The PCL-AI Body of Knowledge.
This defines the competencies the PCL-AI credential is designed to assess. It is published as the First Edition of the PCL-AI Body of Knowledge — thirteen domains, sixty-one Knowledge Areas, weighted 40/40/20 across project accounting & finance, project management principles and governed AI.
The thirteen domains
Project accounting & finance — 40 %
Domains 1–4 · the financial grammar of project controls.
Foundations of Accounting for Project Controls
The double-entry model, the financial statements, accruals and project cost coding.
- Apply the accounting equation and double-entry mechanics
- Build and read the four financial statements
- Run accruals, provisions and project cost coding
Financial Reporting & the Standards
Revenue from contracts (IFRS 15 at its heart) and the standards that shape project numbers.
- Apply the five-step revenue model to project contracts
- Account for contract assets, liabilities and onerous contracts
- Bridge management reporting and statutory reporting
Budgeting & Forecasting
Estimates, baselines, forecasts and project cash flow.
- Build classed estimates and time-phased budgets
- Forecast EAC top-down and bottom-up, with a governed change log
- Model project cash flow, funding and indirect taxes
Performance, Variance & Reporting
Turning numbers into decisions — variances, KPIs and reporting.
- Decompose variances with flexed budgets and bridges
- Design KPI systems with data-derived thresholds
- Report by exception to the decision each audience must take
Project management principles — 40 %
Domains 5–12 · the delivery disciplines controls serves.
Cost Management & Cost Control
The cost engine: commitments, accruals, actuals and control accounts.
- Run the commitment → accrual → actual cycle
- Structure control accounts with work authorisation
- Track quantities, trends and the true cost exposure
Earned Value Management & Forecasting
The flagship: EV, indices and defensible forecasts.
- Measure EV with sound earning rules and the productivity factor
- Select and defend the EAC method, not just compute it
- Apply earned schedule and credibility tests to recovery claims
Contracts & Commercial Management
Contract types, variations, claims, BoQ and the billing cycle.
- Work the risk-allocation spectrum from lump sum to MDMH rates
- Manage variations, claims, LDs, retention and securities
- Run BoQ measurement, certificates and the revenue loop
Project Management Lifecycle
Initiating to closing — gates, benefits and governance.
- Operate stage gates on evidence, pricing decision latency
- Manage stakeholders and communication as a controls discipline
- Close projects with completions, final accounts and lessons
Agile & Adaptive Delivery
Scrum, Kanban, flow metrics and AgileEVM for controls.
- Measure velocity, flow and cycle time honestly
- Apply AgileEVM and reconcile story points to % complete
- Govern hybrid delivery through gates without breaking flow
Project Scheduling
CPM in full — networks, float, compression and delay.
- Run forward and backward passes, float and the critical path
- Compress with crash economics, drag and resource limits
- Progress schedules and analyse delay defensibly
Business Process Cycles
O2C, P2P, time-and-expense and the control environment.
- Operate three-way match, SoD and the audit trail
- Read process mining and continuous monitoring output
- Manage working capital: DSO, DPO, DIO and the cash cycle
Risk Management
Uncertainty quantified — registers, contingency and appetite.
- Quantify risk with EMV, decision trees and Monte Carlo
- Set and govern contingency against P-levels and appetite
- Manage correlation, draw-down and opportunity
AI knowledge & practical approach — 20 %
Domain 13 · governed AI across the controls lifecycle.
AI for Project Controls & PM
Concepts, data, prompting, tools, applied workflows, governance and capability — the governed use of AI across the whole controls lifecycle.
- Apply AI to estimating, forecasting, scheduling, commercial and reporting workflows
- Evaluate outputs with golden sets, precision/recall and priced review steps
- Govern models, data and accountability — AI proposes, the professional disposes
One framework, three views
The PCL-AI describes a single discipline through one framework: the thirteen Body of Knowledge domains above, organised in three groups. The examination blueprint samples the same domains at the published group weighting, and the professional competencies on the credential page map onto them. One structure, from study to exam.
| Domain group | Domains | Exam weight |
|---|---|---|
| Project accounting & finance | Domains 1–4 | 40 % |
| Project management principles | Domains 5–12 | 40 % |
| AI knowledge & practical approach | Domain 13 | 20 % |
The domain groups and their 40/40/20 weighting are those of the published PCL-AI Body of Knowledge, First Edition.
How this will be validated
Job-task analysis
A survey of practising professionals to confirm what the role actually requires — the evidence base for the standard.
SME authoring & review
Subject-matter experts author and peer-review the standard and the items mapped to it.
Founding pilot
A pilot cohort sits the assessment so we can analyse item performance before going live.
Standard-setting
A formal study (e.g., modified Angoff) fixes a defensible pass mark — not an arbitrary percentage.
Have your say
We are actively seeking practitioner input on this draft. If you work in project controls and want to shape the standard, we want to hear from you.
Using the draft to plan your study
The domain structure is the most useful self Treat it as a mirror before you treat it as a syllabus.
Map yourself first
Rate your confidence against each domain's task statements — honestly, from evidence of work you have actually done, not topics you merely recognise.
Weight your effort
The published 40/40/20 weighting shows where assessment attention concentrates. Planning, cost and earned value together carry roughly half the weight in this draft.
Close gaps by doing
The task statements describe applied competence — building, analysing, communicating. Where you can, close a gap on a live project rather than from a textbook alone.
Pair your self-assessment with the published exam structure and the sample questions to see how competence is actually tested.
Thirteen domains, one discipline
The domains are separated for assessment design, but the discipline they describe is one system. The schedule feeds earned value; earned value feeds the forecast; risk analysis sets the contingency the forecast draws on; data quality underpins all of them; and governed AI now runs across the whole chain. That is deliberate. The PCL-AI is designed to certify integrated judgement — a professional who can follow a variance from a slipped activity, through its cost and forecast consequences, to a decision-ready recommendation. Reading the draft, notice how many task statements only make sense with two or three domains held together at once.
A living framework
The Body of Knowledge is maintained under a continuous review programme: refinements from practitioner feedback, the job-task analysis and expert review are incorporated in subsequent editions, and the current edition supersedes all earlier printings. Feedback is most valuable when it is specific and grounded in practice — sector perspective where energy, rail, construction or data-centre practice diverges; terminology that would confuse practitioners in your market; and evidence about the real balance of the job. To contribute, contact the institute and name the domain and Knowledge Area you are commenting on — precise feedback gets used.