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Salesforce’s Marketing Intelligence

Salesforce’s Marketing Intelligence

Introducing Salesforce’s Marketing Intelligence: The Future of Marketing Analytics For the past dozen years, many marketers have been enamored with a powerful marketing analytics tool—Datorama, now known as Salesforce Marketing Cloud Intelligence (MCI). Regarded as one of the most seamless solutions for unifying data across multiple sources, MCI was built by marketers for marketers. It enables users to effortlessly combine data, generate reports, and build dashboards using plain language and pre-built data models. Whether for simple smart-lens dashboards or complex automated reporting triggered by specific events, MCI has been a game-changer. To the delight of marketers everywhere, Salesforce announced on March 18, 2025, a new evolution of the tool: Marketing Intelligence (MI). Built on the Salesforce Platform, MI takes the best of MCI and integrates it with Data Cloud’s unified architecture while introducing agentic AI features that deliver actionable, conversational insights. So, without further ado, let’s explore this exciting new tool. What Is Marketing Intelligence? Marketing Intelligence (MI) is a new Salesforce application designed to simplify marketing data management, deliver trustworthy insights, and maximize marketing ROI. Built on Data Cloud and deeply connected to the Salesforce ecosystem, MI is fully extensible—equipping marketers with everything they need to create powerful, meaningful dashboards with minimal effort. Data Cloud for Marketers, Made Easy One of the standout features of MCI has always been its AI-powered data mapping, which auto-populates based on past usage and logical predictions. Additionally, many APIs come with prebuilt models, reducing the need for manual configuration. These capabilities have helped marketers transition smoothly into data modeling and dashboarding without requiring deep technical expertise. Happily, MI retains and enhances these features. Users can upload a TotalConnect file (a flat file of their choice) or connect via API—with options like Google Ads available at launch and more integrations coming soon. Selecting a connection like Google Ads pulls in formatted data, ready for quick mapping, allowing users to build dashboards in just three clicks. Clean, Intuitive Dashboards MI’s dashboards are sleek, fast-loading, and prebuilt—yet fully customizable. A major upgrade over MCI is the inclusion of generative AI summaries, which analyze campaigns and highlight what’s working (and what isn’t). This feature represents the future of dashboarding: not just displaying data trends but explaining them in plain language and suggesting next steps. Marketers can even ask their AI agent to take action based on these insights, streamlining optimization like never before. This functionality is particularly valuable in large implementations where different users extract different insights from the same dashboard. Instead of manually interpreting data, marketers can now ask their AI agent for recommendations—saving time and reducing guesswork. Harmonized Data Across Channels The core goal of any marketing analytics tool—whether Data Cloud, MCI, or MI—is to unify cross-channel data into actionable insights. Beyond standardized API mapping, MI harmonizes fields across datasets and uses a semantic model to logically connect data (e.g., aligning campaign names across paid media, CRM, and other tools—even when naming conventions differ). For Existing MCI Users: What’s New? Many longtime MCI users may wonder: Can a new version really live up to the original? The answer? Absolutely. Here’s why: 1. Normalization, Simplified In MCI, joining messy and clean data often requires manual effort—using formulas or restructuring campaigns. MI changes that. With Einstein AI-powered normalization, users can automatically standardize data without manual adjustments, making cross-channel reporting smoother than ever. 2. Semantic Modeling Flexibility While MCI offers prebuilt data models, MI introduces semantic modeling, allowing users to define custom relationships across datasets. This means greater flexibility in structuring data, adding fields, and evolving models as business needs change—all while maintaining seamless integration with Salesforce objects. 3. ROI & Attribution, Supercharged The best MCI implementations tie cost/engagement data to real ROI. MI takes this further by integrating with Sales Cloud objects, enabling clearer ROI visualization. Additionally, attribution modeling is now more robust, with support for first- and last-touch attribution—helping marketers validate performance and optimize spend. For New Users: Why Choose MI? 1. Best-in-Class Data Harmonization MCI has long been the gold standard for unifying marketing data. Now, MI enhances this with Data Cloud integration, AI normalization, and three-click setup—making it the ultimate solution for marketers. 2. Smarter, Faster Visualizations While MCI offers strong visualization options, MI improves on two key pain points: 3. Unmatched Customization Want to dynamically rename campaigns, merge traffic sources, or filter data based on custom logic? MI makes it possible with pattern extraction, semantic-layer calculated fields, and Einstein normalization—giving marketers unprecedented control. A New Era for Marketing Analytics Marketing Intelligence launched on March 18, 2025 (requiring Data Cloud and MI licenses). Marketers eager to explore this next-generation tool should contact their Salesforce account executive. MI represents a leap forward in intelligent dashboarding, streamlining marketing data in ways MCI only hinted at. For data-driven marketers, the future is here—and it’s more powerful than ever. 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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B2B Customer Service with Agentforce

Agents are the Future of Customer Engagement

Agentic Customer Engagement is Here There was a time when customer service meant going into a brick and mortar building and talking to a person face to face. It was time consuming and did not guarantee a solution. The mail order business brought on the need for the 800 number to contact a merchant. The dot com boom brought customer engagement opportunities directly to our homes. Ios and Android apps brought customer engagement to our fingertips. Yet we still were dependent upon the availability of humans or at least chatbots. Customer service often repressed customer engagement, not enhanced it. Agents, like Salesforce Agentforce, brought 24 7 customer engagement to us no matter where we are, when it is, or how complicated our issue is. And agents improved customer service! What’s next? Robots and drones who deliver our items and answer our questions? Who knows. AI bots are transforming client relationships and customer service. To achieve unparalleled efficiency, these intelligent systems plan and automate difficult activities, make deft decisions, and blend in seamlessly with current workflows. Yes, it’s widely believed that AI agents will play a crucial role in the future of customer engagement, offering personalized, efficient, and consistent experiences across various channels.  Here’s why AI agents are poised to be a key driver in customer engagement: AI agents are becoming smarter every day, using machine learning and natural language processing to predict customer needs, handle complex queries with empathy and offer real-time, personalized assistance. How AI Agents Are Redefining Customer Engagement Marketing is undergoing a seismic transformation. Tectonic shift, if you will. The past decade was dominated by complex tech stacks and data integration—now, AI is shifting the focus back to what truly matters: crafting impactful content and campaigns. Welcome to the era of agentic customer engagement and marketing. The Rise of Marketing Agents Unlike traditional customer service agents handling one-to-one interactions, marketing agents amplify human expertise to engage audiences at scale—whether targeting broad segments or hyper-personalized personas. They ensure consistent, high-quality messaging across every channel while automating the intricate backend work of delivering the right content to the right customer at the right time. This shift is powered by rapid AI advancements: How Agentic Engagement Amplifies Marketing Marketing agents don’t replace human creativity—they extend it. Once strategists set guidelines, approve messaging, and define brand voice, agents execute with precision across channels. At Typeface, for example, AI securely learns brand tones and styles to generate on-brand imagery, text, and videos—ensuring every asset aligns with the company’s identity. Key Capabilities of Marketing Agents The Human-Agent Partnership AI agents don’t replace marketers—they empower them. Humans bring creativity, emotional intelligence, and strategic decision-making; agents handle execution, data processing, and scalability. Marketers will evolve into “agent wranglers”, setting objectives, monitoring performance, and ensuring alignment with business goals. Meanwhile, agents will work in interconnected ecosystems—where a content agent’s blog post triggers a social agent’s promotion, while a performance agent optimizes distribution, and a brand agent tracks reception. Preparing for the Agent Era To stay ahead, businesses should:✅ Start small, think big – Pilot agents in low-risk areas before scaling.✅ Train teams – Ensure marketers understand agent management.✅ Build governance frameworks – Define oversight and intervention protocols.✅ Strengthen data infrastructure – Clean, structured data fuels agent effectiveness.✅ Maintain human oversight – Regularly audit agent outputs for quality and alignment. Work with a Salesforce partner like Tectonic to prepare for the Agent Era. The Future is Agentic The age of AI-driven marketing isn’t coming—it’s here. Companies that embrace agentic engagement will unlock unprecedented efficiency, personalization, and impact. The question isn’t if you’ll adopt AI agents—it’s how soon. Ready to accelerate your strategy? Discover how Agentforce (Salesforce’s agentic layer) can cut deployment time by 16x while boosting accuracy by 70%. The future of marketing isn’t just automated—it’s autonomous, adaptive, and agentic. Are you prepared? The Future of Customer Experience: AI-Driven Efficiency and Innovation Businesses have long understood the connection between operational efficiency and superior customer experience (CX). However, the rapid advancement of AI-powered technologies, including next-generation hardware and virtual agents, is transforming this connection into a measurable driver of value creation. Increasingly well-documented use cases for generative AI (GenAI) demonstrate that companies can simultaneously deliver a vastly superior customer experience at a significantly lower cost-to-serve, resulting in substantial financial gains. From Customer Journeys to Autonomous Customer Missions To achieve this ideal balance, companies are shifting from traditional customer journeys—where users actively manage their own experiences via apps—to a more comprehensive approach driven by trusted autonomous agents. These agents are designed to complete specific tasks with minimal human involvement, creating an entirely new paradigm for customer engagement. While early implementations may be rudimentary, the convergence of hardware and AI will lead to sophisticated, seamless experiences far beyond current capabilities. AI-Enabled Internal and External Transformation AI is already driving transformation both internally and externally. Internally, it streamlines processes, enhances employee experiences, and significantly boosts productivity. In customer service operations, for example, GenAI has driven productivity improvements of 15% to 30%, with some companies targeting up to 80% efficiency gains. Externally, AI is reshaping customer interactions, making them more personalized, efficient, and intuitive. Virtual co-pilots assist customers by answering inquiries, processing returns, and curating tailored offers—freeing human employees to focus on complex issues that require nuanced decision-making. Linking Operational Efficiency to Customer Experience Leading organizations are demonstrating how AI-driven efficiencies translate into enhanced CX. Despite these gains, companies must raise the bar even further to fully capitalize on AI’s potential. The convergence of next-generation hardware with AI-driven automation presents an unprecedented opportunity to redefine customer engagement. From App-Driven Experiences to Autonomous Agents At Dreamforce 2024, Salesforce CEO Marc Benioff highlighted that service employees waste over 40% of their time on repetitive, low-value tasks. Similarly, customers face friction in making significant purchases or planning events. Google research indicates that travelers may engage in over 700 digital touchpoints when planning a trip—a fragmented and often frustrating experience. Imagine instead a network of proprietary and third-party agents seamlessly executing customer missions—such as purchasing a car or planning a vacation—without requiring constant user input. These AI agents could: This “agentic AI” model represents a shift from passive app-based assistance to proactive, intelligent automation, significantly reducing

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Balancing Security with Operational Flexibility

Balancing Security with Operational Flexibility

Security measures for AI agents must strike a balance between protection and the flexibility required for effective operation in production environments. As these systems advance, several key challenges remain unresolved. Practical Limitations 1. Tool Calling 2. Multi-Step Execution 3. Technical Infrastructure 4. Interaction Challenges 5. Access Control 6. Reliability & Performance The Road Ahead Scaling AI Through Test-Time Compute The future of AI agent capabilities hinges on test-time compute, or the computational resources allocated during inference. While pre-training faces limitations due to finite data availability, test-time compute offers a path to enhanced reasoning. Industry leaders suggest that large-scale reasoning may require significant computational investment. OpenAI’s Sam Altman has stated that while AGI development is now theoretically understood, real-world deployment will depend heavily on compute economics. Near-Term Evolution (2025) Core Intelligence Advancements Interface & Control Improvements Memory & Context Expansion Infrastructure & Scaling Constraints Medium-Term Developments (2026) Core Intelligence Enhancements Interface & Control Innovations Memory & Context Strengthening Current AI systems struggle with basic UI interactions, achieving only ~40% success rates in structured applications. However, novel learning approaches—such as reverse task synthesis, which allows agents to infer workflows through exploration—have nearly doubled success rates in GUI interactions. By 2026, AI agents may transition from executing predefined commands to autonomously understanding and interacting with software environments. Conclusion The trajectory of AI agents points toward increased autonomy, but significant challenges remain. The key developments driving progress include: ✅ Test-time compute unlocking scalable reasoning ✅ Memory architectures improving context retention ✅ Planning optimizations enhancing task decomposition ✅ Security frameworks ensuring safe deployment ✅ Human-AI collaboration models refining interaction efficiency While we may be approaching AGI-like capabilities in specialized domains (e.g., software development, mathematical reasoning), broader applications will depend on breakthroughs in context understanding, UI interaction, and security. Balancing computational feasibility with operational effectiveness remains the primary hurdle in transitioning AI agents from experimental technology to indispensable enterprise tools. 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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itsm

Salesforce Move Into IT Service Management

Salesforce CEO Marc Benioff Signals Bold Move into IT Service Management (ITSM)Salesforce CEO Marc Benioff has once again made headlines, this time with a bold announcement about the company’s expansion into IT Service Management (ITSM). During a recent appearance on the Motley Fool podcast, Benioff revealed that Salesforce is “building new apps, like ITSM.” This follows a subtle hint he dropped during an earnings call, where he teased, “At our TrailheadDX event… You might get a glimpse of the new ITSM product that’s coming if you look hard.” While the ITSM product didn’t take center stage at the event, Salesforce’s intentions to make significant strides in the ITSM space are clear. This move is particularly intriguing given the evolving dynamics between the ITSM and CRM markets, where Salesforce and ServiceNow are increasingly encroaching on each other’s territories. ServiceNow’s CRM Ambitions: A Challenge to Salesforce ServiceNow, the dominant player in the ITSM market, has been making bold moves into CRM, a domain where Salesforce has long been the leader. In fact, Salesforce outsells its closest competitor, Microsoft, by nearly four-to-one in the CRM space. However, ServiceNow is determined to carve out a significant share of the CRM market. Earlier this week, ServiceNow announced its agreement to acquire Moveworks for $2.8 billion. In an interview with CNBC, ServiceNow CEO Bill McDermott emphasized that this acquisition would strengthen the company’s front-office capabilities and bolster its ambition to become “the market leader” in CRM. Unlike traditional CRM competitors who often compete on price, ServiceNow offers a unique value proposition. Its CRM solution integrates with middle- and back-office workflows, encompassing order management, inventory, invoicing, and more. This end-to-end approach provides a more data-rich CRM experience, setting ServiceNow apart from Salesforce. While Salesforce still holds an edge in ease-of-implementation and core CRM functionality—particularly as ServiceNow relies on partners for marketing CRM capabilities—ServiceNow’s differentiated approach poses a long-term threat. Its strong foothold among IT teams, who are increasingly influencing customer-facing technology decisions, adds to its competitive advantage. Salesforce’s ITSM Push: A Strategic Countermove? Benioff’s announcement about Salesforce’s ITSM ambitions could be seen as a strategic countermeasure to ServiceNow’s CRM expansion. Over the years, the two tech giants have steadily encroached on each other’s markets, leveraging their respective strengths to diversify their offerings. As the lines between enterprise technologies continue to blur, the competition between Salesforce and ServiceNow is heating up. With the rise of AI and data platforms, businesses are seeking more integrated and innovative solutions, setting the stage for a fascinating battle of innovation and market dominance. Benioff Takes Aim at Microsoft—Again Adding another layer to this competitive narrative, Benioff didn’t miss the opportunity to critique Microsoft during the podcast. While he expressed amazement at the rapid advancements in AI over the past two years, he also took a jab at Microsoft’s offerings. “I think a lot of our customers have been very disappointed with a lot of the solutions that have been given to them—or even shoved at them,” Benioff said. “Even Microsoft has really disappointed so many of our customers. Copilot has a dozen copilots across its product lines, none of which are connected. It’s not one source of data or one piece of enterprise code.” This isn’t the first time Benioff has targeted Microsoft. He has previously expressed skepticism about its approach to AI, even comparing its Copilot feature to the infamous “Clippy” assistant from the past. A High-Stakes Battle of Innovation As the tech industry continues to evolve, the competition between Salesforce, ServiceNow, and Microsoft is intensifying. With Salesforce venturing into ITSM, ServiceNow pushing into CRM, and Benioff’s recurring critiques of Microsoft, the coming months promise to bring even more innovation—and perhaps a few more pointed remarks. The battle lines are drawn, and the stakes are high. As these tech giants vie for dominance, businesses stand to benefit from the wave of innovation and competition driving the industry forward. 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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FormAssembly Gov Cloud Achieves FedRAMP High Impact Authorization

FormAssembly Gov Cloud Achieves FedRAMP High Impact Authorization

FormAssembly Gov Cloud Achieves FedRAMP High Impact Authorization, Strengthening Secure Data Collection for Federal Agencies FormAssembly, a leader in secure, forms-based data collection solutions, has announced that FormAssembly Gov Cloud is now FedRAMP High Impact Authorized, providing federal agencies and public sector organizations with a fully compliant, secure solution for data collection and process automation. FedRAMP (Federal Risk and Authorization Management Program) is a U.S. government initiative that standardizes security assessment and authorization for cloud-based technologies used by federal agencies. This milestone, achieved in partnership with FedHIVE, ensures agencies can confidently leverage FormAssembly’s no-code platform to streamline workflows while meeting the government’s most rigorous security and compliance requirements. Operating within a High Impact Virtualized Environment, FormAssembly Gov Cloud adheres to more than 420 security controls, safeguarding sensitive federal data and ensuring compliance with stringent federal regulations. “Achieving FedRAMP High Impact authorization underscores our commitment to providing government agencies with a secure, compliant solution for modernizing data collection,” said Jaineesh Davda, CIO at FormAssembly. “With FormAssembly Gov Cloud, agencies can replace outdated manual processes with automated workflows that enhance efficiency, improve citizen services, and ensure data integrity.” Empowering Federal Agencies with Secure, Automated Data Collection FormAssembly Gov Cloud is designed to meet the demanding security and compliance requirements of federally regulated environments. Agencies can confidently collect and manage Controlled Unclassified Information (CUI) while benefiting from advanced security features, including: ✅ Role-Based Access Control – Ensuring only authorized personnel access sensitive data.✅ Data Encryption – Protecting information in transit and at rest.✅ Comprehensive Audit Trails – Providing transparency and accountability in data handling. Streamlining Government Operations with Seamless Integrations Beyond security, FormAssembly Gov Cloud empowers agencies to transform manual, paper-based processes into efficient, automated workflows. Seamless integration with government-preferred platforms such as Salesforce Government Cloud, Microsoft 365, and Google Workspace enables agencies to build a secure, connected data ecosystem. With over 350 five-star reviews on the Salesforce AppExchange, FormAssembly remains the preferred digital form solution for organizations leveraging Salesforce, accelerating implementation, driving long-term adoption, and optimizing data collection at scale. Learn more about FormAssembly Gov Cloud [here] and find us on the FedRAMP Marketplace. About FormAssembly FormAssembly is a leading forms-based data collection platform trusted by organizations worldwide. Known for its ease of use, enterprise-grade security, and seamless Salesforce integration, FormAssembly empowers businesses in highly regulated industries—such as government, financial services, healthcare, higher education, and nonprofits—to streamline data workflows while maintaining strict compliance. To learn more, visit www.formassembly.com. 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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AI in Airport Operations

VINCI Airports Leverages AI to Enhance Passenger Experience and Optimize Operations Across airside, landside, and terminal operations, VINCI Airports— a Corporate Partner of the FTE Digital, Innovation & Startup Hub— is harnessing Artificial Intelligence (AI) to transform passenger experiences, streamline airport flow, and reduce CO2 emissions. As an Innovation Center of Excellence for VINCI Airports, Lyon Airport is at the forefront of testing and implementing Generative AI (GenAI) to enhance customer interactions and operational efficiency. “AI is more than a buzzword—it’s a powerful tool for driving efficiency, improving interactions, personalizing services, and saving time,” says César Clary, Head of Digital & Innovation at Aéroports de Lyon/VINCI Airports. However, he emphasizes that AI should serve as a means to an end, not just a goal in itself. “We are making significant strides in leveraging AI to personalize services, improve efficiency, and reshape airport management.” AI-Powered Enhancements at Lyon Airport With over 10 million passengers passing through Lyon Airport each year, maintaining a cutting-edge customer experience is a priority. VINCI Airports has integrated AI-driven solutions into key customer touchpoints through in-house development and strategic partnerships: “The goal is to create more personalized and seamless interactions for travelers while supporting our staff,” Clary explains. By enabling natural language communication, real-time insights, and personalized recommendations, GenAI and Agentic AI are revolutionizing customer interactions and setting the stage for future service innovations. AI in Airport Operations Beyond customer service, AI is enhancing operational efficiency through: Overcoming Challenges in AI Implementation Despite AI’s vast potential, its adoption comes with challenges. Effective AI integration requires: Clary offers a strategic approach for AI adoption: “Spend time on algorithms and technology, but above all, invest in people, processes, and change management. Start small, demonstrate value, and educate your teams to ensure successful adoption.” With Lyon Airport leading the way, VINCI Airports is proving that GenAI is not just a futuristic concept but a transformative force in modern mobility. 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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Salesforce Unveils Agentforce 2dx

Salesforce Unveils Agentforce 2dx

Salesforce Unveils Agentforce 2dx: A Major Leap in AI Agent Capabilities Proactive, Autonomous AI Agents to Bridge the Skills Gap Salesforce has announced a major upgrade to its AI agent platform with Agentforce 2dx, a next-generation solution designed to move beyond reactive, chat-based interactions. With enhanced efficiency, agility, and scalability, Agentforce 2dx enables AI agents to operate autonomously, integrating seamlessly with existing data systems, business logic, and user interfaces. The Future of Work: AI Agents Filling the Labor Gap “Companies today have more work than workers, and Agentforce is stepping in to fill the gap,” said Adam Evans, EVP and GM of Salesforce’s AI Platform. Unlike traditional AI chatbots that rely on rigid programming or manual prompts, agentic AI dynamically adapts to live data and evolving business needs, making it far more effective in real-world applications. Introducing AgentExchange: A Marketplace for AI Agent Templates Alongside Agentforce 2dx, Salesforce is launching AgentExchange, an online marketplace where businesses can access and share pre-built AI agent templates and actions. From launch, AgentExchange will feature: The AI Agent Race Heats Up Salesforce’s announcement comes amid intensified industry focus on AI agents. Microsoft and AWS have recently made significant moves, with Microsoft research revealing that 72% of business leaders expect AI agents to be fully integrated into their operations soon—21% within the next year and 39% within two years. Meanwhile, AWS is reportedly forming a dedicated AI agent division, led by Swami Sivasubramanian, VP of AI and Data, reporting directly to CEO Matt Garman. Salesforce CEO Marc Benioff has been vocal about the future of AI agents, predicting that tomorrow’s CEOs will need to manage both human employees and AI-powered agents. With Agentforce 2dx and AgentExchange, Salesforce is positioning itself at the forefront of this transformation, empowering businesses to automate, scale, and innovate like never before. Like1 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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The Evolving Role of AI Agents in Key Industries

Legal Services The future of AI in the legal field lies in deeper collaboration between human lawyers and AI systems. AI agents will handle routine document processing, contract analysis, and compliance checks, while legal professionals focus on strategy, negotiation, and final validation. This approach ensures efficiency without compromising accuracy or legal accountability. Finance The financial sector has been an early adopter of AI agents, leveraging them for market analysis, trading, and risk management. 1. Market Analysis & Research 2. Trading & Investment 3. Risk Management Current Limitations: While results are promising, financial AI applications require strict risk management and regulatory oversight. Most firms start with narrowly scoped use cases—such as single-asset trading—before expanding into complex portfolio management. Research & Science AI agents are transforming scientific research by accelerating discovery while maintaining rigorous methodology. A multi-agent approach is proving valuable throughout the research lifecycle: This framework has already shown success in chemistry, where AI agents have identified novel catalysts and reaction pathways. With Google’s Gemini Deep Research, AI-driven knowledge synthesis is expanding beyond specialized fields to broader scientific domains. Challenges & Considerations: The key to success is integrating AI agents into existing research methodologies while preserving scientific rigor. Emerging AI Agent Trends Across industries, three core patterns define the evolution of AI agents: While AI agents hold immense potential, most industries remain in an experimental phase of adoption. Many organizations start with Retrieval-Augmented Generation (RAG) before advancing to fully autonomous agents. The Challenge of Implementation Adopting AI agents requires careful evaluation of their benefits vs. complexity: Organizations must balance innovation, security, and operational efficiency to maximize the impact of AI agents in their industries. 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 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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ai arms race

The Two Types of Voice AI

The Two Types of Voice AI: Assistive AI vs. Autonomous AI Voice AI is transforming customer service by automating tasks, enhancing productivity, and improving customer satisfaction. But not all Voice AI functions the same way — there are two primary types: Assistive AI and Autonomous AI. Understanding their unique roles can help businesses deploy the right solution to optimize efficiency, reduce costs, and deliver exceptional customer experiences. Assistive AI: Empowering Service Representatives Assistive AI works alongside human service representatives, enhancing their efficiency by providing real-time guidance and support during live interactions. Rather than replacing human agents, Assistive AI streamlines workflows, surfaces relevant information, and handles routine tasks — allowing service reps to resolve issues faster and more accurately. Here’s how Assistive AI transforms the customer service experience: 🚀 Real-Time Call Guidance As a customer describes their issue, Assistive AI follows the live call transcript, instantly surfacing relevant knowledge articles, past interaction history, and next-best actions for the agent. This eliminates the need for reps to manually search for information, reducing call times and improving resolution accuracy. For example, if a customer calls to reschedule a hotel stay, Assistive AI can immediately: The result? Faster resolutions and happier customers. 📝 Automated Call Summaries Generative AI capabilities allow Assistive AI to automatically summarize calls once they conclude. Instead of requiring agents to manually document case notes, Assistive AI generates: This significantly reduces post-call administrative work and ensures accurate case documentation. 🎯 Next-Best Action Recommendations Assistive AI can analyze customer sentiment and intent during a call. For example: This proactive support helps agents resolve issues faster, reduce churn, and improve overall customer satisfaction. 📊 Supervisor Alerts Based on Sentiment Assistive AI doesn’t just assist agents — it also helps supervisors. If Assistive AI detects a sharp decline in customer sentiment (such as anger, frustration, or confusion), it can: This prevents escalations from spiraling out of control, protecting the customer experience. ✅ Key Benefits of Assistive AI: Assistive AI empowers human agents — making them smarter, faster, and more effective at delivering outstanding customer service. Autonomous AI: Self-Sufficient Customer Service Agents While Assistive AI works alongside human agents, Autonomous AI can independently handle customer interactions without requiring human intervention. Autonomous AI acts as a fully capable, virtual agent capable of resolving complex requests, completing transactions, and delivering personalized service — all in real-time. This next generation of Voice AI is transforming how businesses handle high call volumes, reducing costs while delivering faster, more accurate service. 💬 Conversational, Human-Like Interactions Unlike traditional IVR systems, Autonomous AI engages in natural, human-like conversations without rigid menu trees or button prompts. Customers can speak in their own words, and the AI agent will: For example: This level of automation significantly reduces operational costs and enhances customer satisfaction. 🔄 Task Execution Across Systems Autonomous AI is not just conversational — it’s actionable. It can directly integrate with: This enables Autonomous AI to complete complex tasks like: No hold times. No transfers. Just fast, efficient resolutions. 💡 Smart Escalation for Complex Cases If a task exceeds the AI agent’s capabilities, it can automatically: This seamless handoff ensures high-quality service without frustrating the customer. 🧠 Continuous Learning and Improvement Like Assistive AI, Autonomous AI continuously learns from customer interactions. Over time, it improves its accuracy, expands its task-handling capabilities, and becomes more effective at resolving complex issues — reducing human intervention further. ✅ Key Benefits of Autonomous AI: Autonomous AI transforms customer service by automating high-volume interactions, allowing human agents to focus on high-value, complex cases. The Power of Voice AI: Assistive + Autonomous Working Together The true power of Voice AI lies in combining Assistive AI and Autonomous AI. Together, they create an optimal balance of automation and human support: Additional Business Benefits of Voice AI 📈 Scalability Without Increasing Costs Voice AI allows businesses to handle thousands of customer calls simultaneously without expanding headcount. This ensures consistent, 24/7 support while keeping operational costs low. 💵 Revenue Growth Through Personalization By analyzing customer history and real-time sentiment, Voice AI can offer: This enables businesses to not only resolve issues but also drive revenue growth. 📊 Data-Driven Insights for Continuous Improvement Voice AI captures and analyzes customer interactions to identify: These insights empower businesses to proactively enhance their products, services, and overall customer experience. 🌐 Enhanced Accessibility for Diverse Customers Voice AI also improves accessibility by enabling voice-based interactions for customers with disabilities or language barriers, ensuring an inclusive support experience. The Future of Customer Service is Voice AI The days of clunky IVR systems and long hold times are over. Voice AI — both Assistive and Autonomous — is revolutionizing customer service by enabling: Forward-thinking businesses that embrace Voice AI now will not only enhance customer experiences but also drive operational efficiency, reduce costs, and increase revenue. ✅ Ready to transform your contact center with Voice AI?Discover how Assistive and Autonomous AI can redefine your customer service — improving satisfaction, reducing costs, and unlocking new growth opportunities. 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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AI-Driven Salesforce Explained

AI-Driven Salesforce Explained

AI-driven Salesforce refers to the integration of Artificial Intelligence (AI) into Salesforce’s Customer Relationship Management (CRM) platform to enhance its capabilities, automate processes, and deliver smarter, data-driven insights. Salesforce has embedded AI into its ecosystem through Salesforce Einstein, its proprietary AI technology. Here’s a breakdown of how AI drives Salesforce: 1. What is AI-Driven Salesforce? AI-driven Salesforce leverages machine learning, natural language processing (NLP), predictive analytics, and automation to help businesses make smarter decisions, improve customer experiences, and streamline operations. It transforms raw data into actionable insights and automates repetitive tasks, enabling teams to focus on strategic activities. 2. Key Features of AI-Driven Salesforce a) Salesforce Einstein Einstein is the AI layer built into Salesforce that powers intelligent features across the platform. Key capabilities include: b) AI-Powered Insights c) Personalization d) Automation e) Predictive Intelligence 3. Benefits of AI-Driven Salesforce a) Enhanced Customer Experience b) Increased Efficiency c) Data-Driven Decision Making d) Improved Sales Performance e) Scalability 4. Use Cases of AI-Driven Salesforce a) Sales b) Marketing c) Customer Service d) Commerce 5. The Future of AI in Salesforce In summary, AI-driven Salesforce empowers businesses to work smarter, not harder, by leveraging data and automation to deliver better customer experiences and drive growth. It’s a game-changer for sales, marketing, service, and beyond! Content updated January 2025. 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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Prioritize Data Quality

Prioritize Data Quality

Prioritize Data Quality: Strengthening Governance for AI and Business Success 86% of analytics and IT leaders agree: AI’s effectiveness depends on the quality of its data inputs. High data quality brings organizations closer to data maturity and AI success—and it all starts with strong data governance. 🔹 92% of analytics and IT leaders say there’s never been a greater need for trustworthy data.📊 The State of Data and Analytics Report, 2023 Building a Strong Data Governance Strategy Data governance is more than compliance—it’s a structured approach to managing data quality, security, and usability. With the right governance in place, teams gain confidence in their data, leading to smarter decision-making and a culture of trust. Follow these six steps to lay the foundation for a successful governance strategy: 1. Align Governance Policies with Business Needs Meet with stakeholders to understand how data is used across teams. Ensure governance policies cover every critical workflow and use case, helping teams get the data they need—accurately and securely. 2. Define What ‘Data Quality’ Means for Your Organization Create a clear framework using these key data quality dimensions: ✅ Completeness: Are all necessary data fields populated?✅ Timeliness: Is data up to date and aligned with business goals?✅ Validity: Does data comply with governance rules and constraints?✅ Usage: How frequently is the data used for reporting and decision-making?✅ Accuracy: Does the data reflect reality, and is it sourced from trusted origins?✅ Consistency: Are data formatting and structure standardized across sources?✅ Reliability: Has data maintained quality and consistency over time? 3. Implement a Robust Quality Control Process Standardized processes—data entry validation, deduplication, cleansing, and archiving—are essential for governance. Leverage AI-powered tools like Tableau CRM Analytics to automate these tasks with built-in data profiling and enrichment features. 4. Schedule Regular Governance Reviews Your business evolves—your governance strategy should too. Establish a review cadence to assess policies, update processes, and address new data challenges. 5. Train Teams on Data Security and Compliance Education is key. Assign role-based security permissions, ensure regulatory compliance, and provide a clear process for reporting data issues (e.g., a dedicated Slack channel or help desk). 6. Define Success with Data Governance KPIs Tracking governance effectiveness is essential. Use these key metrics to measure impact: Metric Example of Smart KPI How to Track Data Quality Improve overall data quality by 4% per quarter. Assign values to frequency, error rates, and data usage. Data Usage Increase customer data-driven decision-making by 30% in 12 months. Measure employee logins, reports accessed, and data utilization. Time-to-Insight Reduce time from customer action → dashboard update to 10 minutes by next quarter. Track time-to-insight vs. benchmarks. Moving Up the Data Maturity Curve A well-governed data strategy leads to: 📈 Higher efficiency in decision-making🚀 More successful AI and analytics initiatives🏆 Competitive advantage through trustworthy data 🔍 “Ascending the data maturity curve unlocks new efficiencies and a competitive edge.”— Funke Bishi, Associate Director, Data and Business Analysis, RBC Capital Markets Take Action: Strengthen Your Data Governance ✅ Define what ‘quality data’ means for your business.✅ Align governance policies with team needs.✅ Use AI-powered tools like Tableau Data Prep for automated cleansing.✅ Train leaders on data best practices and compliance. 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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Salesforce Flow Approval Orchestration

Salesforce Flow Approval Orchestration

Salesforce Flow Approval Orchestration: A Winner for Approval Processes Approval Processes have long been a key feature in Salesforce, enabling professionals to implement structured, multi-step approvals for records. With the shift towards Salesforce Flow as the primary declarative automation tool, it was only a matter of time before approval functionality became natively integrated. Enter Flow Approval Orchestration—a new way to build and manage approvals entirely within Flow Builder at no additional cost. Introducing Flow Approval Orchestration Spring ’25 introduces Approval Orchestration Flow Types, expanding Flow’s capabilities beyond merely invoking an existing Approval Process. These new flows offer a robust solution for both simple and complex approval workflows, accommodating both internal and external users and systems. Approval Orchestration flows come in two types: Stages and Steps: The Core of Approval Orchestration Approval Orchestration relies on Stages and Steps to structure approvals. Stages contain multiple Steps that can execute sequentially or in parallel based on specified criteria. Within each Stage, two types of Steps are available: By default, a Stage is completed once all its Steps are finished, but this can be customized with additional conditions or an Evaluation Flow for more complex logic. Building an Approval Process in Flow Builder Approval Orchestration offers flexibility for various business scenarios. Let’s consider an example where an Opportunity requires approval upon creation, but only if the related Account is classified as a Customer Account. Steps to Implement: For opportunities over 500K, a secondary approver is required. The Decision Element in Flow allows navigation between different Stages, ensuring complex approval hierarchies are handled efficiently. Enhancing User Experience & Management To streamline approval management: Key Considerations Before transitioning to Flow Approval Orchestration, keep these in mind: Final Thoughts Bringing all approval-related automation into Flow Builder is a significant leap forward for Salesforce admins. Flow Approval Orchestration not only simplifies complex approval logic but also enhances visibility and control over approvals. Try it in a sandbox environment to explore its potential and tailor it to your specific business needs. 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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AI Agents

AI Agents in Action: Real-World Applications

The true potential of AI agents lies in their practical use across industries. Let’s explore how different sectors are leveraging AI agents to solve real challenges. Software Development The shift from simple code completion to autonomous software development highlights AI’s expanding role in engineering. While GitHub Copilot introduced real-time coding assistance in 2021, today’s AI agents—like Devin—can manage end-to-end development, from setting up environments to deployment. Multi-agent frameworks, such as MetaGPT, showcase how specialized AI agents collaborate effectively: While AI agents lack human limitations, this shift raises fundamental questions about development practices shaped over decades. AI excels at tasks like prototyping and automated testing, but the true opportunity lies in rethinking software development itself—not just making existing processes faster. This transformation is already affecting hiring trends. Salesforce, for example, announced it will not hire new software engineers in 2025, citing a 30% productivity increase from AI-driven development. Meanwhile, Meta CEO Mark Zuckerberg predicts that by 2025, AI will reach the level of mid-level software engineers, capable of generating production-ready code. However, real-world tests highlight limitations. While Devin performs well on isolated tasks like API integrations, it struggles with complex development projects. In one evaluation, Devin successfully completed only 3 out of 20 full-stack tasks. In contrast, developer-driven workflows using tools like Cursor have proven more reliable, suggesting that AI agents are best used as collaborators rather than full replacements. Customer Service The evolution from basic chatbots to sophisticated AI service agents marks one of the most successful AI deployments to date. Research by Sierra shows that modern AI agents can handle complex tasks—such as flight rebookings and multi-step refunds—previously requiring multiple human agents, all while maintaining natural conversation flow. Key capabilities include: However, challenges remain, particularly in handling policy exceptions and emotionally sensitive situations. Many companies address this by limiting AI agents to approved knowledge sources and implementing clear escalation protocols. The most effective approach in production environments has been a hybrid model, where AI agents handle routine tasks and escalate complex cases to human staff. Sales & Marketing AI agents are now playing a critical role in structured sales and marketing workflows, such as lead qualification, meeting scheduling, and campaign analytics. These agents integrate seamlessly with CRM platforms and communication tools while adhering to business rules. For example, Salesforce’s Agentforce processes customer interactions, maintains conversation history, and escalates complex inquiries when necessary. 1. Sales Development 2. Marketing Operations Core capabilities: However, implementing AI in sales and marketing presents challenges: A hybrid approach—where AI manages routine tasks and data-driven decisions while humans focus on relationship-building and strategy—has proven most effective. Legal Services AI agents are also transforming the legal industry by processing complex documents and maintaining compliance across jurisdictions. Systems like Harvey can break down multi-month projects, such as S-1 filings, into structured workflows while ensuring regulatory compliance. Key capabilities: However, AI-assisted legal work faces significant challenges. Validation and liability remain critical concerns—AI-generated outputs require human review, and the legal responsibility for AI-assisted decisions is still unresolved. While AI excels at document processing and legal research, strategic decisions remain firmly in human hands. Final Thoughts Across industries, AI agents are proving their value in automation, efficiency, and data-driven decision-making. However, fully autonomous systems are not yet replacing human expertise—instead, the most successful implementations involve AI-human collaboration, where agents handle repetitive tasks while humans oversee complex decision-making. As AI technology continues to evolve, businesses must strike the right balance between automation, control, and human oversight to maximize its potential. 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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