Agentic AI, and its potential to create a digital workforce, is reshaping the nature of work itself. Unlike previous waves of automation that primarily replaced manual tasks, autonomous AI agents can collaborate with humans on complex cognitive work ranging from strategic planning and creative problem-solving to relationship management and decision-making. As companies move to the hybrid workforce, we need a complete rethinking of workforce development, moving beyond traditional reskilling approaches to embrace new models of human-agent collaboration.

For business leaders, the question is not whether AI agents will transform their workforce, but how quickly they can prepare their people. Organizations that proactively develop human-agent collaboration capabilities will gain significant competitive advantages, while those that wait risk being left behind by more agile competitors.

Complete, high-quality data is the foundation for any successful AI initiative, and automating data management is essential for a scalable agentic AI solution. Salesforce, with its Data Cloud, provides a solid data foundation, but has gaps in data automation and governance. Informatica fills in those gaps and more. Salesforce is doubling down on its Agentic AI strategy by acquiring enterprise data management leader Informatica for approximately $8 billion. This strategic move positions Salesforce to create what CEO Marc Benioff calls "the most complete, agent-ready data platform in the industry."

Google's annual developer conference delivered a broad portfolio of AI-powered innovations this past week, reshaping how we'll interact with Google search, productivity tools, and creative applications. Here's everything you need to know about the biggest announcements from Google I/O 2025.

Google is accelerating AI integration across all its products, with Gemini 2.5 now powering enhanced search experiences, new subscription tiers offering premium AI access, and revolutionary tools for coding, creativity, and productivity. The company is clearly positioning itself as the leader in practical AI applications.

Your digital workforce is about to become as conversational and collaborative as your human team, thanks to Agentforce's native integration with Slack. With the launch of Agentforce for employees, organizations can now deploy specialized AI agents that work alongside teams in their existing collaboration spaces, transforming how work gets done.

As specialized AI agents gain autonomy, domain expertise, and decision-making capabilities, they're forcing a rethinking of organizational structures that have remained largely unchanged for decades. What began as basic automation tools handling repetitive tasks has evolved into sophisticated AI systems capable of advanced decision-making support and complex reasoning. This evolution is more than just technological advancement, it’s a significant shift in how we conceptualize AI's role in the workplace.

The traditional Go-To-Market (GTM) team and models have been the backbone of revenue generation for businesses of all sizes. Historically structured around distinct human-driven functions: marketing creates awareness, sales drives conversion, and customer success ensures retention. These teams operated in a largely analog world despite digital tools supporting their work. For several reasons this model isn’t working correctly anymore. Customers mostly don’t buy the way sellers sell.

It’s time for a rethinking of this paradigm. Digital transformation has radically altered GTM dynamics, introducing new channels, dissolving boundaries between functions, and creating a mountain of customer data. The emergence of a digital workforce; intelligent systems powered by AI, automation, and digital agents, has potential to accelerate the transformation and to greatly improve outcomes.

For businesses looking to integrate AI capabilities across their digital ecosystem, a significant challenge has been the inefficiency of building separate AI solutions for each application or channel. Enter headless AI agents, a novel approach that decouples intelligence from interfaces, allowing organizations to develop AI capabilities once and deploy them everywhere, invisibly powering decisions across the enterprise.

Autonomous, goal-driven agents are capable of planning, executing actions, and learning from outcomes without continuous human oversight. As these systems grow in complexity and capability, their need for precise, context-aware information retrieval becomes increasingly critical. In the architecture of intelligent agents, retrieval systems serve as both the foundation and scaffolding, determining not just what information is accessible, but how it flows through the reasoning process to produce meaningful actions.

So retrieval isn't merely a supplementary component in agentic architectures—it forms the foundation upon which reasoning, decision-making, and action are built. An agent's ability to access, filter, and synthesize relevant information directly impacts its performance across virtually all domains. Without effective retrieval mechanisms, even the most sophisticated reasoning capabilities fall short when operating on incomplete or irrelevant data.

Artificial intelligence systems are rapidly evolving from reactive tools designed for narrow tasks to autonomous systems capable of tackling complex problems with minimal human intervention. These "agentic" AI systems, with their capability to operate autonomously, offer great potential value for businesses, but a crucial question remains: how do these agents "understand" enough to act meaningfully in our world? The answer lies in semantics – the study of meaning in language and symbols. Semantic understanding provides the conceptual framework that AI agents need to interpret inputs, reason through tasks, and make autonomous decisions that align with human goals. This article explores how semantics enables context-awareness, intent recognition, and decision-making capabilities that define modern agentic AI systems.

Artificial intelligence (AI) is rapidly evolving beyond single, generalist autonomous agents toward ecosystems of specialized AI agents working in concert. While powerful generalist agents provide remarkable versatility, the future of AI systems increasingly resembles human organizations, specialized entities collaborating to tackle complex challenges that exceed any individual's capabilities.

As organizations race to deploy increasingly autonomous AI systems, a critical question emerges: how do we harness their power while maintaining alignment with business goals? We've moved beyond simple reactive tools to autonomous agents capable of goal-driven behavior, what we now call "agentic AI." These systems can independently plan, reason, and take actions to achieve objectives with minimal human supervision. However, as these agentic systems evolve from isolated use cases to orchestrated, multi-agent workflows, their complexity increases exponentially.

Today, we're witnessing a fundamental shift in how enterprises implement AI. As business challenges grow more complex and stakeholders demand measurable outcomes, implementation strategies are evolving from generalized experimentation toward highly specialized, domain-specific, and outcome-driven deployments. This evolution represents more than a technical shift, it signals a new phase of AI maturity where success depends not just on having artificial intelligence, but on having the right kind of intelligence for specific business contexts.

For decades, robots have operated primarily through explicit programming, following precise commands and executing predetermined routines with remarkable precision but limited adaptability. Today, we stand at the threshold of a fundamental shift in how robots interact with the world around them. The surge in agentic artificial intelligence (AI) capabilities is transforming robots from mere instruction-followers into goal-oriented, autonomous decision-makers.

This paradigm shift represents more than a technical evolution; it signifies a new relationship between humans and machines. Rather than programming robots with exact sequences of actions, we can now specify desired outcomes and let intelligent systems determine how to achieve them. Agentic AI powers this transition from command-based to goal-driven robotics is reshaping industries, augmenting human capabilities, and opening new frontiers in machine intelligence.

The next frontier in AI development isn't merely about making systems smarter, but about teaching them to reach beyond their boundaries and manipulate the digital world through tool use. This transition marks a critical inflection point in the development of AI capabilities. The ability to effectively use tools is not merely an add-on feature but represents a core capability for AI systems to progress from being merely assistive to potentially autonomous.

As AI agents increasingly learn to interface with and operate external tools, from APIs and software applications to databases and physical systems, we are entering a new era of digital work orchestration and agentic interoperability that promises to reshape how organizations leverage artificial intelligence.

Artificial Intelligence (AI) has rapidly evolved in capabilities over the past few years as more and more businesses realize the competitive advantage of its use. The need to incorporate AI into business operations has created a situation where organizations face a fundamental challenge: they need artificial intelligence that can reason deeply about specialized domains, but without the massive computational requirements of large language models (LLMs). As businesses increasingly deploy AI solutions for specific business functions, from customer support to legal research, the demand for compact, efficient models has never been higher.

Artificial intelligence is entering a transformative new era. While large language models (LLMs) have revolutionized how we generate and interact with information, the emergence of Agentic AI represents a fundamental shift from reactive to proactive intelligence. This evolution promises to redefine applications, workflows, and value creation across industries.

Multi-Agent Systems (MAS) represent one of the most promising and rapidly evolving areas in artificial intelligence (AI) research and application. At their core, these systems are made up of multiple intelligent agents working together to solve problems that would be difficult or impossible for a single agent to handle alone. Similar to human teams, these digital collaborators combine their individual capabilities, share information, coordinate their actions, and collectively make decisions to achieve common objectives.