Why this matters
AI is becoming part of everyday project controls work, from schedule analysis to risk modelling to acceleration planning. That’s a genuinely useful shift. But it also means project controls professionals are increasingly being asked to make decisions about tools and technology, often without a background in how to evaluate them.
Understanding how an AI tool actually works, what it’s built on, how it handles uncertainty, and what happens when you need to explain a decision to a client or a board, matters more in this profession than in most. When AI is informing decisions about delay recovery, risk, or acceleration on a major programme, you want to be able to trust the answer and explain how it was reached.
What this checklist gives you
This guide sets out the practical questions worth asking when you’re evaluating an AI tool for project controls, covering the areas that matter most in practice: reliability and how a tool handles uncertainty, auditability and explainability, the data and models a tool is built on, how it fits into your existing workflows, and security and governance.
It’s structured as a straightforward checklist you can bring into vendor conversations and demos, alongside a simple comparison table that sets out different approaches to AI in project controls, so you can see where a given tool sits and decide what matters most for your team.
The aim is simple: to help you ask better questions, understand the answers you get back, and make a confident, informed choice, whichever direction that leads you.
Who it’s for
Written for planners, schedulers, project-controls specialists and delivery leaders working on complex capital programmes.
If you’re managing recovery, risk, change or acceleration, this guide offers a practical example of how auditable AI can help you investigate more options and strengthen the evidence behind your decisions.
Contents
Inside the guide
The buyer's checklist
A practical set of questions covering reliability, auditability and explainability, data and model foundations, workflow fit, and security and governance, ready to bring into any vendor conversation.
Approaches to AI in project controls
A simple comparison table setting out different approaches to AI, from analytics-only platforms through to fully agentic, auditable systems, so you can see the range of what’s available and where your priorities sit.
The bottom line
A short summary of what tends to separate AI that simply produces answers from AI that can also show its working.
Nodes & Links AI
An honest look at how our own platform is built, including our assurance layer, audit trail, data foundations, and security certifications, so you can hold us to the same standard as everyone else.