High-Value Applications of AI Technology

The next wave of AI transformation is here – and it’s not just about language-based models anymore. The real breakthroughs are happening now with Large Quantitative Models (LQMs) and cutting-edge quantum technologies. This seismic shift is already unlocking game-changing capabilities that will define the future: Materials & Drug Discovery – LQMs trained on physics and chemistry are accelerating breakthroughs in biopharma, energy storage, and advanced materials. Quantitative AI models are pushing the boundaries of molecular simulations, enabling scientists to model atomic-level interactions like never before. Cybersecurity & Post-Quantum Cryptography – AI is identifying vulnerabilities in cryptographic systems before threats arise. As organizations adopt quantum-safe encryption, they’re securing sensitive data against both current AI-powered attacks and future quantum threats. The time to act is now. Medical Imaging & Diagnostics – AI combined with quantum sensors is revolutionizing medical diagnostics. Magnetocardiography (MCG) devices are providing more accurate cardiovascular disease detection, with potential applications in neurology and oncology. This is a breakthrough that could save lives. LQMs and quantum technologies are no longer distant possibilities—they’re here, and they’re already reshaping industries. The real question isn’t whether these innovations will transform the competitive landscape—it’s how quickly your organization will adapt.

AI is not hype. At IBM we've completed 1,000+ Generative AI projects in the last 12 months, prioritizing business applications over consumer ones. Top use cases are: ▪️ 𝗖𝘂𝘀𝘁𝗼𝗺𝗲𝗿-𝗳𝗮𝗰𝗶𝗻𝗴 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 𝗮𝗻𝗱 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲𝘀 - Customer service: Empower customers to find solutions with easy, compelling experiences. Automate answers with 95% accuracy - Marketing: Increase personalization, and improve efficiency across the content supply chain. Reduce content creation costs by up to 40% - Content creation: ex. enhance digital sports viewing with auto-generative spoken AI commentary. Scale live viewing experiences cost-effectively - Knowledge worker: Enable higher value work, improve decision making, increase productivity. Reduce 90% of text reading and analysis work ▪️ 𝗛𝗥, 𝗙𝗶𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗦𝘂𝗽𝗽𝗹𝘆-𝗖𝗵𝗮𝗶𝗻 𝗳𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀 - HR automation: Reduce Manual work and automate recruiting sourcing and nurturing job candidates. Reduce employee mobility processing time by 50% - Supply chain: Automate source-to-pay processes, reduce resource needs, and improve cycle times. Reduce cost per invoice by up to 50% - Planning and analysis: Make smarter decisions, and focus on higher-value tasks with automated workflows and AI. Process planning data up to 80% faster - Regulatory compliance: Support compliance based on requirements/risks, and proactively respond to regulatory changes. Reduce time spent responding to issues ▪️ 𝗜𝗧 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗮𝗻𝗱 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 - App modernization, migration: Generate code, and tune code generation response in real time. Deliver faster development output - IT automation: Identify deployment issues, avoid incidents, and optimize application demand to supply. Reduce mean time to repair (MTTR) by 50% - AIOps: Assure continuous, cost-effective performance and connectivity across applications. Reduce application support tickets by 70% - Data platform engineering: Redesign the approach for data integration using generative AI. Reduce data integration time by 30% ▪️ 𝗖𝗼𝗿𝗲 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀 - Threat management: Reduce incident response times from hours to minutes or seconds. Contain potential threats 8x faster - Asset management: Optimize critical asset performance and operations while delivering sustainable outcomes. Reduce unplanned downtime by 43% - Product development: ex. expedite drug discovery by inferring structure with AI from simple molecular representations. Faster and less expensive drug discovery - Environmental intelligence: Provide intelligence to proactively manage the impact of severe weather and climate. Increase manufacturing output by 25% ______ Please repost it ♻️ and follow me, Armand Ruiz , for more similar posts.

Here is my list of high-potential AI focus areas (based on a decade of early-stage AI investments) 👇 1️⃣ Intelligent automation → Target industries with manual, inefficient processes → Build adaptive systems that anticipate needs and evolve with usage → Create tools that *dramatically* boost team productivity and decision-making 2️⃣ ML infrastructure No AI company can operate without clean data to feed ML → Develop tools for capturing, cleaning, and processing enterprise data at scale → Build customizable models solving specific high-value business problems → Create algorithms that uncover actionable insights from complex datasets 3️⃣ Cybersecurity Cyber is AI's biggest enemy → Protect against threats like model poisoning and data manipulation → Build robust systems for anomaly detection and rapid response → Develop solutions securing the entire AI pipeline from training to deployment If you're building in any of these areas or know someone who is, leave a comment below with the pitch.