Weekly online
Each four-month module includes 14 interactive two-hour classes and one reading week, following an optional full-day London masterclass. Read selected material, practise between sessions and bring your questions back to the group.
Professional learning · Project Controls & Management
AI in Project Controls & Management.
Build AI workflows that turn project evidence into action.
Project teams spend valuable time reconciling information, rewriting reports and chasing actions. KBC’s AI in Project Controls course develops a practical way to redesign that work. You move from specifying a useful AI task to mapping a process, structuring project data, building dashboards and creating supervised workflows and agents. KBC’s own developing manuscript provides the teaching architecture, supported by PMI’s 2026 AI standard and PMBOK 8. The focus is the complete control loop: reliable evidence, appropriate analysis, a clear recommendation, authorised action and review of the outcome. A practical project brings the learning together.
Weekly route4 months
Live online2 hours a week
Module startsOctober · February · June
Learning structure1 module
The professional challenge
Make the next decision with more confidence.
Your monthly report is assembled from spreadsheets, schedules, emails and meeting notes. By the time it is ready, the decision window has narrowed. The course helps you identify where AI can assist, where deterministic calculations are needed and how a reviewed workflow can preserve evidence, ownership and control.
Who will benefit
For project controllers, PMO professionals, planners, cost and risk specialists, reporting analysts and project managers who want to apply AI to recurring information work. It suits professionals able to describe a real control process and assess its outputs, with technical implementation developed through guided practical work.
What you take back to work
Practical capability, built through learning.
- Specify and evaluate AI tasks using approved evidence, clear outputs, acceptance criteria and decision boundaries.
- Map and redesign a project-controls workflow, supported by a consistent data model and quality checks.
- Create a dashboard or supervised automation that connects project information with a useful decision.
- Demonstrate a practical AI solution with validation, approval routes, recovery arrangements and evidence of value.
Inside the course
A connected learning journey.
The verified KBC manuscript architecture maps directly to 14 teaching sessions: foundations and prompting; process and data; automation and dashboards; agent design and build; communication workflows; integration; and final governance and demonstration. The six learning blocks group those sessions for clarity. Independent practical work is needed to develop and test the solution between classes.
01Project controls as a decision system
Examine how project information becomes a forecast, recommendation, commitment and action. KBC’s opening manuscript connects this control loop with organisational design and the allocation of work between people, rules and AI. Identify a meaningful use case, the accountable owner and the decision it should improve. Define success before selecting a tool or assuming that automation is the answer.
Apply it at work: Create an AI opportunity canvas for a reporting, forecasting or action-management problem.
02Prompting as a management specification
Turn an informal request into a precise instruction with operating context, governing references, trusted evidence, analytical steps, output requirements and limits. KBC’s CONTROL framework structures this work, with acceptance checks that test traceability, calculation, uncertainty and conformance. Practise diagnosing weak outputs and improving the specification, rather than treating fluent prose as evidence of correct analysis.
Apply it at work: Build and test a small prompt library for project-controls outputs, using known examples and failure cases.
03Process design and project data
Map the existing workflow, including hand-offs, sources, delays and exceptions. Design a future process that separates deterministic rules, AI interpretation and human decisions. Build a consistent evidence layer for project, schedule, cost, risk, change and action information, with identifiers, owners and validation. Keep source values, calculations and assumptions distinguishable so the output can be checked.
Apply it at work: Produce current and proposed process maps, a data dictionary and a controls checklist.
04Automation and dashboards
Translate the process specification into a practical workflow and a useful decision interface. Explore triggers, field mapping, branching, structured outputs and error handling, then connect indicators and exception views to user needs. Review how data is refreshed and how missing or inconsistent values appear. The practical sequence prioritises understandable, testable behaviour and a clear route from insight to action.
Apply it at work: Build a reporting workflow or dashboard using controlled training data and document its validation results.
05Supervised agents and connected systems
Specify an agent as a bounded organisational role: purpose, inputs, tools, memory, permissions, measures and escalation. Explore how a workflow can analyse an exception, draft a recommendation and request approval before updating an authorised record. Consider system ownership, duplicate prevention and recovery when connecting applications, so automation does not introduce conflicting versions of the same project fact.
Apply it at work: Demonstrate an agent or connected workflow with an explicit approval step, execution record and recovery path.
06Meetings, assurance and the practical project
Use authorised transcripts and communications to propose actions, decisions, risks and issues while preserving links to the original evidence. Distinguish confirmed commitments from suggestions and uncertainty. Bring the solution under an operating policy covering access, review, version changes, monitoring and incidents. Evaluate the practical project against accuracy, usefulness, review effort and the benefit achieved in its intended process.
Apply it at work: Present a tested solution and an implementation pack covering ownership, evidence, controls and a measured improvement objective.
Learn around your working life
Time to understand. Space to apply.
Online or in a city near you
Face-to-face delivery can be arranged in London, Maidstone, Manchester, Birmingham, Leeds and Liverpool. Your course offer confirms the venue and teaching dates.
A London masterclass to start your module
Meet your tutors and fellow professionals at an optional full-day London event. KBC covers the event, agreed transportation, refreshments and an open buffet dinner. Confirm arrangements with the team before booking travel.
Support between sessions
Return to class recordings and learning resources, bring questions to your tutor and use free one-to-one tutoring and coaching support. Your course plan sets out the guided reading and practice needed alongside live teaching.
Your route to recognition
Know what the assessment involves.
The course develops practical evidence through a tested AI output, workflow, dashboard or agent and an explanation of its purpose, controls and performance. The KBC manuscript architecture culminates in a practical demonstration and governance pack. The confirmed KBC assessment brief should specify submission, marking and completion requirements. This is KBC professional development; using PMI standards as references does not by itself award a PMI certification or establish that the course is a PMI-CPMAI examination-preparation programme.
Your written offer records the award or course completion requirements, any examination fees included, and the relevant eligibility checks.
The professional knowledge behind the course
Explore the books and standards.
The learning themes above bring together the relevant guide, syllabus and workplace practice. KBC’s teaching sequence connects these ideas through discussion and application.
- AI in Project Controls and Project Management: From Human Bureaucracy to the Agentic Project Organisation ↗Amgad Badewi / Kent Business College · Working manuscript, September 2026
Original 14-chapter teaching architecture. Chapters 1–2 full: control-system foundations and managerial prompting. Chapters 3–14 are structured development briefs covering process, data, automation, dashboards, agents, meetings, integration and governance.
- The Standard for Artificial Intelligence in Portfolio, Program and Project Management ↗Project Management Institute · June 2026
Eight principles, five performance domains, life-cycle/tailoring and human oversight.
- PMBOK Guide ↗Project Management Institute · Eighth Edition,2025
Integrated project control, governance, evidence and performance-domain context.
Your investment
Get a course offer that fits your route.
Request the standalone fee for your chosen format, including the teaching, reading materials, assessment and membership arrangements relevant to this course.
Exploring the full PCP apprenticeship?
£27,000 apprenticeship funding + £7,000 additional IPC support. The IPC package covers professional examinations, memberships, graduation, Benenden Health membership, London masterclasses and agreed transport.
These figures relate to the complete Project Controls Professional apprenticeship. Standalone course fees are separate. The team confirms eligibility, any employer contribution and the contents of your written offer.
Explore the PCP pathway →Your questions, answered.
Do I need to be a programmer?
The learning begins with defining the process, evidence and decision. Guided practical work then develops the implementation. Discuss your current spreadsheet, data and workflow confidence with KBC so the course and support match your starting point.
Will AI make the project decisions?
You will design explicit decision boundaries. AI can help analyse, explain and recommend; authority, approval and accountability must be deliberately assigned. The practical work tests what the system may do and when it must ask for review.
Is this an official PMI AI qualification?
The course uses PMI’s AI standard and PMBOK 8 as supporting references. The teaching architecture and practical outputs are KBC’s. Any external certification must be separately identified and confirmed in the course offer.
What would this help you achieve?
Talk to KBC about your experience, your goals and the right learning route for you.