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— AI use case · The public sector —

AI for case management,
governed properly.

Case management is one of the strongest, and most consequential, places AI can help a public body. Done well it gives officers time back and citizens a faster, fairer service. Done carelessly it automates the wrong decision at scale. The Institute of AI helps you tell the two apart.

— State of play —

High stakes, high potential.

Casework is the engine room of the public sector. Benefits and appeals, planning and licensing, adult and children’s social care, immigration and asylum, complaints and safeguarding. Every one of these runs on queues of cases, rising demand and a workforce stretched thin, which is exactly why AI looks so appealing.

The promise is real. Triage, summarisation and drafting can genuinely clear backlogs and free officers for the human judgement only they can make. But case management also produces some of the most consequential decisions the state makes about a person, and the data behind it is sensitive and often biased. That combination is why this use case deserves serious governance rather than a quick pilot.

The Institute of AI works with public bodies as an independent professional body, not a supplier. We have no case management software to sell, so our only interest is whether AI makes your service faster, fairer and defensible, or whether it should not be there at all.

Five

pillars an organisation is assessed against: Strategy, Governance, Skills, Implementation, Impact

Four

accreditation levels for the officers who run the system, Student to Fellow

One

public Charter, free for any organisation to read and sign

— Where it helps —

Six places AI earns its keep in casework.

The strongest use cases give time back to the frontline and keep a named officer accountable for every decision.

Intake, triage and prioritisation

New applications, claims and referrals sorted by urgency, complexity and risk so the most vulnerable cases surface first and caseworkers spend their time where it counts.

Evidence summarisation

Long case files, medical reports and correspondence condensed into a structured summary with citations back to the source, so an officer reviews rather than re-reads.

Next-action and eligibility support

Suggested next steps and eligibility flags surfaced against policy and statutory rules, keeping the caseworker as the person who decides and records the reason.

Correspondence and translation

Drafting standard letters, plain-English rewrites and translation for multilingual caseloads, with a human sign-off before anything reaches a member of the public.

Caseload management

Backlog and ageing analysis across queues that flags cases drifting past statutory deadlines, so managers can rebalance work before a breach or an escalation.

Quality assurance and audit

Sampling and consistency checks across closed cases that catch drift between officers and teams, feeding a documented, defensible audit trail for inspectors.

— What must be governed —

The questions to settle before you scale.

Case management is not a low-risk use case. These are the controls we put around it, each one tailored to public sector casework.

01
Model risk and validation

Prove it works before it touches a real case

Case management decisions carry legal weight and affect livelihoods, so a model earns its place through documented validation against representative data, tested error rates and a clear statement of where it must not be used. Pilots run in shadow mode alongside human decisions long enough to trust the numbers.

1

shadow-mode evaluation before any live decision support

02
Fairness and bias

Test for disparate impact, by cohort

Historic casework data carries historic bias, and a triage or eligibility model can quietly encode it. Outcomes are measured across protected characteristics and known-vulnerable cohorts, and the public sector equality duty is assessed as a live obligation, not a launch-day form.

100%

of models bias-tested across protected-characteristic cohorts

03
Explainability

A reason a person can give back

A citizen has a right to understand a decision that affects them, and a caseworker has to be able to justify it on appeal. Every AI-assisted recommendation carries a plain-English rationale and the evidence it drew on, so the human decision-maker can adopt, amend or reject it on the record.

0

unexplained recommendations reaching a case file

04
Data protection

UK GDPR and a DPIA that means it

Casework runs on special-category data and the most sensitive personal circumstances. A Data Protection Impact Assessment sets lawful basis, minimisation, retention and the line on automated decision-making under Article 22 before any processing begins, and it is revisited when the system changes.

1

DPIA completed and maintained per case-handling system

05
Human accountability

A named officer owns the outcome

AI supports the decision, it does not make it. Meaningful human review has to be real rather than a rubber stamp, which means officers with the time, training and authority to overrule the model, and a record that shows judgement was genuinely exercised.

0

decisions delegated wholly to a model

06
Monitoring after go-live

Watch it in production, forever

Caseloads, policy and populations shift, and a model that was fair at launch can drift. Live monitoring of accuracy, override rates and outcome disparities, with a clear trigger to pause or retrain, keeps the system honest long after the project team has moved on.

24/7

monitoring of drift, override rates and outcome disparity

Make case management faster,
and fairer.

A short call with the Institute of AI. Plain answers on where AI helps your casework, where it should not, and a clear next step. No case management software to sell.