AI Decision Support Systems

The Problem with Unaided Decision-Making at Scale

When organisations process thousands of similar decisions — grant applications, loan assessments, case prioritisations, regulatory reviews — two problems emerge. The first is inconsistency: different analysts apply policy criteria differently, producing outcomes that vary by reviewer rather than by merit. The second is overload: analysts under volume pressure take shortcuts, and the quality of individual decisions degrades.

AI decision support addresses both problems without removing human accountability from the process.

What We Build

Recommendation Engines. Systems that assess incoming cases against defined criteria and return a structured recommendation — approve, decline, escalate, or flag — along with the evidence and reasoning behind it. The analyst sees the recommendation and its rationale before making their own determination.

Consistency Monitoring. Dashboards that track how often human decisions align with or deviate from AI recommendations, broken down by analyst, office, case type, and time period. This is not about disciplining staff — it is about identifying where guidance is ambiguous or where training is needed.

Case Triage and Prioritisation. Systems that rank incoming cases by complexity, urgency, or risk so that analyst time is directed where it has the most impact. Straightforward cases can be handled quickly; complex or high-risk cases receive appropriate attention.

Structured Reasoning Interfaces. Interfaces that walk analysts through the relevant criteria for a decision type, prompting them to record their findings at each step. The AI surfaces relevant precedents, policy text, and prior similar cases to inform the analyst's review.

The Directive on Automated Decision-Making

Federal institutions are subject to the Treasury Board Directive on Automated Decision-Making, which governs how AI may be used in administrative decisions affecting Canadians. The directive establishes four impact levels, each with escalating requirements for human oversight, explainability, and notification.

We assess the impact level of every system we build within this framework. For systems at impact levels two and above, we design explicit human review workflows, ensure the AI cannot render a final decision without human confirmation, and build the notification and recourse mechanisms required by the directive.

This is not compliance theatre. The directive's requirements reflect sound design principles for any decision support system in a public sector environment.

The Canadian Legal and Financial Context

In legal services, AI decision support assists with case assessment, document review prioritisation, and regulatory compliance checks — reducing the time lawyers spend on preliminary analysis. All recommendation output is designed to support solicitor-client work product, with appropriate privilege considerations built into the data architecture.

In financial services, decision support systems assist with credit adjudication, transaction review, and regulatory reporting flags. We ensure alignment with OSFI guidance on model risk management and the requirements of applicable provincial and federal consumer protection legislation.

Keeping Humans in the Loop

We hold a firm design position: AI decision support systems in regulated environments must maintain meaningful human control over consequential decisions. This means the system surfaces recommendations but does not enforce them, every override is recorded and auditable, and the AI model is regularly reviewed against actual outcomes to identify drift or bias.

This architecture also protects the organisation. When a decision is challenged — by a client, a regulator, or a court — the organisation can demonstrate that a human made the decision, understood the basis for it, and exercised independent judgement.