AI in Hospitality: From Chatbots to the Core P&L Engine
AI in hospitality is moving well beyond lobby chatbots. Choice Hotels is rolling out AI across its entire value chain, using Amazon Web Services as the backbone. The company is embedding AI into guest discovery and booking, franchise operations, revenue management, maintenance, guest communications, distribution, and pricing and inventory optimisation. Instead of isolated pilots, Choice is treating AI as production infrastructure, standardising on AgentCore, an AWS‑powered enterprise platform for intelligent agents that can retrieve trusted information and automate workflows. With nearly 7,500 hotels and more than 650,000 rooms across 50 countries and territories, the focus is on tools that reduce manual work for lean franchise teams and generate visible returns at property level. This shift mirrors a broader industry push, with initiatives like the AI Hospitality Alliance asking operators to define what they actually want AI to fix, not just accept tools imposed by vendors.
AI in Manufacturing and Supply Chains: Live on the Factory Floor
At Hannover Messe, SAP demonstrated what mature AI in manufacturing looks like when it leaves the lab. Visitors followed a live ginger‑shot production line, from mixing to packaging and warehouse delivery, seeing AI orchestrate end‑to‑end supply chain processes in real time. SAP’s agentic AI uses trusted data and applications to help manufacturers sense, analyse and act as a single, connected system, including coordination with physical AI partners such as ANYbotics field‑service robots and AIMBO picking and packing robots. This is AI in manufacturing as an operational control layer, not a standalone analytics project. The same logic underpins Striim’s approach to enterprise AI deployment: it streams operational data with low latency into Google Cloud, keeping legacy databases and new cloud‑native systems synchronised while companies modernise. For industrial firms, the real innovation is reliable, always‑current data that AI systems can safely automate against.
AI in Healthcare and MedTech: From Drug Leads to Market Intelligence
AI in healthcare is now tackling both lab science and commercial execution. Johnson & Johnson reports that AI has cut its lead optimisation time for new drug candidates by half, allowing teams to screen a vast universe of chemical compounds and biologics more efficiently. The company is also using AI inside medical devices to speed up cardiac mapping and improve precision in hip and knee replacements, while automating manufacturing steps and regulatory document preparation, slashing clinical report work from hundreds of hours to minutes. On the commercial side, AcuityMD has raised USD 80 million (approx. RM368 million) in Series C funding to expand its AI platform for MedTech. It aggregates claims databases, FDA filings, government records and market signals into a proprietary knowledge graph aligned with each customer’s internal data. Already serving 16 of the top 20 MedTech companies and surfacing more than USD 34 billion (approx. RM156.4 billion) in pipeline, it plans to extend AI beyond sales and marketing across the full product lifecycle.
Enterprise AI Deployment: Process Automation, Real‑Time Data and Financial Use Cases
A new generation of platforms is embedding AI directly into business processes. Appian is anchoring AI in process models, combining agentic automation with AI‑assisted, spec‑driven development and Model Context Protocol integration. Its AI agents operate against structured workflows, unified data fabric and explicit guardrails, giving enterprises more control and auditability over AI process automation. Customers like Global Excel Management are using this to overhaul fragmented healthcare claims workflows. Striim complements this by providing real‑time, trusted data pipelines, synchronising on‑premise Oracle systems with Google Cloud databases so AI‑driven applications can run on continuously updated information without disrupting legacy operations. In financial markets, Calystron Capital is fusing classic systematic trend‑following with AI execution. Humans design rules, while AI executes trades, manages positions and enforces stop‑losses across multiple markets, removing emotional bias. And in iGaming, operators are deploying AI not just for marketing, but for cost prevention—using intelligent monitoring to catch infrastructure degradation before it becomes an outage.
Beyond the Hype: Worker Trust, Compliance and Lessons for Malaysian Firms
Across sectors, a common pattern is emerging: AI projects are shifting from experimentation to measurable ROI, but success hinges on human and regulatory factors as much as technology. Delta Air Lines’ CEO has deliberately avoided the term “artificial intelligence”, preferring “augmented intelligence” to reduce employee fear and emphasise that AI is a tool, not a headcount‑reduction program. Similar sensitivities will shape how AI in hospitality, AI in manufacturing and AI in healthcare are rolled out, especially in heavily regulated markets. Vendors are responding with explainable workflows, strong data governance and clear guardrails. For Malaysian and regional companies, the lesson is to prioritise AI process automation where data is already strong and pain points are clear—such as pricing, supply chain visibility, fraud detection and claims processing—rather than chasing flashy pilots. Start with integration into existing systems, invest in talent and compliance early, and frame AI as co‑pilot technology that augments local teams’ judgement rather than replacing it.
