6 things every AI leader needs to get right in H2 2026
The pilot phase is over. Here are the 6 trends shaping AI strategy in H2 2026, from agentic infrastructure to physical AI and custom builds.
Governed agents are here. Is your stack ready?
Microsoft Build 2026 didn't just announce products. It announced a philosophy: the era of the unmanaged AI agent is over.
Demystifying AI agents: going beyond the buzzwords
"Agent" is the most overused word in AI right now. But strip away the hype and what are you actually working with? Adobe principal scientist Deepak Pai breaks down the real building blocks of agentic systems and when they're worth reaching for.
Why smart companies don’t add AI everywhere
Boards want AI roadmaps. Competitors are shipping AI features. And 74% of companies still can't make it pay. This piece breaks down the eight-point framework that separates disciplined AI adoption from expensive noise.
5 questions AI agent vendors hope you don’t ask
Most AI agent failures don't happen during the demo. They happen when APIs fail, context windows explode, costs spiral, and nobody can explain why the agent made a decision. Here are five questions that separate production-ready platforms from expensive experiments.
6 things to fix before RLHF turns your biases into features
Your reward model is learning exactly what your annotators prefer. The problem is that "better" and "unbiased" are two different things, and RLHF has no way to tell them apart.
Is multi-turn reasoning broken?
Multi-turn reasoning is broken in a way nobody saw coming. The question is; what can we do to fix it?
The AI-first GTM strategist: agents, workflows, and knowing when to stop
Most GTM teams deploy AI where it's most visible. The question worth asking first: is that actually where it's most ready?
The 3 reasons your AI never makes it to production
Most companies don't have an AI problem. They have a throughput problem. And I think that distinction matters a lot when you start talking about how to actually get AI working in production.
Is your AI is evaluating you?
What if the model you've been evaluating has been evaluating you right back? New research finds that LLMs systematically alter their output depending on whether, and by whom, they believe they are being observed. It might have serious implications - are you ready?