ONC Archives - gettectonic.com
The Agentic Future
07Jun

The Agentic Future

The “agentic future” refers to a future where AI agents play a significant role in various aspects of life, including work, decision-making, and even personal tasks. This future envisions AI agents as autonomous entities capable of making decisions, planning actions, and executing tasks without direct human supervision, essentially functioning as assistants or collaborators rather than just tools.  Here’s a look at what this future might entail: 1. Autonomous AI Assistants: 2. AI as a Collaborative Partner: 3. Challenges and Considerations: 4. Examples of Agentic AI in Action: 5. The Rise of Multi-Agent Systems: In conclusion, the “agentic future” is a vision of a world where AI agents are integrated into various aspects of life, enhancing productivity, personalization, and decision-making. While challenges and considerations remain, the potential for innovation and transformation is significant. 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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June 7, 2025in Agentic AI, Data, Google0 Comments
salesforce agentforce rapid deployment
05Jun

Tectonic and Agentforce

Salesforce Agentforce is revolutionizing how businesses deliver personalized and always-available support through powerful, autonomous AI agents. To fully capitalize on this innovative tool, understanding both your business needs and the Salesforce ecosystem is essential. With extensive experience in Salesforce and developing customized AI solutions, Tectonic is well-positioned to help businesses and government agencies visualize a working proof of concept for adopting Agentforce. Together, Tectonic will help companies develop AI agents tailored to their industry, providing 24/7 support for both employees and customers, regardless of location. At Dreamforce 2024, Salesforce unveiled Agentforce, one of the most anticipated AI releases of the year. Built on Salesforce’s advanced AI technology, Agentforce is poised to transform business operations. While Salesforce is known for its exciting announcements, it’s often challenging to discern how these new products apply to your business. So, let’s get past the hype. What does Agentforce really offer, and how can Tectonic help your company take advantage of it today? Key Use Cases for Agentforce CX Agent (Internal Usage) The Customer Experience (CX) Agent is an AI-powered solution designed to enhance customer interactions across various channels. Tectonic’s implementation focuses on providing human agents the information they need from numerous data sources to respond to customer inquiries, resolving issues, and guiding users through processes. By ensuring seamless communication and support, businesses can elevate the overall customer experience and foster loyalty. Customer Service (External Customer Usage) Agentforce transforms customer service operations by deploying AI agents that handle common inquiries, troubleshoot issues, and provide information 24/7. Tectonic’s implementation allows organizations to reduce wait times and enhance service quality, freeing human agents to tackle more complex problems. This shift not only improves operational efficiency but also leads to higher customer satisfaction levels. How Your Business Can Leverage Agentforce Agentforce isn’t just about adding AI—it’s about improving efficiency and reducing the burden on employees. The challenge lies in integrating these AI agents effectively into existing processes. That’s where Tectonic steps in. With a focus on helping businesses quickly realize the value of Agentforce, Tectonic can help you implement a Proof of Concept (POC) to demonstrate how AI could impact operations, whether it’s improving customer service or enhancing sales. Why Start Now? Agentforce’s release has captured the attention of businesses eager to adopt cutting-edge AI technology. However, turning Agentforce into a game-changer requires a practical approach: Availability for these POCs is limited, so now is the time to act if you’re interested in testing Agentforce. This opportunity allows businesses to see firsthand how AI agents can improve efficiency, productivity, and customer experience. How to Get Started Tectonic’s team can walk you through potential use cases and demonstrate how autonomous agents can boost customer service, empower sales teams, optimize marketing, and more. If you’re ready to take the next step, reach out to one of Tectonic’s experts to see how Agentforce can transform your business. 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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June 5, 2025in Agentforce Platform, Data, Salesforce, Technology0 Comments
Apex
04Jun

Comprehensive Guide to Monitoring Apex Jobs in Salesforce

Why Monitoring Apex Jobs Matters Monitoring asynchronous Apex jobs is critical for maintaining Salesforce system health and performance. Batch processes, queueable jobs, and scheduled operations that fail or exceed limits can disrupt business operations. Proactive monitoring helps: Methods for Monitoring Apex Jobs 1. Using the Native Apex Jobs Dashboard Access Path: Key Features: Critical Data Points: Column Description Why It Matters Job Name Class/trigger name Identifies problem components Status Execution outcome Flags failures needing attention Total Batches Batch job iterations Reveals processing volume Submitted By Initiating user Tracks accidental executions Started/Finished Timestamps Calculates duration for optimization 2. Advanced Tracking with SOQL Queries For deeper analysis, query the AsyncApexJob object: sql Copy Download SELECT Id, ApexClass.Name, JobType, Status, CreatedDate, CompletedDate, NumberOfErrors, JobItemsProcessed, TotalJobItems, ExtendedStatus FROM AsyncApexJob WHERE CreatedDate = LAST_N_DAYS:1 ORDER BY CreatedDate DESC Key Fields Explained: 3. Proactive Monitoring with Custom Reports Recommended Report Type: Sample Report Filters: Best Practices for Effective Monitoring Troubleshooting Common Issues Problem Diagnostic Query Solution Stuck jobs WHERE Status = ‘Processing’ AND CreatedDate < LAST_N_HOURS:2 Abort via UI or API Batch job failures WHERE JobType = ‘BatchApex’ AND NumberOfErrors > 0 Check ExtendedStatus field Queueable job limits WHERE JobType = ‘Queueable’ AND CreatedDate = TODAY Implement queue depth monitoring Scheduled job overlaps WHERE JobType = ‘ScheduledApex’ AND Status = ‘Queued’ Adjust schedule frequencies Advanced Monitoring Options Conclusion Effective Apex job monitoring requires combining Salesforce’s native tools with custom queries and proactive alerting. By implementing these strategies, administrators can: ✔ Catch failures before users report them✔ Optimize job scheduling for better performance✔ Maintain clear audit trails of automated processes✔ Prevent governor limit issues Regular review of job metrics should be part of every Salesforce admin’s routine maintenance checklist to ensure system reliability and performance. 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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June 4, 2025in Data, Salesforce0 Comments
health and life sciences
02Jun

Top Ways AI is Transforming Patient Portal Messaging

Healthcare providers are drowning in patient messages—but AI-powered patient portals can help. By automating responses, improving clarity, and streamlining workflows, AI is easing clinician burnout while enhancing patient communication. Here’s how AI is making an impact: 1. Smart Triage: Routing Messages to the Right Person Not every message needs a physician’s attention—some are billing questions, others require nursing input. AI can: Example: 2. AI-Drafted Responses: Saving Clinicians Time Generative AI can craft clear, empathetic, and clinically accurate responses to common patient queries. Key Findings: Caveat: 3. Simplifying Medical Jargon for Better Patient Understanding Many patients struggle with complex medical terms in portal messages. AI can: Example:NYU Langone used GPT-4 to rewrite discharge summaries, making them easier to understand while scoring higher on patient education metrics. 4. Helping Patients Write Better Messages AI doesn’t just assist providers—it can guide patients to ask clearer questions, reducing back-and-forth. How it works: Vanderbilt’s study found AI-generated prompts made patient messages more concise and actionable. 5. Ethical AI Use: Transparency & Human Oversight While AI boosts efficiency, best practices matter:✅ Always review AI responses before sending.✅ Edit for tone—patients value empathy and a personal touch.✅ Consider disclosing AI use—studies (like Duke’s 2025 review) show it doesn’t harm satisfaction. “AI can reduce burnout while maintaining trust—if used responsibly.”—Dr. Anand Chowdhury, Duke University The Future of AI in Patient Portals As AI evolves, expect: The Bottom Line:AI won’t replace clinicians—but it can free them from repetitive tasks, allowing more time for meaningful patient care. Ready to explore AI for your patient portal? Start with triage automation and AI-assisted drafting, then scale as trust in the technology grows. 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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June 2, 2025in Generative AI, Technology0 Comments
02Jun

Salesforce Acquires Informatica for $8B to Supercharge AI and Data Ambitions

San Francisco, CA – Salesforce has finalized a deal to acquire data management giant Informatica in an all-cash transaction worth approximately $8 billion, marking its largest acquisition since the $28 billion Slack purchase in 2021. The agreement comes after failed negotiations in 2023, when talks collapsed over valuation disagreements. Since then, Informatica’s stock price has declined by more than 30%, making the acquisition significantly cheaper for Salesforce. Under the terms, Informatica shareholders will receive $25 per share—a 30% premium over its closing price on May 22. Why Informatica? Because AI Needs Clean Data The acquisition is a strategic move to strengthen Salesforce’s AI and data infrastructure, particularly for its Agentforce autonomous AI platform. “Together, Salesforce and Informatica will create the most complete, agent-ready data platform in the industry,” said Marc Benioff, Salesforce Chair and CEO. “By unifying Data Cloud, MuleSoft, and Tableau with Informatica’s advanced data management, we’ll enable smarter, safer, and more scalable AI outcomes.” Key Drivers Behind the Deal: Potential Challenges Despite the strategic fit, the deal carries risks: What’s Next? The acquisition is expected to close in early fiscal 2027 (February). Once finalized, Salesforce plans to: Bottom Line Salesforce is betting big that AI’s future success depends on data quality—and Informatica gives it the tools to win that race. But with high integration risks and investor skepticism, the real test will be execution. One thing is clear: In the AI era, data is the new currency—and Salesforce just bought a money printer. 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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June 2, 2025in Data, Enterprise, Google, Mulesoft, Salesforce0 Comments
AI-Powered Analytics
30May

AI-Powered Analytics

Tableau Next: AI-Powered Analytics That Works Alongside You Businesses today are drowning in data but burning alive in search of insights. With 75% of business leaders pressured to prove data’s value, the need for fast, trustworthy intelligence has never been greater. Enter Tableau Next—Salesforce’s evolution of its analytics platform, now supercharged with agentic analytics. This isn’t just another dashboard tool. It’s an AI collaborator that speeds up the entire data-to-action process, automating tedious tasks and delivering insights in plain language. What Is Agentic Analytics? Instead of static reports, Tableau Next lets users work with AI agents to: How It Works Built on Salesforce Data Cloud, Tableau Next connects securely to enterprise data while keeping it consistent and reliable. Key features: Why It Matters “We’re moving from static reports to AI as a decision-making partner,” says Ryan Aytay, CEO of Tableau. By blending AI with trusted data, Tableau Next makes analytics faster, more proactive, and accessible to everyone—not just data experts. The result? Smarter decisions, less manual work, and real business impact—without the usual data headaches. Key Takeaways:✅ AI does the grunt work – Automates data prep, analysis, and monitoring.✅ Ask questions, get answers – Natural language queries deliver instant insights.✅ Built for trust – Salesforce’s secure, unified data layer keeps AI accurate.✅ From insight to action – Automated workflows help teams respond faster. Tableau Next isn’t just an upgrade—it’s a new way to work with data. And for businesses racing to stay ahead, that could be a game-changer. 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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May 30, 2025in Analytics Builder, Data, Enterprise, Google Analytics, Salesforce, Salesforce Data Cloud, Salesforce Tableau0 Comments
ai agent interoperability
30May

AI Agents Interoperability

The Future of AI Agents: Why Interoperability is the Key to Unlocking True Automation The Challenge of Multi-Agent Collaboration AI agents like Agentforce can reason, act autonomously, and execute complex workflows—but can they work together seamlessly? Our previous exploration of agentic maturity revealed that reaching Level 4 (multi-agent orchestration) requires more than just advanced AI—it demands industry-wide standards for agents to discover, authenticate, and communicate across platforms. Without a common interoperability framework, the AI agent ecosystem risks fragmentation, limiting the potential for end-to-end automation in enterprise environments. Why Agent Interoperability Matters Customers operate in heterogeneous tech stacks, using diverse AI frameworks like: While Agentforce offers a powerful way to build agents, we recognize that interoperability is essential for true scalability. That’s why we partnered with Google and 50+ leading tech vendors to develop A2A—the industry’s first agent-to-agent interoperability protocol. Salesforce’s Contribution: The Agent Card We introduced the Agent Card—a lightweight JSON contract that defines an agent’s:✔ Capabilities✔ Identity & compliance tags✔ Trust Score Google adopted this concept as the keystone of the A2A protocol, ensuring seamless capability discovery and version negotiation between agents. The Business Case for Agent Interoperability An open agent-to-agent protocol enables: 1. Breaking Down Vendor Silos 2. Accelerating Time-to-Value 3. Strengthening Governance & Security The 10 Building Blocks of Agent Interoperability To enable scalable, trusted agent collaboration, we’ve defined a foundational framework: Building Block Function Agent Metadata Card A digital “business card” detailing an agent’s capabilities, identity, and technical specs. Identity & Authorization (RBAC) Ensures agents only access data and tools they’re permitted to use. Registry & Discovery A dynamic “address book” where agents register and find each other. Communication Patterns Standardized protocols for real-time and async interactions. Agent Trust Score Measures reliability, ethics, and compliance. State & Conversation Management Tracks ongoing interactions with a unique conversation ID. Multimodal Support Enables text, voice, image, and video interactions. Observability & Auditability Monitors performance, flags issues, and logs actions. Security & Compliance Encrypted data, PII masking, and threat detection. Scalability & Performance Handles surges in demand without slowdowns. Real-World Use Cases 1. Customer Service 2. Sales & Partnerships 3. Financial Services The Path Forward Agent interoperability isn’t just a technical challenge—it’s a strategic imperative for enterprises adopting AI at scale. By embracing A2A and open standards, businesses can:✅ Eliminate vendor lock-in✅ Reduce integration costs✅ Unlock seamless automation The future isn’t just AI agents—it’s AI agents that work together. How could agent interoperability transform workflows in your organization? Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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May 30, 2025in Agentforce Platform, Data, Enterprise, Google, Salesforce, Salesforce Data Cloud0 Comments
Understanding the Bag-of-Words Model in Natural Language Processing
29May

Understanding the Bag-of-Words Model in Natural Language Processing

The Foundation of Text Representation The bag-of-words (BoW) model serves as a fundamental technique in natural language processing (NLP) that transforms textual data into numerical representations. This approach simplifies the complex task of teaching machines to analyze human language by focusing on word occurrence patterns while intentionally disregarding grammatical structure and word order. Core Mechanism of Bag-of-Words The Processing Pipeline Practical Applications Text Classification Systems Sentiment Analysis Tools Specialized Detection Systems Comparative Advantages Implementation Benefits Technical Limitations Semantic Challenges Practical Constraints Enhanced Alternatives N-Gram Models TF-IDF Transformation Word Embedding Approaches Implementation Considerations When to Use BoW When to Avoid BoW The bag-of-words model remains a vital tool in the NLP toolkit, offering a straightforward yet powerful approach to text representation. While newer techniques have emerged to address its limitations, BoW continues to serve as both a practical solution for many applications and a foundational concept for understanding more complex NLP methodologies. 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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May 29, 2025in Data, Enterprise, Technology0 Comments
Salesforce in a Mega-Data Deal with Informatica
27May

Salesforce and Informatica Again

Salesforce Revives Talks to Acquire Informatica in Major Data Play San Francisco, CA – Salesforce is reportedly in advanced discussions to acquire cloud data management firm Informatica, reigniting a potential deal that collapsed last year, according to sources familiar with the matter. Deal Details & Strategic Rationale A Deal Years in the Making Strategic Fit & Potential Hurdles Salesforce’s Acquisition History The CRM giant has a track record of blockbuster deals, including: However, activist investors recently pushed Salesforce to curb large acquisitions in favor of organic growth, leading to: The revived Informatica talks suggest a strategic pivot back to growth via acquisitions. Informatica’s Ownership & Market Position What’s Next? Industry Watch: Will Salesforce secure its next big bet, or will history repeat itself with another stalled deal? Key Takeaway: If successful, this acquisition would mark Salesforce’s return to large-scale M&A, positioning it as a stronger player in AI-powered data management. Follow-up Question: How would an Informatica acquisition impact Salesforce’s competition with Microsoft, Oracle, and Snowflake? 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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May 27, 2025in Data, Enterprise, Mulesoft, Salesforce, Snowflake0 Comments
Potential of GenAI in Healthcare
27May

Generative AI Adoption Accelerates in Healthcare, Survey Reveals

Healthcare leaders are increasingly embracing generative AI to enhance administrative efficiency and clinical productivity, according to a new McKinsey & Company survey. The latest findings, based on Q4 2024 responses from 150 executives across payers, health systems, and healthcare technology firms, highlight rapid adoption and shifting investment priorities. Key Survey Insights Growing Adoption of Gen AI Partnerships Drive Implementation Top Use Cases: Efficiency & Engagement ROI Expectations Are High Strategic Recommendations McKinsey’s report emphasizes that successful gen AI integration requires: “As generative AI gains momentum, healthcare leaders must position their organizations for long-term success by prioritizing high-impact use cases and strategic partnerships,” the authors noted. The findings underscore generative AI’s expanding role in transforming healthcare operations, with early adopters already seeing measurable benefits. 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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May 27, 2025in Data, Generative AI, Technology0 Comments
Salesforce Data Cloud Hits $900M in Revenue
24May

Salesforce Data Cloud Hits $900M in Revenue

Salesforce Data Cloud Hits $900M in Revenue, Powering the Future of AI-Driven Business As AI evolves toward autonomous agents, unified data has become the backbone of enterprise intelligence—ensuring accuracy, compliance, and actionable insights. Without it, AI outputs grow unreliable, and compliance risks surge. Salesforce Data Cloud is addressing this challenge by unifying fragmented data sources, enabling smarter AI-powered experiences. The platform just hit a major milestone in FY25, reaching 0M in annual recurring revenue (ARR)—a testament to its rapid adoption. Why Data Cloud Stands Out Unlike traditional data solutions that require costly overhauls, Data Cloud enables real-time data activation with:✔ Zero-copy architecture (no data duplication)✔ 270+ pre-built connectors (Zendesk, Shopify, Snowflake, and more)✔ Unified structured & unstructured data processing Rahul Auradkar, EVP & GM of Unified Data Services and Einstein at Salesforce, explains: “Data Cloud is the leading data activation layer because it harmonizes data from any source—powering every AI action, automation, and insight. Our hyperscale capabilities, governance, and open ecosystem help enterprises break down silos, creating the foundation for trusted AI.” The Strategic Power of Unified Data Data Cloud acts as an intelligent activation layer, pulling data from warehouses, lakes, CRMs, and external systems to create a single customer view. This fuels: Insulet, a medical device company, leveraged Data Cloud to enhance customer experiences. Amit Guliani, acting CTO, says: “Unified data helps us move from insights to action—delivering personalized solutions that simplify life for people with diabetes.” Industry Recognition & Real-World Impact Salesforce Data Cloud has been named a Leader in the 2025 Gartner Magic Quadrant for Customer Data Platforms and praised by IDC, Forrester, and Constellation Research. Wyndham Hotels & Resorts uses it to transform guest experiences. Scott Strickland, Chief Commercial Officer, shares: “Data Cloud gives our agents a unified view of reservations, loyalty, and CRM data—letting us anticipate needs and personalize stays across thousands of properties.” The Future: Agentic AI Powered by Real-Time Data Data Cloud is the foundation for autonomous AI agents, enabling:🔹 Proactive workflows (agents triggered by customer behavior)🔹 Self-optimizing operations (automated risk detection, dynamic responses)🔹 Trusted governance (GDPR compliance, access controls, security) Adam Berlew, CMO at Equinix, notes: “Data Cloud is shifting our marketing strategy, enabling AI-powered personalization and automation at scale—key to our competitive edge.” Conclusion: AI Runs on Unified Data As businesses transition to AI-first models, Salesforce Data Cloud ensures:✅ Agents act autonomously with real-time, trusted data✅ Humans focus on strategy while AI handles routine tasks✅ Every interaction is hyper-personalized With $900M in ARR and rapid enterprise adoption, Data Cloud is proving to be the essential engine for the next wave of AI-driven business. Key Takeaways: Salesforce Data Cloud isn’t just unifying data—it’s powering the future of intelligent business. 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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May 24, 2025in Agentic AI, Data, Enterprise, Salesforce, Salesforce Data Cloud, Snowflake0 Comments
large concept model
24May

Large Concept Models

Large Concept Models (LCMs) are a new type of language model that differ from traditional Large Language Models (LLMs) by working with concepts, rather than individual words, to process and generate language. Instead of focusing on tokens, LCMs focus on semantic representations at the sentence level, allowing for more abstract and nuanced reasoning.  Key Features of LCMs: How LCMs Differ from LLMs: Potential Applications of LCMs: 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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May 24, 2025in Agentic AI, AI Tools, Google0 Comments
Agentic AI Race
22May

The Evolution Beyond AI Agents

The Evolution Beyond AI Agents: What Comes Next? The Rapid Progression of AI Terminology The landscape of artificial intelligence has undergone a remarkable transformation in just three years. What began with ChatGPT and generative AI as the dominant buzzwords quickly evolved into discussions about copilots, and most recently, agentic AI emerged as 2024‘s defining concept. This accelerated terminology cycle mirrors fashion industry trends more than traditional technology adoption curves. Major players including Adobe, Qualtrics, Oracle, OpenAI, and Deloitte have recently launched agentic AI platforms, joining earlier entrants like Microsoft, AWS, and Salesforce. This rapid market saturation suggests the industry may already be approaching the next conceptual shift before many organizations have fully implemented their current AI strategies. Examining the Staying Power of Agentic AI Industry analysts present diverging views on the longevity of the agentic AI concept. Brandon Purcell, a Forrester Research analyst, acknowledges the pattern of fleeting AI trends while recognizing agentic AI’s potential for greater staying power. He cites three key factors that may extend its relevance: Klaasjan Tukker, Adobe’s Senior Director of Product Marketing, draws parallels to mature technologies that have become invisible infrastructure. He predicts agentic AI will follow a similar trajectory, becoming so seamlessly integrated that users will interact with it as unconsciously as they use navigation apps or operate modern vehicles. The Automotive Sector as an AI Innovation Catalyst The automotive industry provides compelling examples of advanced AI applications that transcend current “agentic” capabilities. Modern autonomous vehicles demonstrate sophisticated AI behaviors including: These implementations suggest that what the tech industry currently labels as “agentic” may represent only an intermediate step toward more autonomous, context-aware systems. The Definitional Challenges of Agentic AI The technology sector faces significant challenges in establishing common definitions for emerging AI concepts. Adobe’s framework describes agents as systems possessing three core attributes: However, as Scott Brinker of HubSpot notes, the term “agentic” risks becoming overused and diluted as vendors apply it inconsistently across various applications and functionalities. Interoperability as the Critical Success Factor For agentic AI systems to deliver lasting value, industry observers emphasize the necessity of cross-platform compatibility. Phil Regnault of PwC highlights the reality that enterprise environments typically combine solutions from multiple vendors, creating integration challenges for AI implementations. Three critical layers require standardization: Without such standards, organizations risk creating new AI silos that mirror the limitations of legacy systems. The Future Beyond Agentic AI While agentic AI continues its maturation process, the technology sector’s relentless innovation cycle suggests the next conceptual breakthrough may emerge sooner than expected. Historical naming patterns for AI advancements indicate several possibilities: As these technologies evolve, they may shed specialized branding in favor of more utilitarian terminology, much as “software bots” became normalized after their initial hype cycle. The automotive parallel suggests that truly transformative AI implementations may become so seamlessly integrated that their underlying technology becomes invisible to end users—the ultimate measure of technological maturity. Until that point, the industry will likely continue its rapid cycle of innovation and rebranding, searching for the next paradigm that captures the imagination as powerfully as “agentic AI” has in 2024. 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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May 22, 2025in Agentic AI, Artificial Intelligence, Data, Enterprise, Generative AI, Salesforce, Technology0 Comments
The Paradox of Jagged Intelligence in AI
20May

The Paradox of Jagged Intelligence in AI

AI systems are breaking records on complex benchmarks, yet they falter on simpler tasks humans handle intuitively—a phenomenon dubbed jagged intelligence. This ainsight explores this uneven capability, tracing its evolution in frontier models and the impact of reasoning models. We introduce SIMPLE, a new public benchmark with easy reasoning tasks solvable by high schoolers, vital for enterprise AI where reliability trumps advanced math skills. Since ChatGPT’s 2022 debut, foundation models have been marketed as chat interfaces. Now, reasoning models like OpenAI’s o3 and DeepSeek’s R1 leverage extra inference-time computation for step-by-step internal reasoning, boosting performance in math, engineering, and coding. This shift to scaling inference compute arrives as pretraining gains may be plateauing. Benchmarking the Gaps Traditional AI benchmarks measure peak performance on tough tasks, like graduate exams or complex code, creating new challenges as old ones are mastered. However, they overlook reliability and worst-case performance on basic tasks, masking jaggedness in “solved” areas. Modern models outshine humans on some challenges but stumble unpredictably on others, unlike specialized tools (e.g., calculators or photo editors). Despite advances in modeling and training, this inconsistent jaggedness persists. SIMPLE targets easy problems where AI still lags, offering insights into jaggedness trends. Evolution of Jaggedness Will jaggedness shrink or grow as models advance? This question shapes enterprise AI success. Lacking jaggedness benchmarks, we created SIMPLE—a dataset of 225 simple questions, each solvable by at least 10% of high schoolers. Example Questions from SIMPLE Performance Trends Evaluating current and past top models on SIMPLE traces jaggedness over time. Green tasks are high school-level; blue are expert-level. School-level benchmarks saturated by 2023-2024, shifting focus to harder tasks. SIMPLE, using the best of gpt-4, gpt-4-turbo, gpt-4o, o1, and o3-mini, scores lowest on school-level questions. Yet, reasoning models show a ~30% improvement, suggesting they reduce jaggedness by double-checking work, linking reasoning to better simple-task performance. Case Study Insights and Implications Reasoning models transfer top-line gains to simple tasks to some extent, but SIMPLE remains unsaturated. Jaggedness persists, with top-line progress outpacing worst-case improvements. This mirrors computing’s history: excelling in narrow domains, outpacing human limits once applied, yet always facing new challenges. Jaggedness may not just define AI—it could be computation’s inherent nature. 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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May 20, 2025in Data, Enterprise0 Comments

Once Upon a Time in Data Land

Once Upon a Time in Data Land: Building the Artificial Intelligence-Ready Warehouse In the early days of data, businesses simply wanted to know what had already happened in the past. Questions like “How many units shipped?” or “What were last month’s sales?” drove the first major digital settlements—the Digitally Filed Data Warehouse. Looking back this seems like the aluminum carport you can have erected in your driveway. The Meticulously Organized Library (The Digitally Filed Data Warehouse Era) Imagine a grand, meticulously organized library. Data from sales, finance, and inventory wasn’t just dumped inside—it went through ETL (Extract, Transform, Load), where it was cleaned, standardized, and structured into predefined formats. Need quarterly sales figures? They were always in the same place, ready for reliable reporting. But then, the world outside got messy. Suddenly, businesses weren’t just dealing with neat rows and columns—they faced website clicks, customer emails, sensor data, social media streams, images, and videos. The rigid Digitally Filed Data Warehouse struggled to adapt. Trying to force unstructured data through ETL was like trying to shelve a waterfall—slow, expensive, and often impossible. The Everything Shed (The Rise of the AI-Powered Warehouse) Enter the AI-Powered Warehouse—a vast, flexible storage space built for raw, unstructured data. Instead of forcing structure upfront, it embraced “store first, organize later” (schema-on-read). Data scientists could explore everything, from tweets to video transcripts, without constraints. But freedom had a cost. Without governance, many AI-Powered Warehouses became “data swamps”—cluttered, unreliable, and slow. Finding clean, trustworthy data was a treasure hunt, and building reliable AI pipelines was a challenge. Organizing the Shed (The AI-Ready Warehouse Paradigm) The solution? Structure without sacrifice. The AI-Ready Warehouse kept the flexibility of raw storage but added intelligence on top. Technologies like Delta Lake, Apache Iceberg, and Apache Hudi introduced:✔ ACID transactions (no more corrupted data)✔ Data versioning (“time travel” to past states)✔ Schema enforcement (order without rigidity)✔ Performance optimizations (speed at scale) A key innovation was the Medallion Architecture, organizing data by quality: This hybrid approach unified BI dashboards, analytics, and machine learning—all on the same foundation. The AI Factory (The Modern AI-Functioning Warehouse) Just as businesses adapted, AI evolved. Generative AI, autonomous agents, and real-time decision-making demanded more than batch-processed data. The AI-Ready Warehouse transformed into a fully integrated AI factory, built for: 🔹 Real-Time & Streaming Data 🔹 Seamless MLOps Integration 🔹 Vector Databases & Embeddings 🔹 Robust AI Governance Why This Matters for AI Agents Autonomous AI agents don’t just analyze data—they act on it. The AI-Functioning Warehouse gives them:✔ Context: Real-time data + historical insights✔ Consistency: Features match training data✔ Memory: Logged actions for continuous learning The Future: An AI-Native Data Ecosystem The journey from Digitally Filed Data Warehouse to AI-Powered Warehouse to AI-Functioning Warehouse reflects a shift from static reporting to dynamic intelligence. For businesses embracing AI, the question is no longer “Do we need a data strategy?” but “Is our data foundation AI-ready?” The answer will separate the leaders from the laggards in the age of AI. Next Steps: The future belongs to those who build not just for data, but for AI. 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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May 18, 2025in Artificial Intelligence, Data, Generative AI0 Comments

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