Research intelligence
Applied notes connecting research evidence to consequential transformation choices, work design and implementation architecture.
AI can accelerate project-controls analysis, but reliable control requires schedule, cost, risk, change and reporting evidence to remain connected, traceable and human-accountable.
A workflow-level map for applying AI in project and portfolio management: where current products can help now, where integration is required, and where human authority should remain explicit.
AI in the PMO: which workflows are worth redesigning first? Read More »
An enterprise AI inventory should be a working control surface for use cases, systems, data, authority, vendors, regulatory triggers and lifecycle change—not a static software list.
A practical enterprise AI inventory: what to record, who owns it, and why Read More »
A practical operating guide to the EU AI Act: build the inventory, classify use cases, assign ownership, control transparency and prohibited practices, prepare high-risk systems, and make AI governance operational.
The EU AI Act is in force: what should an enterprise actually do now? Read More »
A practical operating architecture for deciding which AI use is permitted, where data may go, what AI may do, where human authority remains, and how exceptions become governance intelligence.
What an enterprise AI acceptable-use architecture actually needs Read More »
Personal AI at work persists because employees are solving real work problems faster than organizational systems adapt. The durable response is not prohibition alone: it is a managed work environment that combines useful approved tools, data boundaries, identity, controls, adoption and continuous discovery.
Shadow AI is an operating-model problem Read More »
A practical architecture for moving from observed operating friction to a bounded, measurable AI-enabled intervention: evidence, decisions, work design, technical architecture, implementation and learning.
How DENFORTH moves from operational evidence to implementation architecture Read More »
AI capability does not determine the quality of the resulting work system. The consequential design choices concern task allocation, authority, expertise, exceptions, coordination and learning.
Why Human–AI work design matters more than tool adoption Read More »
A practical method for enterprise AI transformation: start with a consequential recurring decision, reconstruct its evidence, work and authority, build the smallest bounded intervention, measure the operating result, and earn scale.
Decision-first Enterprise AI Transformation Read More »
A current evidence note on why widespread AI use is not the same as enterprise-scale value—and why workflow, operating-model, data and adoption redesign now matter more than tool access.
Most enterprises do not have an AI problem. They have a transformation problem. Read More »
