Imagine a self-driving car faced with a split-second decision: veer off the road to avoid a pedestrian but endanger its passengers or stay on course and risk the pedestrian’s life. Such scenarios were once confined to ethics classrooms, but with the rise of agentic AI, they are becoming real-world dilemmas. Agentic AI—systems capable of making autonomous decisions—is now integrated into various sectors, from healthcare to transportation. However, its growing presence raises an urgent question: who is responsible when these systems make decisions that lead to significant consequences?

Agentic AI, a concept emphasizing autonomy, goal-oriented behavior, and decision-making capabilities, is reshaping the AI landscape. Unlike traditional AI systems that heavily rely on user input, agentic AI operates independently, adapting to dynamic environments and collaborating to achieve specific objectives. By leveraging a multi-agent approach, the capabilities of agentic AI can be enhanced further, creating systems that are scalable, flexible, and efficient. Multi-agent systems (MAS) can amplify the power of agentic AI, and have a variety of high value applications across various industries and functions.

The introduction of generative AI a couple of years ago created excitement, some hype and over time, a growing concern about hallucinations or inaccuracies in provided answers. Adding methods to add contextual data to the generative AI tools is proving to ground the LMs and greatly improve model accuracy, reducing or eliminating hallucinations. One of the most common frameworks is retrieval augmented generation (RAG). Advanced forms of RAG, which mash up different approaches, are being introduced, and in the proper use case, are having a large impact on business adoption of generative AI enabled tools. One of the most promising techniques to emerge is GraphRAG (Graph-based Retrieval-Augmented Generation), a cutting-edge framework that combines graph-based data structures with RAG to provide highly accurate, context-driven, and explainable AI solutions. This approach is especially impactful for tasks requiring deep reasoning over interconnected knowledge, such as complex question answering, summarization, and tailored content generation.

Language models (LMs) are rapidly increasing in capabilities, evolving from simple statistical models to sophisticated systems capable of a growing set of use cases for business. The year 2025 is likely to be a pivotal moment in their development, with significant advancements across multiple dimensions. The various types of language models, their sizes, and their impact, including their role in enabling agentic AI, continue to expand and gain more advanced capabilities. From Large Language Models (LLMs) to Liquid Foundation Models (LFMs), these LM innovations open a great deal of value and opportunity for businesses.

Artificial Intelligence (AI) is transforming how software is developed, making it faster, smarter, and more efficient. In Agile product development, where adaptability and speed are foundational, AI is a game-changer. By applying AI to automate coding, testing, and deployment, AI tools not only streamline workflows but also empower teams to focus on innovation. The use of AI enhances the Agile software development lifecycle (SDLC) and changes the roles and necessary skills of employees.

In a business environment marked by continual rapid change, autonomy in digital systems is no longer a luxury—it’s a necessity. Business leaders are seeking smarter solutions that not only streamline operations but also proactively drive innovation and growth. Agentic AI represents a new frontier in this evolution, providing the foundation for a scalable, intelligent digital workforce. Business leaders who embrace this technology can unlock unprecedented efficiency, agility, and competitive advantage.

The manufacturing and logistics industries continue to evolve through a digital transformation, marked by the shift from basic automation to systems that are increasingly autonomous. Automation has long been a cornerstone of industrial operations, but the advent of Agentic AI represents a disruptive leap. This emerging technology goes beyond pre-programmed tasks, enabling systems to learn, adapt, and make decisions independently. By reshaping workflows, optimizing efficiency, and redefining roles, Agentic AI is set to revolutionize these industries.

Sustainability is a critical part of modern product design. With growing awareness of environmental challenges and increasing consumer and regulatory demands, companies are seeking innovative ways to create eco-friendly products. Artificial intelligence (AI) has high potential in this transformation by offering tools and methodologies to optimize material usage, reduce waste, and enhance energy efficiency throughout the product lifecycle.

Agentic AI represents the next evolution of artificial intelligence—a leap from simple automation to creating AI systems capable of autonomous decision-making and self-directed problem-solving. This type of AI is poised to change the nature of work and create what can be thought of as a digital labor force. As organizations increasingly integrate Agentic AI into their operations, they are shifting from traditional labor models towards a blend of human and digital workforces, enabling significant gains in efficiency, productivity, and innovation.

The digital labor force is composed of AI-driven systems that can perform tasks autonomously, without requiring constant human oversight. This transformation has profound implications for the way we work, providing opportunities for businesses to scale operations and freeing human workers to focus on more strategic and creative roles. In this article, we explore what Agentic AI is, how it creates a digital labor force, and the opportunities and challenges that come with this transformation.

In most companies today, the nature of work is undergoing significant transformation, largely driven by advances in artificial intelligence (AI). As organizations seek ways to optimize their operations, increase profitability and enhance productivity, understanding how AI can support different types of work is critical.

Broadly speaking, work can be classified into two categories: structured work and unstructured work. Structured work involves repetitive, rule-based tasks with clear guidelines, while unstructured work encompasses creative, decision-driven activities that require human intuition and emotion. With AI technologies advancing at a rapid pace, they are now increasingly capable of both optimizing structured work and contributing to and improving unstructured work. Let's look at how AI agents are being used to enhance structured and unstructured work, what opportunities this presents for businesses, and the challenges that come with integrating AI into various workflows.

Agentic AI, the latest evolution of artificial intelligence, is poised to transform the workplace by autonomously taking actions to fulfill specific goals. Unlike traditional AI systems that rely heavily on human input for decision-making, Agentic AI can independently perform tasks, learn from interactions, and adapt to changing environments, making it a powerful ally for knowledge workers.

Currently, knowledge work is marked by an overwhelming volume of information, routine tasks, and the need for constant decision-making. Knowledge workers often struggle to balance high-value, creative work with the burden of repetitive, low-impact activities. In this context, the introduction of Agentic AI has the potential to enable workers to shift their focus from mundane tasks to more strategic, meaningful contributions.

The central question is, how will Agentic AI reshape the roles, skills, and value of knowledge workers? The potential of Agentic AI to augment human capabilities can empower individuals to achieve higher productivity, creativity, and job satisfaction.

As businesses attempt to remain competitive in a rapidly changing market, the concept of adaptability has become a key component of strategic planning. Agentic AI is emerging as a catalyst in creating enterprises that can not only respond to change but also anticipate and proactively adapt to it.

An Adaptive Enterprise is one that is designed to be agile, resilient, and capable of evolving in response to shifts in the business environment. Adaptability is no longer just an advantage—it is a necessity for survival in the face of rapid technological advancements, market disruptions, and changing customer expectations.

Artificial intelligence (AI) is redefining how businesses operate, innovate, and compete. However, the success of any AI initiative largely depends on a solid foundation: data management. Without a well-thought-out data strategy, AI projects can falter, delivering suboptimal results or, worse, failing entirely. The effectiveness of AI hinges on quality data, structured governance, and streamlined accessibility—all of which are anchored in a robust data strategy.

In the fast-paced world of product development, time is everything. Companies are under constant pressure to get their ideas off the whiteboard and into the hands of users as quickly as possible. In this environment, building a Minimum Viable Product (MVP) rapidly can be the difference between leading the market or playing catch-up. Enter generative AI—a transformative set of tools that is redefining the way R&D teams innovate and iterate.

Artificial Intelligence (AI) has brought us powerful tools to understand, interact, and make decisions in the world around us. Among these tools, Large Language Models (LLMs) have become incredibly well-known for their ability to understand and generate natural language. However, there’s another class of AI models gaining prominence: Large Behavior Models (LBMs). These models specialize in predicting, simulating, and optimizing human-like actions and are uniquely equipped to tackle challenges where understanding behavior is key.

AI-powered predictive analytics is fundamentally reshaping how marketers develop and execute their strategies, bringing a new level of precision and efficiency to decision-making processes. In today’s competitive market, relying solely on traditional approaches and intuition is no longer sufficient to capture audience attention and deliver high returns on marketing investments. Predictive analytics uses advanced AI and machine learning models to analyze large amounts of historical and real-time data, extracting valuable insights about customer behavior, preferences, and emerging trends. This allows marketers to not only understand their target audience better but also anticipate needs and take preemptive actions to maximize impact.

Imagine sending every prospect a sales email that feels as personal as a handwritten note—but without spending hours writing each one. This is where generative AI steps in, transforming how businesses handle sales outreach. The power lies in enhancing personalization at scale—creating a connection with every lead without sacrificing the quality of engagement.

As financial leaders, CFOs face growing challenges in navigating economic shifts, risk, and fraud prevention. AI has become essential in reshaping financial management by optimizing forecasting, enhancing risk management, detecting fraud, and automating accounting tasks. This post outlines how AI can empower CFOs in these critical areas, with an emphasis on the technologies and types of AI required.

Integrating AI into legacy systems is one of the most critical challenges and opportunities for CIOs today. Legacy systems—typically complex, rigid, and outdated by modern standards—often carry significant technical debt but remain essential to daily operations, particularly in industries like financial services, healthcare, and manufacturing. Here’s how CIOs can approach creating an IT strategy that seamlessly integrates AI with legacy systems, minimizing disruption and maximizing value.

As the urgency for businesses to adopt sustainable practices intensifies, artificial intelligence (AI) has emerged as a transformative force in the quest for environmental responsibility. In an era marked by climate change and resource scarcity, organizations are increasingly turning to AI to navigate the complexities of sustainability. This powerful technology offers innovative solutions for reducing carbon footprints, optimizing energy use, and enhancing the efficiency of supply chains —areas that are of growing concern to today's business leaders.