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Data Governance for the AI Enterprise
17Jun

A Strategic Approach to Governing Enterprise AI Systems

The Imperative of AI Governance in Modern Enterprises Effective data governance is widely acknowledged as a critical component of deploying enterprise AI applications. However, translating governance principles into actionable strategies remains a complex challenge. This article presents a structured approach to AI governance, offering foundational principles that organizations can adapt to their needs. While not exhaustive, this framework provides a starting point for managing AI systems responsibly. Defining Data Governance in the AI Era At its core, data governance encompasses the policies and processes that dictate how organizations manage data—ensuring proper storage, access, and usage. Two key roles facilitate governance: Traditional data systems operate within deterministic governance frameworks, where structured schemas and well-defined hierarchies enable clear rule enforcement. However, AI introduces non-deterministic challenges—unstructured data, probabilistic decision-making, and evolving models—requiring a more adaptive governance approach. Core Principles for Effective AI Governance To navigate these complexities, organizations should adopt the following best practices: Multi-Agent Architectures: A Governance Enabler Modern AI applications should embrace agent-based architectures, where multiple AI models collaborate to accomplish tasks. This approach draws from decades of distributed systems and microservices best practices, ensuring scalability and maintainability. Key developments facilitating this shift include: By treating AI agents as modular components, organizations can apply service-oriented governance principles, improving oversight and adaptability. Deterministic vs. Non-Deterministic Governance Models Traditional (Deterministic) Governance AI (Non-Deterministic) Governance Interestingly, human governance has long managed non-deterministic actors (people), offering valuable lessons for AI oversight. Legal systems, for instance, incorporate checks and balances—acknowledging human fallibility while maintaining societal stability. Mitigating AI Hallucinations Through Specialization Large language models (LLMs) are prone to hallucinations—generating plausible but incorrect responses. Mitigation strategies include: This mirrors real-world expertise—just as a medical specialist provides domain-specific advice, AI agents should operate within bounded competencies. Adversarial Validation for AI Governance Inspired by Generative Adversarial Networks (GANs), AI governance can employ: This adversarial dynamic improves quality over time, much like auditing processes in human systems. Knowledge Management: The Backbone of AI Governance Enterprise knowledge is often fragmented, residing in: To govern this effectively, organizations should: Ethics, Safety, and Responsible AI Deployment AI ethics remains a nuanced challenge due to: Best practices include: Conclusion: Toward Responsible and Scalable AI Governance AI governance demands a multi-layered approach, blending:✔ Technical safeguards (specialized agents, adversarial validation).✔ Process rigor (knowledge certification, human oversight).✔ Ethical foresight (bias mitigation, risk-aware automation). By learning from both software engineering and human governance paradigms, enterprises can build AI systems that are effective, accountable, and aligned with organizational values. The path forward requires continuous refinement, but with strategic governance, AI can drive innovation while minimizing unintended consequences. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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June 17, 2025in Data, Enterprise, Google0 Comments
ai
01May

Fireflies.ai Launches Domain-Specific Mini Apps to Automate Meeting Insights

Khosla Ventures-backed Fireflies.ai, an AI-powered note-taking platform, unveiled a suite of domain-specific “mini apps” on Wednesday, designed to automatically extract actionable insights from meeting transcripts. With the rise of automatic speech recognition (ASR) and generative AI, meeting intelligence startups—such as Otter, Read AI, Circleback, Krisp, and Granola—have seen rapid growth. Fireflies.ai is no exception, with co-founder and CEO Krish Ramineni reporting an 8x increase in users and achieving profitability. To accelerate its expansion, the startup is rolling out over 200 mini apps tailored to various roles and use cases, including: While competitors like Circleback require manual prompting to generate insights from transcripts, Fireflies.ai’s mini apps eliminate the need for user input, streamlining the process. “The time it takes to derive insights post-meeting is significant,” Ramineni told TechCrunch. “These apps close that gap by automating actions immediately after meetings, boosting productivity.” Users can also integrate outputs with platforms like Salesforce, HubSpot, Asana, Jira, Slack, and Microsoft Teams. For instance, a meeting summary can be automatically shared with a manager via Slack once the discussion concludes. Fireflies.ai allows users to deploy mini apps per meeting and even build custom apps for specialized needs. The company plans to introduce team-sharing capabilities in the future. Beyond mini apps, Fireflies.ai is enhancing meeting intelligence with pre-meeting briefs on participants and organizations. The startup is also testing “digital twins”—AI avatars that can attend meetings and respond to basic queries, similar to experiments by Zoom and others. This expansion underscores Fireflies.ai’s push to automate workflows and maximize efficiency in professional collaboration. 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 1, 2025in AI Tools, Generative AI, Generative AI Tools, Salesforce
Mastering Decorators and Lifecycle Hooks in Salesforce LWC
18Mar

Mastering Decorators and Lifecycle Hooks in Salesforce LWC

Introduction to LWC Core Concepts Lightning Web Components (LWC) in Salesforce leverage two fundamental JavaScript features to create efficient, reactive components: decorators and lifecycle hooks. These mechanisms work together to: Deep Dive into LWC Decorators 1. @api – The Public Interface Decorator Purpose: Enables component communication and exposes public properties/methods Key Characteristics: Implementation Patterns: javascript Copy // Child component exposing properties and methods import { LightningElement, api } from ‘lwc’; export default class Modal extends LightningElement { @api title = ‘Default Title’; // Public property with default @api show() { // Public method this.template.querySelector(‘.modal’).classList.remove(‘hidden’); } @api hide() { this.template.querySelector(‘.modal’).classList.add(‘hidden’); } } Best Practices: Performance Considerations: 2. @track – The Reactive Property Decorator (Legacy) Evolution of Reactivity: When to Use Today: Modern Alternatives: javascript Copy // Preferred immutable pattern (no @track needed) updateUser() { this.user = { …this.user, name: ‘Updated Name’ }; } // Array operations addItem(newItem) { this.items = […this.items, newItem]; } 3. @wire – The Data Service Decorator Core Functionality: Implementation Options: javascript Copy // Property syntax (automatic) @wire(getContacts) contacts; // Function syntax (manual control) @wire(getContacts) wiredContacts({ error, data }) { if (data) this.contacts = data; if (error) this.error = error; } Advanced Patterns: Lifecycle Hooks Demystified The Component Lifecycle Journey Practical Implementation Guide Component Communication Patterns Parent-to-Child: html Copy <!– Parent template –> <c-child public-property={value}></c-child> Run HTML Child-to-Parent: javascript Copy // Child component this.dispatchEvent(new CustomEvent(‘notify’, { detail: data })); Performance Optimization Techniques Common Anti-Patterns to Avoid Advanced Patterns and Best Practices State Management Strategies Testing Lifecycle Hooks Example Test Case: javascript Copy import { createElement } from ‘lwc’; import MyComponent from ‘c/myComponent’; describe(‘Lifecycle hooks’, () => { it(‘calls connectedCallback when inserted’, () => { const element = createElement(‘c-my-component’, { is: MyComponent }); spyOn(MyComponent.prototype, ‘connectedCallback’); document.body.appendChild(element); expect(MyComponent.prototype.connectedCallback).toHaveBeenCalled(); }); }); Real-World Component Examples Data Table with Sorting javascript Copy import { LightningElement, api } from ‘lwc’; export default class DataTable extends LightningElement { @api columns = []; @api data = []; sortBy(field) { this.data = […this.data].sort((a, b) => a[field] > b[field] ? 1 : -1 ); } } Dynamic Form Generator javascript Copy import { LightningElement, api } from ‘lwc’; export default class DynamicForm extends LightningElement { @api fields; values = {}; handleChange(event) { this.values = { …this.values, [event.target.name]: event.target.value }; } } Conclusion and Key Takeaways By mastering these concepts, developers can create robust, efficient Lightning Web Components that leverage the full power of the Salesforce platform while maintaining clean, maintainable code architecture. 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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March 18, 2025in Data, Salesforce
13Mar

Why Its Good to be Data-Driven

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

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March 13, 2025in Data, Generative AI, Technology
Salesforce’s AI Energy Score
08Feb

Salesforce’s AI Energy Score

Salesforce’s AI Energy Score: Setting a New Standard for AI Sustainability Understanding AI’s Environmental Impact As AI adoption accelerates globally, concerns about its environmental footprint have grown. Due to AI’s reliance on power-intensive data centers, the technology consumes vast amounts of energy and water, raising sustainability challenges. To address this, Salesforce, in collaboration with Hugging Face, Cohere, and Carnegie Mellon University, has introduced the AI Energy Score—a pioneering tool designed to measure and compare AI models’ energy efficiency. The AI Energy Score Launch The AI Energy Score will debut at the AI Action Summit on February 10, 2025, where leaders from over 100 countries, along with private sector and civil society representatives, will convene to discuss AI’s role in sustainability. Recognized by the French Government and the Paris Peace Forum, this initiative marks a significant step toward transparent and accountable AI development. “We are at a critical moment where the rapid acceleration of both the climate crisis and AI innovation intersect,” says Boris Gamazaychikov, Head of AI Sustainability at Salesforce.“AI’s environmental impact has remained largely opaque, with little transparency around its energy consumption. The AI Energy Score provides a standardized framework to disclose and compare these impacts, removing a key blocker to making sustainable AI the norm.” What Is the AI Energy Score? Developed in partnership with Hugging Face, Cohere, and Carnegie Mellon University, the AI Energy Score aims to establish clear and standardized energy consumption metrics for AI models. “The AI Energy Score is a major milestone for sustainable AI,” says Dr. Sasha Luccioni, AI & Climate Lead at Hugging Face. “By creating a transparent rating system, we address a key blocker for reducing AI’s environmental impact. We’re excited to launch this initiative and drive industry-wide adoption.” Key features of the AI Energy Score include: ✅ Standardized energy ratings – A framework for evaluating AI models’ energy efficiency✅ Public leaderboard – A ranking of 200+ AI models across 10 common tasks (e.g., text and image generation)✅ Benchmarking portal – A platform for submitting and assessing AI models, both open and proprietary✅ Recognizable energy use label – A 1–5 star system for easy identification of energy-efficient models✅ Label generator – A tool for AI developers to create and share standardized energy labels The Impact of the AI Energy Score The introduction of this score is expected to have far-reaching implications for the AI industry: 🔹 Driving market preference – Transparency will push demand for more energy-efficient AI models🔹 Incentivizing sustainable development – Public disclosure will encourage AI developers to prioritize efficiency🔹 Empowering informed decisions – AI users and businesses can make better choices based on energy efficiency data Salesforce’s Commitment to Sustainable AI Salesforce is leading by example, becoming the first AI model developer to disclose energy efficiency data for its proprietary models under this framework. This aligns with the company’s broader sustainability goals and ethical AI approach. Agentforce: AI Efficiency at Scale Salesforce’s Agentforce platform, introduced in 2024, is designed to deploy autonomous AI agents across business functions while maintaining energy efficiency. “Agentforce is built with sustainability at its core, delivering high performance while minimizing environmental impact,” explains Boris Gamazaychikov.“Unlike DIY AI approaches that require energy-intensive model training for each customer, Agentforce is optimized out of the box, reducing costly and carbon-heavy training.” Organizations are already leveraging Agentforce for impact-driven efficiencies: ✅ Good360 uses Agentforce to allocate donated goods more efficiently, cutting waste and emissions while saving 1,000+ employee hours annually✅ Businesses can reduce operational costs by optimizing AI model energy consumption “Reducing AI energy use isn’t just good for the environment—it lowers costs, optimizes infrastructure, and improves long-term profitability,” says Suzanne DiBianca, EVP & Chief Impact Officer at Salesforce.“We’re proud to work with industry leaders to build a more transparent AI ecosystem.” Addressing the AI Energy Challenge With AI-driven data center power usage projected to double by 2026, the AI Energy Score is a timely solution to help organizations manage and reduce their AI-related environmental impact. “The AI Energy Score isn’t just an energy-use metric—it’s a strategic business advantage,” adds Boris Gamazaychikov. “By helping organizations assess and optimize AI model energy consumption, it supports lower costs, better infrastructure efficiency, and long-term profitability.” As AI continues to evolve, sustainability must be part of the equation. The AI Energy Score is a major step in ensuring that the AI industry moves toward a more responsible, energy-efficient future.: Setting a New Standard for AI Sustainability Understanding AI’s Environmental Impact As AI adoption accelerates globally, concerns about its environmental footprint have grown. Due to AI’s reliance on power-intensive data centers, the technology consumes vast amounts of energy and water, raising sustainability challenges. To address this, Salesforce, in collaboration with Hugging Face, Cohere, and Carnegie Mellon University, has introduced the AI Energy Score—a pioneering tool designed to measure and compare AI models’ energy efficiency. The AI Energy Score Launch The AI Energy Score will debut at the AI Action Summit on February 10, 2025, where leaders from over 100 countries, along with private sector and civil society representatives, will convene to discuss AI’s role in sustainability. Recognized by the French Government and the Paris Peace Forum, this initiative marks a significant step toward transparent and accountable AI development. “We are at a critical moment where the rapid acceleration of both the climate crisis and AI innovation intersect,” says Boris Gamazaychikov, Head of AI Sustainability at Salesforce.“AI’s environmental impact has remained largely opaque, with little transparency around its energy consumption. The AI Energy Score provides a standardized framework to disclose and compare these impacts, removing a key blocker to making sustainable AI the norm.” What Is the AI Energy Score? Developed in partnership with Hugging Face, Cohere, and Carnegie Mellon University, the AI Energy Score aims to establish clear and standardized energy consumption metrics for AI models. “The AI Energy Score is a major milestone for sustainable AI,” says Dr. Sasha Luccioni, AI & Climate Lead at Hugging Face. “By creating a transparent rating system, we address a key blocker for reducing AI’s

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February 8, 2025in Agentforce Platform, Data, Salesforce, Technology
salesforce agentforce ai powered agentic agents
24Jan

AI for Membership Sites

AI for Membership Sites: How Artificial Intelligence is Driving New Revenue for Member-Only Platforms Membership sites are entering a transformative era where “AI is the New UI.” Two recent developments illustrate this trend and underscore how artificial intelligence is redefining user interaction and unlocking new revenue streams. The first insight comes from Dr. John Sviokla’s Forbes article, “AI Is The New UI: 3 Steps Business Leaders Must Take Now”. Sviokla emphasizes a fundamental shift: “For decades, we’ve interacted with technology through screens, buttons, and menus. But a fundamental shift is underway — artificial intelligence is becoming the new user interface.” The second example involves a large members-only association in the airline industry. This organization has implemented custom AI chatbots within its member portal to address a growing challenge: members no longer have time to sift through lengthy PDFs or dense slide decks. Instead, they crave fast, ChatGPT-style access to information—and they’re willing to pay for it. A Paradigm Shift in User Interfaces Historically, intuitive gestures and responsive designs revolutionized how people interacted with technology. Today, AI is driving the next evolution, moving interfaces from static designs to dynamic, user-centric experiences. Dr. Sviokla notes: “This transformation isn’t just about chatbots; it’s about AI becoming the primary means through which we interact with systems, data, and machines. For business leaders, this shift represents both an opportunity and an imperative to reimagine how their organizations engage with customers and operate internally.” AI-powered interfaces offer users immediate, conversational, and personalized access to information, bypassing the traditional maze of links and menus. For membership sites, this evolution is particularly significant, as it transforms how members interact with content and services. The “ChatGPT Effect” on Membership Sites The rise of ChatGPT has shifted consumer expectations for digital interactions. Websites are now adopting chatbots and virtual assistants that provide tailored experiences. For membership sites, this technology enables: For example, organizations are deploying AI assistants on their websites to handle various functions, such as sales inquiries, product support, and pricing guidance. These tools enhance member satisfaction and provide opportunities for new revenue streams. AI as a Revenue Generator Membership sites leveraging AI are seeing measurable financial benefits. Consider a crypto token regulation platform that integrated custom AI chatbots. These tools allow members to interact with proprietary data in real time, transforming static content into a dynamic, accessible resource. This shift has significantly increased the platform’s value proposition, attracting and retaining members willing to pay a premium for enhanced access. Starting Small: A Scalable Approach to AI Implementing AI doesn’t require a complete system overhaul. Membership sites can begin with a simple, custom chatbot built using existing content, such as publicly available documents or FAQs. By monitoring member interactions and gathering feedback, organizations can gradually expand their AI capabilities. The key is to focus on enhancing the member experience. Missteps often occur when organizations adopt overly complex solutions that fail to address real user needs. A phased approach ensures AI integration adds value and aligns with member expectations. The Future of AI in Membership Sites The potential for AI in membership sites extends far beyond chatbots. Future applications include: For example, the Martin Trust Center for MIT Entrepreneurship recently launched an AI-powered tool specifically designed to serve its members. These types of innovations highlight how AI can enhance the member experience while driving operational and financial success. Reimagining Member Engagement Membership sites that embrace AI as a foundational component of their user experience are positioning themselves for long-term success. By focusing on solving real problems and delivering meaningful interactions, organizations can strengthen member relationships and drive sustainable growth. For membership sites, the question is no longer whether to adopt AI but how quickly they can integrate it. AI represents an opportunity—and an imperative—to transform the way members interact with content, data, and services. The sites that act now will set the standard for the future of member-driven platforms. 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 24, 2025in Artificial Intelligence, Data, Technology
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 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 18, 2025in Agentic AI, Data, Generative AI, Technology
Scope of Generative AI
07Jan

Exploring Generative AI

Like most employees at most companies, I wear a few different hats around Tectonic. Whether I’m building a data model, creating and scheduing an email campaign, standing up a platform generative AI is always at my fingertips. At my very core, I’m a marketer. Have been for so long I do it without eveven thinking. Or at least, everyuthing I do has a hat tip to its future marketing needs. Today I want to share some of the AI content generators I’ve been using, am looking to use, or just heard about. But before we rip into the insight, here’s a primer. Types of AI Content Generators ChatGPT, a powerful AI chatbot, drew significant attention upon its November 2022 release. While the GPT-3 language model behind it had existed for some time, ChatGPT made this technology accessible to nontechnical users, showcasing how AI can generate content. Over two years later, numerous AI content generators have emerged to cater to diverse use cases. This rapid development raises questions about the technology’s impact on work. Schools are grappling with fears of plagiarism, while others are embracing AI. Legal debates about copyright and digital media authenticity continue. President Joe Biden’s October 2023 executive order addressed AI’s risks and opportunities in areas like education, workforce, and consumer privacy, underscoring generative AI’s transformative potential. What is AI-Generated Content? AI-generated content, also known as generative AI, refers to algorithms that automatically create new content across digital media. These algorithms are trained on extensive datasets and require minimal user input to produce novel outputs. For instance, ChatGPT sets a standard for AI-generated content. Based on GPT-4o, it processes text, images, and audio, offering natural language and multimodal capabilities. Many other generative AI tools operate similarly, leveraging large language models (LLMs) and multimodal frameworks to create diverse outputs. What are the Different Types of AI-Generated Content? AI-generated content spans multiple media types: Despite their varied outputs, most generative AI systems are built on advanced LLMs like GPT-4 and Google Gemini. These multimodal models process and generate content across multiple formats, with enhanced capabilities evolving over time. How Generative AI is Used Generative AI applications span industries: These tools often combine outputs from various media for complex, multifaceted projects. AI Content Generators AI content generators exist across various media. Below are good examples organized by gen ai type: Written Content Generators Image Content Generators Music Content Generators Code Content Generators Other AI Content Generators These tools showcase how AI-powered content generation is revolutionizing industries, making content creation faster and more accessible. I do hope you will comment below on your favorites, other AI tools not showcased above, or anything else AI-related that is on your mind. Written by Tectonic’s Marketing Operations Director, Shannan Hearne. 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 7, 2025in AI Tools, Data, Enterprise, Generative AI, Google, Technology
ai
30Dec

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 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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December 30, 2024in Agentic AI, Artificial Intelligence, Data, Enterprise, Generative AI, Technology
AI Prompts to Accelerate Academic Reading
19Oct

AI Prompts to Accelerate Academic Reading

10 AI Prompts to Accelerate Academic Reading with ChatGPT and Claude AI In the era of information overload, keeping pace with academic research can feel daunting. Tools like ChatGPT and Claude AI can streamline your reading and help you extract valuable insights from research papers quickly and efficiently. These AI assistants, when used ethically and responsibly, support your critical analysis by summarizing complex studies, highlighting key findings, and breaking down methodologies. While these prompts enhance efficiency, they should complement—never replace—your own critical thinking and thorough reading. AI Prompts for Academic Reading 1. Elevator Pitch Summary Prompt: “Summarize this paper in 3–5 sentences as if explaining it to a colleague during an elevator ride.”This prompt distills the essence of a paper, helping you quickly grasp the core idea and decide its relevance. 2. Key Findings Extraction Prompt: “List the top 5 key findings or conclusions from this paper, with a brief explanation of each.”Cut through jargon to access the research’s core contributions in seconds. 3. Methodology Breakdown Prompt: “Explain the study’s methodology in simple terms. What are its strengths and potential limitations?”Understand the foundation of the research and critically evaluate its validity. 4. Literature Review Assistant Prompt: “Identify the key papers cited in the literature review and summarize each in one sentence, explaining its connection to the study.”A game-changer for understanding the context and building your own literature review. 5. Jargon Buster Prompt: “List specialized terms or acronyms in this paper with definitions in plain language.”Create a personalized glossary to simplify dense academic language. 6. Visual Aid Interpreter Prompt: “Explain the key takeaways from Figure X (or Table Y) and its significance to the study.”Unlock insights from charts and tables, ensuring no critical information is missed. 7. Implications Explorer Prompt: “What are the potential real-world implications or applications of this research? Suggest 3–5 possible impacts.”Connect theory to practice by exploring broader outcomes and significance. 8. Cross-Disciplinary Connections Prompt: “How might this paper’s findings or methods apply to [insert your field]? Suggest potential connections or applications.”Encourage interdisciplinary thinking by finding links between research areas. 9. Future Research Generator Prompt: “Based on the limitations and unanswered questions, suggest 3–5 potential directions for future research.”Spark new ideas and identify gaps for exploration in your field. 10. The Devil’s Advocate Prompt: “Play devil’s advocate: What criticisms or counterarguments could be made against the paper’s main claims? How might the authors respond?”Refine your critical thinking and prepare for discussions or reviews. Additional Resources Generative AI Prompts with Retrieval Augmented GenerationAI Agents and Tabular DataAI Evolves With Agentforce and Atlas Conclusion Incorporating these prompts into your routine can help you process information faster, understand complex concepts, and uncover new insights. Remember, AI is here to assist—not replace—your research skills. Stay critical, adapt prompts to your needs, and maximize your academic productivity. 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 19, 2024in AI Tools, Data, Generative AI
More Cool AI Tools
15Sep

More Cool AI Tools

In today’s fast-paced digital world, AI is no longer a luxury but a necessity for maximizing work efficiency. With the right AI tools, businesses and individuals can automate tasks, enhance creativity, improve customer engagement, and streamline operations. Here’s a breakdown of the Top 21 AI tools you should explore to elevate your productivity and stay ahead of the curve! 1️⃣ Video CreationSynthesiaWebsite: SynthesiaAn AI video creation tool that lets you generate high-quality videos from text. Ideal for creating marketing, training, and explainer videos quickly and professionally. VeedWebsite: VeedVeed helps you create, edit, and share professional videos with ease, incorporating AI to streamline the process of adding captions, effects, and edits. SubmagicWebsite: SubmagicSubmagic uses AI to automatically generate subtitles for videos, improving accessibility and viewer engagement. 2️⃣ Customer Relationship Management (CRM)HubSpotWebsite: HubSpotHubSpot’s AI-powered CRM system streamlines customer interactions, helping businesses improve customer satisfaction, sales, and retention. FreshworksWebsite: FreshworksThis tool offers AI-driven solutions for customer service, sales, and marketing, helping companies improve relationships and resolve issues faster. HighLevelWebsite: HighLevelHighLevel integrates AI to improve customer management processes, including lead nurturing and campaign tracking. 3️⃣ Website Design and BrandingWizard AIWebsite: Wizard AIA design tool that helps you create stunning visuals and branding for your website using AI. Whether you’re looking to revamp your website or create a logo, Wizard AI makes it simple. LookaWebsite: LookaLooka offers AI-powered logo creation, making it easy for businesses and startups to design professional logos in just minutes. TurbologoWebsite: TurbologoTurbologo is another intuitive logo maker that uses AI to generate custom logo designs based on your business type and preferences. 4️⃣ Project Management and CollaborationMondayWebsite: MondayAn all-in-one project management platform that uses AI to automate workflows, track progress, and enhance team collaboration. ClickUpWebsite: ClickUpClickUp leverages AI to provide real-time project insights, task automation, and comprehensive team collaboration tools for businesses of all sizes. Golf AIWebsite: Golf AIGolf AI helps golfers refine their game with AI insights, but its technology can also be applied in the professional world, improving focus, strategy, and decision-making in various projects. 5️⃣ Marketing and Lead GenerationPipedriveWebsite: PipedriveA popular tool that helps businesses track leads and automate marketing workflows, making lead generation more efficient and scalable. Apollo AIWebsite: Apollo AIApollo enables businesses to automate sales and lead generation by using AI to find and reach potential customers, helping you connect with decision-makers faster. EnvizWebsite: EnvizThis platform uses AI to provide intelligent data analysis and insights, allowing businesses to fine-tune their marketing strategies. 6️⃣ AI for Audio and VoiceMurf AIWebsite: Murf AIAn AI voice generator that converts text into lifelike voiceovers. Ideal for creators, marketers, and educators who want to generate high-quality audio content. SpeechifyWebsite: SpeechifySpeechify turns written text into audio, helping users consume content faster. It’s perfect for multitaskers and individuals with reading disabilities. ElevenLabsWebsite: ElevenLabsElevenLabs offers state-of-the-art AI technology to generate and clone natural-sounding voices, ideal for podcasts, audiobooks, and interactive audio experiences. 🌐 Explore More AI-Powered ToolsUnlock your productivity potential with these top AI tools. Whether you’re managing projects, creating content, or building customer relationships, AI is your key to efficiency. 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 15, 2024in AI Tools, Data, Technology
Generative AI Overview
19Aug

Generative AI Overview

Editor’s Note: AI Cloud, Einstein GPT, and other cloud GPT products are now Einstein. For the latest on Salesforce Einstein The Rise of Generative AI: What It Means for Business and CRM Generative artificial intelligence (AI) made headlines in late 2022, sparking widespread curiosity and questions about its potential impact on various industries. What is Generative AI? Generative AI is a technology that creates new content—such as poetry, emails, images, or music—based on a set of input data. Unlike traditional AI, which focuses on classifying or predicting, generative AI can produce novel content with a human-like understanding of language, as noted by Salesforce Chief Scientist Silvio Savarese. However, successful generative AI depends on the quality of the input data. “AI is only as good as the data you give it, and you must ensure that datasets are representative,” emphasizes Paula Goldman, Salesforce’s Chief Ethical and Humane Use Officer. How Does Generative AI Work? Generative AI can be developed using several deep learning approaches, including: Other methods include Variational Autoencoders (VAEs) and Neural Radiance Fields (NeRFs), which generate new data or create 2D and 3D images based on sample data. Generative AI and Business Generative AI has captured the attention of global business leaders. A recent Salesforce survey found that 67% of IT leaders are focusing on generative AI in the next 18 months, with 33% considering it a top priority. Salesforce has long been exploring generative AI applications. For instance, CodeGen helps transform simple English prompts into executable code, and LAVIS makes language-vision AI accessible to researchers. More recently, Salesforce’s ProGen project demonstrated the creation of novel proteins using AI, potentially advancing medicine and treatment development. Ketan Karkhanis, Salesforce’s Executive VP and GM of Sales Cloud, highlights that generative AI benefits not just large enterprises but also small and medium-sized businesses (SMBs) by automating proposals, customer communications, and predictive sales modeling. Challenges and Ethical Considerations Despite its potential, generative AI poses risks, as noted by Paula Goldman and Kathy Baxter of Salesforce’s Ethical AI practice. They stress the importance of responsible innovation to ensure that generative AI is used safely and ethically. Accuracy in AI recommendations is crucial, and the authoritative tone of models like ChatGPT can sometimes lead to misleading results. Salesforce is committed to building trusted AI with embedded guardrails to prevent misuse. As generative AI evolves, it’s vital to balance its capabilities with ethical considerations, including its environmental impact. Generative AI can increase IT energy use, which 71% of IT leaders acknowledge. Generative AI at Salesforce Salesforce has integrated AI into its platform for years, with Einstein AI providing billions of daily predictions to enhance sales, service, and customer understanding. The recent launch of Einstein GPT, the world’s first generative AI for CRM, aims to transform how businesses interact with customers by automating content creation across various functions. Salesforce Ventures is also expanding its Generative AI Fund to $500 million, supporting AI startups and fostering responsible AI development. This expansion includes investments in companies like Anthropic and Cohere. As Salesforce continues to lead in AI innovation, the focus remains on creating technology that is inclusive, responsible, and sustainable, paving the way for the future of CRM and business. The Future of Business: AI-Powered Leadership and Decision-Making Tomorrow’s business landscape will be transformed by specialized, autonomous AI agents that will significantly change how companies are run. Future leaders will depend on these AI agents to support and enhance their teams, with AI chiefs of staff overseeing these agents and harnessing their capabilities. New AI-powered tools will bring businesses closer to their customers and enable faster, more informed decision-making. This shift is not just a trend—it’s backed by significant evidence. The Slack Workforce Index reveals a sevenfold increase in leaders seeking to integrate AI tools since September 2023. Additionally, Salesforce research shows that nearly 80% of global workers are open to an AI-driven future. While the pace of these changes may vary, it is clear that the future of work will look vastly different from today. According to the Slack Workforce Index, the number of leaders looking to integrate AI tools into their business has skyrocketed 7x since September 2023. Mick Costigan, VP, Salesforce Futures In the [still] early phases of a major technology shift, we tend to over-focus on the application of technology innovations to existing workflows. Such advances are important, but closing the imagination gap about the possible new shapes of work requires us to consider more than just technology. It requires us to think about people, both as the customers who react to new offerings and as the employees who are responsible for delivering them. Some will eagerly adopt new technology. Others will resist and drag their feet. 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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August 19, 2024in AI Tools, Artificial Intelligence, Data, Enterprise, Generative AI, Salesforce, Salesforce Einstein, Technology
generative adversarial network
24Jul

What is a Generative Adversarial Network?

A Generative Adversarial Network (GAN) is a type of machine learning model that consists of two neural networks, a generator and a discriminator, that compete in a “game” to generate realistic data samples. The generator tries to produce convincing fake data, while the discriminator tries to distinguish between real and generated data, leading to the generator improving its ability to create realistic data.  Here’s a more detailed explanation: Key Components: How it Works: Applications: Challenges: Content updated March 2025. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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July 24, 2024in Data
Exploring Google Vertex AI
21Feb

Vertex AI

Exploring Google Vertex AI Conversation — Dialogflow CX with Generative AI, Data Stores, and Generators Vertex AI Conversation, built on Dialogflow and Vertex AI, introduces generative conversational features that utilize large language models (LLMs) for natural language understanding, crafting responses, and managing conversation flow. These advancements streamline agent design and enhance the quality of interactions. With Vertex AI Conversation, you can employ a state machine approach to develop sophisticated, generative AI-powered agents for dynamic conversation design and automation. In this insight, we’ll delve into the cutting-edge Dialogflow CX Generative AI technology, focusing on Data Stores and Generators. Data Stores: The Library of Information for Conversations Imagine Data Stores as an extensive library. When a question is asked, the virtual assistant acts as a librarian, locating relevant information. Dialogflow CX’s Data Store feature makes it easy to create conversations around stored information from various sources: For data preparation guidance, visit Google’s official documentation. Generators: LLM-Enhanced Dynamic Responses Dialogflow CX also enables Generators to use an LLM directly in Dialogflow CX without webhooks. Generators can perform tasks like summarization, parameter extraction, and data manipulation. Sourced from Vertex AI, they create real-time responses based on your prompts. For example, a Generator can be customized to summarize lengthy answers—an invaluable feature for simplifying conversations in chat or voice applications. You can find common Generator configurations in Google Cloud Platform (GCP) documentation. Creating a Chat Application with Vertex AI To start building, go to the Search and Conversation page in Google Cloud, agree to the terms, activate the API, and select “Chat.” Setting Up Your Agent After naming your agent and configuring data sources, like a Cloud Storage bucket with PDF documents, you’ll see your new chat app under Search & Conversation | Apps. Navigate to Dialogflow CX, where you can use your data store by setting up parameters for the agent and configuring responses. Once your agent is ready, you can test it in the Agent simulator. Adding a Generator for Summarization Using the Generator feature, you can further refine responses. Set parameters to target the Generator’s summarization feature, and link it to a specific page for summarized responses. This improves chat flow, providing concise answers for faster interactions. Integrating with Discord If you want to deploy your agent on platforms like Discord, follow Google’s integration guide for Dialogflow and adjust your code as needed. With the integration, responses will include hyperlinks for easy reference. Conclusion Vertex AI Conversation, with Dialogflow CX, enables powerful, human-like chat experiences by combining LLMs, Data Stores, and Generators. Ready to build your own dynamic conversational experiences? Now is the perfect time to experiment with this technology and see where it can take you. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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February 21, 2024in Data, Generative AI, Google, Technology
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