The quiet default: why most AI projects choose the safe option
Most AI projects make a conservative choice somewhere that limits what they can accomplish. The choice is rarely announced as conservative. It is presented as sensible, pragmatic, or appropriately scoped. Understanding why this happens is the first step toward making decisions that are actually right rather than merely defensible.
How AI is reshaping competitive strategy
The competitive advantages that have held for decades are being stress-tested by AI. Speed of implementation is no longer a durable moat. The organizations rethinking where their real advantages lie are better positioned than those optimizing harder for advantages that are eroding.
The AI product manager: a new role taking shape
Building products with AI components requires product managers to develop new skills, own new responsibilities, and apply different judgment than traditional software PM work demands. The role is evolving faster than most PM playbooks have caught up.
What to prioritize in your AI roadmap for 2027
Most AI roadmaps list capabilities the team wants to build. The ones that actually deliver value are organized around a different set of questions: where is the current system falling short, what infrastructure enables multiple use cases, and what can the organization realistically absorb?
Why AI features need different success metrics
Organizations routinely measure AI feature success using the same metrics they apply to traditional software features. The mismatch produces misleading signal, misallocated investment, and AI systems that optimize for the wrong outcomes.