Autonomous marketing is the prize. But if you're still approving every AI output, you're the bottleneck. The shift from "human-in-the-loop" to "human-on-the-loop" is how marketing leaders will scale agentic AI in 2026—designing guardrails and monitoring outcomes instead of rubber-stamping every action. New post: what it means, why it matters, and three design patterns to make it real.

Your AI pilot demos beautifully. The ROI projections are compelling. And then... nothing happens. Sound familiar? Our 5 Autonomous Marketing Success Drivers help you escape pilot purgatory and ship production AI in 8 weeks. We address the real blockers: scope creep, perfectionism, and organizational friction disguised as technical problems. Real results before Q1 ends.

Every CTO has had the same nightmare: 200 SaaS tools, infinite integration complexity, mounting costs. But when it comes to AI, tool sprawl isn't the problem. It's your biggest untapped advantage.

When it comes to AI, the biggest gains live in the white spaces, the system boundaries created back when coordination was too expensive to happen.

Many companies approach AI by identifying tasks they can automate. The bigger opportunity is not making current processes cheaper. It's recognizing that AI enables coordination across fragmented parts of organizations that couldn't align before.

Enterprise leaders need a structured approach that bridges the gap between AI experimentation and business transformation. This is where Agentic Foundry's 5-phase FORGE planning and implementation framework earns its salt.

The GenAI gold rush is on, and companies making bets on new technologies should consider three paths to AI implementation: Buy off-the shelf, boost with proprietary data and build custom solutions.

Too many AI projects remain stuck in a "brilliant advisor trap" – failing to harness its power to get stuff done in the real-world.

Two years into the LLM era, the commodification of powerful foundation models has shifted the competitive dynamics from biggest brain to deepest moat.

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.