Generative AI for Business

Introduce text-generating AI, common strengths and limitations, and the difference between a plausible response and a verified fact. Discuss approved tools, access, data handling and human responsibility.

• Recognise drafting, summarising and information-organisation use cases.
• Distinguish plausible generated text from verified business facts.
• Identify approved access, data boundaries and human responsibilities.

Review a fictional internal update and label which facts are supplied, which details are missing and which questions need a manager before any AI prompt is written.

Build prompts that define a task, intended reader, source material, constraints and review format. Compare a broad request with a structured one without assuming one model response is authoritative.

• State the business task, audience and required output.
• Provide bounded source context and explicit constraints.
• Request uncertainty, missing information and review points.

Use a sanitised sample supplier note to request a short draft, a list of open questions and a separate set of claims requiring source verification.

Explore possible assistance for meeting-note organisation, internal communication, first-draft FAQs and summaries of approved documents. Consider where confidentiality and version control affect the task.

• Organise meeting notes into actions, decisions and questions.
• Draft neutral internal messages from approved information.
• Preserve confidentiality, source context and document versions.

Turn fictional meeting notes into a draft action register, then verify each owner, date and action against the mock source before marking uncertain entries.

Create a human-review checklist, define tasks that should remain out of scope and consider how a manager could evaluate a proposed pilot without assuming savings or business results.

• Define reviewer, approval and escalation responsibilities.
• Keep high-risk decisions and restricted data out of scope.
• Set pilot boundaries without promising business results.

Prepare a one-page fictional pilot brief that names the task, approved inputs, reviewer, escalation trigger and a measure to discuss with management.

Break a routine business process into inputs, transformations, decisions and handoffs, then identify where a generated draft may support work without taking ownership of a controlled decision.

• Separate repeatable preparation from accountable decisions.
• Identify process inputs, owners, handoffs and source records.
• Prioritise low-risk tasks that can be checked.

Map a fictional procurement or production-update process and mark one possible AI-assisted preparation step, its source record, reviewer and stop condition.

Apply the core method to operations, planning, procurement, QA/QC, customer service and office coordination while respecting role permissions and approved information.

• Adapt outputs to different business audiences and roles.
• Use neutral drafts for operations, procurement and customer replies.
• Keep QA/QC and operational judgements with authorised owners.

Choose a fictional warehouse discrepancy, supplier query or QA/QC observation and create two audience-specific draft outputs, then ask the responsible owner to verify each one.

Use practical checks for completeness, accuracy, source coverage, assumptions and tone. Record unresolved issues and escalate content that cannot be verified or that may affect quality, safety, customers or commitments.

• Trace material statements to supplied or approved sources.
• Check omissions, invented details, figures and commitments.
• Escalate uncertain, sensitive or consequential outputs.

Audit a fictional AI-generated business brief against its source pack, classify each issue as correction, clarification or escalation, and prepare a reviewer checklist.

Turn a selected use case into a reviewable team playbook with permitted inputs, prompt guidance, approval steps, feedback handling and a decision about whether to continue, revise or stop.

• Document an approved task, inputs, owner and reviewer.
• Create reusable prompt and checking guidance for the team.
• Review feedback before changing the workflow or scope.

Draft a fictional one-page team playbook for meeting-note summarisation, including data rules, prompt fields, quality checks, escalation route and a review date.