Jon Stojan

Big business has found ways to integrate tech for decades. They’ve built increasingly sophisticated digital experiences that often blend seamlessly into in-store experiences.

Consider a high-profile professional with a two-decade track record, no compliance flags, and a reputation that holds up in any room. Ask an AI system about them, a due diligence tool, a language model used in underwriting, a platform ranking algorithm; what comes back may be a partial portrait, a misclassification, or a silence that carries its own consequences. This is not a fringe scenario. According to Alesh.ai, it describes most high-credibility professionals operating today.

The Tel Aviv startup, founded by veterans of McAfee and RSA, is defining a new category it calls agentic cloud security. Its Context Lake gives AI agents the architectural understanding to investigate and remediate threats without human intervention, aimed at reducing reliance on manual workflows that have kept security teams buried in alerts.

Five months later, he passed the exam on his first try.

Most organizations don’t fail because of strategy. They fail because execution breaks down between intent and reality. Many leadership teams invest heavily in defining direction, shaping vision, and aligning on long-term ambition. On paper, the path forward is often well-articulated. Still, somewhere between that clarity at the top and the reality on the ground, progress stalls in ways leaders often sense but rarely confront directly.

Projects to build data center infrastructure in low-Earth orbit have been increasingly discussed in the business press recently. AI is rapidly developing, permeating everything, and now it's setting its sights on space. Arctic Ventures is tracking the emerging market for orbital data centers as AI infrastructure demand grows.

When David Natroshvili founded SPRIBE in 2018, the company operated with a small team working from Tbilisi, Georgia. As the organization expanded internationally and added teams across multiple locations, the technical challenges of scaling were only part of the story. Maintaining alignment across people, priorities, and communication became just as important.

A survey of 152 B2B professionals suggests that enterprise AI translation may be moving beyond early adoption and toward a greater focus on governance, where platform considerations can begin to outweigh the model itself.

Day10 is developing a software delivery model that it believes reflects changes in how engineering services may evolve. Co-founder and CEO Konstantin Tsybulko and founding investor Yury Yakubchyk outline their view that the sector is entering a period of transition.

Nearly nine in ten companies now deploy artificial intelligence in some capacity, according to McKinsey. But a 2025 Harvard Business Review survey found that only six percent fully trust AI to run core business processes. Damini Rijhwani has spent most of her career working in the space between those two numbers. After nearly a decade in AI and machine learning, with several years building clinical imaging systems at Philips, she founded Automation Core Inc., developing patient management software for medical aesthetics, a market projected to reach $200 billion globally by 2033 according to Straits Research.

Current AI systems are effective at storing surface-level information, but they are still developing when it comes to capturing deeper context, such as the reasoning behind decisions or the progression of strategic thinking. That knowledge lives in one person's chat history, invisible to everyone else.

Creative work has a habit of compressing time. A soundtrack cue that once had room for planning, recording, and revision now often has to come together on a much tighter production schedule. That pressure has made AI music tools more relevant to working creators because they shorten the distance between an idea and a usable piece of audio.