AI Adoption for Professionals

Identify which of your own tasks AI can take over, do it reliably, verify the output, and make a measured productivity case to your manager.

5 Weeks4 Sessions/WeekIn PersonBeginner-friendly

Prompt WritingWorkflow AutomationAI Ethics

Share this course

Overview Curriculum Capstone Assessment Enquire

What you'll be able to do

Graduates can identify which of their own tasks AI can take over, do it reliably, verify the output and document the method so a colleague can repeat it.

Choose an appropriate tool for a task and explain the trade-offs
Write prompts that produce usable output on the first or second attempt
Build reusable prompt templates and custom assistants for recurring work
Work with their own documents, spreadsheets and data through AI tools
Automate a multi-step workflow without writing code
Produce written material, presentations, images and video with AI assistance
Verify output, recognise fabrication and know which tasks to keep manual
Handle confidential information appropriately and apply a workplace AI policy
Present a measured productivity case to a manager

Positioning

This course teaches people to use AI tools well inside their existing job. It does not teach software development, and it does not lead to a developer role. Students who want to build AI applications should take Generative AI and Agent Engineering, which requires Python.

Every exercise is run against the student's own work. Students are asked to bring three real recurring tasks to the first session, and those tasks are the material for the rest of the course.

Who it's for

Office and administrative staff, operations and HR teams
Marketing, sales and customer support staff
Finance, accounting and audit staff
Teachers, researchers, journalists and content professionals
Managers and business owners evaluating AI for their teams

Prerequisites

Comfortable using a computer, a browser, email and a spreadsheet
No programming, mathematics or technical background required

Engineering Onboarding does not apply to this course. Students set up their accounts and workspace in Session 1.

Tools and technologies

ClaudeChatGPTGeminiNotebookLM for document workMicrosoft CopilotGoogle Workspace AIA no-code automation platform such as Zapier or MakeAI features inside Excel and Google SheetsCanvaA presentation generatorAn image generation toolAn AI meeting notetakerA transcription toolA research assistant

Target Outcomes

Improved individual productivity in an existing roleInternal AI champion or trainerProcess automation ownerA credible basis for a department-level AI pilot

Course curriculum

5 modules · 4-5 weeks

Concepts: what a large language model does and what it cannot do; why it produces confident errors, and what fabrication looks like in practice; the difference between a chatbot, a search engine and a database; free against paid tiers and what the difference buys; comparing the major assistants on writing, reasoning, document handling and image work; the anatomy of a working prompt covering role, task, context, constraints, format and examples; iterating rather than restarting; giving the model examples of what you want; asking it to work through a problem step by step; controlling tone, length and reading level; common prompting mistakes.
Lab: run the same task across three assistants and compare the output; take a poor prompt and improve it through four rounds, recording what each change did; rewrite one of your own recurring tasks as a structured prompt; produce a deliberately fabricated answer and identify the warning signs that were present.
Project: Deliverable: a personal task audit listing your three recurring tasks with an initial assessment of which parts AI can take.

Concepts: uploading documents, spreadsheets and images, and what the tool can and cannot read; summarising long documents without losing the substance; extracting structured information from unstructured files such as invoices, contracts, CVs and reports; comparing multiple documents; question answering over a document set; the limits of long documents and how to split work; working with tables and spreadsheet data, including formula generation, cleaning, categorisation and analysis; interpreting a chart or a scanned page; transcription and meeting notes; language work covering translation, bilingual drafting and Nepali content; building a reusable prompt library; custom assistants and projects that carry your context between sessions.
Lab: summarise a forty-page report and verify every claim in the summary against the source; extract a structured table from twenty messy documents; clean and categorise a real spreadsheet; build one custom assistant configured for a task you repeat weekly, then hand it to a classmate to test.
Project: Deliverable: a prompt library of at least eight tested templates, and one working custom assistant.

Concepts: drafting with AI while keeping your own voice, and why undisclosed generated text reads badly; email, proposal, report and policy drafting; editing and proofreading your own writing; structuring a long document; presentation generation and where it needs manual repair; image generation covering prompt structure, style control, iteration, and the licensing and rights questions that matter commercially; editing existing images; short video and voice generation, and their current limits; social and marketing content, campaign copy and content calendars; data visualisation; research support covering literature search, source verification and citation, and the specific risk of invented references; accessibility and plain-language rewriting.
Lab: produce a complete client-facing document from a rough brief, then have a classmate mark every sentence that reads as machine-written; generate a presentation for a real internal topic and repair it to a standard you would present; run five rounds of image iteration toward a specified brief; verify ten AI-supplied citations and record how many were real.
Project: Deliverable: a portfolio of three finished work products, each with the prompt trail that produced it.

Concepts: the difference between prompting a tool and building a workflow; mapping a process before automating it; identifying the steps worth automating and the steps to leave alone; triggers, actions and conditions in a no-code automation platform; connecting email, calendar, forms, spreadsheets, storage and chat; inserting an AI step into an automated chain; handling errors and building in a human checkpoint; AI features inside the tools you already own, covering Copilot in Office and AI in Google Workspace; meeting workflows from recording through notes to task assignment; customer response workflows with templates and escalation rules; recurring reporting; testing an automation before trusting it; the cost of automating something poorly understood.
Lab: map one of your own processes end to end, then build a working automation for it with an AI step and a human approval point; break the automation deliberately and observe what happens to the data; build an automated weekly report from a live spreadsheet; measure the time your automation saves against the time it took to build.
Project: Mini-project: one working, tested automation covering a real process, with a written process map and a measured time saving.

Concepts: verification as a discipline, and which categories of output must always be checked, including numbers, names, dates, legal statements, citations and code; where AI is currently unreliable; bias in output and how it appears in hiring, lending and assessment contexts; confidentiality and what happens to data you paste into a tool; personal and customer data, and consent; enterprise against consumer tiers and data retention; drafting a workplace AI usage policy; disclosure, and when to tell a client or reader that AI was used; copyright and ownership of generated material; professional obligations in accounting, law, medicine and education; the effect on your own skills if you stop doing the work yourself; evaluating a new tool without being sold to; building the productivity case for a manager, with measured before and after figures; running a pilot with a team.
Lab: audit your own outputs from the previous four weeks for unverified claims; write an AI usage policy for your workplace and defend it against a compliance challenge; evaluate a tool you have not used against a fixed criteria set; rehearse a proposal to a sceptical manager.

Capstone project

An AI adoption case for a real process in the student's own workplace or a supplied scenario for students not currently employed.

Requirements

A current-state process map with measured time and cost
A defined scope covering which steps AI will take, which stay manual, and why
Working implementation, whether a custom assistant, a prompt system, an automation or a combination
A tested prompt library or configuration that a colleague can use without the student present
A verification procedure stating what must be checked and by whom
Measured before and after figures covering time, volume or error rate
A risk and confidentiality assessment
A written AI usage guideline for the process
A one-page proposal for a decision-maker
A ten-minute presentation followed by questioning, including a live demonstration of the working system

Assessment

30%Weekly labs and deliverables

15%Peer testing of another student's assistant or automation

40%Capstone, weighted on measured improvement and on whether a colleague can operate the result

15%Final presentation and questioning

There is no written examination. A student passes by demonstrating a working system that saves measurable time and that someone else can run.

Corporate Delivery

This syllabus is also delivered as a condensed in-house programme: a two-day intensive covering Modules 1 and 2, or a four-session variant covering Modules 1, 2, 4 and 5, tailored to a department's own processes and confidentiality requirements.

Out of scope

Programming and software development
Building AI applications or agents through code
Machine learning theory
Model fine-tuning
Data science
Students wanting these should take Generative AI and Agent Engineering or AI / Machine Learning.