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The world of generative AI is moving away from the novelty of text chat and stepping directly into heavy-duty automation. Microsoft has announced the global general availability of Copilot Cowork, marking a major shift from assistive AI tools to autonomous systems. More than half of the Fortune 500 have already adopted the platform during its preview in Frontier, including global brands like Accenture and Zurich Insurance..

This article is contributed by Varinderjit Singh, General Manager, Lenovo Malaysia Recent research reveals EMEA organisations are past the hype phase. Now, they’re getting strategic about enterprise AI and tangible results. AI has been around for years, largely in the form of predictive and interpretive tools operating behind the scenes. That changed with the rise of generative AI, which brought AI into everyday workflows and sparked widespread interest.

Imagine this scenario: It’s 3:00 PM on a Friday, and your company’s primary customer-facing mobile application stutters. Transactions fail, users loop infinitely on the checkout screen, and the complaints start flooding social media. Your DevOps team rushes into a war room, but instead of fixing the problem, they spend the next two hours pointing fingers.

Collecting data isn’t anything new for global biopharmaceutical companies. For years, the life sciences industry has sat on massive mountains of clinical data, compliance records, and commercial metrics. Truth be told, most of this data historically ended up trapped behind thousands of static dashboards or buried inside rigid, legacy software applications. Sanofi is looking to completely dismantle that old corporate model.

Mobile gaming smartphones have evolved from simple novelties into sustained performance powerhouses that openly challenge traditional desktop engineering. REDMAGIC has officially anchored its latest flagship contender in Malaysia with the release of the REDMAGIC 11S Pro. Designed to tackle the persistent thermal bottlenecks that plague high-intensity mobile workflows, this new iteration balances raw speed with daily practicality.

Enterprise AI has spent the last few years completely dominating technology headlines, but truth be told, most large organisations are still stuck in the mud when it comes to practical deployment. It’s easy to spin up a basic chatbot to summarise generic internal documents, but building a system that can autonomously handle high-stakes legal, tax, and compliance data is a completely different ballgame.

Synology has officially expanded its footprint in Malaysia with the general availability of the PAS7700 storage system. This is an enterprise solution explicitly designed to anchor mission-critical workloads where even a few seconds of unexpected downtime is completely unacceptable. At the core of this new enterprise lineup is the main PAS7700 appliance, a massive 4U rackmount chassis that marks a significant step forward for Synology’s high-availability storage portfolio.

This article is contributed by Arran Mulvaney, Regional Director – South East Asia & India at OneTrust When to implement? What tools to use? How do I get the most out of it? You would be hard pushed to find a Singaporean business leader who has not asked these questions in relation to AI over the last 12-18 months, but it’s clear that other nations’ organisations are not quite following the same trajectory.

Data management has always suffered from a lingering, hidden tax: fragmentation. Historically, organisations scaling up their analytics or AI pipelines have had to grapple with an inefficient cycle of copying, stitching, and duplicating data across separate cloud environments just to make it usable. It is a slow, expensive process that creates inconsistent baselines and introduces massive structural friction.

Enterprise AI has spent a lot of time in the headlines lately, but truth be told, most projects suffer from a massive bottleneck: trust. Spinning up a temporary proof-of-concept chatbot is easy enough, but letting an autonomous AI agent loose on your actual production data is a completely different story. If your corporate data definitions are fragmented across isolated systems, a bot will guess, hallucinate, or make critical operational mistakes.