Generative AI for Business Productivity Training Course

Participants can prioritise business use cases using structured value, feasibility and risk criteria.

• Opportunity scoring matrix using volume, variability, risk and value criteria
• Process maps identify handoffs, rework, delays and information bottlenecks
• Task decomposition separates judgment, retrieval, drafting and execution steps
• Jobs-to-be-done statements define users, triggers, outcomes and constraints
• Baseline measures capture cycle time, quality, cost and adoption
• Prioritisation combines feasibility, expected benefit and control requirements

Participants can construct reusable prompts that produce specified, usable and reviewable outputs.

• Prompt anatomy: role, task, context, constraints, examples and output format
• Few-shot prompting establishes style, classification rules and response patterns
• Structured prompts specify acceptance criteria, source boundaries and assumptions
• Chain-of-thought replaced by concise rationale requests and verification steps
• JSON schemas constrain fields, data types and downstream system consumption
• Prompt versioning records purpose, inputs, model settings and results

Participants can produce source-grounded AI outputs and verify material claims before use.

• Source-grounded research separates evidence, interpretation, assumptions and recommendations
• RAG patterns connect approved knowledge bases to generated answers
• Citation checks test source existence, relevance, currency and claim support
• Verification ladders escalate high-impact claims to subject-matter review
• Triangulation compares primary sources, internal records and independent references
• Confidence labels distinguish verified findings from provisional generated content

Participants can use generative AI to draft, edit and review decision-ready business documents.

• Document briefs define audience, decision, evidence, tone and approval route
• Outline-first drafting creates logical sections before sentence-level generation
• Executive summaries use situation, implication, recommendation and next-step structure
• Editing passes check factual accuracy, tone, specificity and unsupported claims
• Brand voice guides translate into reusable prompt constraints and examples
• Redlining compares generated drafts against policy, contract and source documents

Participants can use AI-assisted analysis while validating formulas, data quality and reporting outputs.

• Spreadsheet copilots generate formulas, explain logic and flag anomalies
• Natural-language analysis translates business questions into testable data requests
• Formula validation tests ranges, references, units and exceptional values
• Data dictionaries define fields, owners, permitted uses and quality rules
• Chart critique checks scales, aggregation, labels and decision relevance
• Sensitive data minimisation removes identifiers before model interaction

Participants can apply generative AI to meeting records, knowledge articles and internal service processes.

• Meeting preparation converts agendas, attendee roles and decisions into prompts
• Transcript summarisation distinguishes decisions, actions, risks and unresolved questions
• Action registers assign owners, due dates, dependencies and escalation triggers
• Knowledge articles follow problem, context, procedure, exception and source fields
• Taxonomies improve retrieval through controlled terms, synonyms and metadata
• Search evaluation measures answer relevance, grounding and retrieval coverage

Participants can design AI-enabled workflows with appropriate automation boundaries and human oversight.

• Automation suitability assesses repeatability, exception rates, permissions and accountability
• Human approval gates control commitments, payments, personnel and regulated communications
• Agent workflows define goals, tools, state, boundaries and stop conditions
• API concepts connect prompts with forms, records and business applications
• Exception handling routes low-confidence outputs to named human reviewers
• Benefits cases quantify hours saved, error reduction and service improvements

Participants can apply practical governance controls to employee use of generative AI.

• NIST AI RMF functions organise governance, measurement and treatment activities
• Data classification determines permitted prompts, storage locations and retention
• Privacy impact assessments identify personal-data processing and mitigation controls
• Bias testing defines affected groups, failure modes and review thresholds
• Copyright checks distinguish licensed inputs, public sources and generated outputs
• Incident response logs model failures, escalation paths and corrective actions

Participants can prepare a controlled pilot and personal application plan for priority AI use cases.

• Use-case charters specify sponsor, users, workflow, measures and guardrails
• Pilot plans define scope, cohorts, baselines, support and review cadence
• Adoption communications explain changed tasks, controls and escalation channels
• Operating procedures document approved prompts, reviewers and record-keeping requirements
• Performance dashboards track usage, quality, cycle time and realised benefits
• Personal application plans commit next actions, stakeholders and evidence milestones