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Commerce Cloud and Agentic AI
09Apr

5 Mindset Shifts That Revolutionized Salesforce Help with AI

When Salesforce challenged us to reinvent our help portal in just five days, we didn’t just redesign a UI—we reimagined how AI could transform customer support. Here’s how we turned Salesforce Help into an intuitive, agent-driven experience—and the key mindset shifts that made it possible. The Challenge: A Help Portal at Scale Salesforce Help serves 60 million annual visitors across 750,000+ articles in 18 languages. Yet, despite this vast knowledge base: Our mission? Reduce friction, boost self-service, and make help feel human—fast. From Static Portal to AI-Powered Guide: 5 Key Shifts 1. From Navigation to Conversation Old Approach: New Mindset: Result: Faster resolutions, fewer drop-offs. 2. From Content Management to Knowledge Engineering Old Approach: New Mindset: Result: Smarter self-service, fewer support tickets. 3. From Siloed Teams to Rapid Collaboration Old Approach: New Mindset: Result: A full UI overhaul in 5 days. 4. From Rigid UI to Adaptive Engagement Old Approach: New Mindset: Result: Feels like a helpful conversation, not a maze. 5. From Feature-Centric to Outcome-Driven Old Approach: New Mindset: Result: Cleaner, faster, higher adoption. The Impact: A Blueprint for AI-Powered Help Watch the full story: Salesforce+ Video Your Turn: How Will You Rethink Support? AI isn’t just about adding chatbots—it’s about redesigning experiences around how people actually seek help. Ask yourself: Less is more. Clarity is king. And sometimes, a 5-day sprint can change everything. 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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April 9, 2025in Agentforce Platform, Data, Google, Salesforce0 Comments
time series artificial intelligence
07Apr

Revolutionizing Time Series AI

Revolutionizing Time Series AI: Salesforce’s Synthetic Data Breakthrough for Foundation Models Revolutionizing Time Series AI. Time series analysis is hindered by critical challenges in data availability, quality, and diversity—key factors in building powerful foundation models. Real-world datasets often suffer from regulatory constraints, inherent biases, inconsistent quality, and a lack of paired textual annotations, making it difficult to develop robust Time Series Foundation Models (TSFMs) and Time Series Large Language Models (TSLLMs). These limitations stifle progress in forecasting, classification, anomaly detection, reasoning, and captioning, restricting AI’s full potential. To tackle these obstacles, Salesforce AI Research has pioneered an innovative approach: leveraging synthetic data to enhance TSFMs and TSLLMs. Their groundbreaking study, “Empowering Time Series Analysis with Synthetic Data,” introduces a strategic framework for using synthetic data to refine model training, evaluation, and fine-tuning—while mitigating biases, expanding dataset diversity, and enriching contextual understanding. This approach is particularly transformative in regulated sectors like healthcare and finance, where real-world data sharing is heavily restricted. The Science Behind Synthetic Data Generation Salesforce’s methodology employs advanced synthetic data generation techniques tailored to replicate real-world time series dynamics, including trends, seasonality, and noise patterns. Key innovations include: These methods enable controlled yet highly varied data generation, capturing a broad spectrum of time series behaviors essential for robust model training. Proven Benefits: How Synthetic Data Supercharges Model Performance Salesforce’s research reveals significant performance gains from synthetic data across multiple stages of AI development: ✅ Pretraining Boost – Models like ForecastPFN, Mamba4Cast, and TimesFM showed marked improvements when pretrained on synthetic data. ForecastPFN, for instance, excelled in zero-shot forecasting after full synthetic pretraining. ✅ Optimal Data Blending – Chronos found peak performance by mixing 10% synthetic data with real-world datasets, beyond which excessive synthetic data could reduce diversity and effectiveness. ✅ Enhanced Evaluation – Synthetic data allowed precise assessment of model capabilities, uncovering hidden biases and gaps. For example, Moment used synthetic sinusoidal waves to analyze embedding sensitivity and trend detection accuracy. Future Directions: Overcoming Limitations While synthetic data offers immense promise, Salesforce identifies key areas for improvement: 🔹 Systematic Integration – Developing structured frameworks to strategically fill gaps in real-world datasets.🔹 Beyond Statistical Methods – Exploring diffusion models and other generative AI techniques for richer, more realistic synthetic data.🔹 Fine-Tuning Potential – Leveraging synthetic data adaptively to address domain-specific weaknesses during fine-tuning. The Path Forward Salesforce AI Research demonstrates that synthetic data is a game-changer for time series analysis, enabling stronger generalization, reduced bias, and superior performance across AI tasks. While challenges like realism and alignment remain, the future is bright—advancements in generative AI, human-in-the-loop refinement, and systematic gap-filling will further propel the reliability and applicability of time series models. By embracing synthetic data, Salesforce is laying the foundation for the next generation of AI-driven time series innovation—ushering in a new era of accuracy, adaptability, and intelligence. 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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April 7, 2025in Data, Generative AI, Salesforce0 Comments
B2B Customer Service with Agentforce
24Mar

Agents are the Future of Customer Engagement

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

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March 24, 2025in Agentforce Platform, Agentic AI, Data, Generative AI, Google, Salesforce0 Comments
25Jan

Direct Manipulation to Intent-Driven Design

The Shift from Direct Manipulation to Intent-Driven Design: How AI is Reshaping User Interaction The way we interact with software is constantly evolving — sometimes through gradual changes, other times through disruptive leaps. Today, as AI-powered applications gain momentum, design pioneers like Vitaly Friedman, Emily Campbell, and Greg Nudelman are exploring the emerging design patterns that are reshaping user experiences. This shift is more than just another tech trend — it represents a fundamental transformation in human-computer interaction. The shift can be compared to the transition from film cameras to digital photography. In the past, users manually adjusted settings like exposure and film speed. With digital cameras, the process became automated — users simply clicked a button, and the camera handled the rest. AI is now bringing a similar transformation to software interfaces, allowing users to express their desired outcomes without dictating every step of the process. As Jakob Nielsen notes, this shift moves us away from rigid, step-by-step commands toward a goal-driven approach. In his words: “With new AI systems, the user no longer tells the computer what to do. Rather, the user tells the computer what outcome they want.” This transformation is not just technological — it’s philosophical. It challenges traditional ideas about control, agency, and human-computer collaboration. Where users once defined every step in an interaction, they now express their intent and let AI determine the optimal approach to achieving it. Direct Manipulation: The Foundation of Intuitive Design Before diving into how AI reshapes user interaction, it’s essential to understand the principles of direct manipulation, which have long defined intuitive user interfaces. In 1985, Edwin Hutchins, James Hollan, and Don Norman introduced the concept of direct manipulation in user interfaces. Direct manipulation refers to interaction styles where users directly engage with on-screen objects using physical, incremental, and reversible actions. For example, when you drag a file from one folder to another, you are directly manipulating the object. This interaction style is intuitive because it minimizes cognitive load — users can see immediate feedback as they interact with digital objects, reinforcing a sense of control. The Transition to Goal-Oriented Interactions AI is now challenging the dominance of direct manipulation by introducing goal-driven interactions. Instead of dictating each step of a process, users now express their desired outcome, and the system interprets and executes the task. Consider the AI-powered ‘Erase’ feature in Windows Photos. Instead of manually editing pixels to remove an unwanted object from a photo, users simply select the object and let the AI complete the task. This shifts the interaction from direct manipulation to intent-driven collaboration. Researchers like Desolda have explored this shift in their model of human-AI interaction. In traditional direct manipulation, users act in a linear sequence — recognize a goal, take an action, receive feedback. With AI, interactions become iterative and dynamic — users provide high-level input, AI executes, and users refine the output as needed. Enhancing Rather Than Replacing Direct Manipulation While AI introduces new interaction paradigms, it does not eliminate direct manipulation; instead, it layers new capabilities on top of it. For example, open input fields — like those found in ChatGPT or generative design tools — are built upon familiar UI patterns. These patterns reduce friction while allowing AI to extend the user‘s capabilities. Similarly, emerging frameworks like ‘Promptframes’ — introduced by Evan Sunwall — blend traditional wireframing with AI-generated content, accelerating design workflows without discarding familiar structures. This hybrid approach illustrates how AI can augment direct manipulation rather than replace it. Designing Seamless AI Interactions The ultimate goal of AI in design is to make interactions seamless. The most effective AI experiences do not draw attention to themselves — they quietly enhance user workflows. A prime example is Netflix’s recommendation engine. It does not ask users to configure settings or provide detailed input — it simply learns, adapts, and presents relevant content. This is the gold standard for AI-powered design: reducing friction, minimizing cognitive load, and allowing users to focus on their goals rather than the mechanics of interaction. As we design for AI, the focus should remain on enabling users to achieve outcomes effortlessly, rather than demanding their continuous attention. The future of user experience lies in balancing direct manipulation with AI-driven augmentation — empowering users to act with minimal friction while achieving powerful, intelligent outcomes. 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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January 25, 2025in Salesforce
18Jan

From Generative AI to Agentic AI

Understanding the Coming Shift: From Generative AI to Agentic AI Large Language Models (LLMs), such as GPT, excel at generating text, answering questions, and supporting various tasks. However, they operate reactively, responding only to the input they receive based on learned patterns. LLMs cannot make decisions independently, adapt to new situations, or plan ahead. Agentic AI addresses these limitations. Unlike Generative AI, Agentic AI can set goals for itself, take initiative by itself, and learn from its experiences. It is proactive, capable of adjusting its actions over time, and can manage complex, evolving tasks that demand continuous problem-solving and decision-making. This transition from reactive to proactive AI unlocks exciting new possibilities across industries. In this insight, we will explore the differences between Agentic AI and Generative AI, examining their distinct impacts on technology and industries. Let’s begin by understanding what sets them apart. What is Agentic AI? Agentic AI refers to systems capable of autonomous decision-making and action to achieve specific goals. These systems go beyond generating content—they interact with their environments, respond to changes, and complete tasks with minimal human guidance. For example: What is Generative AI? Generative AI focuses on creating content—text, images, music, or video—by learning from large datasets to identify patterns, styles, or structures. For instance: Generative AI acts like a creative assistant, producing content based on what it has learned, but it remains reactive and task-specific. Key Differences in Workflows Agentic AI employs an iterative, cyclical workflow that includes stages like “Thinking/Research” and “Revision.” This adaptive process involves self-assessment, testing, and refinement, enabling the system to learn from each phase and tackle complex, evolving tasks effectively. Generative AI, in contrast, follows a linear, single-step workflow, moving directly from input to output without iterative improvements. While efficient for straightforward tasks, it lacks the ability to revisit or refine its results, limiting its effectiveness for dynamic or nuanced challenges. Characteristics of Agentic AI vs. Generative AI Feature Agentic AI Generative AI Autonomy Acts independently, making decisions and executing tasks. Requires human input to generate responses. Behavior Goal-directed, proactively working toward specific objectives. Task-oriented, reacting to immediate prompts. Adaptation and Learning Learns from experiences, adjusting actions dynamically. Operates based on pre-trained patterns, without learning. Decision-Making Handles complex decisions, weighing multiple outcomes. Makes basic decisions, selecting outputs based on patterns. Environmental Perception Understands and interacts with its surroundings. Lacks awareness of the physical environment. Case Study: Agentic Workflow in Action Andrew Ng highlighted the power of the Agentic Workflow in a coding task. Using the HumanEval benchmark, his team tested two approaches: This illustrates how iterative methods can enhance performance, even for older AI models. Conclusion As AI becomes increasingly integrated into our lives and workplaces, understanding the distinction between Generative AI and Agentic AI is essential. Generative AI has transformed tasks like content creation, offering immediate, reactive solutions. However, it remains limited to following instructions without true autonomy. Agentic AI represents a significant leap in technology. From chatbots to today. By setting goals, making decisions, and adapting in real-time, it can tackle complex, dynamic tasks without constant human oversight. Approaches like the Agentic Workflow further enhance AI’s capabilities, enabling iterative learning and continuous improvement. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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January 18, 2025in Agentic AI, Data, Generative AI, Technology
AI Captivates the World
01Jan

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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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January 1, 2025in AI Tools, Artificial Intelligence, Data
Generative AU and the Future of UCD
13Dec

Generative AU and the Future of UCD

Generative AI and the Future of UCD: Adapting to New Challenges Discussions about generative AI seem endless—and while the topic may feel saturated, revisiting it in the context of user-centered design (UCD) and service delivery reveals critical opportunities and challenges worth exploring. The Current Landscape of Generative AI Generative AI is being increasingly evaluated for its potential to enhance research and public services. At the Ministry of Justice, for example, teams are exploring how generative AI can streamline user journeys, reduce duplication, and improve access to information—key pillars of effective service design. While enthusiasm and investment in generative AI are high, the reality is more cautious. Most projects remain in the proof-of-concept phase, and feedback often reflects attitudes rather than real-world behaviors. Public trust in AI is low, and many people lack an understanding of how it works or how they might interact with it. In government and public services, unresolved questions about risk tolerance, error management, and human oversight signal that AI integration is still in its early stages. Instead of declaring generative AI as the solution to user problems—or worrying about AI replacing jobs—it’s more productive to focus on adapting UCD practices to harness AI responsibly and effectively. The Risk of ‘Solutionizing’ in UCD Generative AI introduces a familiar challenge for UCD professionals: the risk of “solutionizing.” Many projects prioritize developing AI solutions, even before confirming they meet user needs. While experimentation is vital for exploring AI’s potential, there’s a danger in stakeholders prematurely assuming these proofs-of-concept validate AI as the ultimate solution. This underscores the enduring importance of UCD in the “age of AI.” UCD professionals must ensure that user needs remain central, educating stakeholders not just about AI’s capabilities but also about why user-centered design leads to better outcomes. To achieve this, UCD teams must prioritize ongoing user research and create opportunities for solution-agnostic ideation. Avoiding the “innovation trap”—assuming that the newest technologies inherently produce the best outcomes—requires openly acknowledging biases and finding creative ways to test assumptions. By doing so, decision-making becomes more transparent and adaptable, enabling cost-effective course corrections when needed. How UCD Will Evolve While the foundations of UCD will remain intact, generative AI will require adjustments to specific practices. For example, traditional usability testing might not fully address the variability of AI responses, which can differ even for identical user inputs. This unpredictability challenges conventional testing methods and demands new approaches. Collaboration between UCD teams, data scientists, and AI developers will be essential. By working closely, these teams can better understand how generative AI interacts with users, ensuring its capabilities are leveraged effectively. Rethinking Design Thinking Generative AI might also shift how design thinking is applied within UCD. The traditional double diamond model emphasizes deep discovery and iterative solution exploration. However, when incorporating generative AI, it may be beneficial to experiment with AI’s capabilities earlier in the discovery phase, blending user problem exploration with rapid technical experimentation. This approach would require guardrails to ensure user needs remain the priority, but it could lead to more innovative and practical solutions by aligning technical feasibility with user-centered insights from the outset. Conclusion Generative AI isn’t ready to replace jobs, but it does demand that UCD professionals evolve their practices. By adapting methods, increasing AI literacy, and holding innovation accountable to user needs, UCD teams can ensure that generative AI enhances, rather than detracts from, effective service design. How do you see UCD adapting to the challenges and opportunities of generative AI? What other considerations should we anticipate? Let’s continue the conversation! Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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December 13, 2024in Data, Generative AI
AI Agent Workflows
24Oct

AI Agent Workflows

AI Agent Workflows: The Ultimate Guide to Choosing Between LangChain and LangGraph Explore two transformative libraries—LangChain and LangGraph—both created by the same developer, designed to build Agentic AI applications. This guide dives into their foundational components, differences in handling functionality, and how to choose the right tool for your use case. Language Models as the Bridge Modern language models have unlocked revolutionary ways to connect users with AI systems and enable AI-to-AI communication via natural language. Enterprises aiming to harness Agentic AI capabilities often face the pivotal question: “Which tools should we use?” For those eager to begin, this question can become a roadblock. Why LangChain and LangGraph? LangChain and LangGraph are among the leading frameworks for crafting Agentic AI applications. By understanding their core building blocks and approaches to functionality, you’ll gain clarity on how each aligns with your needs. Keep in mind that the rapid evolution of generative AI tools means today’s truths might shift tomorrow. Note: Initially, this guide intended to compare AutoGen, LangChain, and LangGraph. However, AutoGen’s upcoming 0.4 release introduces a foundational redesign. Stay tuned for insights post-launch! Understanding the Basics LangChain LangChain offers two primary methods: Key components include: LangGraph LangGraph is tailored for graph-based workflows, enabling flexibility in non-linear, conditional, or feedback-loop processes. It’s ideal for cases where LangChain’s predefined structure might not suffice. Key components include: Comparing Functionality Tool Calling Conversation History and Memory Retrieval-Augmented Generation (RAG) Parallelism and Error Handling When to Choose LangChain, LangGraph, or Both LangChain Only LangGraph Only Using LangChain + LangGraph Together Final Thoughts Whether you choose LangChain, LangGraph, or a combination, the decision depends on your project’s complexity and specific needs. By understanding their unique capabilities, you can confidently design robust Agentic AI 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 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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October 24, 2024in Agentforce Platform, AI Tools, Artificial Intelligence, Data, Enterprise, Generative AI, Salesforce
Salesforce LlamaRank
07Oct

Salesforce LlamaRank

Document ranking remains a critical challenge in information retrieval and natural language processing. Effective document retrieval and ranking are crucial for enhancing the performance of search engines, question-answering systems, and Retrieval-Augmented Generation (RAG) systems. Traditional ranking models often struggle to balance result precision with computational efficiency, especially when dealing with large datasets and diverse query types. This challenge underscores the growing need for advanced models that can provide accurate, contextually relevant results in real-time from continuous data streams and increasingly complex queries. Salesforce AI Research has introduced a cutting-edge reranker named LlamaRank, designed to significantly enhance document ranking and code search tasks across various datasets. Built on the Llama3-8B-Instruct architecture, LlamaRank integrates advanced linear and calibrated scoring mechanisms, achieving both speed and interpretability. The Salesforce AI Research team developed LlamaRank as a specialized tool for document relevancy ranking. Enhanced by iterative feedback from their dedicated RLHF data annotation team, LlamaRank outperforms many leading APIs in general document ranking and sets a new standard for code search performance. The model’s training data includes high-quality synthesized data from Llama3-70B and Llama3-405B, along with human-labeled annotations, covering a broad range of domains from topic-based search and document QA to code QA. In RAG systems, LlamaRank plays a crucial role. Initially, a query is processed using a less precise but cost-effective method, such as semantic search with embeddings, to generate a list of potential documents. The reranker then refines this list to identify the most relevant documents, ensuring that the language model is fine-tuned with only the most pertinent information, thereby improving accuracy and coherence in the output responses. LlamaRank’s architecture, based on Llama3-8B-Instruct, leverages a diverse training corpus of synthetic and human-labeled data. This extensive dataset enables LlamaRank to excel in various tasks, from general document retrieval to specialized code searches. The model underwent multiple feedback cycles from Salesforce’s data annotation team to achieve optimal accuracy and relevance in its scoring predictions. During inference, LlamaRank predicts token probabilities and calculates a numeric relevance score, facilitating efficient reranking. Demonstrated on several public datasets, LlamaRank has shown impressive performance. For instance, on the SQuAD dataset for question answering, LlamaRank achieved a hit rate of 99.3%. It posted a hit rate of 92.0% on the TriviaQA dataset. In code search benchmarks, LlamaRank recorded a hit rate of 81.8% on the Neural Code Search dataset and 98.6% on the TrailheadQA dataset. These results highlight LlamaRank’s versatility and efficiency across various document types and query scenarios. LlamaRank’s technical specifications further emphasize its advantages. Supporting up to 8,000 tokens per document, it significantly outperforms competitors like Cohere’s reranker. It delivers low-latency performance, ranking 64 documents in under 200 ms with a single H100 GPU, compared to approximately 3.13 seconds on Cohere’s serverless API. Additionally, LlamaRank features linear scoring calibration, offering clear and interpretable relevance scores. While LlamaRank’s size of 8 billion parameters contributes to its high performance, it is approaching the upper limits of reranking model size. Future research may focus on optimizing model size to balance quality and efficiency. Overall, LlamaRank from Salesforce AI Research marks a significant advancement in reranking technology, promising to greatly enhance RAG systems’ effectiveness across a wide range of applications. With its powerful performance, efficiency, and clear scoring, LlamaRank represents a major step forward in document retrieval and search accuracy. The community eagerly anticipates its broader adoption and further development. 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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October 7, 2024in Data, Salesforce, Technology
Predictive Analytics
30Sep

Predictive Analytics in Salesforce

Predictive Analytics in Salesforce: Enhancing Decision-Making with AI In an ever-changing business environment, companies seek tools to forecast trends and anticipate challenges, enabling them to remain competitive. Predictive analytics, powered by Salesforce’s AI capabilities, offers a cutting-edge solution for these needs. In this guide, we’ll explore how predictive analytics works and how Salesforce empowers businesses to make smarter, data-driven decisions. What is Predictive Analytics? Predictive analytics uses historical data, statistical modeling, and machine learning to forecast future outcomes. With the vast amount of data organizations generate—ranging from transaction logs to multimedia—unifying this information can be challenging due to data silos. These silos hinder the development of accurate predictive models and limit Salesforce’s ability to deliver actionable insights. The result? Missed opportunities, inefficiencies, and impersonal customer experiences. When organizations implement proper integrations and data management practices, predictive analytics can harness this data to uncover patterns and predict future events. Techniques such as logistic regression, linear regression, neural networks, and decision trees help businesses gain actionable insights that enhance planning and decision-making. Einstein Prediction Builder A key component of the Salesforce Einstein Suite, Einstein Prediction Builder enables users to create custom AI models with minimal coding or data science expertise. Using in-house data, businesses can anticipate trends, forecast customer behavior, and predict outcomes with tailored precision. Key Features of Einstein Prediction Builder Note: Einstein Prediction Builder requires an Enterprise or Unlimited Edition subscription to access. Predictive Model Types in Salesforce Salesforce employs various predictive models tailored to specific needs: Building Custom Predictions Salesforce supports custom predictions tailored to unique business needs, such as forecasting regional sales or calculating appointment attendance rates. Tips for Building Predictions Prescriptive Analytics: Turning Predictions into Actions Predictive insights are only as valuable as the actions they inspire. Einstein Next Best Action bridges this gap by providing context-specific recommendations based on predictions. How Einstein Next Best Action Works Data Quality: The Foundation of Accurate Predictions The effectiveness of predictive analytics depends on the quality of your data. Poor data—whether due to errors, duplicates, or inconsistencies—can skew results and undermine trust. Best Practices for Data Quality Modern tools like DataGroomr can automate data validation and cleaning, ensuring that predictions are based on trustworthy information. Empowering Smarter Decisions with Predictive Analytics Salesforce’s AI-driven predictive analytics transforms decision-making by providing actionable insights from historical data. Businesses can anticipate trends, improve operational efficiency, and deliver personalized customer experiences. As predictive analytics continues to evolve, companies leveraging these tools will gain a competitive edge in an increasingly dynamic marketplace. Embrace the power of predictive analytics in Salesforce to make faster, more strategic decisions and drive sustained success. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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September 30, 2024in Data, Enterprise, Salesforce, Salesforce Einstein
Exploring Emerging LLM
27Sep

Exploring Emerging LLM

Exploring Emerging LLM Agent Types and Architectures The Evolution Beyond ReAct AgentsThe shortcomings of first-generation ReAct agents have paved the way for a new era of LLM agents, bringing innovative architectures and possibilities. In 2024, agents have taken center stage in the AI landscape. Companies globally are developing chatbot agents, tools like MultiOn are bridging agents to external websites, and frameworks like LangGraph and LlamaIndex Workflows are helping developers build more structured, capable agents. However, despite their rising popularity within the AI community, agents are yet to see widespread adoption among consumers or enterprises. This leaves businesses wondering: How do we navigate these emerging frameworks and architectures? Which tools should we leverage for our next application? Having recently developed a sophisticated agent as a product copilot, we share key insights to guide you through the evolving agent ecosystem. What Are LLM-Based Agents? At their core, LLM-based agents are software systems designed to execute complex tasks by chaining together multiple processing steps, including LLM calls. These agents: The Rise and Fall of ReAct Agents ReAct (reason, act) agents marked the first wave of LLM-powered tools. Promising broad functionality through abstraction, they fell short due to their limited utility and overgeneralized design. These challenges spurred the emergence of second-generation agents, emphasizing structure and specificity. The Second Generation: Structured, Scalable Agents Modern agents are defined by smaller solution spaces, offering narrower but more reliable capabilities. Instead of open-ended design, these agents map out defined paths for actions, improving precision and performance. Key characteristics of second-gen agents include: Common Agent Architectures Agent Development Frameworks Several frameworks are now available to simplify and streamline agent development: While frameworks can impose best practices and tooling, they may introduce limitations for highly complex applications. Many developers still prefer code-driven solutions for greater control. Should You Build an Agent? Before investing in agent development, consider these criteria: If you answered “yes,” an agent may be a suitable choice. Challenges and Solutions in Agent Development Common Issues: Strategies to Address Challenges: Conclusion The generative AI landscape is brimming with new frameworks and fervent innovation. Before diving into development, evaluate your application needs and consider whether agent frameworks align with your objectives. By thoughtfully assessing the tools and architectures available, you can create agents that deliver measurable value while avoiding unnecessary complexity. 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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September 27, 2024in Data, Enterprise, Generative AI
Chatbot-less AI-ifying
19Sep

Chatbot-less AI-ifying

AI-ify Your Product Without Adding a Chatbot: Inspiration from Top AI Use Cases Artificial intelligence doesn’t always need to look like a chatbot. Some of the most innovative implementations of AI have created intuitive user experiences (UX) without relying on traditional conversational interfaces. Here are seven standout patterns from leading companies and startups that demonstrate how AI can elevate your product in ways that feel natural and empowering for users. These are just a preview of the 24 trending AI-UX patterns featured in the “Trending AI-UX Patterns” ebook by AIverse—perfect for borrowing (or expensing to your company). Pattern 1: Linear Back-and-Forth (Classic Chat) While chat interfaces revolutionized access to AI, this pattern is just the beginning. Think of ChatGPT—its conversational simplicity opened the door to powerful LLMs for non-tech audiences. But beyond basic chat, consider integrating generative UI commands or API-based functionality into your product to transform linear data access into something seamless and engaging. Pattern 2: Non-Linear Conversations Inspired by Subform, this pattern mirrors how humans think—connecting ideas in a web, not a straight line. Non-linear exploration allows users to navigate through information like dots on a map, offering a flexible, intuitive flow. For example, imagine an AI that surfaces related ideas or actions based on user input—ideal for creative tools or brainstorming apps. Pattern 3: Context Bundling Why stop at simple text input when you can bundle context visually? Figma’s dual-tone matrix simplifies tone adjustments for text by letting users drag across a 2D grid. It eliminates the need for complex prompts while maintaining control over customization. Think of ways to integrate pre-bundled prompts directly into your UI to create an intuitive, visually driven experience. Pattern 4: Living Documents Tools like Elicit bring AI into familiar interfaces like spreadsheets by enhancing workflows without disrupting them. Elicit’s bulk data extraction uses subtle animations and transparency—highlighting “low confidence” answers for clarity. This hybrid approach integrates AI in a way that feels natural and predictable, making it a great choice for data-heavy tools or reporting systems. Pattern 5: Work With Me One of the most human-centered AI patterns comes from Granola, which uses meeting summaries based on your rough notes. Instead of overwhelming users with full transcriptions, it creates concise, actionable insights, perfectly blending human oversight with AI-powered efficiency. This pattern exemplifies the “human-in-the-loop” trend, ensuring collaboration between the user and AI. Pattern 6: Highlight and Curate Take inspiration from Lex’s “@lex” comment feature, which allows users to highlight and comment directly in the flow of their work—no app switching or disruption required. By building on familiar text-interaction patterns, this approach integrates AI subtly, offering suggestions or enhancements without breaking the user’s autonomy. Pattern 7: Invisible AI (Agentive UX) AI can work quietly in the background until needed, as demonstrated by Ford’s lane assist. This feature seamlessly takes control during critical moments (e.g., steering) and hands it back to the user effortlessly. Visual, auditory, and haptic feedback make the transition intuitive and reassuring. This “agentive” pattern is perfect for products where AI acts as a silent partner, ready to assist only when necessary. Tectonic Conclusions These patterns prove that AI can elevate your product without resorting to a chatbot. Whether through non-linear exploration, visual bundling, or seamless agentive experiences, the key is to integrate AI in a way that feels intuitive, empowering, and aligned with user needs. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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September 19, 2024in Artificial Intelligence, Data
einstein discovery dictionary
06Sep

Einstein Discovery Dictionary

Familiarize yourself with terminology that is commonly associated with Einstein Discovery. Actionable VariableAn actionable variable is an explanatory variable that people can control, such as deciding which marketing campaign to use for a particular customer. Contrast these variables with explanatory variables that can’t be controlled, such as a customer’s street address or a person’s age. If a variable is designated as actionable, the model uses prescriptive analytics to suggest actions (improvements) the user can take to improve the predicted outcome. Actual OutcomeAn actual outcome is the real-world value of an observation’s outcome variable after the outcome has occurred. Einstein Discovery calculates model performance by comparing how closely predicted outcomes come to actual outcomes. An actual outcome is sometimes called an observed outcome. AlgorithmSee modeling algorithm. Analytics DatasetAn Analytics dataset is a collection of related data that is stored in a denormalized, yet highly compressed, form. The data is optimized for analysis and interactive exploration. AttributeSee variable. AverageIn Einstein Discovery, the average represents the statistical mean for a variable. BiasIf Einstein Discovery detects bias in your data, it means that variables are being treated unequally in your model. Removing bias from your model can produce more ethical and accountable models and, therefore, predictions. See disparate impact. Binary Classification Use CaseThe binary classification use case applies to business outcomes that are binary: categorical (text) fields with only two possible values, such as win-lose, pass-fail, public-private, retain-churn, and so on. These outcomes separate your data into two distinct groups. For analysis purposes, Einstein Discovery converts the two values into Boolean true and false. Einstein Discovery uses logistic regression to analyze binary outcomes. Binary classification is one of the main use cases that Einstein Discovery supports. Compare with multiclass classification. CardinalityCardinality is the number of distinct values in a category. Variables with high cardinality (too many distinct values) can result in complex visualizations that are difficult to read and interpret. Einstein Discovery supports up to 100 categories per variable. You can optionally consolidate the remaining categories (categories with fewer than 25 observations) into a category called Other. Null values are put into a category called Unspecified. Categorical VariableA categorical variable is a type of variable that represents qualitative values (categories). A model that represents a binary or multiclass classification use case has a categorical variable as its outcome. See category. CategoryA category is a qualitative value that usually contains categorical (text) data, such as Product Category, Lead Status, and Case Subject. Categories are handy for grouping and filtering your data. Unlike measures, you can’t perform math on categories. In Salesforce Help for Analytics datasets, categories are referred to as dimensions. CausationCausation describes a cause-and-effect relationship between things. In Einstein Discovery, causality refers to the degree to which variables influence each other (or not), such as between explanatory variables and an outcome variable. Some variables can have an obvious, direct effect on each other (for example, how price and discount affect the sales margin). Other variables can have a weaker, less obvious effect (for example, how weather can affect on-time delivery). Many variables have no effect on each other: they are independent and mutually exclusive (for example, win-loss records of soccer teams and currency exchange rates). It’s important to remember that you can’t presume a causal relationship between variables based simply on a statistical correlation between them. In fact, correlation provides you with a hint that indicates further investigation into the association between those variables. Only with more exploration can you determine whether a causal link between them really exists and, if so, how significant that effect is .CoefficientA coefficient is a numeric value that represents the impact that an explanatory variable (or a pair of explanatory variables) has on the outcome variable. The coefficient quantifies the change in the mean of the outcome variable when there’s a one-unit shift in the explanatory variable, assuming all other variables in the model remain constant. Comparative InsightComparative insights are insights derived from a model. Comparative insights reveal information about the relationships between explanatory variables and the outcome variable in your story. With comparative insights, you isolate factors (categories or buckets) and compare their impact with other factors or with global averages. Einstein Discovery shows waterfall charts to help you visualize these comparisons. CorrelationA correlation is simply the association—or “co-relationship”—between two or more things. In Einstein Discovery, correlation describes the statistical association between variables, typically between explanatory variables and an outcome variable. The strength of the correlation is quantified as a percentage. The higher the percentage, the stronger the correlation. However, keep in mind that correlation is not causation. Correlation merely describes the strength of association between variables, not whether they causally affect each other. CountA count is the number of observations (rows) associated with an analysis. The count can represent all observations in the dataset, or the subset of observations that meet associated filter criteria.DatasetSee Analytics dataset. Date VariableA date variable is a type of variable that contains date/time (temporal) data.Dependent VariableSee outcome variable. Deployment WizardThe Deployment Wizard is the Einstein Discovery tool used to deploy models into your Salesforce org. Descriptive InsightsDescriptive insights are insights derived from historical data using descriptive analytics. Descriptive insights show what happened in your data. For example, Einstein Discovery in Reports produces descriptive insights for reports. Diagnostic InsightsDiagnostic insights are insights derived from a model. Whereas descriptive insights show what happened in your data, diagnostic insights show why it happened. Diagnostic insights drill deeper into correlations to help you understand which variables most significantly impacted the business outcome you’re analyzing. The term why refers to a high statistical correlation, not necessarily a causal relationship. Disparate ImpactIf Einstein Discovery detects disparate impact in your data, it means that the data reflects discriminatory practices toward a particular demographic. For example, your data can reveal gender disparities in starting salaries. Removing disparate impact from your model can produce more accountable and ethical insights and, therefore, predictions that are fair and equitable. Dominant ValuesIf Einstein Discovery detects dominant values in a variable, it means that the data is unbalanced. Most values are in the same category, which can limit the value of the analysis. DriftOver time, a deployed model’s performance can drift, becoming less accurate in predicting outcomes. Drift can occur due to changing factors in the data or in your business environment. Drift also results from now-obsolete assumptions built into the story

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September 6, 2023in Artificial Intelligence, Data, Salesforce
Why BANT Isn’t Always Enough
31May

BANT

Why BANT Isn’t Always Enough: Rethinking Lead Qualification for Modern Sales In the world of sales, the BANT framework (Budget, Authority, Need, Timing) has long been a go-to method for qualifying leads. For companies selling familiar or low-complexity products where price is the primary differentiator, BANT can be an effective tool for identifying prospects ready for aggressive sales pursuit. However, in today’s rapidly evolving business landscape—particularly for B2B tech companies offering innovative or paradigm-shifting solutions—relying solely on BANT can actually do more harm than good. In this article, we’ll explore the limitations of the BANT framework, why it falls short in certain scenarios, and how you can adapt your lead qualification process to better align with modern sales challenges. What is BANT, and Why is it Popular? BANT, a framework popularized by companies like IBM, is designed to help sales teams quickly assess whether a prospect is worth pursuing. The acronym stands for: For straightforward sales scenarios, BANT works well. It helps sales teams prioritize leads that are most likely to convert, saving time and resources. However, as sales environments become more complex—especially in B2B tech—the limitations of BANT become increasingly apparent. The Limitations of BANT in Modern Sales While BANT is a useful starting point, it has several inherent limitations that can hinder your sales efforts, particularly when selling innovative or high-complexity solutions. Here’s why: When BANT Falls Short: Real-World Scenarios Let’s look at a few scenarios where BANT might not be the best fit: How to Overcome BANT’s Limitations The good news is that you don’t have to abandon BANT entirely. Instead, you can augment it with additional strategies to create a more holistic lead qualification process. Here’s how: Conclusion: Evolving Beyond BANT While BANT remains a useful tool for certain sales scenarios, it’s not a one-size-fits-all solution. For B2B tech companies selling innovative or high-complexity offerings, a more nuanced approach to lead qualification is essential. By addressing the limitations of BANT and incorporating strategies like value-based selling, stakeholder mapping, and relationship building, you can better align your sales process with the realities of modern B2B buying. In a world where customer needs and decision-making processes are constantly evolving, the ability to adapt and think beyond traditional frameworks like BANT will set you apart from the competition. So, the next time you’re qualifying leads, ask yourself: Is BANT enough, or is it time to rethink your approach? 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 31, 2022in Enterprise, Technology

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