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Mulesoft

Salesforce’s MuleSoft Paves the Way for Autonomous AI Agents in Enterprise IT

AI agents are coming to the enterprise—and MuleSoft is building the roads they’ll run on. As AI agents emerge as the next evolution of workplace automation, MuleSoft—Salesforce’s integration powerhouse—is rolling out new standards to bring order to the chaos. The company recently introduced two key protocols, Model Context Protocol (MCP) and Agent2Agent (A2A), designed to help AI agents operate autonomously across enterprise systems while maintaining security and oversight. This builds on Salesforce’s Agentforce toolkit, now in its third iteration, which provides developers with the building blocks to create AI agents within the Salesforce ecosystem. The latest update adds a centralized control hub and support for MCP and A2A—two emerging standards that could help AI agents work together seamlessly, even when built by different vendors. Why MuleSoft? The Missing Link for AI Agents MuleSoft, acquired by Salesforce in 2018, originally specialized in connecting siloed enterprise systems via APIs. Now, it’s applying that same expertise to AI agents, ensuring they can access data, execute tasks, and collaborate without requiring custom integrations for every new bot. The two new protocols serve distinct roles: But autonomy requires guardrails. MuleSoft’s Flex Gateway acts as a traffic controller, determining which agents can access what data, what actions they’re permitted to take, and when to terminate an interaction. This lets enterprises retrofit existing APIs for agent use without overhauling their infrastructure. How AI Agents Could Reshape Workflows A typical use case might look like this: This kind of multi-agent collaboration could automate complex workflows—but only if the agents play by the same rules. The Challenge: Agents Are Still Unpredictable While the vision is compelling, AI agents remain more promise than product. Unlike traditional software, agents interpret, learn, and adapt—which makes them powerful but also prone to unexpected behavior. Early adopters like AstraZeneca (testing agents for research and sales) and Cisco Meraki (using MuleSoft’s “AI Chain” to connect LLMs with partner portals) are still in experimental phases. MuleSoft COO Ahyoung An acknowledges the hesitation: many enterprises are intrigued but wary of the risks. Early implementations have revealed issues like agents stuck in infinite loops or processes that fail to terminate. To ease adoption, MuleSoft is offering training programs, entry-level pricing for SMBs, and stricter security controls. The Bigger Picture: Who Controls the Interface Controls the Market Salesforce isn’t trying to build the best AI agent—it’s building the platform that connects them all. Much like early cloud providers didn’t just sell storage but the tools to manage it, MuleSoft aims to be the orchestration layer for enterprise AI. The two protocols are set for general release in July. If successful, they could help turn today’s fragmented AI experiments into a scalable ecosystem of autonomous agents—with MuleSoft at the center. Key Takeaways: ✅ MuleSoft’s new protocols (MCP & A2A) standardize how AI agents interact with systems and each other.✅ Flex Gateway provides governance, ensuring agents operate within defined boundaries.✅ Early use cases show promise, but widespread adoption hinges on reliability and security.✅ Salesforce is positioning MuleSoft as the “operating system” for enterprise AI agents. The bottom line: AI agents are coming—and MuleSoft is laying the groundwork to make them enterprise-ready. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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The Gap Between Marketing Technology and Measurable Results

The Gap Between Marketing Technology and Measurable Results

Despite advancements in marketing tech, many organizations struggle to tie efforts to tangible outcomes. Tools like Salesforce offer robust campaign tracking, yet converting data into actionable insights remains elusive. Operational inefficiencies, disjointed workflows, and inconsistent data inputs stall progress. Without tackling these root issues, even top-tier CRMs fail to provide the unified view marketers need to gauge impact and ROI. The Problem with Rigid Campaign Structures Tracking engagement is key to optimizing touchpoints and boosting conversions. Salesforce treats campaigns as customizable objects, but its top-down rigidity often curbs flexibility. A common approach starts with broad initiatives (e.g., a Q1 marketing push), then splits into channels (social, email), and drills down to specific campaigns. This structure aids organization but hampers dynamic analysis. Marketers must adapt creatively to regain agility. Why Attribution Reporting Falls Short Customer journeys rarely follow a straight line. A prospect might click an email, browse the website, and convert via another source—or engage with a social post, vanish, and return weeks later to buy. Rigid frameworks leave these touchpoints disconnected, obscuring the full journey. A true 360-degree view demands linking every interaction to map and refine the customer path. Breaking Down Data Silos Salesforce’s one-to-many data model struggles with complex many-to-many relationships. For instance, an email with multiple CTAs shouldn’t be locked into a single campaign. The fix? Systems that dismantle data barriers, tracking interactions across the entire journey. Content poses another hurdle—often reused but forced into duplication or oversimplification in rigid setups. Centralizing assets and linking them dynamically cuts redundancy and sharpens performance insights. A Better Approach: Automation & Dynamic Modeling Many marketers lack visibility into content performance, yet proving ROI hinges on it. High-quality content demands resources, but without tracking, teams stumble blindly, missing what drives success. Manual campaign setup adds strain—creating campaigns, adding UTMs, and coordinating teams is time-consuming and error-prone. Automating UTM generation and campaign creation slashes effort while ensuring accurate engagement data. Flexible data models empower multi-angle analysis, dodging confirmation bias and revealing deeper audience insights. Maximizing ROI Without New Tools Rather than adding platforms, marketers should maximize existing tools. With the right strategy, Salesforce can manage complex attribution without pricey integrations. Automation handles the grunt work—logging every touchpoint, attributing influence accurately, and closing reporting gaps. The payoff? Less manual labor, clearer insights, and a seamless view of performance. This isn’t just about efficiency—it’s about harnessing data to refine strategies, boost ROI, and turn content into measurable impact. Turn to Tectonic for help. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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ai and robotics set to transform manufacturing

AI and Robotics Set to Transform Manufacturing

AI and Robotics Set to Transform Manufacturing, Says Salesforce’s Arundhati Bhattacharya Salesforce South Asia CEO highlights workforce evolution and $1B India milestone as intelligent technologies reshape industry Artificial intelligence (AI) and robotics are poised to revolutionize manufacturing, with profound implications for factory workforces, according to Arundhati Bhattacharya, President and CEO of Salesforce South Asia. While adoption on plant floors has been gradual, she notes AI is already driving significant efficiency gains in sales, distribution, and supply chains. Key Insights from Bhattacharya: India Growth Highlights: AI’s Manufacturing Impact: Operational Transformations Future Outlook “The convergence of autonomous agents and robotics will redefine manufacturing ecosystems,” Bhattacharya predicts. As Salesforce expands its Indian presence with industry-specific cloud solutions and AI training infrastructure, the stage is set for intelligent manufacturing to take flight. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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when ai decides

When AI Decides

The Algorithm That Sentenced a Man—And No One Knows Why Meet Eric Loomis. In 2016, he was pulled over in La Crosse, Wisconsin, driving a car linked to a recent shooting. Loomis wasn’t charged with the shooting itself but pleaded guilty to lesser offenses: attempting to flee an officer and driving a vehicle without the owner’s consent. On paper, these were relatively minor felonies. But when it came time for sentencing, something unusual happened. Loomis’s fate wasn’t decided solely by a judge or jury—it was shaped by an algorithm. Wisconsin had adopted a proprietary risk-assessment tool called COMPAS (Correctional Offender Management Profiling for Alternative Sanctions) as part of a push for “data-driven justice.” The software was designed to predict a defendant’s likelihood of reoffending, theoretically helping judges make fairer sentencing decisions. COMPAS scored Loomis as high-risk, suggesting he was likely to commit another crime. That score became a key factor in the judge’s decision to sentence him to six years in prison. Here’s the catch: No one—not Loomis, not his lawyers, not even the judge—knew how that score was calculated. The algorithm was a black box, its inner workings kept secret by its developers. What data was used? What factors mattered most? No one could say. Loomis appealed, arguing that sentencing someone based on unreviewable, unexplained evidence violated due process. The case reached the Wisconsin Supreme Court, which ruled—shockingly—that the use of COMPAS was acceptable. The court acknowledged the tool’s flaws and warned against overreliance on it but ultimately decided that as long as a human judge had the final say, the algorithm’s role was permissible. In other words: An AI made a life-altering decision, no one could explain why, and the court said that was fine—as long as a human rubber-stamped it. Trucks may not yet be pulling up to gas stations demanding we mere humans use our opposable thumbs to fill their tanks, but they could be thinking about it. Accountability: From Campfires to Courtrooms Accountability isn’t just a human invention—it’s a biological imperative. Social species, from apes to humans, enforce norms to maintain order. Apes punish cheaters, share food based on contribution, and even exhibit a rudimentary sense of fairness. For early humans, accountability was immediate and visceral. Steal from the tribe? Face exile. Endanger the group? Risk death. Over millennia, these instincts hardened into customs, then laws. The evolution of justice has been a slow march from arbitrary power to reasoned rule. Kings once claimed divine right—rule “because I said so.” But revolutions in thought—Magna Carta, Locke’s social contract, Beccaria’s arguments for proportionate punishment—shifted accountability from gods to people. Yet now, after centuries of demanding transparency from power, we’re handing decision-making back to unquestionable authorities—not kings or priests, but algorithms we can’t interrogate. The Problem with Machine “Decisions” When a human makes a choice, we expect a reason. Maybe it’s flawed, maybe it’s biased—but it’s something we can challenge, debate, and refine. Machines don’t work that way. AI doesn’t reason—it calculates. It doesn’t weigh morality—it optimizes for probability. Ask an AI why it made a decision, and the answer is always some variation of: “Because the data suggested it.” Consider AlphaGo, the AI that defeated world champion Lee Sedol in 2016. At one point, it made a move so bizarre that commentators thought it was a glitch. But Move 37 wasn’t a mistake—it was a game-winning play. When engineers asked why AlphaGo made that move, the answer was simple: It didn’t know. It had just calculated that the move had the highest chance of success. Brilliant? Yes. Explainable? No. Agentic AI: Decision-Making Without Oversight If black-box algorithms in courtrooms worry you, brace yourself. AI isn’t just recommending decisions anymore—it’s acting autonomously. Enter Agentic AI: systems that don’t wait for instructions but pursue goals independently. They schedule meetings, draft reports, negotiate deals, and even delegate tasks to other AIs—all without human input. Google’s Agent-to-Agent (A2A) protocol enables AI systems to coordinate directly. Workday touts AI handshakes, where agents manage workflows like hyper-efficient middle managers. But here’s the terrifying part: We can’t audit these systems. As Dr. Adnan Masood, Chief AI Architect at UST, warns: “AI-to-AI interactions operate at a speed and complexity that makes traditional debugging and inspection almost useless.” When AI agents collaborate, their decision chains become unfathomably complex. “Explainable AI” tools offer plausible-sounding rationales, but they’re often post-hoc justifications, not true explanations. Who’s Responsible When AI Goes Rogue? In human systems, accountability is clear. If a judge sentences someone unfairly, we can vote them out. If a manager makes a bad call, they can be fired. But in an AI-driven world, who takes the blame? The answer is no one—or worse, everyone and no one at the same time. The Future: “Because the Algorithm Said So” Eric Loomis’s case was a warning. Today, AI shapes who gets hired, who gets loans, who gets parole. Tomorrow, it could dictate medical treatments, military strikes, and legal outcomes—all without explanation. We’re outsourcing judgment to machines that can’t justify their choices. And once we accept that, we’re left with only one answer when we ask why: “Because the AI said so.” Is that the future we want? Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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AI evolves with tools like Agentforce and Atlas

AI Development Agents: The New Productivity Powerhouse for Tech Teams

The New Productivity Powerhouse for Tech Teams The Rise of AI in Software Development Tech companies are rapidly adopting AI-powered developer agents to supercharge productivity and accelerate generative AI integration. These intelligent systems are transforming key workflows—from code generation to large-scale system migrations—delivering unprecedented efficiency gains. How AI Agents Are Revolutionizing Development According to Anupam Mishra, Director of Developer Programs at AWS India and South Asia, AI agents are now handling:✔ Moderate-complexity coding tasks✔ Automated test case generation✔ Security vulnerability detection✔ Legacy system modernization Real-World Impact: AWS Case Studies At the AWS Summit Bengaluru 2025, Mishra revealed staggering results from AI-assisted development: 1. 4X Faster .NET to Linux Migration 2. 83% Faster Java Version Upgrades 3. $260M Annual Savings from AI Automation Why AI Development Agents Are a Game-Changer ✅ Faster time-to-market – Automate repetitive coding tasks✅ Lower costs – Reduce manual debugging & refactoring✅ Enhanced security – Proactively detect vulnerabilities✅ Seamless legacy modernization – Accelerate cloud migrations The Future of AI-Assisted Development As AI agents grow more sophisticated, expect:🔹 Autonomous feature development🔹 Self-healing code that fixes bugs in real time🔹 AI-powered DevOps pipelines “We’re entering an era where AI doesn’t just assist developers—it collaborates with them,” says Mishra. “The best developers won’t be replaced by AI—they’ll be the ones using it best.” Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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Agentforce to the Team

How Agentforce 2.0’s New Model Changes the Game

Salesforce Reinvents AI Pricing: How Agentforce 2.0’s New Model Changes the Game From Conversations to Actions: Salesforce’s Bold Pricing Shift When Salesforce launched Agentforce 2.0 in October 2024, it raced ahead of competitors like Microsoft, SAP, and ServiceNow, positioning itself as the go-to platform for enterprise AI agents. The initial -per-conversation model worked well for simple use cases—like AI handling frontline customer chats—but as businesses experimented further, limitations emerged. Now, Salesforce is rolling out a game-changing update: action-based pricing. The New Pricing Model: Pay for What the AI Actually Does Bill Patterson, EVP of Corporate Strategy at Salesforce, explains: “We’re moving to an action-oriented model—charging for the actual work AI agents perform, not just conversations.” Key Features of the New Pricing: ✅ Flex Credits – Universal currency for AI actions across Sales, Service, and Marketing Clouds✅ $0.10 per action (20 credits) – Only pay when the AI completes a task✅ No hidden fees – Unlike hyperscalers, no separate charges for compute, storage, or LLM calls Example: “Think of it like electricity—you don’t pay differently for your fridge vs. your stove. Flex Credits power all AI agents uniformly.”— Bill Patterson Two Major Additions: Flex Agreement & Digital Wallet 1. Flex Agreement: Convert Unused Licenses into AI Credits Many companies overbuy CRM licenses during hiring surges. Now, they can trade unused licenses into Flex Credits for AI agents. Why It Matters: 2. Digital Wallet: Control & Monitor AI Spending A new centralized dashboard lets companies:📊 Track AI agent usage in real-time🛑 Set spending limits (e.g., cap expensive agents)📈 Measure ROI per agent “This isn’t about nickel-and-diming customers—it’s about fair, scalable pricing that grows with AI adoption.” How Does Salesforce Compare to Competitors? Pricing Model Salesforce Hyperscalers (AWS, Azure) AI Startups Basis Actions completed Compute + microservices “Employee replacement” flat fees Flexibility ✅ Universal Flex Credits ❌ Complex tiered pricing ❌ Rigid per-agent costs Transparency ✅ Clear per-action cost ❌ Hidden API/LLM fees ✅ Fixed but inflexible Salesforce’s edge? Agentforce One: The Next Evolution Coming in July 2025, Salesforce is rebranding Einstein One as Agentforce One—a bundled AI package for Sales & Service Cloud users. What’s Included? Goal: Lower the barrier to entry and accelerate AI adoption across Salesforce’s 150,000+ customers. Will This Boost Agentforce Adoption? ✅ 8,000 companies already use Agentforce (fastest-growing Salesforce product ever).✅ Flex Credits remove cost uncertainty.✅ Digital Wallet enables better budgeting. But… 8,000 is just 5% of Salesforce’s customer base. The new pricing could be the push needed to unlock mass adoption. The Bottom Line Salesforce’s pricing shift isn’t just about cost—it’s about trust. By moving to action-based billing, they’re ensuring customers:✔ Only pay for valuable AI work✔ Can scale AI across departments✔ Gain full visibility into ROI What’s next? As AI costs normalize, Salesforce’s flexible, transparent model could set the industry standard. 🚀 Ready to explore Agentforce?Contact us today! “This is the pricing model AI-powered businesses have been waiting for.”— CIO, Fortune 500 Salesforce Customer Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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Salesforce Unified Knowledge

Unified Data & AI-Driven Insights

The Future of Wealth Management: Unified Data & AI-Driven Insights In today’s fast-moving financial world, real-time, unified client data is the most powerful tool a wealth manager can possess. Unified Data & AI-Driven Insights make for personalized care every client wants. Clients now expect hyper-personalized, proactive advice—but legacy systems with siloed data, manual processes, and compliance risks make delivering this nearly impossible. Salesforce transforms wealth management by centralizing client information, automating workflows, and enabling AI-powered insights—helping advisors provide smarter, faster, and more compliant financial guidance. The Impact of Unified Data Firms using Salesforce report:📈 34% increase in sales productivity⚡ 37% faster decision-making💡 Higher client satisfaction & retention Let’s explore how Salesforce solves wealth management’s biggest challenges—and how Tectonic ensures seamless implementation. The Challenges of Fragmented Wealth Management Systems 1. Disconnected Client Data Information scattered across CRMs, portfolio tools, and spreadsheets makes it impossible to get a single client view. 2. Wasted Time on Manual Work Advisors lose hours compiling reports instead of advising clients—increasing errors and inefficiencies. 3. Slow, Generic Recommendations Without real-time insights, advisors miss opportunities to offer timely, personalized strategies. 4. Compliance Risks Outdated or incomplete client profiles raise regulatory red flags, exposing firms to penalties. How Salesforce Transforms Wealth Management 1. Financial Services Cloud (FSC) A purpose-built platform for wealth management, featuring: 2. 360-Degree Client View Integrates data from sales, service, marketing, and external systems—ensuring every advisor has real-time client insights. Example: A client’s updated contact details or investment preferences automatically sync across all touchpoints. 3. AI-Powered Insights with Einstein 4. Compliance & Security Why Choose Tectonic for Your Salesforce Implementation? At Tectonic, we don’t just set up Salesforce—we optimize it for your firm’s unique needs. Our Expertise: 🔹 Tailored Salesforce Solutions – Customized for wealth management workflows🔹 Seamless Integrations – Connect portfolio tools, compliance systems & more🔹 AI & Automation – Deploy Einstein for smarter client insights🔹 Ironclad Security – Ensure data protection & regulatory compliance🔹 Ongoing Support – Continuous optimization as your business grows The Future Is Unified, AI-Driven, & Client-Centric Salesforce isn’t just a CRM—it’s a competitive advantage for wealth managers ready to:✔ Deliver hyper-personalized advice at scale✔ Operate with real-time data & compliance confidence✔ Focus on clients—not manual busywork 🚀 Ready to transform your firm?Let Tectonic guide your Salesforce journey. Contact us! “With Salesforce and Tectonic, we’ve shifted from reactive to proactive client relationships—driving growth and trust.”— CFO, Top 50 Wealth Management Firm Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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ai agent communication protocols

AI Agent Communication Protocols

AI agent communication protocols are sets of rules that define how AI agents interact and exchange information within multi-agent systems. They provide a standardized way for agents to collaborate, share knowledge, and coordinate their actions to achieve complex goals. Key examples include Agent Communication Protocol (ACP), Model Context Protocol (MCP), and Agent2Agent (A2A).  Elaboration: Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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LLMs and AI

Why Writers Are Disappointed with LLMs

Researchers Explore Why Writers Are Disappointed with LLMs—And Propose a Solution Despite their transformative impact on writing, communication, and creativity, large language models (LLMs) often leave professional writers unsatisfied. A collaborative study by Stony Brook University and Salesforce AI Research investigates this disconnect, identifying key shortcomings in AI-generated text and proposing a manually refined model to better align machine output with human expression. While LLMs like GPT, Claude, and Llama have revolutionized tasks—from scientific writing to creative storytelling—they still struggle to match the depth and originality of human-authored content. A recent study led by Stony Brook’s Assistant Professor Tuhin Chakrabarty, in collaboration with professional writers, pinpoints these limitations and suggests pathways for improvement. The paper received a Best Paper nomination and Honorable Mention at CHI 2025. “A major issue is that LLM-generated text often lacks originality and variation,” says Chakrabarty. The overreliance on LLMs has led to what researchers call algorithmic monoculture—a homogenization of style, where outputs become repetitive, clichéd, and rhetorically shallow. Unlike human writers, who employ nuanced narrative techniques, LLMs frequently default to telling rather than showing, missing the layered complexity that defines compelling writing. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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Salesforce prompt builder

Mastering Salesforce Prompt Builder

Mastering Salesforce Prompt Builder: The Complete Guide to AI-Powered Productivity Why Prompt Engineering Matters in the Salesforce Ecosystem As Salesforce doubles down on generative and agentic AI investments, teams across the ecosystem are racing to implement AI solutions. Yet many struggle with: Enter Prompt Builder — Salesforce’s native tool for declarative, no-code prompt engineering. This insight walks through everything from setup to advanced techniques. Understanding Prompts: The Foundation of Salesforce AI What Exactly is a Prompt? A prompt is a structured instruction that guides AI to generate relevant, consistent responses. In Salesforce, prompts can: Example Prompt Use Case: “As a sales assistant (ROLE), draft a 100-word follow-up email (TASK) for [Contact.Name] about [Opportunity.Name]. Use a professional but friendly tone and include next steps (FORMAT).” Getting Started with Prompt Builder Enablement Checklist Pro Tip: Refresh your browser after enabling to access Prompt Builder. Building Your First Prompt: A Step-by-Step Walkthrough Step 1: Configure Prompt Details Field Description Prompt Type Choose from: Sales Email, Field Generation, Record Summary, Knowledge Answers, or Flex Templates Name/API Name Unique identifiers for your prompt Related Object The Salesforce object this prompt will reference Step 2: Craft the Prompt Template Apply the Role-Task-Format framework: Advanced Techniques: Step 3: Test & Iterate Step 4: Activate & Deploy Embed prompts in: Prompt Engineering Best Practices 1. Design with Purpose 2. Implement Guardrails Risk Solution Hallucinations Add “When unsure, respond: ‘I don’t have enough context’” Tone inconsistencies Specify: “Use [brand] voice guidelines from Knowledge Article #123” Data leakage Leverage CRM data grounding and Einstein Trust Layer 3. Measure & Optimize Track key metrics via Agentforce Analytics:✅ Prompt usage frequency✅ User acceptance rates✅ Downstream KPIs (e.g., case resolution time) Scaling AI Responsibly Governance Framework DevOps Integration Beyond Prompts: The Bigger AI Picture While Prompt Builder excels at generative tasks, combine it with: Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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Government-Citizen Communication

Salesforce’s Vision for Smarter, More Efficient Government Services

Agentic AI: Salesforce’s Vision for Smarter, More Efficient Government Services Transforming Public Sector Service Delivery with AI At the recent Agentforce World Tour in London, Kishan Chetan, Salesforce’s Global Service Cloud Lead, outlined how agentic AI is revolutionizing government operations—streamlining citizen services, reducing bureaucratic friction, and improving efficiency. The Challenge: Meeting Rising Citizen Expectations Citizens today expect fast, seamless, and personalized services—similar to what they experience with private sector giants like Amazon or Uber. Yet many government agencies struggle with:🔹 Complex, manual processes (e.g., DMV queues, permit approvals)🔹 Disjointed data silos (policy docs, case files, eligibility criteria)🔹 Overburdened staff (high administrative workloads leading to burnout) The Solution: Agentic AI + Unified Data Salesforce’s approach combines intelligent automation with harmonized data to: ✔ Automate Routine Tasks – License renewals, appointment scheduling, FAQs✔ Retrieve Policy & Eligibility Info Instantly – No more digging through PDFs✔ Proactively Notify Citizens – Alerts for deadlines, document submissions Real-World Impact: The Data Foundation: Zero-Copy Integration Why Traditional Systems Fail Most agencies store data across:📁 Legacy databases📝 Unstructured documents (PDFs, policies, case notes)🌐 External sources (press releases, regulatory updates) Problem: AI can’t work effectively with fragmented data. Salesforce Data Cloud: The Key to Smarter AI Salesforce’s “zero-copy” integration allows agencies to:🔹 Access data in real time without costly migrations🔹 Unify structured & unstructured sources (e.g., policy docs + CRM records)🔹 Power AI with context-aware insights “Government is knowledge-centric—you need to understand policies, eligibility, and case history. AI can’t do that without clean, connected data.”— Kishan Chetan, Salesforce Agentic AI in Action: Use Cases 1. Social Care Management 2. Grant & Permit Approvals 3. Citizen Self-Service 4. Policy Compliance The Human-AI Partnership Contrary to fears of job displacement, Chetan emphasized that agentic AI augments—not replaces—civil servants:✅ Frees up time for complex decision-making✅ Reduces burnout by automating repetitive tasks✅ Enhances service quality with 24/7 availability Example: Global Alignment with Digital Government Initiatives Salesforce’s strategy aligns with:🇬🇧 UK’s Blueprint for Digital Government – AI as a core enabler🇪🇺 EU’s Digital Decade – 100% online public services by 2030🇺🇸 US AI Executive Order – Modernizing federal workflows The Road Ahead 2025 Priorities for Public Sector AI:🚀 Expanding pre-built solutions (e.g., welfare eligibility engines)🤖 Multi-agent collaboration – AI systems coordinating across departments🔐 Ethical AI governance – Bias detection, transparency tools Bottom Line: Agentic AI is not just a tech upgrade—it’s a public trust accelerator. By delivering faster, fairer, and more transparent services, governments can rebuild citizen confidence in the digital age. 🔗 Explore Salesforce’s Public Sector AI Solutions🔗 Read Forrester’s Take on GovTech Trends “The future of government isn’t just digital—it’s intelligently autonomous.” Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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ai agents

Pacers Sports & Entertainment Teams Up With Salesforce to Revolutionize Fan Experience Through AI

Partnership Leverages Salesforce’s Agentforce AI Platform to Capitalize on Indiana Fever’s Record Growth INDIANAPOLIS – Pacers Sports & Entertainment (PS&E), parent organization of the Indiana Fever and Indiana Pacers, has entered a transformative partnership with Salesforce to redefine fan engagement through cutting-edge AI technology. The collaboration comes as the Indiana Fever franchise rides an extraordinary wave of popularity, boasting: The AI-Powered Fan Engagement Revolution PS&E is deploying Salesforce’s new Agentforce AI platform alongside Marketing Cloud and Data Cloud to create: Strategic Impact “This partnership establishes the technological foundation for what we believe will become one of professional sports’ most valuable fan engagement ecosystems,” said a PS&E executive. The integration allows: The Fever Effect The timing coincides with the Indiana Fever’s meteoric rise, fueled by star power and surging WNBA popularity. Salesforce’s AI capabilities will help PS&E: Industry Implications As sports organizations compete for fan attention in the digital age, PS&E’s AI implementation represents a new playbook for: The partnership signals a new era where every fan interaction—from ticket purchase to concession stand visit—becomes part of a continuous, intelligent conversation powered by artificial intelligence. About the Partners Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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Salesforce Data Cloud

Data Cloud and Data Cloud One

Salesforce Data Cloud is a platform for building unified customer profiles by consolidating data from various sources. Data Cloud One, on the other hand, is a feature within Data Cloud that facilitates bidirectional data sharing and access across multiple Salesforce organizations.  Here’s the difference: In essence: Data Cloud is the underlying platform for unifying customer data, while Data Cloud One is a feature that simplifies the use of Data Cloud across multiple Salesforce orgs. Data Cloud One enhances the power of Data Cloud by enabling easy access to a unified customer 360 across multiple orgs, rather than requiring separate Data Cloud instances for each. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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Salesforce Launches Marketing Cloud Next

Salesforce Launches Marketing Cloud Next

Salesforce Launches Marketing Cloud Next: The End of “Do-Not-Reply” Marketing Say goodbye to one-way marketing. Salesforce just unveiled Marketing Cloud Next, a fully agentic AI-powered platform that transforms how brands engage with customers—turning static campaigns into dynamic, two-way conversations. Why This Changes Everything Today’s consumers expect personalized, real-time interactions—yet most marketing emails still come from “no-reply@company.com“ addresses, offering zero ability to respond. Salesforce is flipping the script: How It Works: AI as Your Co-Pilot Marketing Cloud Next doesn’t replace humans—it augments them. Think of it as a “seasoned team member” that handles grunt work while marketers focus on strategy: “It’s the end of ‘do-not-reply,’” says Bobby Jania, CMO of Salesforce Marketing Cloud. “Humans don’t send emails expecting no response—why should brands?” The Bigger Shift: AI-Driven Expectations Once customers experience conversational marketing, they’ll demand it everywhere. (Remember how ride-sharing made waiting 10 minutes for a taxi feel archaic?) Salesforce is betting that static, one-way campaigns will soon seem just as outdated. But there’s a catch: Not every brand is ready to hand the reins to AI. While some will use Agentforce for full autonomy, others will keep humans in the loop—for now. Available Now—But Is the Market Ready? Marketing Cloud Next rolls out to existing customers in July 2025, integrating with Salesforce’s CRM, Data Cloud, and LinkedIn for closed-loop analytics. The bottom line? Salesforce isn’t just selling a tool—it’s pushing a new paradigm: marketing where every message is a conversation, and AI does the heavy lifting. The question is: Will customers embrace chatty bots—or miss the simplicity of “STOP” to unsubscribe? Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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