From the breakout of DeepSeek R1 to the viral “Raising a lobster” trend (OpenClaw adoption), all within just a year, AI in China is being adopted and scaled in a fundamentally different way, rapidly translating into tangible commercial value.
At the recent TCG Retail Summit, Edouard de Mézerac set out to cut through the noise around AI. His message: the era of scattered use cases is over. What comes next is something far more structural and far more demanding. For years, companies have experimented with AI in isolated pockets described as “dots of colour” across the organization. Useful, perhaps. Transformational? Not quite. Now, the ground is shifting. Agentic AI isn’t about adding another layer of technology. It’s about rethinking how work gets done, end to end.
A patient wakes up with recurring back pain and asks an AI assistant what to do. The app checks its history, notes a previous prescription, and suggests switching to a different anti-inflammatory. Your molecule isn't mentioned. By the time the patient sees their doctor that afternoon for a prescription, the framing is set.
Imagine a CFO reviewing the quarterly cloud spend. The AI team presents a compelling chart: per-token inference costs have dropped 75% year-over-year. The models are faster, the APIs are cheaper, and the vendor is offering volume discounts. Everything points toward savings. Then the actual invoice arrives, and the total is higher than last quarter.
Artefact’s new ebook, Scaling Data Collaboration in the AI Era, explores how organizations can unlock greater value by breaking down data silos and enabling seamless collaboration across teams. As AI adoption accelerates, success depends not just on technology, but on the ability to connect data, people, and processes effectively. The ebook highlights how modern approaches to data collaboration empower faster insights, stronger governance, and more impactful AI-driven outcomes, turning data into a true strategic asset.
Human Resources is undergoing a fundamental shift from a reactive cost center to a proactive driver of value. Yet many organizations remain anchored in a minimalist approach to people analytics. While generative AI and autonomous agents are gaining traction across the enterprise, HR’s use of data is still often limited to basic turnover prediction.
For decades, placemaking in Britain has been governed as much by judgment as by methodology. Practitioners speak of "character", "vibrancy", and the elusive "feel" of a streetscape; qualities refined through experience, human use, and professional instinct rather than formalised metrics. The accomplished practitioner was often the one who had seen enough places to recognise what worked, even when the causal mechanisms remained partly intangible.
AI is reshaping professional practice across the built environment, and the surveying profession is no exception. With the Royal Institution of Chartered Surveyors (RICS) having published its first Responsible Use of Artificial Intelligence in Surveying Practice professional standard (effective from 9 March 2026) the question for many firms is no longer whether to engage with AI, but how to do so in a way that is compliant, considered, and professionally defensible.
Red Hat built a $34 billion business on Linux. IBM bought it. What the deal validated was a hypothesis that had held for four decades: that companies extracting enormous value from shared code would, in self-interest, keep funding the projects they depended on. That hypothesis is now under stress. Not because anyone decided to stop funding open source. Because the industry that funded it most — SaaS — is being dismantled by the industry that depends on it most — AI.
