01
Reduce repetitive reporting
Use structured data and automation to reduce time spent gathering, formatting and drafting routine project controls information.
Efficiency
Practical capability for modern project environments
AI in Project Controls Certificate
Build responsible AI workflows, dashboards, automations and governed agents that work with real project controls data - not isolated classroom demonstrations.
Live and tutor-led
No prior coding required
Portfolio and capstone
Human-in-the-loop governance
14Live practical sessions
2hEach tutor-led session
28hTotal live learning time
4mDelivered over four
months
Applied toolchain
ChatGPTPrompting & analysis
ClaudeWorkflow design
n8nAutomation & agents
Google SheetsControlled data layer
LovableDashboard & app UI
ChatGPTPrompting & analysis
ClaudeWorkflow design
n8nAutomation & agents
Google SheetsControlled data layer
LovableDashboard & app UI
Course proposition
Move from AI awareness to a working, governed solution.
The programme follows a demonstrate-build-test-review pattern. Every session creates reusable workplace assets and evidence for professional development.
02
Improve analysis consistency
Create repeatable prompts, quality checks, thresholds and workflow rules for clearer decision-ready narratives.
Quality03
Connect existing tools
Use Google Sheets, APIs and n8n to connect project applications without a disruptive rip-and-replace programme.
Integration04
Keep people accountable
Design approvals, escalation points and audit trails so professionals remain responsible for quality and decisions.
Governance05
Build portfolio evidence
Leave with tested prompts, workflows, dashboards, data models, logs and a practical capstone demonstration.
Progression06
Measure workplace value
Compare baseline and pilot performance using reporting time, data quality, action closure and control compliance measures.
ImpactDesigned for both sides of the workplace
Built for project professionals and the employers who support them.
Suitable for experienced professionals and developing practitioners who understand the basics of project work and want hands-on AI and automation skills.
Build practical capability you can show.
Create visible evidence for workplace reviews, interviews, professional development and apprenticeship discussions.
- ✓Project controllers
- ✓Planners and schedulers
- ✓Cost and risk professionals
- ✓PMO analysts
- ✓Project managers
- ✓Programme teams
Turn individual learning into organisational improvement.
Retain reusable workflows, dashboards, templates, mappings and operating procedures while introducing AI through controlled pilots.
- ✓Faster reporting cycles
- ✓Better data discipline
- ✓Controlled innovation
- ✓Improved action follow-through
- ✓Reusable digital assets
- ✓Capability and retention
Learning outcomes
Ten capabilities that connect directly to project controls work.
Each outcome is applied through practical activities, testing and portfolio evidence rather than passive content consumption.
AI
Select valuable AI use cases
Assess value, feasibility, data needs, risks and human control points.
P
Engineer reliable prompts
Produce structured, testable outputs for schedule, cost, risk, change and reporting.
WF
Redesign workflows
Map current processes and specify AI-enabled future workflows with safe failure paths.
GS
Structure governed data
Build a controlled Google Sheets data model with validation and data-quality checks.
n8n
Automate analysis and reporting
Read data, detect exceptions, call AI and write controlled outputs through n8n.
UI
Create real-data dashboards
Design action-focused Lovable dashboards backed by approved spreadsheet data.
AG
Build governed AI agents
Define tools, permissions, stop conditions, approvals, logging and escalation.
MT
Convert meetings into actions
Extract decisions, risks and actions from transcripts with validation and approval.
API
Integrate project applications
Map IDs and fields across an API-ready platform, n8n, Sheets and dashboards.
Solution architecture
Build an integrated AI project controls solution
Learners connect structured project data, automation, AI analysis, dashboards and governance into one working course solution.
Project Applications
Input source
ClickUp, Asana, Jira or monday.com project records.
API connectedGoogle Sheets
Structured data layer
Project, schedule, cost, risk, change and action data.
Records availableChatGPT
AI model
Prompt engineering, analysis and reporting commentary.
Model readyClaude
Workflow intelligence
Workflow design, meeting analysis and documentation.
Analysis readyAutomation & AI Orchestration
Core workflow engine
Triggers, transformations, AI calls, integrations, routing, validation and controlled write-back.
Workflow executedAI Project Analysis
Generated output
Detect exceptions, draft commentary and recommend actions.
Output generatedHuman Approval
Required governance step
Review AI output, verify evidence and approve important actions before write-back.
Approval requiredLovable Dashboard / App
Presentation layer
Project health, schedule, cost, risk, changes, actions and approved AI commentary.
Dashboard updatedAudit Trail
Control record
Log decisions, approvals, changes and system activity.
LoggedHuman-in-the-Loop Governance
Reviews AI outputs, approves actions, checks evidence, controls write-back and keeps a complete audit trail.
Learner and employer voices
Real experiences from across the learning journey.
Present multiple approved testimonials from learners, employers and workplace sponsors. The animated carousel pauses when a visitor hovers over it.
★★★★★Learner voice
Approved learner testimonial goes here. Focus on the practical skills developed, the tools used and the workplace result.
★★★★★Learner voice
Approved learner testimonial goes here. Explain how the course improved confidence with AI, reporting, dashboards or automation.
★★★★★Learner voice
Approved learner testimonial goes here. Include a specific example of a workflow, dashboard or agent created during learning.
★★★★★Learner voice
Approved learner testimonial goes here. Describe the value of live teaching, practical builds and portfolio evidence.
★★★★★Employer voice
Approved employer testimonial goes here. Describe the organisational value, capability improvement or reporting benefit observed.
★★★★★Employer voice
Approved employer testimonial goes here. Explain how the learner applied new skills responsibly within the workplace.
★★★★★Learner voice
Approved learner testimonial goes here. Highlight responsible AI, human approval and improved project-controls decision making.
★★★★★Employer voice
Approved employer testimonial goes here. Highlight team development, reusable assets and the benefit of an applied final project.
Content status: The testimonial cards are professionally designed placeholders. Replace them only with approved, attributable learner or employer feedback before publishing.
Seven modules, fourteen sessions
A structured four-month build journey.
Every module progresses the same solution from opportunity and data design through automation, dashboards, agents, integration and governance.
Session 1
AI opportunity and architecture
Map one reporting or control process, define baseline measures and identify human control points.
Session 2
Prompt engineering with ChatGPT
Build and test a reusable project controls prompt library with structured outputs and quality checks.
Session 3
Map the current workflow
Document inputs, outputs, decisions, roles, bottlenecks, exceptions and ownership.
Session 4
Design the AI-enabled workflow
Create a to-be process with triggers, approvals, error handling and safe failure paths.
Session 5
Build the project controls data model
Create project, schedule, cost, risk, change, action and configuration tables with validation.
Session 6
n8n automation fundamentals
Read data, detect exceptions, call AI and write a controlled draft output with logs.
Session 7
Dashboard design and prototype
Design around user decisions, KPIs, RAG rules, accessibility and action-focused layouts.
Session 8
Connect Google Sheets to Lovable
Replace mock data, validate field mapping and apply controlled refresh or approved updates.
Session 9
Design the project controls agent
Define purpose, tools, permissions, evidence, confidence, stop conditions and tests.
Session 10
Build a governed agent in n8n
Generate recommendations, request approval, write back approved actions and retain an audit trail.
Session 11
Analyse meetings with AI
Extract decisions, RAID items, actions and unresolved questions from source evidence.
Session 12
Automate meeting-to-action
Validate owners and dates, prevent duplicates, approve outputs and update project systems.
Session 13
Integrate API-ready applications
Map records, handle credentials and errors, prevent duplicates and document source-of-truth decisions.
Session 14
Policy and capstone showcase
Present a complete solution and defend its data, workflow, approvals, controls and measured value.
Apply the full toolchain
Choose a relevant practical project.
Use an approved workplace case or an anonymised KBC training scenario. Every capstone must be useful, traceable, controlled, tested, measurable and transferable.
AI Project Controls Dashboard
Google Sheets data, automated commentary and a Lovable view for schedule, cost, risk, change and actions.
Meeting-to-Action Automation
Transcript analysis, validation, approval and controlled action creation in a selected project application.
Risk and Change Control Agent
An agent that reviews records, requests missing data, recommends next steps and escalates priority cases.
Monthly Reporting Assistant
Exception detection, narrative generation, quality checks and a human-approved reporting pack.
Project Application Integration Bridge
Synchronise an employer platform, Google Sheets, n8n and the Lovable dashboard with controlled write-back.
Portfolio Early-Warning System
Use thresholds and AI analysis to identify emerging issues, route review and highlight priority actions.
Value for learners and employers
Practical benefits beyond the classroom.
The strongest outcomes happen when learning is connected to a relevant use case, appropriate data access, a workplace sponsor and timely review.
Move from awareness to tested prompts, workflows, dashboards, agents and integrations.
Leave with visible evidence for reviews, interviews and progression discussions.
Automate preparation while keeping professional review and accountability.
Recognise privacy, quality, bias and hallucination risks and apply safeguards.
Reduce collecting, formatting, drafting and chasing effort across routine reporting.
Share data structures, prompts, thresholds and workflow rules across teams.
Introduce AI with permissions, logs, approvals, policies and clear accountability.
Retain templates, workflows, dashboards, mappings and operating procedures.
Delivery and assessment
Understand. Design. Build. Govern. Demonstrate.
Four months provides time between selected sessions for practice, employer review, troubleshooting and portfolio refinement.
1
UnderstandUse cases and risks
2
DesignData and workflows
3
BuildDashboards and agents
4
GovernReview and approval
5
DemonstrateValue and evidence
Assessment approach
Assessment is based on practical evidence created throughout the programme.
- ✓Practical portfolio produced across all modules.
- ✓Working capstone solution or controlled prototype.
- ✓Evidence of testing, controls and human approval.
- ✓Reflection on value, limitations and next steps.
- ✓Tutor review against communicated criteria.
KBC AI in Project Controls Certificate
Awarded following successful completion of required practical work, portfolio, attendance and KBC quality requirements. Detailed criteria are confirmed during onboarding.
Entry and funding
Choose the route that fits the learner and employer.
Funding, delivery arrangements and certificate requirements are confirmed in writing before enrolment.
Project Control Professional Level 6
The certificate is designed to be available through Kent Business College's Project Control Professional Level 6 pathway, subject to current rules and individual and employer eligibility.
Employer-supportedEligibility checked before enrolmentWorkplace evidenceFunding confirmed in writing
Check apprenticeship eligibilityCommercial cohort or employer route
Organisations may discuss a dedicated cohort, tailored project application and delivery arrangements.
Commercial pricing and scope are confirmed separately.Recommended entry profile
Basic familiarity with spreadsheets and project reporting. A workplace use case or willingness to use a KBC scenario. No prior coding required.
Data and security
Use anonymised or synthetic data by default. Employer data requires explicit permission, approved access and human review before external communication or write-back.
Questions before enrolment
Frequently asked questions.
Clear answers about entry requirements, technology, data use, professional alignment and funding.
No. The course uses low-code tools, structured logic, field mapping and practical API concepts. Optional expressions may be introduced where useful.
Yes. Learners can connect approved Google Sheets data directly or through an n8n and API/webhook layer. Training data is used until access is approved.
ChatGPT supports prompt engineering, analysis and structured drafting. Claude supports workflow design, documentation and meeting analysis. n8n can orchestrate approved model access.
No. It is a Kent Business College certificate. The curriculum is informed by public themes in the PMI AI standard and supports evidence relevant to the APM ChPP journey. External certification and chartership require separate eligibility and assessment.
Only with explicit organisational permission and appropriate controls. An anonymised workplace dataset or KBC training dataset can be used instead.
It is designed to be available through the Project Control Professional Level 6 pathway. Funding is subject to current rules and confirmed learner and employer eligibility.
September 2026 intake
Build it. Govern it. Use it.
Book an information session to discuss eligibility, employer needs, delivery arrangements and the most suitable final project.
