How to read this catalogue
Generative AI is not one capability — it is a set of language and content abilities applied to whatever your team does with words, documents, and data. The use cases below are organised by function so you can find the ones nearest your own work, but they share a common shape: AI produces a fast draft or analysis, and a human verifies and decides. The right way to use this list is to score candidates by task volume, manual cost, and how text-heavy they are, then pilot the highest-scoring two or three with a clear metric. Breadth here is for orientation; value comes from depth on a few well-chosen applications.
Marketing, sales, and customer service
Marketing is the densest cluster: drafting blog posts and landing copy, generating dozens of ad and subject-line variants for testing, repurposing one long asset into social, email, and video scripts, writing SEO meta descriptions, producing first-draft images, summarising campaign performance, and personalising copy by segment. Sales benefits from drafting tailored outreach, summarising call notes into CRM updates, qualifying and scoring inbound leads, generating proposal and follow-up drafts, and answering reps’ product questions from a knowledge base. Customer service is often the fastest payback: drafting replies for agent review, deflecting repetitive questions with a knowledge-grounded bot, summarising long ticket threads, classifying and routing incoming requests, translating responses, and turning resolved tickets into help-centre articles. These three functions alone span well over twenty concrete, proven applications.
Operations, HR, finance, and legal
Operations uses AI to draft standard operating procedures from process descriptions, summarise long reports, generate first-draft supplier and logistics communications, and surface anomalies in operational data. HR applies it to drafting and de-biasing job descriptions, screening and summarising applications, generating interview question sets, writing onboarding materials, and drafting policy documents for review. Finance gets value from summarising earnings and analyst reports, drafting commentary on variances, explaining figures in plain English for non-finance stakeholders, and categorising transactions. Legal benefits from first-pass contract review and clause extraction, plain-English summaries of dense documents, drafting standard agreements from templates, and research triage. Product and engineering round out the fifty with code generation, test writing, documentation, bug triage, user-feedback synthesis, and changelog drafting.
Turning the list into results
The catalogue is worthless without discipline behind it. For every use case you adopt, capture a baseline number — hours, cost, cycle time, resolution rate — before you switch on AI, then measure the AI-assisted version against it. Keep a human in the loop for anything customer-facing, legally binding, or financially material; the universal safe pattern is AI drafts, human approves. Start with a general assistant for the writing and analysis tasks, and reach for specialised tools only where deep workflow integration adds genuine value. Prioritise by value, not novelty: the most boring, highest-volume, most text-heavy task on the list will almost always deliver more return than the most exciting one. Pick two, measure honestly, and expand from proven wins.