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Tableau Next
19Feb

Tableau Next

Introducing Tableau Next: Agentic Analytics, Reimagined We’re entering a new era of autonomous analytics—where insights meet action, and data becomes a daily driver of decisions. Tableau Next is at the forefront of this transformation: a next-gen analytics platform that brings AI-powered insights directly into the flow of work. Formerly known as Tableau Einstein, Tableau Next is now built entirely on the Salesforce Platform and deeply integrated with Agentforce, enabling organizations to deliver proactive, personalized insights using trusted, unified data. What Is Tableau Next? Tableau Next is an agentic analytics platform that reinvents self-service BI with a modular, composable architecture. It empowers every department, user, and industry to create agile, contextual analytics experiences—powered by reusable components, semantic AI, and consistent data embedded into every workflow. With Salesforce’s secure, privacy-first foundation, teams can confidently use Tableau Next to make smarter decisions, faster. AI-Powered Insights, Embedded Where You Work In many businesses, valuable insights go unused—buried in dashboards or siloed across systems. Tableau Next changes that by delivering real-time, contextual insights directly where people work. With built-in metrics, predictive forecasting, and generative AI, users can: Features like Tableau Agent and Tableau Pulse combine to power an intelligent, always-on analytics experience—whether you’re a business user, analyst, or IT leader. Data You Can Trust with Tableau Semantics Turning data into value starts with trust. Tableau Next’s semantic layer brings meaning and consistency to your data, making insights easier to discover, understand, and act on. Key capabilities include: Built for Real-Time, Scalable Data Operations Your data lives everywhere—in the cloud, on-prem, across countless systems. Tableau Next connects to it all without duplication or delay. Built on the Salesforce Platform and powered by Data Cloud, Tableau Next offers: This allows you to connect, analyze, and act on data at every touchpoint—without the headaches of traditional data infrastructure. Composable, Reusable Analytics—Your Way Most organizations struggle to reuse dashboards, data models, and workflows across teams. Tableau Next unlocks those assets through an open, API-first design and a new analytics marketplace. Benefits include: This flexibility drives efficiency and supports enterprise-wide customization. Future-Proof Your Data Strategy with Tableau Next With over 20 years of innovation in visual analytics, Tableau now takes a bold leap forward with the intelligence, scalability, and automation needed for today’s data-driven world. Integrated with Agentforce, Data Cloud, and Salesforce AI, Tableau Next ensures that everyone in your business gets the answers—and the actions—they need, right when they need them. Start Using Tableau Next Today Many features of Tableau Next are already available in Tableau+, including: Now’s the time to elevate your data strategy. With Tableau Next, your data works as smart as you do. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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February 19, 2025in Data, Enterprise, Generative AI, Mulesoft, Salesforce
$15 Million to AI Training for U.S. Government Workforce
19Feb

AI Adoption in the Federal Government

AI Adoption in the Federal Government: A New Era Under the Trump Administration With a new administration in Washington and a $500 billion AI infrastructure initiative underway, the U.S. federal government may be entering a phase of accelerated AI adoption. Federal AI Expansion AI adoption grew under the Biden administration, with agencies leveraging it for fraud detection, workflow automation, and data analysis. However, experts predict that the Trump administration will further expand federal AI use. “Trump and his advisers have spoken about ‘unleashing AI,’ signaling a push for broader adoption within government agencies,” said Darrell West, a senior fellow at the Brookings Institution’s Center for Technology Innovation. As the administration scales back AI safety regulations and deepens ties with major tech firms, federal AI usage is expected to rise. However, ensuring transparency and educating the public remain crucial for building trust in government AI applications. AI Governance Framework The foundation for federal AI governance was established under Trump’s first term, with executive orders EO 13859 (2019) and EO 13960 (2020). EO 13960 mandated an annual AI use case inventory, significantly expanding under Biden—from 710 cases in 2023 to 2,133 in 2024. Reggie Townsend, VP of Data Ethics at SAS and a National AI Advisory Committee (NAIAC) member, emphasized the importance of this transparency: “The inventory was a crucial first step in building public trust.” Biden’s EO 14110 (2023) introduced stronger AI guardrails, requiring agencies to designate chief AI officers, disclose safety-related AI use cases, and implement risk management guidelines. However, on his first day in office, Trump rescinded EO 14110, signaling a shift toward deregulation. AI Applications in Government The 2024 federal AI inventory reported 2,133 AI use cases across 41 agencies. The Department of Health and Human Services (HHS) led with 271 cases, reflecting a 66% increase from the previous year. Key applications include: Harvard Kennedy School adjunct lecturer Bruce Schneier anticipates even broader AI integration in government, from automating reports to drafting legislation and conducting audits. Despite growing interest, the federal government lags behind the private sector in AI adoption, especially for generative AI, due to concerns over bias, reliability, and transparency. AI Under a Second Trump Term Trump’s return to office in 2025 signals an AI policy shift favoring reduced oversight and enhanced global AI leadership. “Federal AI adoption will accelerate under Trump,” West said, citing efforts to integrate major tech figures into federal initiatives. Notably, Trump appointed xAI owner Elon Musk to lead the newly rebranded Department of Government Efficiency, formerly the U.S. Digital Service. This agency is tasked with modernizing federal technology, reducing costs, and driving deregulation. With EO 14110 rescinded, the scope of AI governance under Trump remains uncertain. “Will he eliminate all guardrails, or keep some protections? That’s something to watch,” West noted. Big Tech’s Role in Federal AI Trump’s inauguration underscored tech industry influence, with Elon Musk, Mark Zuckerberg, Jeff Bezos, and Sundar Pichai in attendance. Major tech firms, including Amazon, Google, and Microsoft, each contributed $1 million to the event, while OpenAI CEO Sam Altman made a personal $1 million donation. Some companies are aligning with the administration’s stance on AI and content moderation. Meta, for instance, has replaced its fact-checking services with a community-driven model similar to X’s Community Notes and relaxed its moderation policies. A deregulated AI landscape could benefit big tech, particularly in areas like AI safety standards and data copyright issues, while advancing the administration’s vision for U.S. AI dominance. AI’s Future in Government On his second day in office, Trump announced a $500 billion AI infrastructure investment, forming Stargate—a coalition of OpenAI, SoftBank, MGX, and Oracle—to expand AI infrastructure nationwide. “This will be the largest AI infrastructure project in history,” Trump declared, emphasizing the need for AI leadership against global competitors like China. However, West warned that accelerated adoption must be managed carefully: “It’s critical that AI is implemented fairly, with privacy and security safeguards in place.” Building AI Literacy Effective AI deployment requires education within federal agencies. “Many government workers lack AI expertise, making it difficult to procure and implement AI solutions effectively,” West said. NAIAC’s Townsend advocates for structured AI training, tailored to different federal roles. Public AI literacy is also crucial, with initiatives like the National AI Research Resource (NAIRR) promoting equitable access to AI education and development. “The public must be informed enough to hold the government accountable on AI issues,” Townsend concluded. As AI adoption accelerates, striking a balance between innovation, oversight, and public trust will define the next phase of federal AI policy. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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February 19, 2025in Data, Generative AI, Google, Public Sector Solutions, Technology
AI Arms Race
14Feb

AI-Powered Cancer Immunotherapy

AI-Powered Cancer Immunotherapy: How Predictive Models Are Personalizing Treatment The Challenge of Predicting Immunotherapy Success Immunotherapy—particularly immune checkpoint inhibitors (ICIs)—has revolutionized cancer treatment, offering long-term remission for some patients with lung cancer, melanoma, and kidney cancer. However, only 20-40% of patients respond to ICIs, and clinicians struggle to predict who will benefit. Current biomarkers like tumor mutational burden (TMB) and PD-L1 expression are expensive, inconsistent, and not universally applicable. This leaves doctors relying on trial-and-error approaches, delaying effective treatment and increasing costs. SCORPIO: An AI Tool Using Routine Blood Tests to Predict Treatment Response Researchers from Mount Sinai’s Tisch Cancer Institute and Memorial Sloan Kettering Cancer Center have developed SCORPIO, an AI model that predicts ICI effectiveness using routine blood tests and clinical data—eliminating the need for costly genomic sequencing. How SCORPIO Works Key Advantages Over Traditional Methods ✔ More accurate than PD-L1 & TMB testing in trials✔ Works across 21 cancer types (validated in 10,000+ patients)✔ Low-cost & scalable—uses existing lab tests✔ No specialized equipment needed, ideal for resource-limited settings Why This Matters for Cancer Care Next Steps: From Research to Real-World Use Before widespread adoption, SCORPIO will undergo prospective clinical trials to confirm real-world performance. Challenges include: The Future of AI in Immunotherapy SCORPIO is part of a growing wave of AI tools transforming oncology: As Diego Chowell, PhD (Mount Sinai) notes: “SCORPIO represents a major step toward democratizing precision oncology—making advanced cancer care accessible to all patients, not just those at specialized centers.” The Bottom Line AI is shifting immunotherapy from trial-and-error to predictive, personalized medicine. With tools like SCORPIO, the future of cancer treatment is smarter, faster, and more equitable. Next Frontier? Combining AI with real-time patient monitoring to dynamically adjust therapies—bringing us closer to truly adaptive cancer care. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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February 14, 2025in AI Tools, Artificial Intelligence, Data, Generative AI, Life Sciences Cloud, Salesforce, Salesforce Development Services, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud
No-Code Generative AI
11Feb

Generative-Driven Development

Nowhere has the rise of generative AI tools been more transformative than in software development. It began with GitHub Copilot’s enhanced autocomplete, which then evolved into interactive, real-time coding assistants like Aider and Cursor that allow engineers to dictate changes and see them applied live in their editor. Today, platforms like Devin.ai aim even higher, aspiring to create autonomous software systems capable of interpreting feature requests or bug reports and delivering ready-to-review code. At its core, the ambition of these AI tools mirrors the essence of software itself: to automate human work. Whether you were writing a script to automate CSV parsing in 2005 or leveraging AI today, the goal remains the same—offloading repetitive tasks to machines. What makes generative AI tools distinct, however, is their focus on automating the work of automation itself. Framing this as a guiding principle enables us to consider the broader challenges and opportunities generative AI brings to software development. Automate the Process of Automation The Doctor-Patient Strategy Most contemporary generative AI tools operate under what can be called the Doctor-Patient strategy. In this model, the GenAI tool acts on a codebase as a distinct, external entity—much like a doctor treats a patient. The relationship is one-directional: the tool modifies the codebase based on given instructions but remains isolated from the architecture and decision-making processes within it. Why This Strategy Dominates: However, the limitations of this strategy are becoming increasingly apparent. Over time, the unidirectional relationship leads to bot rot—the gradual degradation of code quality due to poorly contextualized, repetitive, or inconsistent changes made by generative AI. Understanding Bot Rot Bot rot occurs when AI tools repeatedly make changes without accounting for the macro-level architecture of a codebase. These tools rely on localized context, often drawing from semantically similar code snippets, but lack the insight needed to preserve or enhance the overarching structure. Symptoms of Bot Rot: Example:Consider a Python application that parses TPS report IDs. Without architectural insight, a code bot may generate redundant parsing methods across multiple modules rather than abstracting the logic into a centralized model. Over time, this duplication compounds, creating a chaotic and inefficient codebase. A New Approach: Generative-Driven Development (GDD) To address the flaws of the Doctor-Patient strategy, we propose Generative-Driven Development (GDD), a paradigm where the codebase itself is designed to enable generative AI to enhance automation iteratively and sustainably. Pillars of GDD: How GDD Improves the Development Lifecycle Under GDD, the traditional Test-Driven Development (TDD) cycle (red, green, refactor) evolves to integrate AI processes: This complete cycle eliminates the gaps present in current generative workflows, reducing bot rot and enabling sustainable automation. Over time, GDD-based codebases become easier to maintain and automate, reducing error rates and cycle times. A Day in the Life of a GDD Engineer Imagine a GDD-enabled workflow for a developer tasked with updating TPS report parsing: By embedding AI into the development process, GDD empowers engineers to focus on high-level decision-making while ensuring the automation process remains sustainable and aligned with architectural goals. Conclusion Generative-Driven Development represents a significant shift in how we approach software development. By prioritizing architecture, embedding automation into the software itself, and writing GenAI-optimized code, GDD offers a sustainable path to achieving the ultimate goal: automating the process of automation. As AI continues to reshape the industry, adopting GDD will be critical to harnessing its full potential while avoiding the pitfalls of bot rot. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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February 11, 2025in AI Tools, Generative AI, Laws
Tectonic Salesforce Integrations
08Feb

Guide to Reinventing Your Business for the Digital Age

Digital Transformation: The Complete Guide to Reinventing Your Business for the Digital Age What Is Digital Transformation? Digital transformation is the strategic adoption of digital technologies to fundamentally reshape business processes, culture, and customer experiences. It’s not just about upgrading IT systems—it’s a holistic reinvention of how a company operates, competes, and delivers value in an increasingly digital world. “Every digital transformation begins and ends with the customer.”— Marc Benioff, Chairman & Co-CEO, Salesforce Digitization vs. Digitalization vs. Digital Transformation Concept Definition Example Digitization Converting analog data to digital Scanning paper invoices into PDFs Digitalization Using digital tools to improve existing processes CRM systems replacing Rolodexes Digital Transformation Reimagining business models with digital-first strategies Netflix shifting from DVDs to streaming Why Digital Transformation Matters 1. Customer Expectations Are Evolving 2. Employees Demand Modern Tools 3. Industry Disruption Is Accelerating Key Drivers of Digital Transformation Real-World Examples of Digital Transformation 1. Banking: From Branches to Apps 2. Retail: Personalization at Scale 3. Insurance: Proactive Risk Management How to Start Your Digital Transformation Step 1: Assess Your Current State Step 2: Build a Cross-Functional Strategy Step 3: Choose the Right Technology Step 4: Foster a Digital-First Culture Avoiding Common Pitfalls 🚫 Mistake: Buying disconnected point solutions✅ Fix: Invest in an integrated platform 🚫 Mistake: Treating it as an “IT project”✅ Fix: Make it a company-wide initiative 🚫 Mistake: Ignoring change management✅ Fix: Get employee buy-in early The Future of Digital Transformation Ready to Transform? Start small, think big, and put your customers at the center. The businesses that thrive in the next decade will be those that embrace continuous digital evolution. Need help? Contact us. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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February 8, 2025in Cloud Computing, Data, Salesforce, Technology
copilots and agentic ai
03Feb

Transforming Industries and Redefining Workflows

The Rise of Agentic AI: Transforming Industries and Redefining Workflows Artificial Intelligence (AI) is evolving faster than we anticipated. No longer limited to predicting outcomes or generating content, AI systems are now capable of handling complex tasks and making autonomous decisions. This new era—driven by Agentic AI—is set to redefine the workplace and transform industries. From Prediction to Autonomy: The Three Waves of AI To understand where we’re headed, it’s important to see how far AI has come. Arun Parameswaran, SVP & MD of Salesforce India, describes it as a fundamental shift: “What has changed with agents is their ability to handle complex reasoning… and, most importantly, to take action.” Unlike previous AI models that recommend or predict, Agentic AI executes tasks, reshaping customer experiences and operational workflows. Agentic AI in Action: Industry Applications At a recent Mint x Salesforce India deep-dive event on AI, industry leaders explored how Agentic AI is driving transformation across sectors. The panel featured: Here’s how Agentic AI is already making an impact: 1. Revolutionizing Customer Support Traditional chatbots have limited capabilities. Agentic AI, however, understands urgency and context. 2. Accelerating Business Decisions In finance and supply chain management, AI agents analyze vast amounts of data and execute decisions autonomously. 3. Transforming Travel & Aviation Airlines are leveraging AI to optimize booking systems, reduce costs, and enhance efficiency. 4. Automating Wealth Management AI agents in financial services monitor markets, adjust strategies, and offer personalized investment recommendations in real time. The Risks & Responsibilities of Agentic AI With great autonomy comes great responsibility. The potential of Agentic AI is vast—but so are the challenges: The Future of Work: AI as a Partner, Not a Replacement Despite concerns about job displacement, AI is more likely to reshape rather than replace roles. What Are AI Agents? AI agents go beyond traditional models like ChatGPT or Gemini. They are proactive, self-learning systems that: They fall into two categories: “AI agents don’t just wait for commands; they anticipate needs and act,” says Dr. Tomer Simon, Chief Scientist at Microsoft Research Israel. AI Agents in the Workplace: A Shift in Roles AI agents streamline processes, but they don’t eliminate the need for human oversight. Salesforce’s Agentforce is a prime example: “Companies need to integrate AI, not fear it. Those who fail to adopt AI tools risk drowning in tasks AI can handle,” warns Dr. Omri Allouche, Chief Scientist at Gong. The Road Ahead: AI-Driven Business Growth Agentic AI is not about replacing people—it’s about empowering them. As organizations re-evaluate workflows and embrace AI collaboration, the companies that act early will gain a competitive edge in efficiency and innovation. Final Thought The AI revolution is here, and Agentic AI is at its forefront. The key question isn’t whether AI will transform industries—it’s how organizations will adapt and thrive in this new era. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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February 3, 2025in Agentic AI, AI Tools, Artificial Intelligence, Data, Generative AI, Google, Laws, Salesforce, Technology
Basejump on AI Analytics
01Feb

Basejump on AI Analytics

The Evolution of Data and AI: A New Frontier Once considered the new oil, data is no longer the sole focus of business transformation. The emergence of artificial intelligence (AI) has shifted the conversation toward compute power as the fundamental resource driving innovation. Sequoia Capital emphasizes AI’s transformative impact, stating: “The fields that generative AI addresses — knowledge work and creative work — comprise billions of workers. [If] Generative AI can make these workers at least 10% more efficient and/or creative: they become not only faster and more efficient, but more capable than before. Therefore, Generative AI has the potential to generate trillions of dollars of economic value.”— Sonya Huang, Pat Grady, Generative AI: A Creative New World Whether this vision materializes remains to be seen, but the comparisons to past technological revolutions—such as the internet and mobile computing—are hard to ignore. The Role of AI in Knowledge Work and Data Analytics Data analytics has seen unprecedented growth, prompting businesses to reassess its value. With a projected compound annual growth rate (CAGR) of 27.3% through 2030, the market’s rapid expansion has led to a push for better tools and standards. Data analytics has evolved from a discipline primarily rooted in physics, mathematics, accounting, and economics to a more structured academic field. This shift is evident in the surge of data science degrees. The National Center for Education Statistics reported a 968% increase in data science bachelor’s degrees awarded from 2020 to 2022, highlighting growing demand. Modern Data Analytics: From ETL to ELTL Traditional extract-transform-load (ETL) processes have struggled to keep up with modern analytical demands. The introduction of extract-load-transform (ELT) architectures, championed by tools like dbt, empowered data teams by shifting transformation ownership from data architects to analysts. However, this speed came at a cost: fragmented data governance and inconsistencies in reporting. A more refined approach—ELTL (extract-load-transform-load)—adds a critical governance step, ensuring structured data models before visualization and reporting. Data Governance, the Semantic Layer, and AI Unstructured data delivery has led to discrepancies across dashboards and reports. To address this, solutions such as dbt’s semantic layer and SQLMesh provide standardized testing, metadata management, and auditing mechanisms. AI plays a crucial role here, offering: AI-Driven Precision Analytics AI is set to revolutionize data querying by replacing cumbersome dashboards with precise, real-time insights. Borrowing from the concept of precision medicine, “precision analytics” ensures that businesses receive tailored data transformations instantly. This shift reduces reliance on static filters and rigid dashboards, allowing AI to dynamically generate and refine queries. The Rise of Data Objects AI-generated SQL queries—what we term “data objects”—are distinct from traditional dashboards in that they are: AI Solutions: Retrieving and Querying Data AI-powered data analytics falls into two broad categories: Building Trust in AI-Driven Analytics AI-generated analytics must be trustworthy, requiring: The Future of AI in Data Analytics AI will not replace data teams but will enhance their efficiency by reducing bottlenecks and accelerating insights. Organizations that integrate AI with strong data governance will achieve a competitive edge, enabling faster, more accurate, and more relevant analytics. The shift toward AI-driven data analytics is inevitable. The question is: will organizations embrace the change and refine their strategies, or will they be left behind in the wake of innovation? Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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February 1, 2025in Artificial Intelligence, Data, Enterprise, Generative AI, Technology
Unstructured Data
27Jan

Unstructured Data

Ultimately, combining the semantic depth of embeddings with the precision of structured retrieval methods will create more intelligent, robust AI systems—ones that understand the full context of the data and can deliver more precise, relevant results.

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January 27, 2025in Data, Generative AI
20Jan

Einstein Copilot No More

Salesforce Rebrands Einstein Copilot as Agentforce Salesforce has officially retired its Einstein Copilot feature, rebranding it as “Agentforce (Default).” While hints of this change surfaced earlier, with confirmation arriving in January 2025, many users were still surprised by the move. Ashish Agarwal, a Salesforce Architect, Consultant, and Trainer, highlighted the change in a LinkedIn post, noting that the conversational AI assistant had been integrated into Salesforce’s broader Agentforce platform. Salesforce’s release notes confirmed the update, stating there would be no changes to functionality but minor refinements to permissions, user interface elements, and help documentation. “In Setup, you’ll see Agentforce or Agentforce (Default) for the agent name and type. This change won’t impact your implementations,” Salesforce assured its users. The company reiterated that the tool remains a vital resource for assisting Salesforce users with daily business interactions. The Journey of Einstein Copilot Einstein Copilot, introduced in April 2024, was Salesforce’s flagship generative AI (GenAI) solution. Integrated into Salesforce applications, it allowed users to perform workflows and ask questions using natural language. Built on the Einstein 1 Platform, it leveraged Salesforce’s Data Cloud and metadata to deliver personalized experiences. Despite its relatively short lifespan, the update positions Einstein Copilot as a component of Agentforce, aligning it with Salesforce’s focus on AI agents. Moving Beyond the “Copilot” Label Salesforce CEO Marc Benioff’s critical remarks about Microsoft’s Copilot may offer clues to the rebranding decision. Over recent months, Benioff has labeled Microsoft’s Copilot as “repackaged ChatGPT” and “Clippy 2.0,” aiming to distinguish Salesforce’s offerings from its competitor. This shift also reflects Salesforce’s evolving AI strategy. The company’s “Four Waves of AI” framework identifies copilots as part of the second wave, now replaced by the third wave: autonomous AI agents. Agarwal encapsulated this shift in his LinkedIn post, stating: “In today’s Agentic world, you don’t need a Copilot to assist you. Agents are taking the controls themselves. They can pilot the plane, handle turbulence, and even serve the in-flight snacks—all while you sit back, relax, and enjoy the ride.” Reactions and Reservations Despite Salesforce’s enthusiasm, the rebranding has drawn some skepticism. Richard Clark, Vice President of EMEA Alliances and Success at Elements.cloud, expressed surprise at the decision, citing Einstein Copilot’s unique role as an internal user agent. Agarwal also questioned the naming convention, arguing that “Agentforce” represents Salesforce’s entire AI platform, which includes components like the Prompt Builder, agents, and the Einstein Trust Layer. Assigning the name to a single agent, he suggested, could create unnecessary confusion. The Future of Agentforce Since its debut in October 2024, Agentforce has quickly evolved. The introduction of Agentforce 2.0, dubbed “The Digital Labor Platform,” has expanded its capabilities, allowing users to create and deploy autonomous AI agents. Salesforce has also tailored its offerings for specific industries. For example, Agentforce for Retail provides AI-driven solutions for customer service, guided shopping, order management, and appointment scheduling. Conclusion Salesforce Agentforce Details The transition from Einstein Copilot to Agentforce signifies Salesforce’s commitment to advancing its AI strategy. While some users may need time to adjust, the move aligns with the company’s vision of autonomous AI agents driving productivity and innovation. Whether viewed as a strategic pivot or a deliberate effort to distance itself from competitors, the rebranding underscores Salesforce’s ongoing evolution in the AI space. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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January 20, 2025in Agentforce Platform, Data, Einstein 1 Platform, Generative AI, Salesforce
agetnforce for nonprofits
08Jan

A Revolution in Enterprise AI

Salesforce Unveils Agentforce 2.0: A Revolution in Enterprise AI Salesforce has launched Agentforce 2.0, a cutting-edge update poised to redefine how enterprises design, deploy, and manage AI-driven solutions. With a suite of innovative features, this upgrade emphasizes seamless collaboration, enhanced integration, and unprecedented flexibility in creating AI-powered workflows. Agentforce 2.0 focuses on three core advancements: headless agents for programmatic control, deep Slack integration for streamlined teamwork, and a revamped API-driven architecture that simplifies development and deployment. Core Highlights of Agentforce 2.0 1. Enhanced Integration Architecture Agentforce 2.0 introduces a sophisticated integration framework designed to streamline workflows and accelerate development. By leveraging MuleSoft for Flow, it provides 40 pre-built connectors for seamless integration with enterprise systems. At the heart of this architecture is the API Catalog, a centralized hub for discovering, managing, and optimizing APIs within Salesforce. The Topic Center simplifies deployment by embedding Agentforce metadata into API design workflows, minimizing manual configurations and reducing time-to-market. Key features of the API Catalog include: This API-first approach empowers DevOps teams to manage and optimize AI capabilities efficiently through a single interface. 2. Upgraded Atlas Reasoning Engine The Atlas Reasoning Engine takes enterprise AI capabilities to the next level, enabling smarter and more effective AI-driven operations. Key enhancements include: Preliminary testing shows a 33% improvement in response accuracy and a doubling of relevance in complex scenarios compared to previous models. The engine uniquely combines System 1 reasoning (fast, intuitive responses) with System 2 reasoning (deep analytical processing), setting a new benchmark for enterprise AI intelligence. 3. Headless Agents for Greater Control A game-changing feature of Agentforce 2.0 is the introduction of headless agents, which operate autonomously without requiring direct user interaction. Key capabilities include: This feature enables enterprises to build robust, autonomous workflows, making Agentforce 2.0 an indispensable tool for optimizing digital operations. 4. Deep Slack Integration Agentforce 2.0 seamlessly integrates AI with Slack, Salesforce’s collaboration platform, fostering real-time, AI-enhanced teamwork. Technical advancements include: By embedding AI agents directly into Slack, teams can break down silos, enabling smoother collaboration and faster decision-making across departments. 5. Data Cloud Integration Agentforce 2.0 leverages Salesforce’s Data Cloud to enhance data accessibility and intelligence. Key features include: This integration ensures that AI agents can provide real-time, actionable insights by tapping into a comprehensive, unified data ecosystem. Final Thoughts Agentforce 2.0 represents a transformative leap in enterprise AI, combining cutting-edge technologies like headless agents, deep Slack integration, and an advanced API-driven architecture. By empowering organizations to create intelligent, autonomous workflows, Agentforce 2.0 sets a new standard for AI innovation in the enterprise sector. With its ability to streamline collaboration, optimize operations, and unlock new levels of productivity, Agentforce 2.0 positions Salesforce as a leader in the evolving AI landscape, helping businesses stay ahead in the digital age. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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January 8, 2025in Data, Enterprise, Mulesoft, Salesforce
Standards in Healthcare Cybersecurity
21Dec

Deploying Large Language Models in Healthcare

Study Identifies Cost-Effective Strategies for Deploying Large Language Models in Healthcare Efficient deployment of large language models (LLMs) at scale in healthcare can streamline clinical workflows and reduce costs by up to 17 times without compromising reliability, according to a study published in NPJ Digital Medicine by researchers at the Icahn School of Medicine at Mount Sinai. The research highlights the potential of LLMs to enhance clinical operations while addressing the financial and computational hurdles healthcare organizations face in scaling these technologies. To investigate solutions, the team evaluated 10 LLMs of varying sizes and capacities using real-world patient data. The models were tested on chained queries and increasingly complex clinical notes, with outputs assessed for accuracy, formatting quality, and adherence to clinical instructions. “Our study was driven by the need to identify practical ways to cut costs while maintaining performance, enabling health systems to confidently adopt LLMs at scale,” said Dr. Eyal Klang, director of the Generative AI Research Program at Icahn Mount Sinai. “We aimed to stress-test these models, evaluating their ability to manage multiple tasks simultaneously and identifying strategies to balance performance and affordability.” The team conducted over 300,000 experiments, finding that high-capacity models like Meta’s Llama-3-70B and GPT-4 Turbo 128k performed best, maintaining high accuracy and low failure rates. However, performance began to degrade as task volume and complexity increased, particularly beyond 50 tasks involving large prompts. The study further revealed that grouping tasks—such as identifying patients for preventive screenings, analyzing medication safety, and matching patients for clinical trials—enabled LLMs to handle up to 50 simultaneous tasks without significant accuracy loss. This strategy also led to dramatic cost savings, with API costs reduced by up to 17-fold, offering a pathway for health systems to save millions annually. “Understanding where these models reach their cognitive limits is critical for ensuring reliability and operational stability,” said Dr. Girish N. Nadkarni, co-senior author and director of The Charles Bronfman Institute of Personalized Medicine. “Our findings pave the way for the integration of generative AI in hospitals while accounting for real-world constraints.” Beyond cost efficiency, the study underscores the potential of LLMs to automate key tasks, conserve resources, and free up healthcare providers to focus more on patient care. “This research highlights how AI can transform healthcare operations. Grouping tasks not only cuts costs but also optimizes resources that can be redirected toward improving patient outcomes,” said Dr. David L. Reich, co-author and chief clinical officer of the Mount Sinai Health System. The research team plans to explore how LLMs perform in live clinical environments and assess emerging models to determine whether advancements in AI technology can expand their cognitive thresholds. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 21, 2024in Data, Generative AI, Technology

Transitioning to Granular Case Categorization in Salesforce

Making Data Work: Transitioning to Granular Case Categorization in Salesforce As businesses grow and their reporting needs evolve, the demand for more granular data categorization becomes essential to maintaining clarity and driving decision-making. Salesforce’s Case Object, which traditionally uses Type and Sub-type fields to classify cases, may no longer suffice for organizations with increasingly complex reporting standards. To address this, a new approach is being implemented: introducing New Type, New Sub-type, and New Sub-sub-Type fields. These fields enable deeper categorization and allow organizations to align their data architecture with modern reporting demands. However, adopting these new fields presents challenges, especially for legacy systems that rely on the older fields. Here’s how organizations can navigate this transition seamlessly using Apex and custom metadata. The Challenge: Transitioning to New Fields The move toward granular categorization brings clear benefits, but it also introduces complexities: To ensure a smooth transition, organizations need a solution that automates field mapping while maintaining data consistency across both legacy and updated systems. The Solution: Apex Mapping with Custom Metadata By leveraging Apex and custom metadata, organizations can create an automated system to map old field values (Type and Sub-type) to the new fields (New Type, New Sub-type, and New Sub-sub-Type). Here’s how the mapping process works: The Benefits of Apex Mapping The Technical Process Real-World Impact Final Thoughts In today’s data-driven world, evolving reporting standards demand more precise data categorization. Transitioning from Type and Sub-type fields to New Type, New Sub-type, and New Sub-sub-Type fields ensures your organization can meet modern reporting needs. By implementing Apex mapping with custom metadata, you can automate the transition, maintain legacy compatibility, and unlock the full potential of granular reporting. While the shift requires planning and effort, the long-term benefits of improved accuracy, efficiency, and decision-making make it an invaluable step for any organization. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 20, 2024in Data
Meta Joins the Race to Reinvent Search with AI
18Dec

Meta Joins the Race to Reinvent Search with AI

Meta Joins the Race to Reinvent Search with AI Meta, the parent company of Facebook, Instagram, and WhatsApp, is stepping into the evolving AI-driven search landscape. As vendors increasingly embrace generative AI to transform search experiences, Meta aims to challenge Google’s dominance in this space. The company is reportedly developing an AI-powered search engine designed to provide conversational, AI-generated summaries of recent events and news. These summaries would be delivered via Meta’s AI chatbot, supported by a multiyear partnership with Reuters for real-time news insights, according to The Information. AI Search: A Growing Opportunity The push comes as generative AI reshapes search technology across the industry. Google, the long-standing leader, has integrated AI features such as AI Overviews into its search platform, offering users summarized search results, product comparisons, and more. This feature, now available in over 100 countries as of October 2024, signals a shift in traditional search strategies. Similarly, OpenAI, the creator of ChatGPT, has been exploring its own AI search model, SearchGPT, and forging partnerships with media organizations like the Associated Press and Hearst. However, OpenAI faces legal challenges, such as a lawsuit from The New York Times over alleged copyright infringement. Meta’s entry into AI-powered search aligns with a broader trend among tech giants. “It makes sense for Meta to explore this,” said Mark Beccue, an analyst with TechTarget’s Enterprise Strategy Group. He noted that Meta’s approach seems more targeted at consumer engagement than enterprise solutions, particularly appealing to younger audiences who are shifting away from traditional search behaviors. Shifting User Preferences Generational changes in search habits are creating opportunities for new players in the market. Younger users, particularly Gen Z and Gen Alpha, are increasingly turning to platforms like TikTok for lifestyle advice and Amazon for product recommendations, bypassing traditional search engines like Google. “Recent studies show younger generations are no longer using ‘Google’ as a verb,” said Lisa Martin, an analyst with the Futurum Group. “This opens the playing field for competitors like Meta and OpenAI.” Forrester Research corroborates this trend, noting a diversification in search behaviors. “ChatGPT’s popularity has accelerated this shift,” said Nikhil Lai, a Forrester analyst. He added that these changes could challenge Google’s search ad market, with its dominance potentially waning in the years ahead. Meta’s AI Search Potential Meta’s foray into AI search offers an opportunity to enhance user experiences and deepen engagement. Rather than pushing news content into users’ feeds—an approach that has drawn criticism—AI-driven search could empower users to decide what content they see and when they see it. “If implemented thoughtfully, it could transform the user experience and give users more control,” said Martin. This approach could also boost engagement by keeping users within Meta’s ecosystem. The Race for Revenue and Trust While AI-powered search is expected to increase engagement, monetization strategies remain uncertain. Google has yet to monetize its AI Overviews, and OpenAI’s plans for SearchGPT remain unclear. Other vendors, like Perplexity AI, are experimenting with models such as sponsored questions instead of traditional results. Trust remains a critical factor in the evolving search landscape. “Google is still seen as more trustworthy,” Lai noted, with users often returning to Google to verify AI-generated information. Despite the competition, the conversational AI search market lacks a definitive leader. “Google dominated traditional search, but the race for conversational search is far more open-ended,” Lai concluded. Meta’s entry into this competitive space underscores the ongoing evolution of search technology, setting the stage for a reshaped digital landscape driven by AI innovation. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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December 18, 2024in Enterprise, Generative AI, Google, Laws, Salesforce, Technology

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