Successful AI projects begin with a clear understanding of how work actually happens. Intelligence Mapping reveals where AI agents can create value before code is written — preventing costly failures and ensuring AI investments solve real problems.

With the gap between imagination and deployment shorter than ever, understanding agentic patterns is a critical step in mastering the emerging AI-driven marketplace. We review six key patterns needed to drive value for customers and enterprises.

In her seminal AI report, Mary Meeker looks back at a seeming paradox from the first Industrial Age to underscore how the enormous productivity potential of artificial intelligence is poised to unleash entirely new categories of human activity.

Can AI shopping assistants close sales like a human expert? Our test shows they can, and how! Perplexity leads the pack, delivering expert-level, contextually nuanced product knowledge while ChatGPT impresses. Amazon's Rufus and Google Gemini trail behind. As shopping shifts from keywords to conversations, brands must adapt or risk invisibility in the AI-mediated future.

We tested AI shopping agents from Amazon and Perplexity to see how they fare helping prepare a family for Iceland's famously fickle Laugavegur Trek. Our quick takeaway? Highly capable LLMs such as Perplexity have the clear upper hand over bots like Rufus, at least for the moment, in providing expert-level guidance for complex purchasing, even if Perplexity's buy flow remains a tad glitchy. 

How do you keep up with a technology that transforms faster than you can implement it? Start with a a problem-first mindset.

Do android-powered project teams dream of better solutions? The answer, according to a recent Harvard Business School working paper, is yes.

The tech industry buzzes with speculation about the end of software engineering. The reality is more nunaces, and more interesting.

When you're building an agent, your first hurdle isn't market fit – it's proving that your AI thinks and acts autonomously.

Complexity theory provides a cogent framework for understanding how agentic AI can drive corporate reinvention.

Agentic ecommerce moves beyond recommendation algorithms to focus on what the buyer is actually looking for.

When people interact with AI models it often feels like magic. The reality, if mundane, is deeply fascinating.

Organizations adopting agentic tools need to reinvent themselves according to the KPIs of tomorrow.

Remember when strategic decisions followed a neat quarterly cycle? That traditional model has become an existential liability.

As AI agents become mainstream, leaders need to prepare for a profound shift in human-machine collaboration.

Realizing the full potential of AI agents presents a curious challenge: learning from mistakes.

Stochastic modeling is the secret weapon of AI success, showing leaders where AI can deliver on its promise.

The AI revolution demands a fundamental reimagining of organizational structure and operation, argues Nancy Wang.

In this season of tech predictions, Human+Machine has proven prescient about the nonlinear roots of AI superpowers, writes David Golub.

AI hallucination is frustrating, but the problem often stems from how the underlying information was processed and stored.