"model accuracy"

PagerDuty Executive Chair Jenn Tejada explains why AI's next challenge isn't building smarter agents—it's keeping them from drifting, failin...

The AI-verified engineer is a different profile: They own outcomes, not prompts.

AI models without strong business context risk costly errors, but vendor approaches to “context” vary. Enterprises must take ownership of th...

What the user has been doing in the session, not just what they're looking at, is a separate context layer that most teams haven't scoped ye...

Many AI initiatives fail not because of poor technology but because organizations lack shared definitions, context and decision-making frame...

A decision that helped one quarter was making the business harder to defend in every quarter after it.​

The failure is not in the technology. It is in the operating model surrounding it, and it shows up in three interconnected places.

Start by figuring out if the systems organizations build around AI are designed to produce trustworthy outcomes. That's an architectural que...

While the model is often the first suspect for AI pilots stalling, the architecture is the more likely culprit.

Even though enterprise AI is advancing rapidly, when organizations move beyond prototypes, their AI systems often fail in production.

Autonomous networking requires that every action be verified against a mathematically accurate model of the full production network before e...

A surprisingly easy way to multiply an AI model’s profit is to drive decisions via expected value instead of predictive scores. Here's how,...