For years, content creation in global marketing has been treated as a creative exercise. In reality, it has become an operational bottleneck. Unilever is among the first large-scale companies to confront that reality directly. Its use of digital twins — high-fidelity virtual replicas of physical products — is not just a creative
The current wave of artificial intelligence in marketing has largely been defined by one capability: content generation. From images to copy, AI tools promise faster production and lower costs. Yet for global enterprises, the real challenge is not generating content—it is ensuring that content is accurate, consistent, and compliant
Nestlé’s latest AI move should not be read as a clever creative shortcut. It is a structural change in how a global brand manufactures marketing content. In June 2025, the company said it launched an in-house AI-powered content service that uses digital twins to generate high-quality product visuals for
A lot of companies talk about AI. Far fewer have turned it into operating leverage. That is why Walmart stands out. The interesting thing about Walmart’s AI story is not that it has launched a chatbot, or that it is experimenting with generative AI like everyone else. The real
Artificial intelligence is no longer something reserved for engineers, coders, or tech insiders. It is quickly becoming one of the most important skills of our time — shaping how people work, learn, create, communicate, and build businesses. But for many beginners, AI still feels confusing, intimidating, and full of technical language.
Artificial intelligence is increasingly seen as a real productivity story, not just a technology story. The OECD’s recent work estimates that AI could raise annual total factor productivity growth by roughly 0.25 to 0.6 percentage pointsover a 10-year horizon, equivalent to about 0.4 to 0.9
Artificial intelligence is advancing at a breathtaking pace in research labs and tech giants — yet across everyday business operations, adoption remains uneven, fragmented, and often disappointingly slow. The result is a widening gap between what AI can do and what most organizations actually deploy at scale. The Paradox: Ubiquity Without
For years, the world’s biggest artificial intelligence gatherings sounded like emergency meetings: catastrophic risk, existential threats, runaway systems. Today, the tone has changed. Across continents, global AI summits are increasingly focused on deployment, economic growth, public services, and measurable outcomes — not just hypothetical dangers. This shift is not speculation.
Artificial intelligence is no longer advancing in isolated labs or single companies. Instead, it is being shaped in high-level forums where governments, startups, corporations, investors, and academia converge. Major AI leadership conferences have become strategic infrastructure for the digital age — places where policy is negotiated, partnerships are formed, and the
AI is rapidly transforming smartphone cameras from passive imaging tools into autonomous creative systems. The next wave of devices doesn’t just capture reality — it interprets, reconstructs, and even invents it. Evidence from recent flagship launches, manufacturer briefings, and technical previews shows that AI-centric camera systems with generative editing and
Artificial intelligence is no longer just a software story. It is becoming a hard-infrastructure story — about where the data centers sit, who controls advanced chips, how much electricity is available, and whether a country has domestic access to the compute needed to train and run modern AI systems. The OECD
What once looked like a fringe experiment is starting to look more like a new production layer. AI-focused film events are no longer limited to tech demos or online curiosities: Runway’s AI Festival, launched in 2022, has expanded beyond film into design, fashion, advertising and gaming, while the Reply