Most professionals know how to prompt a chatbot. Far fewer know where AI can create real value, how to verify its outputs before they reach a client, or how to demonstrate its business impact.
Over six weeks, you’ll apply that judgement to a real workplace problem, developing an AI Opportunity Analysis, AI Risk and Verification Plan, and AI Business Value Analysis. These outputs come together in a final AI Business Case: an evidence-based recommendation to proceed, revise or stop a specific AI application—ready to take into the meeting where the decision is made.
This TSIBA short course is delivered in partnership with Alwin Education Ltd. Alwin Education is a UK based digitally native network of locally accredited higher-education institutions that focus on expanding access, strengthening quality, and aligning every programme to labour-market demand. This course is aimed at giving working professionals a practical, career-focused learning experience underpinned by rigorous academic standards.
Integrate all six module outputs — the opportunity analysis, risk and verification plan, planning and prioritisation analysis, value analysis, implementation approach and future-ready recommendation — ending in a justified call to proceed, revise or not proceed
A structured AI Opportunity Analysis naming where AI fits and where human judgement has to stay, built by testing a real workplace process against task features like repetition, data quality, risk and judgement
A practical verification routine — source checking, cross-checking, human review — scaled to how much checking a task deserves against its stakes, written up as an AI Risk and Verification Plan
Measures you can defend — productivity gains separated from real business value, current-state compared with intended-state outcomes, and impact estimated on one or two business measures with assumptions stated
Tools arrived, everyone experimented, and nobody agreed what's safe to put into them or who checks the output. You want to be the person who can settle that.
Typical job titles: operations coordinator, project manager, business analyst, marketing manager, client service lead.
You know your function and can see where AI might help, but you can't yet build the argument that would convince a decision-maker.
Typical job titles: financial accountant, HR business partner, procurement specialist, compliance officer, legal adviser.
You're being asked whether a tool is appropriate, whether its output can be trusted, and what it's actually delivering.
Typical job titles: team lead, department manager, quality manager, risk and compliance manager, L&D specialist.
Orientation module + 6 modules + capstone. Each module runs one week at 4–6 hours.
Learn the common workplace applications for AI — drafting, summarising, organising information, searching, pattern support — then test a real process from your own work against task features like repetition, data quality, risk and judgement, and what's appropriate to put into an AI tool.
Output: an AI Opportunity Analysis.
Move from prompting to verifying: examine why AI outputs can be inaccurate, incomplete, biased or misleading, build a practical verification routine — source checking, cross-checking, human review — and scale how much checking a task deserves against its stakes.
Output: an AI Risk and Verification Plan.
Bring AI into planning without handing over the decision: apply prioritisation techniques including the Eisenhower Matrix and strategic centring, brief AI with enough workplace context to be useful, and interrogate its suggestions for missing context and assumptions.
Output: an AI-Supported Planning and Prioritisation Analysis.
Answer the question most AI pilots never answer — did it work? Separate measures that are easy to count from measures that show value, distinguish productivity gains from business value, compare current-state with intended-state outcomes, and estimate the effect on one or two measures with assumptions made explicit.
Output: an AI Business Value Analysis.
Work out what has to be true before an AI application can land: classify the organisational factors that support or block adoption, compare approaches from pilots to process redesign, sequence implementation against organisational priorities, and address the people, processes, tools and data adoption depends on.
Output: an AI Implementation Approach.
Set up how the work keeps improving after rollout: see how agility, continuous learning and human-centred leadership shape practice as AI changes what work looks like, use practical AI support for planning, reflection and coaching, and identify the behaviours that keep you effective as tools, teams and priorities change.
Output: a Future-Ready Management and AI Action Plan, plus a personal toolkit.
Bring together all six module outputs into one coherent business case, ending in a justified recommendation to proceed, revise the proposal, or not proceed.
| Item | Detail |
|---|---|
| Total course fee | R3,990 |
| Included | All course materials, facilitated online delivery, portfolio feedback |
| Group / employer enrolment | Price determined by group size |
AI can produce a confident, polished answer in seconds. But before you act on it or put your name to it, you need to know whether you can trust it. In this course, you’ll learn to spot inaccurate information, invented details, missing context and overconfident claims in AI-generated content. You’ll build a practical process for checking facts, tracing citations to their original sources and deciding how much verification is needed based on the potential risk. You’ll also learn to use AI to support goal-setting, prioritisation and decision-making. You’ll finish with an AI Risk and Verification Plan and an AI-Supported Planning and Prioritisation Analysis based on your own work.
Leave your details and a TSIBA course advisor will contact you.
By submitting this form, you consent to TSIBA processing your information in accordance with POPIA and our Privacy Policy.Your people are already using AI. What you probably don't have is a shared way of deciding which tasks it should touch, how much checking an output needs before it reaches a client, and whether any of it is producing value — enrol a cohort and close that gap using your own processes as the material.
Talk to Us About CohortsEvery participant finishes with an AI Business Case on a real opportunity in your organisation, ending in a recommendation to proceed, revise or not proceed.
4–6 hours per week, fully online, across six weeks. No travel, no days out of the business.
The same opportunity test, verification logic, value measures and implementation sequence across the whole cohort.
No. The course is built for professionals who already use AI day to day, not for data scientists. You'll apply what you learn to a real process or decision from your own workplace.
4–6 hours a week across six weeks, plus an orientation module. Fully online.
A completed AI Business Case: six rubric-marked outputs building to a final recommendation to proceed, revise or not proceed.
Yes — cohorts can be enrolled for a team, and the course runs on your organisation's own processes from week one. Pricing and invoicing depends on group size
