Audience
- Project managers and PMO professionals improving information workflows.
- Planners, controllers and analysts producing recurring reports.
- Transformation practitioners connecting business needs with AI tools.
Project management
AI in Project Management
Explore practical AI applications across project planning, communication and control.
Learning with a practical purpose.
Use AI to reduce repetitive project administration while keeping decisions traceable. This practical module starts with a reporting problem and helps you design, test and improve a useful assistant or workflow. You will work with synthetic project information and learn to distinguish reliable calculation from generated interpretation. Teaching and course completion certification are provided through Kent Business College UK.
The semester contains 14 weekly online sessions of two hours each, giving 28 live teaching hours plus practical development between classes. Build a small solution that you can explain and demonstrate, with clear conditions for human review. This is KBC professional development. References to project-management standards do not make the module a PMI certification or a PMI-CPMAI examination-preparation programme.
Live onlineTeaching through Kent Business College, UK
2 hoursOne live session each teaching week
14 sessionsA semester for each module
28 live hoursPer module, plus independent study
The outline below is a 14-session online module, with one two-hour live session each teaching week. Reading weeks, breaks and additional activities follow the agreed cohort calendar. The cohort syllabus, full route, dates, fees, independent study and assessment requirements are confirmed before enrolment.
Who this module is designed for.
Starting knowledge and preparation
- Be able to describe a project process and work with ordinary spreadsheets.
- Have access to a laptop and approved training tools; any paid licences are identified before enrolment.
What you will work towards.
- Specify an AI task with clear inputs, outputs and success criteria.
- Prepare structured project information for a repeatable workflow.
- Test an assistant against known examples and realistic failure cases.
- Demonstrate a supervised solution with a measurable operational purpose.
Explore the 14-session learning plan.
Explore the focus and practical output of each teaching session. Open individual sessions below, or expand the complete plan.
Session 01 Finding the right problem
Compare recurring project tasks and select one with a clear owner and measurable burden.
Practical output: A practical AI opportunity brief.
Session 02 Understanding model behaviour
Explore why plausible language can be wrong and how model limits affect project reporting.
Practical output: A list of acceptance checks.
Session 03 Specifying the task
Turn an informal reporting request into instructions with trusted inputs, boundaries and required outputs.
Practical output: A structured task specification.
Session 04 Building test examples
Create known cases that reveal omissions, unsupported claims and incorrect handling of uncertainty.
Practical output: A small evaluation dataset.
Session 05 Mapping the process
Trace the current handovers and design a revised process with explicit review points.
Practical output: Current and proposed workflow maps.
Session 06 Preparing project data
Choose consistent identifiers and fields for the information used by the reporting workflow.
Practical output: A project data dictionary.
Session 07 Keeping calculations reliable
Use ordinary formulas for numerical measures and reserve AI for appropriate explanatory tasks.
Practical output: A checked calculation and commentary example.
Session 08 Grounding the response
Provide approved project evidence and require the assistant to distinguish sources from assumptions.
Practical output: A source-linked reporting prompt.
Session 09 Connecting a workflow
Configure a simple trigger, transformation and review step using controlled demonstration records.
Practical output: A working workflow prototype.
Session 10 Designing the interface
Show users the exceptions and decisions that deserve attention without overwhelming them with output.
Practical output: A focused dashboard or review screen.
Session 11 Defining an agent role
Limit a proposed agent’s tools, actions and escalation responsibilities around a specific task.
Practical output: An agent permission and responsibility brief.
Session 12 Testing failure and recovery
Introduce missing information and service errors, then check the workflow’s fallback behaviour.
Practical output: A failure-handling and recovery record.
Session 13 Measuring practical value
Compare the prototype with the original task using time, accuracy and review-effort measures.
Practical output: A balanced pilot evaluation.
Session 14 Demonstration and handover
Present the solution and explain who maintains, reviews and improves it after the pilot.
Practical output: A demonstration and operating guide.
Apply the learning
A supervised project reporting assistant
Build a prototype that turns a controlled dataset into a draft report, exception summary or action-review queue. Define what it may do, what a person must approve and how errors become visible. Demonstrate the result against examples that were not used to design the initial prompt.
Your project evidence can include
- Working prototype and process explanation.
- Test cases, results and identified limitations.
- Pilot measures and a practical operating guide.
Use anonymised, public or suitable synthetic material where workplace information is confidential. The issued assessment brief confirms the required evidence.
Assessment, feedback and certification.
- Weekly demonstrations and peer review of outputs.
- A final prototype, evaluation record and professional explanation.
- A KBC course completion certificate follows the agreed participation and assessment requirements.
Teaching and the relevant course certificate are provided through Kent Business College in the UK. A Kent Business College course certificate is distinct from a separate professional-body qualification or examination. Eligibility, the exact award and any examination inclusions are confirmed for the chosen route.
Questions worth asking.
Do I need to write software?
The module begins with processes, evidence and decisions. Guided implementation can use accessible tools; discuss your existing technical confidence when choosing the learning route.
May I upload employer information to an AI tool?
Use the supplied synthetic information unless your employer has authorised another dataset and the chosen tool. Practical learning does not depend on confidential records.
Will the prototype run my project automatically?
The intended outcome is a bounded assistant or workflow with accountable review. You define which actions require approval and demonstrate those boundaries.
Programme references.
Further reading and programme references · reviewed 18 October 2026