Long-form pieces on putting AI to work in project delivery. Written from practice, not theory.
Turn a prompt you rerun every week into an AI loop: objective, criteria, a check, a stop rule, sign-off and feedback, on one page.
Prepare for a gate review with AI: check your pack against each gate criterion, then predict the board's questions from past minutes. Two prompts.
AI memory is on in many work tools. Ask what yours holds about your project, check its settings, and fix stale items at the source.
When AI drafts the status report and chases actions, the project manager role moves to three places. How to move the freed hours there on purpose.
The best way to use AI for a steering committee deck is to agree the argument with your sponsor first and let the tool build the slides from it.
The useful way to test a new AI model is against your own work, in two parts. First, re-run a few things you already do and compare the output with what you produced before.
An AI agent charter check is a thirty-minute test you run on an agent's brief before it starts work. You ask an AI to play a literal agent that wants to finish the job as fast and as cheaply as possible, and to list every way it could satisfy the brief's words while missing what you meant.
Agentic user experience (AUX) is the shift in interface design from "build something a human can navigate" to "build something an agent can act on": where a person states an outcome and the system, with an agent doing the orchestration, works out the steps.
Most project teams approach AI the same way: they look at who's already doing what, then ask where AI could help.
An AI competency framework for project managers maps AI capability across the disciplines PMs already own: prioritisation, stakeholder communication, risk management, delivery tracking, at novice,…
AI skills for project managers are encoded, reusable workflows that run from the same instructions each time, without you re-explaining them. That's the step up from writing good prompts.
AI trust calibration for project management means treating a confident, well-written AI recommendation as a starting point for verification, not a finished answer.
Managing an AI token budget on a project means giving agent spend its own line in the budget baseline, not lumping it into a flat subscription fee and hoping it stays flat.
AI agent costs are rising for complex tasks - sometimes close to human rates. Here's a simple framework for deciding when delegation is actually worth it.
AI-readable project documentation means writing in short, self-contained paragraphs with explicit decisions and clear topic sentences - not narrative prose that requires the whole document to interpret.
Parallel AI agent orchestration is now the production default. Here's what running agent fleets - not individual agents - means for delivery governance and practice.
AI agents fail in predictable ways. This pre-launch QA checklist gives PMs two test gates to catch reasoning and output errors before stakeholders do.
In production AI deployments, 59% of agentic token usage now routes across multiple models from different providers.
Rich project context consistently produces better AI outputs than better prompts with the same model. The most common reason AI tools give generic, unreliable, or frustrating results in project management isn't the model - it's that the model has never read the project.
Status reports are produced on schedule and read by nobody. An exception-triggered monitoring agent is a different tool entirely - here's how to think about the design.
The PMs getting consistent results from AI aren't better prompters - they're managing context deliberately. Here's what that looks like in practice.
When every PM on your team uses AI differently, the outputs are inconsistent and the knowledge disappears when people leave. A governed prompt library fixes that.
Most enterprise project management tools weren't designed with AI agents as users. They expose atomic API calls where agents need compound outcomes - and the resulting friction is invisible until you actually try to deploy.
An AI delegation register is a shared document that maps every PM task to its current automation status: fully autonomous, AI-assisted, or human-owned.
Inconsistent AI outputs in PM workflows usually trace to context quality, not the model. Here's a practical design doc approach to fix it.
An AI agent governance charter is a one-page document that defines which workflows your team has delegated to AI agents, who reviews the outputs, and what happens when something goes wrong.
AI output quality in project management depends on what you fed in, what the tool can't know, and your willingness to override it. Accountability is yours.
Most PMs use AI ad hoc. A personal AI workflow - repeatable prompts with defined inputs - turns that into a capability that compounds. Here's how to build one.
By mid-delivery, your project record is scattered and unsearchable. NotebookLM turns documents, transcripts, and recordings into a project brain.
The most strategic PMs prevent crises rather than react to them. How AI prediction gives project managers evidence-based foresight from their own data.
Most project analysis is just reporting. AI changes the ratio by handling aggregation so PMs can focus on interpretation. A practical guide for project managers.
AI expands what PMs can automate. Not just date triggers, but messy human inputs like status updates and meeting outputs. A practical guide.
One PM with the right AI tools can produce the documentation output of a whole team. How the Generate pillar works, and what the PMO of One means.
Five domains where AI augments the project manager's role: Assist, Generate, Automate, Analyse, Predict. A practical framework for where to start.
AI changes more than how PMs work. It reshapes team resourcing, stakeholder expectations, and governance. The second and third-order effects for PMs.
Strip back the tools and the PM role comes down to one thing: delivering under uncertainty. A clear look at the fundamentals of project management.
AI is a powerful PM tool, but accountability still sits with you. What responsible AI use actually looks like for project managers.
AI isn't replacing project managers. It's taking on the admin. Here's how PMs can use AI to reclaim time for the work that actually needs their experience.
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