its important limitations and who owns it.

4 — Embedded: Checks on data, source material and versions are part of the normal process.

5 — Assured: We can show that the data and source material remain fit for this use and act when they are not.

Pensions Administration AI Readiness Check

FIRST — WHAT ARE YOU TESTING?

What are you asking AI to do?

Who relies on what the AI produces?

How is the AI output used?