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The Subscription Economy

The Subscription Economy: A $1.5 Trillion Opportunity by 2025The subscription economy has expanded by 435% over the last decade and is projected to reach a staggering $1.5 trillion market size by 2025, cementing its status as a dominant business model. Companies leveraging subscription and servitization strategies are unlocking consistent revenue streams and delivering tailored services that foster customer loyalty and satisfaction. The Challenge of Managing RenewalsWhile the subscription economy thrives, managing renewals effectively remains a critical challenge. Without a robust system, businesses risk: This is where Salesforce CPQ (Configure, Price, Quote) becomes essential. It not only automates the renewal process but also empowers businesses to deliver accurate, timely, and personalized renewal experiences. The Role of Salesforce CPQ in Renewal ManagementSalesforce CPQ streamlines the renewal process by automating workflows, ensuring pricing accuracy, and creating personalized customer experiences. Key benefits include: With Salesforce CPQ, renewals become a strategic opportunity to retain customers, drive revenue growth, and deliver exceptional service. The Importance of Renewal Metrics Tracking the right renewal metrics is crucial for understanding performance and identifying areas for improvement. These metrics provide actionable insights that drive better customer retention and revenue growth. 1. Renewal Rate 2. Churn Rate 3. Renewal Revenue 4. Net Revenue Retention (NRR) 5. Customer Lifetime Value (CLV) 6. Time to Renewal (TTR) 7. Renewal Upsell Rate 8. Renewal Discount Utilization Enhancing Renewal Metrics with Salesforce CPQ Salesforce CPQ transforms how you track and manage renewal metrics through automation, analytics, and workflow optimization. Here’s how: 1. Automate Renewal Tracking 2. Enhance Reporting and Analytics 3. Optimize Workflows 4. Analyze and Improve 5. Train and Support Teams Conclusion Salesforce CPQ revolutionizes renewal management by automating processes, reducing errors, and delivering actionable insights through renewal metrics. By implementing these tools and strategies, you can improve customer retention, increase revenue, and build stronger customer relationships. Whether managing hundreds or thousands of renewals, Salesforce CPQ ensures your business stays ahead with precision and care. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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 AI Research Introduces BLIP-3-Video

Salesforce AI Research Introduces BLIP-3-Video

Salesforce AI Research Introduces BLIP-3-Video: A Groundbreaking Multimodal Model for Efficient Video Understanding Vision-language models (VLMs) are transforming artificial intelligence by merging visual and textual data, enabling advancements in video analysis, human-computer interaction, and multimedia applications. These tools empower systems to generate captions, answer questions, and support decision-making, driving innovation in industries like entertainment, healthcare, and autonomous systems. However, the exponential growth in video-based tasks has created a demand for more efficient processing solutions that can manage the vast amounts of visual and temporal data inherent in videos. The Challenge of Scaling Video Understanding Existing video-processing models face significant inefficiencies. Many rely on processing each frame individually, creating thousands of visual tokens that demand extensive computational resources. This approach struggles with long or complex videos, where balancing computational efficiency and accurate temporal understanding becomes crucial. Attempts to address this issue, such as pooling techniques used by models like Video-ChatGPT and LLaVA-OneVision, have only partially succeeded, as they still produce thousands of tokens. Introducing BLIP-3-Video: A Breakthrough in Token Efficiency To tackle these challenges, Salesforce AI Research has developed BLIP-3-Video, a cutting-edge vision-language model optimized for video processing. The key innovation lies in its temporal encoder, which reduces visual tokens to just 16–32 tokens per video, significantly lowering computational requirements while maintaining strong performance. The temporal encoder employs a spatio-temporal attentional pooling mechanism, selectively extracting the most informative data from video frames. By consolidating spatial and temporal information into compact video-level tokens, BLIP-3-Video streamlines video processing without sacrificing accuracy. Efficient Architecture for Scalable Video Tasks BLIP-3-Video’s architecture integrates: This design ensures that the model efficiently captures essential temporal information while minimizing redundant data. Performance Highlights BLIP-3-Video demonstrates remarkable efficiency, achieving accuracy comparable to state-of-the-art models like Tarsier-34B while using a fraction of the tokens: For context, Tarsier-34B requires 4608 tokens for eight video frames, whereas BLIP-3-Video achieves similar results with only 32 tokens. On multiple-choice tasks, the model excelled: These results highlight BLIP-3-Video as one of the most token-efficient models in video understanding, offering top-tier performance while dramatically reducing computational costs. Advancing AI for Real-World Video Applications BLIP-3-Video addresses the critical challenge of token inefficiency, proving that complex video data can be processed effectively with far fewer resources. Developed by Salesforce AI Research, the model paves the way for scalable, real-time video processing across industries, including healthcare, autonomous systems, and entertainment. By combining efficiency with high performance, BLIP-3-Video sets a new standard for vision-language models, driving the practical application of AI in video-based systems. 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 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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Agentic AI is Here

Agentic AI Revolution

The Agentic AI Revolution: Lead, Follow, or Get Out of the Way The era of agentic AI is here, and the message is clear—if you’re not leading the charge, you’re falling behind. Companies like Wiley and OpenTable are reshaping their industries with autonomous AI agents that don’t just assist but also analyze, strategize, and execute tasks with unparalleled efficiency. As these organizations demonstrate, the key to AI success lies in rewriting the rules of your industry rather than playing catch-up. Rewriting Industry Standards with Agentic AI Wiley: The education giant leveraged Agentforce, a digital labor platform for deploying autonomous AI agents, to transform its customer service operations. By onboarding representatives 50% faster and improving case resolution by 40%, Wiley streamlined its processes in just a few weeks. AI agents now handle registration and payment inquiries, directing students to resources and reducing the workload on human representatives. OpenTable: As the go-to reservation platform for 1.7 billion diners annually, OpenTable deploys AI agents to manage reservation changes and loyalty points. This allows employees to focus on customer relationships. Even a two-minute efficiency gain per interaction translates to massive operational savings. Salesforce Help Site: With over 60 million annual visits, the Salesforce Help site integrated Agentforce to resolve 83% of queries without human involvement. In just weeks, Agentforce doubled its capacity, handling over 32,000 automated conversations. These examples showcase a new era of digital labor where AI agents orchestrate high-value, multistep tasks, working tirelessly to deliver results. Far from replacing humans, they supercharge productivity and innovation, enabling companies to do more than ever before. How to Empower Your Workforce with AI Empowering your workforce for the next wave of AI doesn’t require months of preparation or millions of dollars. You don’t need to build or train your own large language model (LLM). Instead, integrating AI with existing data, automation, and workflows is the key to success, as demonstrated by leaders like Wiley and OpenTable. Here’s how to get started: 1. Real-Time Data Access AI thrives on real-time, high-quality data. Platforms like Salesforce Data Cloud unify structured and unstructured data, connecting it seamlessly to the LLM. Techniques such as retrieval-augmented generation (RAG) and semantic search ensure AI agents can access the most relevant data for any task. 2. Advanced Reasoning AI agents aren’t just about answering queries—they execute complex, multistep tasks. For example, they can process returns, reorder items, and even flag anomalies. Powered by reasoning engines, these agents draw data from systems like CRM, refine plans, and adapt dynamically until the task is completed correctly. 3. Built-In Security AI agents must operate within clear guardrails, knowing their limits and handing tasks off to humans when necessary. Strong permissions and security protocols are essential to ensure data protection and prevent unauthorized actions. 4. Action-Oriented Workflows Generative AI’s real value lies in action. By integrating tools like Salesforce Flow for task automation and MuleSoft APIs for system connectivity, AI agents can execute business workflows such as fraud detection, customer outreach, and case management. 5. Human-AI Collaboration The future of work isn’t AI replacing humans—it’s AI and humans working together. While agents handle data-intensive and repetitive tasks, humans bring strategic thinking, empathy, and creativity. This synergy leads to smarter decisions and redefines workflows across industries. Why Training Your Own LLM May Not Be the Answer Many companies assume training a proprietary LLM will give them a competitive edge. In reality, this process is costly, time-intensive, and requires constant updates to remain accurate. An LLM trained on static data quickly becomes outdated, much like a GPS that fails after the first detour. Instead, companies are turning to out-of-the-box AI solutions that integrate seamlessly with their existing systems. These tools offer the flexibility to scale quickly and adapt in real time, enabling businesses to stay competitive without the heavy lift of building from scratch. Scaling AI for the Future Many organizations remain stuck in pilot phases with AI due to data quality issues and a limited understanding of use cases. Companies like Wiley and OpenTable, however, have cracked the code: integrating prebuilt AI systems with robust data flows, automation, and workflows. By embracing agentic AI, forward-thinking organizations are creating digital labor forces that unlock new efficiencies, enhance customer experiences, and position themselves for long-term success. The trillion-dollar AI opportunity awaits—will you lead or trail behind? 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 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 Captivates the World

AI Captivates the World

In the late 1990s, a transformative moment unfolded that expanded the world to enquiring minds—the screeching of a dial-up modem, followed by a pixelated “Welcome” screen that connected users to a vast, invisible network spanning the globe. The internet revolution redefined how people interacted with information and one another, permanently reshaping digital communication. Fast forward to 2024, and a similar wave of innovation is underway. Artificial intelligence is captivating the world with its ability to understand, create, and process information. Massive datasets can now be uploaded to AI tools, which instantly distill complex insights—tasks that once took teams of analysts weeks to complete are now executed in seconds. Just as the internet linked people and information, AI is deepening connectivity across all aspects of life, from healthcare and finance to workplaces and homes. In this evolving digital divide, designers hold a critical role—not only in making AI usable but in ensuring it remains understandable, trustworthy, and human-centered. As Fei-Fei Li, Co-Director of Stanford’s Human-Centered AI Institute, states, “If we want machines to think, we need to teach them to see.” The traditional linear process of problem ideation, design, prototyping, and delivery is no longer sufficient for AI design. Instead, designers find themselves on an “AI design rollercoaster”—a dynamic cycle of constant iteration. One day, a seemingly impossible feature is prototyped, and the next, the entire approach pivots due to breakthroughs in large language model (LLM) capabilities. Many teams develop working prototypes before even defining their target audience. It is akin to painting a landscape from a moving train—compelling, challenging, and occasionally bewildering. However, this state of flux is where innovation thrives. Strategies for Designers: Understanding AI’s Capabilities and Limitations Designing for AI requires an understanding of its strengths and weaknesses. While designers do not need to become machine learning engineers, they must grasp AI fundamentals to communicate effectively with technical teams. For example, neural networks excel at recognizing patterns in unstructured data but often struggle with logical reasoning. Recognizing these limitations prevents the development of features that sound promising in theory but fail in practice. Strategies for Designers: Designing for Data Scalability Data is the lifeblood of AI systems, yet its quality and availability fluctuate over time. Designers must create interfaces that can adapt to changing data landscapes. For instance, an AI-powered personal finance app may initially rely on basic transaction data but later incorporate richer datasets for advanced investment recommendations. Interfaces should be modular and scalable, capable of accommodating evolving AI functionalities. Strategies for Designers: The Role of Prototyping in AI Design Static wireframes and basic mockups are insufficient for AI-driven products. AI prototypes must capture the responsive, dynamic nature of intelligent systems. Interactive prototypes offer stakeholders a tangible preview of AI’s potential, highlighting both opportunities and challenges early in the design process. Strategies for Designers: Developing AI Design Intuition To navigate AI design effectively, professionals must cultivate an “AI design sixth sense”—an intuitive understanding of what works well in AI-driven interactions. Immersing in AI experiences, exploring different tools, and analyzing emerging design patterns help build this expertise. Strategies for Designers: Pushing Boundaries in AI Design There are no established rulebooks for AI design—only a vast frontier waiting to be explored. The absence of rigid norms offers designers the freedom to experiment and push boundaries. Some of the most groundbreaking innovations stem from unconventional ideas once deemed impractical. Strategies for Designers: Strengthening Collaboration Between Design and Engineering In AI product design, the traditional “design then handoff” model is giving way to a more integrated approach. Designers and engineers increasingly work in tandem, refining AI experiences through continuous iteration. Some of the most effective design solutions emerge from close collaboration with technical teams. Strategies for Designers: The Next Frontier of Design As AI design continues to evolve, the parallels to the early days of the internet are striking. The excitement, potential, and magnitude of change are reminiscent of Web 1.0, yet amplified in scope. Looking ahead, the field must address profound questions: Will AI become indistinguishable from human intelligence? Will designers craft interfaces for AI-human hybrids yet to be imagined? Designers play an essential role in shaping this future—not as passive observers, but as architects of the next digital revolution. The experiences they create will define humanity’s interactions with artificial intelligence. This responsibility should inspire innovation, challenge conventions, and push the boundaries of what is possible. Call to Action Begin the AI design journey today. Choose an AI tool, explore its interface, and analyze its capabilities. Identify strengths, weaknesses, and opportunities for improvement. Share insights with fellow designers and contribute to the evolving conversation on AI design. The next breakthrough may arise from a single moment of curiosity. 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 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 Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

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ai in supply chain

AI in Supply Chain

AI has the potential to transform supply chain management by boosting efficiency, enhancing forecasting, and improving customer satisfaction. However, it’s crucial for leaders to weigh the associated costs, technical challenges, and risks to ensure a smooth and strategic implementation. By addressing these disadvantages, companies can fully unlock AI’s benefits while minimizing its drawbacks.

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AI is revolutionizing BI by transforming it from a retrospective tool into a proactive, real-time decision-making engine.

AI in Business Intelligence

AI in Business Intelligence: Applications, Benefits, and Challenges AI is rapidly transforming business intelligence (BI) by enhancing analytics capabilities and streamlining processes. This shift is reshaping how organizations leverage data for decision-making. Here’s an in-depth look at how AI complements BI, its advantages, and the challenges it introduces. The Evolution of Business Intelligence with AI BI has traditionally focused on aggregating historical and current data to provide insights into business operations—a process known as descriptive analytics. However, many decision-makers seek more: insights into future trends (predictive analytics) and actionable recommendations (prescriptive analytics). AI bridges this gap. With advanced tools like natural language processing (NLP) and machine learning (ML), AI enables businesses to move beyond static dashboards to dynamic, real-time insights. It also simplifies complex analytics, making data more accessible to business users and fostering more informed, proactive decision-making. Key Benefits of AI in Business Intelligence AI brings significant benefits to BI, including: Real-World Applications of AI in BI AI’s integration into BI goes beyond internal efficiency, delivering external value by enhancing customer experiences and driving business growth. Notable applications include: Challenges of AI in Business Intelligence Despite its potential, integrating AI into BI comes with challenges: Best Practices for AI-Driven BI To successfully integrate AI with BI, organizations should: Future Trends in AI and BI AI is expected to augment rather than replace BI, enhancing its capabilities while keeping human expertise central. Emerging trends include: Conclusion AI is revolutionizing BI by transforming it from a retrospective tool into a proactive, real-time decision-making engine. While challenges remain, thoughtful implementation and adherence to best practices can help organizations unlock AI’s full potential in BI. By integrating AI into existing BI workflows, businesses can drive innovation, improve decision-making, and create more agile and data-driven operations. 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 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 Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

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2024 The Year of Generative AI

Was 2024 the Year Generative AI Delivered? Here’s What Happened Industry experts hailed 2024 as the year generative AI would take center stage. Operational use cases were emerging, technology was simplifying access, and general artificial intelligence felt imminent. So, how much of that actually came true? Well… sort of. As the year wraps up, some predictions have hit their mark, while others — like general AI — remain firmly in development. Let’s break down the trends, insights from investor Tomasz Tunguz, and what’s ahead for 2025. 1. A World Without Reason Three years into our AI evolution, businesses are finding value, but not universally. Tomasz Tunguz categorizes AI’s current capabilities into: While prediction and search have gained traction, reasoning models still struggle. Why? Model accuracy. Tunguz notes that unless a model has repeatedly seen a specific pattern, it falters. For example, an AI generating an FP&A chart might succeed — but introduce a twist, like usage-based billing, and it’s lost. For now, copilots and modestly accurate search reign supreme. 2. Process Over Tooling A tool’s value lies in how well it fits into established processes. As data teams adopt AI, they’re realizing that production-ready AI demands robust processes, not just shiny tools. Take data quality — a critical pillar for AI success. Sampling a few dbt tests or point solutions won’t cut it anymore. Teams need comprehensive solutions that deliver immediate value. In 2025, expect a shift toward end-to-end platforms that simplify incident management, enhance data quality ownership, and enable domain-level solutions. The tools that integrate seamlessly and address these priorities will shape AI’s future. 3. AI: Cost Cutter, Not Revenue Generator For now, AI’s primary business value lies in cost reduction, not revenue generation. Tools like AI-driven SDRs can increase sales pipelines, but often at the cost of quality. Instead, companies are leveraging AI to cut costs in areas like labor. Examples include Klarna reducing two-thirds of its workforce and Microsoft boosting engineering productivity by 50-75%. Cost reduction works best in scenarios with repetitive tasks, hiring challenges, or labor shortages. Meanwhile, specialized services like EvenUp, which automates legal demand letters, show potential for revenue-focused AI use cases. 4. A Slower but Smarter Adoption Curve While 2023 saw a wave of experimentation with AI, 2024 marked a period of reflection. Early adopters have faced challenges with implementation, ROI, and rapidly changing tech. According to Tunguz, this “dress rehearsal” phase has informed organizations about what works and what doesn’t. Heading into 2025, expect a more calculated wave of AI adoption, with leaders focusing on tools that deliver measurable value — and faster. 5. Small Models for Big Gains In enterprise AI, small, fine-tuned models are gaining favor over massive, general-purpose ones. Why? Small models are cheaper to run and often outperform their larger counterparts when fine-tuned for specific tasks. For example, training an 8-billion-parameter model on 10,000 support tickets can yield better results than a general model trained on a broad corpus. Legal and cost challenges surrounding large proprietary models further push enterprises toward smaller, open-source solutions, especially in highly regulated industries. 6. Blurring Lines Between Analysts and Engineers The demand for data and AI solutions is driving a shift in responsibilities. AI-enabled pipelines are lowering barriers to entry, making self-serve data workflows more accessible. This trend could consolidate analytical and engineering roles, streamlining collaboration and boosting productivity in 2025. 7. Synthetic Data: A Necessary Stopgap With finite real-world training data, synthetic datasets are emerging as a stopgap solution. Tools like Tonic and Gretel create synthetic data for AI training, particularly in regulated industries. However, synthetic data has limits. Over time, relying too heavily on it could degrade model performance, akin to a diet lacking fresh nutrients. The challenge will be finding a balance between real and synthetic data as AI advances. 8. The Rise of the Unstructured Data Stack Unstructured data — long underutilized — is poised to become a cornerstone of enterprise AI. Only about half of unstructured data is analyzed today, but as AI adoption grows, this figure will rise. Organizations are exploring tools and strategies to harness unstructured data for training and analytics, unlocking its untapped potential. 2025 will likely see the emergence of a robust “unstructured data stack” designed to drive business value from this vast, underutilized resource. 9. Agentic AI: Not Ready for Prime Time While AI copilots have proven useful, multi-step AI agents still face significant challenges. Due to compounding accuracy issues (e.g., 90% accuracy over three steps drops to ~50%), these agents are not yet ready for production use. For now, agentic AI remains more of a conversation piece than a practical tool. 10. Data Pipelines Are Growing, But Quality Isn’t As enterprises scale their AI efforts, the number of data pipelines is exploding. Smaller, fine-tuned models are being deployed at scale, often requiring hundreds of millions of pipelines. However, this rapid growth introduces data quality risks. Without robust quality management practices, teams risk inconsistent outputs, bottlenecks, and missed opportunities. Looking Ahead to 2025 As AI evolves, enterprises will face growing pains, but the opportunities are undeniable. From streamlining processes to leveraging unstructured data, 2025 promises advancements that will redefine how organizations approach AI and data strategy. The real challenge? Turning potential into measurable, lasting impact. 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 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 Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

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ThoughtSpot AI agent Spotter enables conversational BI

ThoughtSpot AI agent Spotter enables conversational BI

ThoughtSpot Unveils Spotter: A Generative AI-Powered Data Agent ThoughtSpot, a leading analytics vendor, has launched Spotter, an advanced generative AI-powered agent designed to revolutionize how users interact with data. Spotter enables conversational data exploration, contextual understanding, and autonomous analysis, making it a significant leap forward in the analytics landscape. Spotter’s Role in ThoughtSpot’s Evolution Spotter replaces Sage, ThoughtSpot’s earlier generative AI-powered interface, which debuted in March 2023. Despite moving from private to public preview and gaining new capabilities, Sage never reached general availability. Spotter is now generally available for ThoughtSpot Analytics, while its embedded version is in beta testing. Unlike earlier AI tools that focused on question-and-answer interactions, such as Sage and Microsoft’s copilots, Spotter takes the concept further by integrating contextual awareness and autonomous decision-making. Spotter doesn’t just respond to queries; it suggests follow-up questions, identifies anomalies, and provides proactive insights, functioning more like a virtual analyst than a reactive chatbot. Key Features of Spotter Spotter is built to enhance productivity and insight generation through the following capabilities: Generative AI’s Growing Impact on BI ThoughtSpot has long aimed to make analytics accessible to non-technical users through natural language search. However, previous NLP tools often required users to learn specific vocabularies, limiting widespread adoption. Generative AI bridges this gap. By leveraging extensive vocabularies and LLM technology, tools like Spotter enable users of all skill levels to access and analyze data effortlessly. Spotter stands out with its ability to deliver proactive insights, identify trends, and adapt to user behavior, enhancing the decision-making process. Expert Perspectives on Spotter Donald Farmer, founder of TreeHive Strategy, highlighted Spotter’s autonomy as a game-changer: “Spotter is a big move forward for ThoughtSpot and AI. The natural language interface is more conversational, but the key advantage is its autonomous analysis, which identifies trends and insights without users needing to ask.” Mike Leone, an analyst at TechTarget’s Enterprise Strategy Group, emphasized Spotter’s ability to adapt to users: “Spotter’s ability to deliver personalized and contextually relevant responses is critical for organizations pursuing generative AI initiatives. This goes a long way in delivering unique value across a business.” Farmer also pointed to Spotter’s embedded capabilities, noting its growing appeal as an embedded analytics solution integrated with productivity tools like Salesforce and ServiceNow. Competitive Positioning Spotter aligns ThoughtSpot with other vendors embracing agentic AI in analytics. Google recently introduced Conversational Analytics in Looker, and Salesforce’s Tableau platform now includes Tableau Agent. ThoughtSpot’s approach builds on its core strength in search-based analytics while expanding into generative AI-driven capabilities. Leone observed: “ThoughtSpot is right in line with the market in delivering an agentic experience and is laying the groundwork for broader AI functionality over time.” A Step Toward the Future of Analytics With Spotter, ThoughtSpot is redefining the role of AI in business intelligence. The tool combines conversational ease, proactive insights, and seamless integration, empowering users to make data-driven decisions more efficiently. As generative AI continues to evolve, tools like Spotter demonstrate how businesses can unlock the full potential of their data. 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 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 Leader Salesforce

Sales Leads and Lead Scoring

Sales teams often face a growing pile of leads, making it overwhelming to determine where to focus their energy. How do you prioritize effectively? Lead scoring is the answer. This methodology helps rank prospects based on their likelihood to convert into customers. By mastering lead scoring, sales teams can win more deals and drive revenue growth. What is Lead Scoring? Lead scoring is a strategy used by sales teams to evaluate and rank potential customers by assigning values based on their behavior, demographics, and interactions with the business. This process identifies high-quality leads and determines their likelihood of conversion. By implementing lead scoring, sales teams can focus their time and resources on the most promising prospects. Why is Lead Scoring Important? According to the Salesforce State of Sales Report, sales reps spend 25% of their workweek researching, prospecting, and prioritizing leads. These activities are essential for moving prospects through the sales funnel, yet balancing them with other responsibilities is a challenge. Lead scoring streamlines this process, enabling teams to be more productive by focusing on high-value leads. This improves conversion rates while helping sales leadership better forecast pipelines and revenue. For example, imagine a sales rep for a medical software company trying to close deals with 100 hospital leads. Pursuing them randomly wastes time. However, with lead scoring, they can identify the top 10 most promising leads based on specific criteria, saving time and increasing success rates. Key Components of an Effective Lead Scoring System 1. Data Categories 2. Implicit vs. Explicit Data 3. Quality Data A reliable lead scoring system depends on accurate and up-to-date data. Keeping CRM records current and synced ensures a dependable scoring process. 4. Rule Definition Define criteria based on your most successful customer profiles. Identify patterns of attributes and behaviors that consistently lead to conversion. Similarly, assess unconverted leads to understand traits that signal low potential. 5. Manual vs. Predictive Scoring Steps to Implement Lead Scoring Common Lead Scoring Mistakes to Avoid Tools and Software for Lead Scoring The right tools can make lead scoring more efficient: If you’re short on data, opt for tools that can leverage anonymized external datasets to build your scoring model, transitioning to your own data over time as you scale. Real-World Examples Lead Scoring: Your Path to Higher Conversions By effectively implementing lead scoring, your sales team can prioritize high-value leads, boost conversion rates, and achieve sustainable revenue growth. Whether you choose manual or predictive methods, the key is to focus on what drives success for your business. Take control of your sales pipeline—lead scoring will show you the way. 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 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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1 Billion Enterprise AI Agents

Inside Salesforce’s Ambition to Deploy 1 Billion Enterprise AI Agents Salesforce is making a bold play in the enterprise AI space with its recently launched Agentforce platform. Introduced at the annual Dreamforce conference, Agentforce is positioned to revolutionize sales, marketing, commerce, and operations with autonomous AI agents, marking a significant evolution from Salesforce’s previous Einstein AI platform. What Makes Agentforce Different? Agentforce operates as more than just a chatbot platform. It uses real-time data and user-defined business rules to proactively manage tasks, aiming to boost efficiency and enhance customer satisfaction. Built on Salesforce’s Data Cloud, the platform simplifies deployment while maintaining powerful customization capabilities: “Salesforce takes care of 80% of the foundational work, leaving customers to focus on the 20% that truly differentiates their business,” explains Adam Forrest, SVP of Marketing at Salesforce. Forrest highlights how Agentforce enables businesses to build custom agents tailored to specific needs by incorporating their own rules and data sources. This user-centric approach empowers admins, developers, and technology teams to deploy AI without extensive technical resources. Early Adoption Across Industries Major brands have already adopted Agentforce for diverse use cases: These real-world applications illustrate Agentforce’s potential to transform workflows in industries ranging from retail to hospitality and education. AI Agents in Marketing: The New Frontier Salesforce emphasizes that Agentforce isn’t just for operations; it’s poised to redefine marketing. AI agents can automate lead qualification, optimize outreach strategies, and enhance personalization. For example, in account-based marketing, agents can analyze customer data to identify high-value opportunities, craft tailored strategies, and recommend optimal engagement times based on user behavior. “AI agents streamline lead qualification by evaluating intent signals and scoring leads, allowing sales teams to focus on high-priority prospects,” says Jonathan Franchell, CEO of B2B marketing agency Ironpaper. Once campaigns are launched, Agentforce monitors performance in real time, offering suggestions to improve ROI and resource allocation. By integrating seamlessly with CRM platforms, the tool also facilitates better collaboration between marketing and sales teams. Beyond B2C applications, AI agents in B2B contexts can evaluate customer-specific needs and provide tailored product or service recommendations, further enhancing client relationships. Enabling Creativity Through Automation By automating repetitive tasks, Agentforce aims to free marketers to focus on strategy and creativity. Dan Gardner, co-founder of Code and Theory, describes this vision: “Agentic AI eliminates friction and dissolves silos in data, organizational structures, and customer touchpoints. The result? Smarter insights, efficient distribution, and more time for creatives to do what they do best: creating.” Competitive Landscape and Challenges Despite its promise, Salesforce faces stiff competition. Microsoft—backed by its integration with OpenAI’s ChatGPT—has unveiled AI tools like Copilot, and other players such as Google, ServiceNow, and HubSpot are advancing their own AI platforms. Salesforce CEO Marc Benioff has not shied away from the rivalry. On the Masters of Scale podcast, he criticized Microsoft for overpromising on products like Copilot, asserting that Salesforce delivers tangible value: “Our tools show users exactly what is possible, what is real, and how easy it is to derive huge value from AI.” Salesforce must also demonstrate Agentforce’s scalability across diverse industries to capture a significant share of the enterprise AI market. A Transformative Vision for the Future Agentforce represents Salesforce’s commitment to bringing AI-powered automation to the forefront of enterprise operations. With its focus on seamless deployment, powerful customization, and real-time capabilities, the platform aims to reshape how businesses interact with customers and optimize internal processes. By targeting diverse use cases and emphasizing accessibility for both technical and non-technical users, Salesforce is betting on Agentforce to drive adoption at scale—and position itself as a leader in the increasingly competitive AI market. 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 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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Why Build a General-Purpose Agent?

A general-purpose LLM agent serves as an excellent starting point for prototyping use cases and establishing the foundation for a custom agentic architecture tailored to your needs. What is an LLM Agent? An LLM (Large Language Model) agent is a program where execution logic is governed by the underlying model. Unlike approaches such as few-shot prompting or fixed workflows, LLM agents adapt dynamically. They can determine which tools to use (e.g., web search or code execution), how to use them, and iterate based on results. This adaptability enables handling diverse tasks with minimal configuration. Agentic Architectures Explained:Agentic systems range from the reliability of fixed workflows to the flexibility of autonomous agents. For instance: Your architecture choice will depend on the desired balance between reliability and flexibility for your use case. Building a General-Purpose LLM Agent Step 1: Select the Right LLM Choosing the right model is critical for performance. Evaluate based on: Model Recommendations (as of now): For simpler use cases, smaller models running locally can also be effective, but with limited functionality. Step 2: Define the Agent’s Control Logic The system prompt differentiates an LLM agent from a standalone model. This prompt contains rules, instructions, and structures that guide the agent’s behavior. Common Agentic Patterns: Starting with ReAct or Plan-then-Execute patterns is recommended for general-purpose agents. Step 3: Define the Agent’s Core Instructions To optimize the agent’s behavior, clearly define its features and constraints in the system prompt: Example Instructions: Step 4: Define and Optimize Core Tools Tools expand an agent’s capabilities. Common tools include: For each tool, define: Example: Implementing an Arxiv API tool for scientific queries. Step 5: Memory Handling Strategy Since LLMs have limited memory (context window), a strategy is necessary to manage past interactions. Common approaches include: For personalization, long-term memory can store user preferences or critical information. Step 6: Parse the Agent’s Output To make raw LLM outputs actionable, implement a parser to convert outputs into a structured format like JSON. Structured outputs simplify execution and ensure consistency. Step 7: Orchestrate the Agent’s Workflow Define orchestration logic to handle the agent’s next steps after receiving an output: Example Orchestration Code: pythonCopy codedef orchestrator(llm_agent, llm_output, tools, user_query): while True: action = llm_output.get(“action”) if action == “tool_call”: tool_name = llm_output.get(“tool_name”) tool_params = llm_output.get(“tool_params”, {}) if tool_name in tools: try: tool_result = tools[tool_name](**tool_params) llm_output = llm_agent({“tool_output”: tool_result}) except Exception as e: return f”Error executing tool ‘{tool_name}’: {str(e)}” else: return f”Error: Tool ‘{tool_name}’ not found.” elif action == “return_answer”: return llm_output.get(“answer”, “No answer provided.”) else: return “Error: Unrecognized action type from LLM output.” This orchestration ensures seamless interaction between tools, memory, and user queries. When to Consider Multi-Agent Systems A single-agent setup works well for prototyping but may hit limits with complex workflows or extensive toolsets. Multi-agent architectures can: Starting with a single agent helps refine workflows, identify bottlenecks, and scale effectively. By following these steps, you’ll have a versatile system capable of handling diverse use cases, from competitive analysis to automating workflows. 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 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 Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

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being ai-driven

The Impact of AI on Jobs

The Impact of AI on Jobs: A Historical and Transformative Perspective For centuries, people have feared losing jobs to technological advancements. From the introduction of the printing press in 1440 to the widespread adoption of assembly lines in manufacturing, history has followed a familiar pattern: a wave of panic followed by a surge of innovation. Today, with AI in the spotlight, headlines warn of job-stealing robots. Yet, AI is not here to take jobs; it’s revealing new ones—and at an unprecedented pace. A Paradigm Shift: AI as a Job Creator Contrary to popular belief, AI is reshaping the job market for the better. Rather than replacing workers, it amplifies human potential, pushing society toward work that is creative, strategic, and uniquely human. Instead of asking, “Will AI take my job?” the better question is, “What new opportunities can AI unlock?” The answers are exciting and transformative. Lessons from the Past Technological disruption is far from new. The printing press, the weaving loom, and even the internet all provoked fears of mass unemployment. Yet, each time, these innovations sparked transformation rather than devastation. Consider the ATM, introduced in the 1960s. Initially, bank tellers feared redundancy. However, rather than replacing tellers, ATMs automated routine tasks, freeing human workers to focus on customer service and financial advising. In fact, the number of bank tellers increased in the decades following ATM adoption. AI follows the same trajectory. By handling repetitive tasks like sorting emails or managing schedules, AI frees workers to focus on areas requiring emotional intelligence, creativity, and problem-solving. AI: A Partner, Not a Competitor AI excels in areas that humans struggle with, such as processing vast datasets, recognizing patterns, and executing repetitive tasks with precision. However, it lacks empathy, context, and abstract thinking—traits that remain uniquely human. For example, IBM Watson can analyze millions of medical journals to suggest treatment options. Yet, a doctor’s role remains indispensable, as patients need empathy, understanding, and a human touch. Similarly, legal AI tools like CaseText can streamline research, but building persuasive arguments and negotiating terms require skills no algorithm can match. Rather than replacing professionals, AI enhances their productivity, enabling them to focus on higher-value tasks. The Birth of Entirely New Industries AI is not only reshaping existing jobs but also creating new roles and industries. The rise of generative AI has introduced positions like prompt engineers, who design effective queries to maximize AI’s output. Similarly, the need for unbiased algorithms has created the field of data ethics, where specialists ensure AI systems prioritize equity and fairness. These roles underscore an important reality: AI doesn’t eliminate opportunities—it redefines them. Addressing Ethical Challenges AI’s reliance on data is both its strength and its vulnerability. Algorithms trained on biased data can perpetuate harmful stereotypes, as seen in Amazon’s failed hiring algorithm, which penalized women. This challenge has given rise to data ethicists tasked with auditing algorithms and designing fair systems, further showcasing how AI disruption creates new fields and opportunities. Augmentation Over Replacement Fear of AI stems from misunderstanding its role. Machines are adept at repetitive and analytical tasks, but they lack the nuanced understanding required for roles in fields like art, music, and medicine. AI tools such as Adobe Sensei or AIVA enhance creativity, allowing artists and musicians to experiment, iterate, and push boundaries. Just as the printing press democratized writing rather than ending it, AI empowers workers to focus on what makes us uniquely human. A Future Worth Working Toward AI represents a profound shift in how society views work. It is not a destroyer of jobs but a catalyst for transformation. By automating inefficiencies and reinforcing human strengths, AI unlocks opportunities yet to be imagined. Rather than fearing the rise of AI, embracing its potential can lead to a future where work is more meaningful, creative, and impactful—an evolution worth striving for. 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 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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What is a CRM

Time to Modernize CRM

Enterprises Must Modernize CRM by 2026 to Unlock AI Potential, Says ISG By 2026, half of all enterprises will need to modernize their Customer Relationship Management (CRM) systems to harness the full power of AI, according to a new report from global technology research and advisory firm Information Services Group (ISG) (Nasdaq: III). The ISG Buyers Guides™ for Customer Relationship Management, produced by ISG Software Research, highlight the evolution of CRM from basic data repositories to sophisticated AI-powered platforms. Modern CRM systems now drive sales performance, improve customer engagement, and provide insights that empower both sales and service teams to meet business objectives. The Need for AI-Driven CRM “CRM systems ensure customer-facing teams have access to up-to-date information, but outdated systems often lead to inefficiencies,” says Stephen Hurrell, Director of Research, Office of Revenue, ISG Software Research. “AI-driven CRM technology is essential for delivering actionable insights, market intelligence, and effective engagement. Enterprises must continuously align their CRM strategies with evolving business needs to remain competitive.” Traditional CRM approaches often rely on fragmented customer interactions, consuming unnecessary resources and limiting growth. While some providers offer add-ons to enhance CRM functionality, their effectiveness depends on the quality of foundational data, such as customer profiles and activity records. Enterprises now face a pivotal decision: reimplement their existing CRM systems or transition to new platforms to unlock the capabilities of AI. For many enterprises migrating to Salesforce’s Agentforce will be the logical answer. Modern CRM Capabilities To address today’s demands, modern CRM systems should: In sales, AI-driven CRM systems offer insights on the best times and strategies for engagement. In service, these systems transform customer interactions into additional revenue streams through more integrated and proactive support. ISG’s Evaluation of CRM Providers The 2024 ISG Buyers Guides for Customer Relationship Management assessed 37 providers across eight CRM-related categories: Suite, Platform, Digital Commerce, Marketing, Partners, Sales, Sales Engagement, and Service. Top Providers by Category (Ranked in Order): Industry Challenges Despite advancements, many enterprises struggle with disconnected and overly customized CRM systems that are difficult to upgrade for AI and automation. “Today’s CRM platforms should be intelligent systems that optimize customer interactions and drive growth,” says Mark Smith, Partner, ISG Software Research. “Unfortunately, many enterprises face inefficiencies due to outdated deployments.” About the ISG Buyers Guides The ISG Buyers Guides are the culmination of over a year of market research and provide insights to help enterprises make informed software investments. These reports are independent of vendor influence and serve as a valuable resource for selecting CRM providers and optimizing business technology strategies. To learn more about the ISG Buyers Guides for Customer Relationship Management and access detailed rankings and insights, visit the ISG Software Research website. About ISG Software Research ISG Software Research, formerly Ventana Research, provides authoritative insights into the business and IT software landscape. It offers consulting, research, and educational services for enterprises and software providers, supporting informed decision-making and software selection through methodologies like the ISG Software Research Value Index. 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 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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