Liquid Foundation Models (LFMs) are a new concept in AI and machine learning that aims to create flexible, adaptive, and continuously improving AI systems. The idea behind LFMs is to move beyond static, pre-trained models to create dynamic, self-updating systems that can evolve over time based on new data and user interactions. This approach makes AI more responsive, context-aware, and aligned with real-world scenarios.

Spatial AI combines artificial intelligence with spatial awareness, enabling machines to understand and interact with the physical world in ways that were once science fiction. It allows AI systems to map, recognize, and interpret their surroundings in three dimensions, giving them the ability to "see" the world much like humans do. This form of AI powers innovations like autonomous drones, robots, and augmented reality applications.

AI presents both opportunities and risks for businesses, making it essential to conduct thorough risk assessments. One significant risk stems from bias in AI algorithms. If the data used to train models is skewed or incomplete, AI systems can produce discriminatory or inaccurate outcomes, potentially leading to unfair treatment of customers or employees. This can damage a company's reputation and expose it to legal challenges. Additionally, businesses relying heavily on AI systems for decision-making may face issues related to transparency. AI models, particularly those involving deep learning, often operate as "black boxes," making it difficult to understand how decisions are made. This opacity can raise trust concerns among stakeholders and hinder accountability.

As AI and automation evolve, the nature of knowledge work is expected to change. Routine and repetitive tasks are likely to be further automated, pushing knowledge workers to focus on strategic thinking, creativity, and complex problem-solving. This trend makes collaboration between AI and human intelligence critical for future workplace productivity.

Building an AI-first IT infrastructure requires a robust, scalable foundation capable of managing the high computational demands, large datasets, and complex algorithms needed to power AI applications. In this post we explores the key components of such infrastructure, from computing power with CPUs, GPUs, and specialized AI chips like TPUs and FPGAs, to efficient data storage systems like data lakes and distributed file systems. In addition it looks at best practices for networking, cloud and edge computing, AI development tools, and model deployment while addressing common challenges like high infrastructure costs, data management, skill gaps, and integration with legacy systems​.

The introduction of artificial intelligence (AI) to IT Service Management (ITSM) is creating the opportunity to reshape how organizations manage and deliver IT services. With the rapid increase in the use of AI technologies, businesses are automating routine tasks, predicting potential IT issues, and enhancing user experiences in ways that were previously difficult to impossible. AI-powered chatbots and virtual autonomous agents now handle common IT inquiries, providing immediate assistance for tasks such as password resets or troubleshooting, significantly reducing the workload on human agents. At the same time, AI is enabling predictive maintenance, allowing IT teams to anticipate and address infrastructure failures before they cause disruptions. By analyzing large datasets, AI-driven systems can classify and route tickets, ensuring that issues are addressed quickly and accurately, while also offering real-time solutions to support teams based on historical knowledge.

LLMs often face challenges when tasked with solving complex problems that require multi-step reasoning or logical deductions. Without clear guidance, they can leap to conclusions, miss crucial intermediate steps, or provide overly simplified answers that don't account for the full depth of the problem. This limitation arises because traditional prompts tend to encourage LLMs to produce direct responses rather than methodically breaking down tasks. For complex reasoning, such an approach can lead to mistakes or incomplete explanations, as the model doesn't always naturally follow the nuanced steps required to solve a problem properly. This is where Chain of Thought (CoT) prompting comes in.

Table Augmented Generation (TAG) is a new approach in natural language processing (NLP) that merges structured data, such as tables, with text generation models. The goal of TAG is to enhance the accuracy, relevance, and depth of machine-generated content by allowing models to reference and reason over tabular data. By integrating tables into the generation process, TAG can significantly improve outputs in areas like report generation, business intelligence, and customer service, where both numerical and textual information are crucial.

The rapid integration and embedding of AI into enterprise applications reshapes how businesses operate, particularly with the fusion of embedded generative AI and traditional AI. Embedded traditional AI has long powered data-driven decision-making and operational efficiencies across industries, but the rise of generative AI introduces a new dimension—enabling systems to create, reason, and generate insights dynamically. As these technologies converge, a new era of AI-driven innovation is emerging, particularly with autonomous agents that not only automate tasks but also learn and adapt to complex scenarios. By combining the precision and reliability of traditional AI with the creativity and adaptability of generative AI, enterprises can leverage more sophisticated solutions, enhancing everything from customer service to product development. This evolution signals a shift toward AI systems that are both proactive and reactive, marking a transformative moment in enterprise technology.

As generative AI continues to evolve it is moving beyond its origins of content creation and into autonomous actions. Initially celebrated for its ability to generate text, images, and even code, generative AI is now being engineered to take decisive, independent actions based on the information it produces. This transition marks a fundamental shift in how we interact with AI, no longer seeing it solely as a tool for creativity and output but as an intelligent agent capable of making decisions, executing tasks, and autonomously solving problems. This evolution is a key component for the growing use of decision intelligence tools to fully automate many business tasks, as well as support automation with human oversight and human in the loop decisions.

Predictive analytics powered by AI is transforming IT operations by enabling a proactive approach to managing infrastructure, service delivery, and system performance. Rather than responding reactively to system failures and disruptions, organizations can now harness AI-driven insights to anticipate issues before they arise. This shift allows for early detection of potential failures, enabling IT teams to resolve problems swiftly and reduce downtime. Predictive analytics also plays a key role in optimizing resource allocation by forecasting demand and dynamically scaling IT resources to meet business needs. By enhancing incident prioritization and automating routine tasks, AI helps improve the speed and efficiency of service delivery. Predictive maintenance, powered by AI models, reduces the risk of unplanned outages by identifying and addressing hardware and system vulnerabilities before they lead to failure. With the added benefits of enhanced security, automated threat mitigation, and operational intelligence, AI-powered predictive analytics is paving the way for more efficient, cost-effective IT operations while significantly minimizing downtime and improving overall service quality.

Assessing your AI maturity is a critical first step in developing a robust and effective AI strategy. As organizations increasingly adopt AI technologies to drive innovation, efficiency, and competitive advantage, understanding where you stand in your AI journey becomes an essential part of the process. AI maturity assessment provides a comprehensive view of your organization’s current capabilities, identifying strengths, weaknesses, and areas for growth. By evaluating your maturity level, you gain valuable insights into how well your organization is prepared to integrate AI into its operations, scale initiatives, and ultimately realize the full potential of these technologies.

The other way to examine generative AI is in specific industry vertical applications. The path of horizontal technology often leads to more industry focused functionality that addresses the unique needs and requirements of each vertical. This infographic breaks down some of the more compelling industry vertical use cases. These use cases build off the base categories (see the previous use case infographic) to meet specific needs in each covered vertical.

With the rapid growth and availability of artificial intelligence (AI), building or refreshing an IT strategy is crucial for companies to stay competitive, agile, and resilient in an increasingly digital and data-driven world. AI is rapidly transforming industries, creating new opportunities for innovation, efficiency, and customer engagement. However, to fully leverage the potential of AI, companies need an IT strategy that is not only aligned with their business goals but also adaptable to the rapid pace of technological change.

The business use cases for generative AI are very diverse, with new applications of the technology popping up nearly weekly. To better understand the potential of generative AI, it’s useful to break it down into the major use categories, and then work down from there into more detail. The model is an attempt to capture the current use cases as categories.

The rapid growth of artificial intelligence (AI) has prompted governments around the world to grapple with how best to regulate the technology and provide a balance between safety and innovation. As AI systems become increasingly integrated into various industries—ranging from healthcare and finance to law enforcement and education—the need for regulatory frameworks that ensure safety, fairness, and accountability are urgent.

The current business landscape is marked by a strong desire to adopt AI tools quickly, driven by the promise of transformative benefits such as increased efficiency, enhanced decision-making, and competitive advantage. However, many businesses are grappling with significant challenges in establishing a robust business case and accurately measuring the return on investment (ROI) for their AI projects. Here’s a closer look at these dynamics:

Companies are increasingly adopting a hybrid infrastructure, which combines public cloud, private cloud, and on-premises systems, to balance flexibility, control, and security in their IT operations. Moving to hybrid infrastructure can optimize their IT operations by balancing the benefits of the cloud with the control and security of on-premises and private cloud environments. This approach offers the flexibility to adapt to various business needs, from regulatory compliance and cost management to performance optimization and disaster recovery.

Large Action Models (LAMs) are a new advancement in AI that builds upon the capabilities of Large Language Models (LLMs). LAMs leverage a combination of existing AI technologies to bridge the gap between understanding language and taking action in the digital world.

Generative Customer Segmentation leverages generative AI to create highly detailed and nuanced customer segments. Unlike traditional segmentation methods that group customers based on broad characteristics like demographics, purchasing behavior, or psychographics, generative customer segmentation uses AI to dynamically analyze large amounts of data and generate segments that reflect intricate patterns and preferences of individual customers.