Cambridge
Academy
In London

AI can modernize records and archives management by accelerating classification, improving metadata quality, enabling faster retrieval, and supporting retention and compliance activities. This practical program equips documents and archives leaders with AI-enabled methods to implement auto-classification, metadata tagging, and lifecycle controls—while maintaining governance, privacy, and audit-ready evidence.

 Course Objectives

By the end of this course, participants will be able to:

·        Identify high-value AI use cases across records and archives lifecycle management

·        Design AI-assisted classification, metadata tagging, and indexing standards

·        Apply AI to support retention scheduling, disposition workflows, and compliance evidence

·        Improve retrieval using AI-enabled search, summarization, and knowledge discovery (with controls)

·        Establish governance, privacy, and quality assurance for trustworthy AI use in records

·        Build a 90-day pilot plan and 12-month roadmap for AI adoption in records and archives

 Target Audience

This course is designed for:

·        Leads and managers of documents, archives, and records management

·        Document controllers and information management coordinators

·        Compliance, risk, and internal audit professionals overseeing information controls

·        IT and digital platform administrators supporting ECM/DMS repositories

·        Teams responsible for retention schedules, archiving, and controlled documents

Course Outlines

Day 1: AI Foundations for Records & Archives + Use-Case Discovery

·        Where AI fits in records: classification, metadata, redaction support, search, retention, and audits

·        AI capabilities and limits: errors, hallucinations, bias, and human-in-the-loop needs

·        Data readiness: document inventories, taxonomies, metadata quality, and access constraints

·        Selecting use cases: value sizing (time saved, retrieval speed, compliance risk reduction)

·        Activity: Build an AI records use-case backlog + value/feasibility prioritization matrix

Day 2: Auto-Classification, Taxonomies & Metadata Tagging

·        Classification fundamentals: record types, functional taxonomies, and sensitivity levels

·        AI-assisted tagging: entities, topics, dates, owners, and retention triggers

·        Designing confidence thresholds: when AI auto-tags vs. routes for review

·        Metadata standards: required fields, validation rules, and quality checks

·        Workshop: Create a classification model (taxonomy + metadata schema + tagging rules)

Day 3: Retention Scheduling, Disposition & Compliance Support

·        Retention concepts: triggers, holds, exceptions, and legal/regulatory constraints

·        AI support for retention: identifying record series, suggesting schedules (with verification)

·        Disposition workflows: approvals, evidence capture, and secure disposal controls

·        Legal holds and investigations: search, preservation, and access governance

·        Practical activity: Build a retention and disposition workflow (roles + controls + evidence pack)

Day 4: AI-Enabled Retrieval, Search & Knowledge Discovery

·        Semantic search concepts: improving findability beyond keywords

·        Summarization and extraction: key facts, decisions, and action items (with validation)

·        Deduplication and version detection concepts for archives cleanliness

·        User experience design: search filters, access permissions, and safe outputs

·        Case study: Retrieval simulation (find, summarize, and compile an evidence bundle for audit)

Day 5: Governance, Privacy, QA & Implementation Roadmap

·        Responsible AI governance: roles, approvals, accountability, and escalation paths

·        Privacy and confidentiality: access control, redaction workflows, and safe handling

·        Quality assurance: sampling checks, error tracking, and model drift monitoring concepts

·        Success metrics: classification accuracy, retrieval time, compliance findings, user satisfaction