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Role of Trusted Data in AI Success

Role of Trusted Data in AI Success

The Critical Role of Trusted Data in AI Success As enterprises accelerate their investment in AI applications—including generative AI—the success of these innovations hinges on trusted data. For AI to deliver value, the data that powers it must be high-quality, accurate, complete, and consistent. Equally important, users must have confidence in the data behind AI tools—whether chatbots that enable natural language data analysis or intelligent agents that automate repetitive tasks. Without trusted data, AI applications risk generating inaccurate outputs. And if users don’t trust the data, they may avoid using the applications altogether. This leads to missed opportunities—from failing to strengthen customer relationships to overlooking cross-sell and revenue growth potential. Conversely, reliable data leads to more accurate AI outputs, driving greater adoption. When employees trust AI-generated insights, they can make smarter, data-driven decisions that fuel business growth. The Data Trust Challenge A recent 2025 Data Trust Report by Ataccama, a Toronto-based data management vendor, in collaboration with Hanover Research, surveyed over 300 senior data leaders to assess AI adoption and challenges. The findings were striking: Why Data Trust Is More Critical Than Ever While data quality has always been vital, its importance has grown with the rise of AI-powered decision-making, according to Mike McKee, CEO of Ataccama. AI is no longer just an analytics tool—it’s becoming the primary interface for data analysis and automation. “AI is rapidly evolving into a core decision-making tool, and its effectiveness depends entirely on trusted data,” McKee noted. “As enterprises expand their AI investments, ensuring data quality and trust will only become more critical.” As AI adoption deepens, organizations must prioritize data governance and quality to unlock AI’s full potential—ensuring accuracy, reliability, and user confidence in every AI-driven decision. 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 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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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

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Shift From AI Agents to AI Agent Tool Use

Building Scalable AI Agents

Building Scalable AI Agents: Infrastructure, Planning, and Security The key building blocks of AI agents—planning, tool integration, and memory—demand sophisticated infrastructure to function effectively in production environments. As the technology advances, several critical components have emerged as essential for successful deployments. Development Frameworks & Architecture The ecosystem for AI agent development has matured, with several key frameworks leading the way: While these frameworks offer unique features, successful agents typically share three core architectural components: Despite these strong foundations, production deployments often require customization to address high-scale workloads, security requirements, and system integrations. Planning & Execution Handling complex tasks requires advanced planning and execution flows, typically structured around: An agent’s effectiveness hinges on its ability to: ✅ Generate structured plans by intelligently combining tools and knowledge (e.g., correctly sequencing API calls for a customer refund request).✅ Validate each task step to prevent errors from compounding.✅ Optimize computational costs in long-running operations.✅ Recover from failures through dynamic replanning.✅ Apply multiple validation strategies, from structural verification to runtime testing.✅ Collaborate with other agents when consensus-based decisions improve accuracy. While multi-agent consensus models improve accuracy, they are computationally expensive. Even OpenAI finds that running parallel model instances for consensus-based responses remains cost-prohibitive, with ChatGPT Pro priced at $200/month. Running majority-vote systems for complex tasks can triple or quintuple costs, making single-agent architectures with robust planning and validation more viable for production use. Memory & Retrieval AI agents require advanced memory management to maintain context and learn from experience. Memory systems typically include: 1. Context Window 2. Working Memory (State Maintained During a Task) Key context management techniques: 3. Long-Term Memory & Knowledge Management AI agents rely on structured storage systems for persistent knowledge: Advanced Memory Capabilities Standardization efforts like Anthropic’s Model Context Protocol (MCP) are emerging to streamline memory integration, but challenges remain in balancing computational efficiency, consistency, and real-time retrieval. Security & Execution As AI agents gain autonomy, security and auditability become critical. Production deployments require multiple layers of protection: 1. Tool Access Control 2. Execution Validation 3. Secure Execution Environments 4. API Governance & Access Control 5. Monitoring & Observability 6. Audit Trails These security measures must balance flexibility, reliability, and operational control to ensure trustworthy AI-driven automation. Conclusion Building production-ready AI agents requires a carefully designed infrastructure that balances:✅ Advanced memory systems for context retention.✅ Sophisticated planning capabilities to break down tasks.✅ Secure execution environments with strong access controls. While AI agents offer immense potential, their adoption remains experimental across industries. Organizations must strategically evaluate where AI agents justify their complexity, ensuring that they provide clear, measurable benefits over traditional AI models. 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 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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Salesforce Data Cloud

Maximizing Salesforce Data Cloud

Maximizing Salesforce Data Cloud: Post-Implementation Strategies for Long-Term Success The Fastest-Growing Salesforce Product Salesforce Data Cloud is seeing explosive growth, with a 130% year-over-year increase in paid customers. In just one quarter, it processed an astonishing 2.3 quadrillion records—a 147% jump from the previous year. Businesses leveraging Data Cloud have seen a 165% boost in web engagement, with major brands like The Adecco Group, Aston Martin, and FedEx using it to strengthen customer relationships and drive growth. The Power of Data Cloud: Turning Information into Action What makes Salesforce Data Cloud so impactful is its ability to unify vast amounts of data, creating 360-degree customer profiles and transforming insights into action. But unlocking its full potential doesn’t stop at implementation—it requires ongoing optimization to keep your data clean, your systems efficient, and your AI models accurate. Post-Implementation Best Practices for Salesforce Data Cloud Once your Salesforce Data Cloud is up and running, the next step is ensuring long-term performance and business value. Here’s how to optimize and sustain your Data Cloud investment: 1. Maintain Data Integrity with Ongoing Quality Management 2. Optimize System Performance for Speed and Efficiency 3. Drive User Adoption with Tailored Training 4. Strengthen Data Governance and Compliance 5. Proactively Manage and Optimize Integrations 6. Refine Customer Segmentation for More Personalization 7. Keep AI and Predictive Models Up to Date 8. Measure ROI and Optimize for Business Impact 9. Foster a Data-Driven Culture Through Clear Communication 10. Stay Agile with Continuous Innovation and Community Engagement Conclusion: The Real Value of Data Cloud Begins After Implementation Salesforce Data Cloud is at the heart of next-gen customer engagement, but its true impact lies in how well you maintain and optimize it. Success depends on:✅ Involving key stakeholders in ongoing improvements✅ Enforcing strong data governance for security and accuracy✅ Continuously refining processes to adapt to changing business needs By committing to post-implementation optimization, your organization can stay agile, data-driven, and ahead of the competition—ensuring that Salesforce Data Cloud delivers maximum value now and in the future. 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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Why Its Good to be Data-Driven

The Power of Data-Driven Decision Making Success in business hinges on the ability to make informed decisions. Every operational aspect, from minor choices like office furniture selection to critical investments such as multi-million-dollar marketing campaigns, is shaped by a series of interrelated decisions. While instinct and intuition may play a role, most business choices rely on relevant data—covering aspects such as objectives, pricing, technology, and potential risks. However, excess irrelevant data can be just as detrimental as insufficient accurate data. Why Its Good to be Data-Driven organization… The Evolution of Data-Driven Decision Making Organizations that prioritize data-driven strategies rely on accurate, relevant, complete, and timely data. Simply amassing large volumes of information does not equate to better decision-making; companies must democratize data access, ensuring it is available to all employees rather than limited to data analysts. The practice of using data to inform business decisions gained traction in the mid-20th century when researchers identified decision-making as dynamic, complex, and often ambiguous. Early techniques like decision trees and prospect theory emerged in the 1970s alongside computer-aided decision-making models. The 1980s saw the rise of commercial decision support systems, and by the early 21st century, data warehousing and data mining revolutionized analytics. However, without clear governance and organizational policies, these vast data stores often fell short of their potential. Today, the goal of data-driven decision-making is to combine automated decision models with human expertise, creativity, and critical thinking. This approach requires integrating data science with business operations, equipping managers and employees with powerful decision-support tools. Characteristics of a Data-Driven Organization A truly data-driven organization understands the value of its data and maximizes its potential through structured alignment with business objectives. To safeguard and leverage data assets effectively, businesses must implement governance frameworks ensuring compliance with privacy, security, and integrity standards. Key challenges in establishing a data-driven infrastructure include: The Benefits of a Data-Driven Approach Businesses recognize that becoming data-driven requires more than just investing in technology; success depends on strategy and execution. According to KPMG, four critical factors contribute to the success of data-driven initiatives: A data-driven corporate culture accelerates decision-making, enhances employee engagement, and increases overall business value. Integrating ethical considerations into data usage is crucial for mitigating biases and maintaining data integrity. Transitioning to a Data-Driven Business With the rapid advancement of generative AI, data-driven organizations are poised to unlock trillions of dollars in economic value. McKinsey estimates that AI-driven decision-making could add between .6 trillion and .4 trillion annually across key sectors, including customer operations, marketing, software engineering, and R&D. To successfully transition into a data-driven organization, companies must: By embracing a data-driven model, organizations enhance their ability to make automated yet strategically sound decisions. With seamless data integration across CRM, ERP, and business applications, companies empower human decision-makers to apply their expertise to high-quality, actionable insights—driving innovation and competitive advantage in a rapidly evolving marketplace. 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 Introduces Agentblazer Status

Salesforce Introduces Agentblazer Status

Salesforce Introduces Agentblazer Status: Your AI Learning Roadmap on Trailhead Trailblazers have always been at the forefront of innovation, using Salesforce to empower businesses and communities. Now, as AI transforms the way we work and solve problems, the need for a deeper understanding of this technology has never been greater. Technology is evolving—becoming smarter, more powerful, and more creative. To thrive in the era of agentic AI, we must evolve too. That means staying curious, embracing change, and continuously building new skills. At Salesforce, we’ve seen our community embody this mindset, always ready to learn and explore new possibilities. Today, we’re making that journey even easier with a structured path to mastering AI and Agentforce. Introducing Agentblazer Status Meet Agentblazers—Trailblazers who leverage Salesforce’s digital labor platform to build Agentforce-powered companies. Whether you’re just starting with AI or already developing advanced autonomous agents, Agentblazer Status provides a clear, structured learning path tailored to your expertise level. Your Agentblazer journey includes three levels: Each level is tied to dedicated Trails, including interactive modules and hands-on challenges that build your expertise. As you progress, you’ll earn badges and points that showcase your Agentblazer Status on your Trailblazer Profile. Building the Skills for the Future The skills that got you here today won’t be enough to take you where you need to go next. AI isn’t just a tool—it’s a collaborator. The future of work isn’t about whether you’ll work with AI but how you’ll integrate, manage, and optimize autonomous agents to elevate customer experiences and business operations. That’s why developing Agentforce literacy is essential for every professional. The Agentblazer program provides a structured roadmap to help you move from AI fundamentals to real-world implementation, ensuring you stay ahead in an AI-driven workplace. Join the Agentblazer Community We’re committed to supporting your AI learning journey. That’s why Salesforce is offering the first attempt at the Agentforce Specialist Certification exam free of charge through 2025. Get started today: The AI revolution is here—are you ready to lead it? Let Tectonic guide you! 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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cloudingo for duplicates

Duplicate Data in Salesforce

Duplicate Data in Salesforce: A Sign of Growth, Not Just a Problem Most businesses see duplicate records in Salesforce as a nuisance—an inevitable byproduct of disconnected systems, human error, or inconsistent data entry. And while duplicates do create challenges, they also reveal something important: your business is growing. Duplicates often emerge when scaling—new teams, more tools, and increased customer interactions mean data pours in from multiple sources (marketing automation, sales outreach, support tickets, etc.). The issue isn’t the duplicates themselves; it’s how you handle them. The Hidden Opportunity in Duplicate Data Instead of treating duplicates as purely negative, consider them a chance to: 1. Build a More Complete Customer Picture A duplicate record often represents multiple touchpoints with the same customer. One entry may contain marketing engagement data, another sales notes, and a third support history. Merging them correctly creates a richer, more valuable profile—helping teams personalize interactions and improve customer experiences. 2. Identify Growth Trends A rise in duplicates can signal positive momentum: Tracking duplicates helps pinpoint where processes need refinement as your business scales. 3. Strengthen Data Governance Duplicates often indicate it’s time to revisit data management policies. A clear governance strategy—defining how records should be entered, maintained, and merged—keeps Salesforce clean and reliable. But strategy alone isn’t enough; training and buy-in ensure adoption across teams. 4. Improve Cross-Team Alignment Multiple records for the same contact may suggest siloed teams. Strategic deduplication creates a single source of truth, fostering collaboration and ensuring no opportunities slip through the cracks. Balancing Flexibility & Control Overly strict data entry rules can discourage Salesforce adoption—if users face too many hurdles, they may avoid logging critical information altogether. Yet, no structure leads to chaos: redundant efforts, misaligned reporting, and frustrated teams. The solution? Don’t prevent duplicates—manage them intelligently. The Right Way to Handle Duplicates Instead of blocking record creation, adopt a smarter approach that:✅ Allows users to work freely—Keep data entry frictionless while educating teams on governance best practices.✅ Automatically flags potential duplicates—Use real-time alerts to surface possible matches without manual checks.✅ Merges records intelligently—Preserve valuable data by consolidating records based on quality rules, not just deletion. How Cloudingo Helps Cloudingo enables businesses to maintain user flexibility while keeping data clean. By automating deduplication, it ensures Salesforce remains both easy to use and reliably structured—giving teams the freedom to work while leadership gets accurate insights. See Duplicates Differently Next time you spot duplicates in Salesforce, don’t just see a problem—see a sign of growth and an opportunity to refine your data strategy. When managed well, duplicates aren’t just acceptable; they’re a key part of scaling successfully. Ready to turn duplicates into an advantage? Explore how Cloudingo can help. 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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spring 25

Spring 25 Revealed

Spring ’25 Salesforce Release: What’s New and How to Prepare As winter lingers, the Salesforce Ohana is already embracing the promise of spring—a season of renewal, growth, and transformation. The Salesforce Spring ’25 Release brings fresh innovations designed to enhance productivity, streamline integrations, and optimize your CRM experience. With powerful AI enhancements, security updates, and UI improvements, this release is set to elevate the way you work. Let’s explore the key updates and how you can prepare to make the most of these enhancements. 1. Changes to Einstein Activity Capture Permissions What’s New?Salesforce is refining access to Einstein Activity Capture (EAC), ensuring more controlled and secure usage. Sales Engagement Basic Users will no longer have default access to EAC and must be assigned the Standard Einstein Activity Capture permission set to continue using the feature. Why It Matters:This update enhances security by limiting access to users with the appropriate permissions, improving governance over activity data. How to Prepare: 2. Transition to ICU Locale Formats What’s New?Salesforce is shifting from Oracle’s JDK Locale formats to ICU Locale Formats for handling date, currency, and numeric data. Why It Matters:ICU formats provide better internationalization and localization, improving accuracy and consistency across different regions. How to Prepare: 3. Introduction of LWC Stacked Modals What’s New?Lightning Web Components (LWC) now support stacked modals, allowing multiple modal windows to remain open simultaneously. Why It Matters:This improves the user experience by enabling seamless navigation between modal windows without losing context. How to Prepare: 4. Secure Redirection for Flows What’s New?Salesforce now requires that retURL parameters used in Flow redirections be explicitly added to the trusted URLs list. Why It Matters:This security enhancement mitigates risks associated with unauthorized redirections, protecting user data. How to Prepare: 5. Rollbacks for Apex Action Exceptions in REST API What’s New?Salesforce now enforces automatic rollbacks for exceptions occurring during Apex actions invoked via REST API. Why It Matters:This update ensures data integrity by preventing incomplete or failed operations from saving partial updates. How to Prepare: Final Thoughts The Spring ’25 release brings vital enhancements to permissions, security, UI, and API reliability. By proactively testing and preparing, you can ensure a seamless transition while unlocking the full potential of these updates. Stay ahead of the curve and leverage these improvements to create a more efficient, secure, and innovative Salesforce environment. 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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Achieving AI Success Starts with Data Maturity

Achieving AI Success Starts with Data Maturity

True AI success depends on data maturity. But what does that mean in practice? Organizations with high data maturity: The Path to Data Maturity Reaching data maturity requires a strategic commitment to: ✅ Develop a unified data strategy that aligns business and data teams toward common goals.✅ Implement strong data management and governance to ensure accuracy and trust.✅ Leverage advanced data solutions to transform raw data into actionable insights.✅ Prioritize security and compliance to protect data from breaches.✅ Foster a data-driven culture where every employee has the skills to analyze and act on insights. See How John Lewis & Partners Unlocks AI + Data-Driven Personalization “Investing in Salesforce has enabled us to make decisions faster and develop deeper relationships with our customers by providing a more personalized, convenient, and seamless customer experience.” — Libby Hickey, Tableau Product Manager, John Lewis & Partners Assess Your Data Maturity Ready to accelerate your data transformation? 📊 Take the free assessment to: Start your data maturity journey today. Contact Tectonic. 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 Data Cloud and Integration

It is Time to Implement Data Cloud

With Salesforce Data Cloud you can: With incomplete data your 360-degree customer view is limited and often leads to multiple sales reps working on the same lead. Slow access to the right leads at the right time leads to missed opportunties and delayed closings. If your team cannot trust the data due to siloes and inaccuracies, they avoid using it. It is Time to Implement Data Cloud. Unified Connect and harmonize data from all your Salesforce applications and external data systems. Then activate your data with insights and automation across every customer touchpoint. Powerful With Data Cloud and Agentforce, you can create the most intelligent agents possible, giving them access to the exact data they need to deliver any employee or customer experience. Secure Securely connect your data to any large language model (LLM) without sacrificing data governance and security thanks to the Einstein 1 trust layer. Open Data Cloud is fully open and extensible – bring your own data lake or model to reduce complexity and leverage what’s already been built. Plus, share out to popular destinations like Snowflake, Google Ads, or Meta Ads. Salesforce Data Cloud is the only hyperscale data engine native to Salesforce. It is more than a CDP. It goes beyond a data lake. You can do more with Data Cloud. Your Agentforce journey begins with Data Cloud. Agents need the right data to work. With Data Cloud, you can create the most intelligent agents possible, giving them access to the exact data they need to deliver any employee or customer experience. Use any data in your organization with Agentforce in a safe and secure manner thanks to the Einstein 1 Trust Layer. Datablazers are Salesforce community members who are passionate about driving business growth with data and AI powered by Data Cloud. Sign up to join a growing group of members to learn, connect, and grow with Data Cloud. Join today. The path to AI success begins and ends with quality data. Business, IT, and analytics decision makers with high data maturity were 2x more likely than low-maturity leaders to have the quality data needed to use AI effectively, according to our State of Data and Analytics report. “What’s data maturity?” you might wonder. Hang tight, we’ll explain in chapter 1 of this guide. Data-leading companies also experience: Your data strategy isn’t just important, it’s critical in getting you to the head of the market with new AI technology by your side. That’s why this Salesforce guide is based on recent industry findings and provides best practices to help your company get the most from your data. Tectonic will be sharing a focus on the 360 degree customer view with Salesforce Data Cloud in our insights. Stay tuned. It is Time to Implement Data Cloud 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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Understanding AI Agent Capabilities

AI agents vary widely in their autonomy and complexity. Some tasks require only basic tool use and response generation, while others demand advanced reasoning and independent decision-making. Recognizing these capability levels helps determine when to use simpler, predictable systems versus fully autonomous agents. The Core Capabilities of AI Agents Three fundamental capabilities distinguish AI agents from basic AI tools: Reasoning and Planning Tool Use Memory and Learning The AI Agent Spectrum The evolution from simple AI tools to fully autonomous agents follows a progression of increasing complexity: Not every problem demands the highest level of autonomy. In many cases, tool-using models or orchestrated systems are more practical and cost-effective. Balancing Capability with Control As AI agents become more autonomous, striking the right balance between capability and oversight is critical. Key factors to consider include: Security and Governance Reliability and Trust Cost and Resource Optimization Understanding where your needs fall on this spectrum is essential for effective AI deployment. Not every task requires a fully autonomous agent—sometimes, a simpler, well-structured system is the smarter, more cost-efficient choice. 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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