Why the AI Era Demands an Intelligent Data Infrastructure NetApp aims to bridge the gap between cloud-based AI models and on-premises data management systems, addressing a major challenge many organizations face as they explore emerging AI capabilities, such as generative AI. AI Era Demands an Intelligent Data Infrastructure. While the most advanced AI tools often
Salesforce, now one of the most influential companies in tech history, was founded by Marc Benioff after his successful tenure at Oracle. Having witnessed the potential of the internet, Benioff realized it would usher in a new era for delivering enterprise software through the cloud. However, this new approach demanded a complete overhaul of traditional
When sales and service teams are seamlessly aligned, organizations can transform every service interaction into a powerful opportunity to drive revenue and build customer loyalty. By delivering contextual, personalized offers at the right time, businesses can turn each customer touchpoint into a chance to solve problems, create value, and enhance engagement. With the help of
Salesforce Data Cloud Drives Customer Experience Transformation with New Data and AI Innovations Salesforce has unveiled new innovations within its Data Cloud, enhancing the way organizations transform customer experiences by leveraging data and AI. Key advancements include expanded support for unstructured data, enabling native processing of audio and video content—such as webinars and calls—to unlock
Use Case Highlights Introduction Salesforce is a powerful platform for building strong customer relationships. However, to maximize the value of your data, a strategic approach is essential. Unmanaged data can lead to inconsistencies, security vulnerabilities, and compliance issues, making it difficult to rely on Salesforce data for strategic decision-making. This data governance insight looks into
Exploring Small Language Models (SLMs): Capabilities and Applications Large Language Models (LLMs) have been prominent in AI for some time, but Small Language Models (SLMs) are now enhancing our ability to work with natural and programming languages. While LLMs excel in general language understanding, certain applications require more accuracy and domain-specific knowledge than these models
With the unprecedented surge in data—expected to grow by 22% this year according to the Salesforce State of Data and Analytics 2023 report—and the rapid evolution of AI, maintaining data quality and security has become more critical than ever. The AI and Information Management 2024 Report highlights that 52% of organizations encountered data quality issues
OpenAI Restructures: Increased Flexibility, But Raises Concerns OpenAI’s decision to restructure into a for-profit entity offers more freedom for the company and its investors but raises questions about its commitment to ethical AI development. Founded in 2015 as a nonprofit, OpenAI transitioned to a hybrid model in 2019 with the creation of a for-profit subsidiary.
AI Agents: Bridging Tool Calling and Reasoning in Generative AI Exploring Problem Solving and Tool-Driven Decision Making in AI Introduction: The Emergence of Agentic AI Recent advancements in libraries and low-code platforms have simplified the creation of AI agents, often referred to as digital workers. Tool calling stands out as a key capability that enhances
The release of OpenAI’s o1 model has sparked some confusion. Unlike previous models that focused on increasing parameters and capabilities, this one takes a different approach. Let’s explore the technical distinctions first, share a real-world experience, and wrap up with some recommendations on when to use each model. Technical Differences The core difference is that
Salesforce and SnapLogic integrations couldn’t be easier with the Tray platform’s robust Salesforce and SnapLogic connectors, which can connect to any service without the need for separate integration tools. Salesforce provides customer relationship management service software, and has a complementary suite of enterprise applications as well. These are focused on customer service, marketing automation, analytics,
Salesforce has announced several major updates to its Data Cloud at the annual Dreamforce conference, aimed at enhancing data analysis and boosting the capabilities of its new AI agents, Agentforce. These updates include support for unstructured data in audio and video formats, 50 new data connectors, and a low-latency pipeline to ensure faster AI responses.
With investor enthusiasm for AI beginning to fade, Salesforce is shifting focus to its next AI wave, “Agentforce,” which will be showcased at the Dreamforce customer conference. This announcement comes at a time when Salesforce stock has underperformed, with revenue growth slowing and expectations building that AI-related revenue may not materialize until 2025. Salesforce and
Increasing productivity is an ongoing challenge for businesses. A recent McKinsey study reveals that over half of employees feel they are relatively unproductive at work. This widespread issue underscores a deeper problem. Historically, productivity was seen as a measure of individual employee performance—high performers versus low performers. But with the growing reliance on software for
Salesforce is launching its AI-powered Agentforce platform across its key clouds, including Sales, Service, Marketing, and Commerce. However, a significant question remains: Will enterprises be willing to pay $2 per conversation for Agentforce, especially without volume discounts? The introduction of Agentforce wasn’t unexpected, as CEO Marc Benioff has been discussing autonomous AI agents since the
