Salesforce Einstein Archives - gettectonic.com

09Sep

Predictive Analytics

Industry forecasts predict an annual growth rate of 6% to 7%, fueled by innovations in cloud computing, artificial intelligence (AI), and data engineering. In 2023, the global data analytics market was valued at approximately $41 billion and is expected to surge to $118.5 billion by 2029, with a compound annual growth rate (CAGR) of 27.1%. This significant expansion reflects the growing demand for advanced analytics tools that provide actionable insights. AI has notably enhanced the accuracy of predictive models, enabling marketers to anticipate customer behaviors and preferences with impressive precision. “We’re on the verge of a new era in predictive analytics, with tools like Salesforce Einstein Data Analytics revolutionizing how we harness data-driven insights to transform marketing strategies,” says Koushik Kumar Ganeeb, a Principal Member of Technical Staff at Salesforce Data Cloud and a distinguished Data and AI Architect. Ganeeb’s leadership spans initiatives like AI-powered Salesforce Einstein Data Analytics, Marketing Cloud Connector for Data Cloud, and Intelligence Reporting (Datorama). His expertise includes architecting vast data extraction pipelines that process trillions of transactions daily. These pipelines play a crucial role in the growth strategies of Fortune 500 companies, helping them scale their data operations efficiently by leveraging AI. Ganeeb’s visionary work has propelled Salesforce Einstein Data Analytics into the forefront of business intelligence. Under his guidance, the platform’s advanced capabilities—such as predictive modeling, real-time data analysis, and natural language processing—are now pivotal in transforming how businesses forecast trends, personalize marketing efforts, and make data-driven decisions with unprecedented precision. AI and Machine Learning: The Next Frontier Beginning in 2018, Salesforce Marketing Cloud, a leading engagement platform used by top enterprises, faced challenges in extracting actionable insights and enhancing AI capabilities from rapidly growing data across diverse systems. Ganeeb was tasked with overcoming these hurdles, leading to the development of the Salesforce Einstein Provisioning Process. This process involved the creation of extensive data import jobs and the establishment of standardized patterns based on consumer adoption learning. These automated jobs handle trillions of transactions daily, delivering critical engagement and profile data in real-time to meet the scalability needs of large enterprises. The data flows seamlessly into AI models that generate predictions on a massive scale, such as Engagement Scores and insights into messaging and language usage across the platform. “Integrating AI and machine learning into data analytics through Salesforce Einstein is not just a technological enhancement—it’s a revolutionary shift in how we approach data,” explains Ganeeb. “With our advanced predictive models and real-time data processing, we can analyze vast amounts of data instantly, delivering insights that were previously unimaginable.” This innovative approach empowers organizations to make more informed decisions, driving unprecedented growth and operational efficiency. Real-World Success Stories Under Ganeeb’s technical leadership, Salesforce Einstein Data Analytics has delivered remarkable results across industries by leveraging AI and machine learning to provide actionable insights and enhance business performance. In the past year, leading companies like T-Mobile, Fitbit, and Dell Technologies have reported significant improvements after integrating Einstein. Ganeeb’s proficiency in designing and scaling data engineering solutions has been critical in helping these enterprises optimize performance. “Scalability with Salesforce Einstein Data Analytics goes beyond managing data volumes—it ensures that every data point is converted into actionable insights,” says Ganeeb. His work processing petabytes of data daily underscores his commitment to precision and efficiency in data engineering. Navigating Data Ethics and Quality Despite the rapid growth of predictive analytics, Ganeeb emphasizes the importance of data ethics and quality. “The accuracy of predictive models depends on the integrity of the data,” he notes. Salesforce Einstein Data Analytics addresses this by curating datasets to ensure they are representative and free from bias, maintaining trust while delivering reliable insights. By implementing rigorous data quality checks and ethical considerations, Ganeeb ensures that Einstein Analytics not only delivers actionable insights but also fosters transparency and trust. This balanced approach is key to the responsible use of predictive analytics across various industries. Future Trends in Predictive Analytics The future of predictive analytics looks bright, with AI and machine learning poised to further refine the accuracy and utility of predictive models. “Success lies in embracing technological advancements while maintaining a human touch,” Ganeeb notes. “By combining AI-driven insights with human intuition, businesses can navigate market complexities and uncover new opportunities.” Ganeeb’s contributions to Salesforce Einstein Data Analytics exemplify this balanced approach, integrating cutting-edge technology with human insight to empower businesses to make strategic decisions. His work positions organizations to thrive in a data-driven world, helping them stay agile and competitive in an evolving market. Balancing Benefits and Challenges – Predictive Analytics While predictive analytics offers vast potential, Ganeeb recognizes the challenges. Ensuring data quality, addressing ethical concerns, and maintaining transparency are crucial for its responsible use. “Although challenges remain, the future of AI-based predictive analytics is promising,” Ganeeb asserts. His work with Salesforce Einstein Data Analytics continues to push the boundaries of marketing analytics, enabling businesses to harness the power of AI for transformative growth. 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

September 9, 2024in Google Analytics, Salesforce Data Cloud, Salesforce Einstein, Salesforce Marketing Cloud

29Aug

Salesforce Einstein SDR and Sales Coach Agents

Salesforce Introduces Autonomous AI Sales Agents: Einstein SDR Agent and Einstein Sales Coach Agent Salesforce, the leading CRM for sales, has announced two new fully autonomous AI sales agents: Einstein Sales Development Rep (SDR) Agent and Einstein Sales Coach Agent. These groundbreaking agents, set to be generally available in October, are designed to help sales teams accelerate growth by handling key sales functions autonomously. Built on the Einstein 1 Agentforce Platform, these agents are poised to transform how sales teams operate, allowing them to focus on more complex deals while automating routine tasks. Einstein SDR Agent: Automating Pipeline 24/7 The Einstein SDR Agent autonomously engages with inbound leads, nurturing pipelines around the clock. Unlike traditional chatbots, which can only respond to pre-programmed queries, the Einstein SDR Agent uses advanced AI to make decisions, prioritize actions, and handle various lead interactions. Whether it’s answering product questions, managing objections, or booking meetings, the SDR Agent ensures that every response is trusted, accurate, and personalized, grounded in your company’s CRM and external data. Key features of the Einstein SDR Agent include: Einstein Sales Coach Agent: Enhancing Seller Performance Through AI-Driven Role-Play Einstein Sales Coach Agent takes sales enablement to the next level by autonomously engaging in role-plays with sellers. Whether simulating a buyer during discovery, pitch, or negotiation calls, the Sales Coach Agent uses generative AI to convert text into speech, providing a realistic training environment. This agent helps sellers refine their skills by offering personalized feedback based on real deal contexts. Key features of the Einstein Sales Coach Agent include: Accenture’s Collaboration with Salesforce Accenture, a global leader in business consulting, will leverage these new AI agents to enhance deal team effectiveness, scale support for more deals, and allow their sales teams to concentrate on the most complex transactions. According to Sara Porter, Global Sales Excellence Lead at Accenture, these AI-driven tools will empower their sales practitioners with advanced technology and processes to drive more intelligent customer conversations and accelerate revenue. Salesforce’s Vision for AI in Sales Salesforce sees these autonomous AI agents as a key part of the future of sales. By integrating AI that can generate high-quality pipeline and provide personalized coaching, sales teams can focus on higher-value deals and better prepare for them. Ketan Karkhanis, EVP and GM of Sales Cloud, emphasizes that every AI conversation must translate into ROI, and these new agents are designed to do just that by augmenting human sales teams to accelerate growth. Availability Both Einstein SDR Agent and Einstein Sales Coach Agent will be generally available in October, with additional functionalities expected to be rolled out throughout the year. Learn More: Note: Any unreleased services or features mentioned here are not currently available and may be subject to changes. Customers should base their purchasing decisions from Salesforce on currently available features. 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

August 29, 2024in Agentforce Platform, Generative AI, Salesforce Einstein

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

August 19, 2024in AI Tools, Artificial Intelligence, Data, Enterprise, Generative AI, Salesforce, Salesforce Einstein, Technology

18Aug

Salesforce on AI

Marketing success hinges on delivering consistent, timely, and engaging content. According to the Salesforce State of Marketing report, 78% of high-performing marketers identify data as their most critical asset for creating cohesive customer journeys. Yet, only 49% report having a unified view of customer data sources. This disconnect highlights a significant challenge many marketing teams face in effectively leveraging their data. For organizations already invested in Salesforce, incorporating AI-driven business intelligence (BI) tools offers numerous benefits. These include reduced time to deliver insights, enhanced automation, increased innovation, improved agility, and cost savings. However, realizing these benefits depends on having high-quality data and robust data strategies. This insight explores AI-driven BI from a Salesforce perspective, highlighting its benefits, applications, and future trends. By understanding the potential of AI in BI, organizations can better harness their data to drive success and innovation. The Role of AI in Business Intelligence Integrating AI into BI systems elevates data analysis by offering deeper insights and predictive capabilities. Here’s how AI enhances BI: These examples demonstrate AI’s ability to improve BI systems by enhancing data accuracy, providing real-time insights, and improving forecasting. Salesforce’s AI Capabilities in BI Salesforce’s AI capabilities in BI are embodied in the versatile tool, Salesforce Einstein. Easily integrated with BI, Einstein automates tasks and delivers personalized insights. Companies using Einstein have reported a 20% increase in sales productivity. Here’s how Einstein can be utilized in various scenarios: These examples illustrate how Salesforce’s AI tools, particularly Einstein, can transform BI by automating routine tasks and delivering personalized insights, ultimately driving customer satisfaction and business growth. Future Trends in AI and BI The future of AI and BI promises even more advanced capabilities and innovations. As AI evolves, so too will the tools for BI. Here are some trends expected to emerge in the near future: These trends show that AI and BI are evolving rapidly. Companies that stay ahead of these developments will be well-positioned to leverage AI for greater innovation and efficiency. Next Steps AI-powered BI, especially with Salesforce, is transforming how businesses operate by providing deeper insights and better decision-making capabilities. To stay competitive and foster innovation, organizations must embrace AI. The quest is no longer just to be data-driven. It is to be data-decisioned. Here are some actionable steps: By taking these steps, businesses can fully leverage AI-driven BI and maintain a competitive edge in the fast-evolving digital playinf field of AI. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

August 18, 2024in Salesforce Einstein

10Aug

Generative AI Tools

One of the most significant use cases for generative AI in business is customer service and support. Most of us have likely experienced the frustration of dealing with traditional automated systems. However, today’s advanced AI, powered by large language models and natural language chatbots, is rapidly improving these interactions. While many still prefer human agents for complex or sensitive issues, AI is proving highly capable of handling routine inquiries efficiently. Here’s an overview of some of the top AI-powered tools for automating customer service. Although the human element will always be essential in customer experience, these tools free up human agents from repetitive tasks, allowing them to focus on more complex challenges requiring empathy and creativity. Cognigy Cognigy is an AI platform designed to automate customer service voice and chat channels. It goes beyond simply reading FAQ responses by delivering personalized, context-sensitive answers in multiple languages. Cognigy’s AI Copilot feature enhances human contact center workers by offering real-time AI assistance during interactions, making both fully automated and human-augmented support possible. IBM WatsonX Assistant IBM’s WatsonX Assistant helps businesses create AI-powered personal assistants to streamline tasks, including customer support. With its drag-and-drop configuration, companies can set up seamless self-service experiences. The platform uses retrieval-augmented generation (RAG) to ensure that responses are accurate and up-to-date, continuously improving as it learns from customer interactions. Salesforce Einstein Service Cloud Einstein Service Cloud, part of the Salesforce platform, automates routine and complex customer service tasks. Its AI-powered Agentforce bots manage “low-touch” interactions, while “high-touch” cases are overseen by human agents supported by AI. Fully customizable, Einstein ensures that responses align with your brand’s tone and voice, all while leveraging enterprise data securely. Zendesk AI Zendesk, a leader in customer support, integrates generative AI to boost its service offerings. By using machine learning and natural language processing, Zendesk understands customer sentiment and intent, generates personalized responses, and automatically routes inquiries to the most suitable agent—be it human or machine. It also provides human agents with real-time guidance on resolving issues efficiently. Ada Ada is a conversational AI platform built for large-scale customer service automation. Its no-code interface allows businesses to create custom bots, reducing the cost of handling inquiries by up to 78% per ticket. By integrating domain-specific data, Ada helps improve both support efficiency and customer experience across omnichannel support environments. More AI Tools for Customer Service There are numerous other AI tools designed to enhance automated customer support: While AI tools are transforming customer service, the key lies in using them to complement human agents, allowing for a balance of efficiency and personalized care. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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

August 10, 2024in AI Tools, Data, Enterprise, Generative AI, Salesforce, Salesforce Einstein

07Aug

Data Cloud and Genpact

Genpact (NYSE: G), a global professional services and solutions firm delivering outcomes that shape the future, announced today its integration with Salesforce Data Cloud to offer AI-driven industry-specific cloud solutions to transform operations and drive competitive advantages for enterprises. Genpact’s integration with Data Cloud will solve issues associated with disconnected and unstructured data, including poor quality, accessibility, and scalability. By combining Genpact’s deep industry knowledge in consumer goods, life sciences, manufacturing, banking, and insurance, with Data Cloud, businesses can improve decision-making, optimize operations, and drive growth. “To navigate an increasingly complex business environment, business leaders must unlock the full potential of their data assets – but they can only do so if they have a more holistic view,” said Riju Vashisht, Chief Growth Officer, Genpact. “Our partnership with Salesforce combines our data, technology, and AI expertise and a global talent pool with the Salesforce Data Cloud, helping businesses break down data silos, gain real-time insights, and deliver personalized experiences at scale.” Data Cloud offers a 360-degree view of the customer every team can act on by seamlessly connecting, unifying, and activating data that lives in silos across an organization. This allows Genpact to better enable automation, analytics, and personalized engagement. Genpact has also launched a comprehensive training program for its employees on Salesforce’s Einstein AI and Data Cloud platforms to enhance skills and boost innovation to stay ahead in the AI landscape. “Data Cloud is the platform powering organizations’ data and AI driven engagement. Data Cloud seamlessly harmonizes and unifies structured and unstructured data to deliver integrated experiences,” said Rahul Auradkar, EVP and GM, Unified Data Services and Einstein, Salesforce. “The AI revolution is about data, Data Cloud in concert with Salesforce Einstein 1 platform drives predictive and generative AI, automation, and analytics for customer engagement.” Genpact’s collaboration with Salesforce underscores the company’s commitment to delivering high-quality solutions and achieving client satisfaction within the Salesforce ecosystem. Visit here for more information about Genpact and Salesforce. About Genpact Genpact (NYSE: G) is a global professional services and solutions firm delivering outcomes that shape the future. Our 125,000+ people across 30+ countries are driven by our innate curiosity, entrepreneurial agility, and desire to create lasting value for clients. Powered by our purpose – the relentless pursuit of a world that works better for people – we serve and transform leading enterprises, including the Fortune Global 500, with our deep business and industry knowledge, digital operations services, and expertise in data, technology, and AI. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

August 7, 2024in Einstein 1 Platform, Generative AI, Salesforce Data Cloud, Salesforce Einstein

30Jul

Understand Salesforce Automated Case Routing

Simplified Case Management with Automation Customer service becomes easier, faster, and more effective with automation tools. A well-organized case management system ensures that customer inquiries are routed to the right person, get the correct answers, and are resolved promptly. Delays or errors in responses can lead to customer dissatisfaction, making efficient case routing critical. Salesforce Service Cloud offers robust automation tools to simplify case management and ensure the right service agents handle cases efficiently, minimizing errors and maximizing customer satisfaction. Efficient Handling of Multi-Channel Case Creation With cases being generated from multiple service channels like web, email, phone, and chat, managing them efficiently can be challenging. Service agents often spend significant time prioritizing, sorting, and assigning cases manually, which can reduce productivity. Moreover, identifying agents with specialized skills and assigning appropriate cases to them can be time-consuming. Automating this process ensures optimal resource utilization, faster resolutions, and higher customer satisfaction. Salesforce provides several tools for routing cases to the right agents: Additionally, advanced automation tools enhance case-routing efficiency: Omni-Channel Routing: Revolutionizing Case Management Omni-Channel Routing is a powerful feature that transforms inefficient systems into streamlined workflows. Without it, agents often rely on manual processes, such as selecting cases from lists, which can result in: Omni-Channel automatically assigns cases to qualified and available agents in real-time, ensuring balanced workloads and prioritizing urgent cases. It seamlessly integrates with both Salesforce Classic and Lightning Experience, saving time and enabling agents to focus on resolving cases quickly. Routing Techniques: Case Assignment Rules: Simplifying Small Business Needs Case Assignment Rules automate case ownership by assigning cases to specific users or queues based on predefined criteria. These rules apply to all cases, regardless of origin, including web-to-case, email-to-case, and more. Key Features: Escalation Rules: Prioritizing Unresolved Cases Escalation Rules help identify and resolve cases that remain unresolved within a specific timeframe. They reassign cases to specific users or teams and send notifications, ensuring: Rules are configured with business hours and time-based criteria to determine when cases should be escalated. Einstein Case Classification & Routing: AI-Powered Efficiency Salesforce Einstein uses machine learning to predict and populate case record fields automatically, reducing agent effort and improving accuracy. How It Works: Einstein Case Routing combines AI predictions with assignment or skill-based routing rules to assign cases to the most suitable agents, improving resolution speed and customer satisfaction. Conclusion Automated case routing transforms customer service by improving productivity, efficiency, and resolution speed. While Omni-Channel Routing is the most comprehensive tool for case management, Escalation and Assignment Rules also play vital roles for smaller business needs. Together, these tools enable businesses to deliver exceptional customer experiences. 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

July 30, 2024in Data, Salesforce, Salesforce Einstein, Salesforce Service Cloud

25Jul

Salesforce Data Cloud Pioneer

While many organizations are still building their data platforms, Salesforce Data Cloud Pioneer has made a significant leap forward. By seamlessly incorporating metadata integration, Salesforce has transformed the modern data stack into a comprehensive application platform known as the Einstein 1 Platform. Led by Muralidhar Krishnaprasad, executive vice president of engineering at Salesforce, the Einstein 1 Platform is built on the company’s metadata framework. This platform harmonizes metadata and integrates it with AI and automation, marking a new era of data utilization. The Einstein 1 Platform: Innovations and Capabilities Salesforce’s goal with the Einstein 1 Platform is to empower all business users—salespeople, service engineers, marketers, and analysts—to access, use, and act on all their data, regardless of its location, according to Krishnaprasad. The open, extensible platform not only unlocks trapped data but also equips organizations with generative AI functionality, enabling personalized experiences for employees and customers. “Analytics is very important to know how your business is doing, but you also want to make sure all that data and insights are actionable,” Krishnaprasad said. “Our goal is to blend AI, automation, and analytics together, with the metadata layer being the secret sauce.” Salesforce Data Cloud Pioneer In a conversation with George Gilbert, senior analyst at theCUBE Research, Krishnaprasad discussed the platform’s metadata integration, open-API technology, and key features. They explored how its extensibility and interoperability enhance usability across various data formats and sources. Metadata Integration: Accommodating Any IT Environment The Einstein 1 Platform is built on Trino, the federated open-source query engine, and Spark for data processing. It offers a rich set of connectors and an open, extensible environment, enabling organizations to share data between warehouses, lake houses, and other systems. “We use a hyper-engine for sub-second response times in Tableau and other data explorations,” Krishnaprasad explained. “This in-memory overlap engine ensures efficient data processing.” The platform supports various machine learning options and allows users to integrate their own large language models. Whether using Salesforce Einstein, Databricks, Vertex, SageMaker, or other solutions, users can operate without copying data. The platform includes three levels of extensibility, enabling organizations to standardize and extend their customer journey models. Users can start with basic reference models, customize them, and then generate insights, including AI-driven insights. Finally, they can introduce their own functions or triggers to act on these insights. The platform continuously performs unification, allowing users to create different unified graphs based on their needs. “We’re a multimodal system, considering your entire customer journey,” Krishnaprasad said. “We provide flexibility at all levels of the stack to create the right experience for your business.” The Triad of AI, Automation, and Analytics The platform’s foundation ingests, harmonizes, and unifies data, resulting in a standardized metadata model that offers a 360-degree view of customer interactions. This approach unlocks siloed data, much of which is in unstructured forms like conversations, documents, emails, audio, and video. “What we’ve done with this customer 360-degree model is to use unified data to generate insights and make these accessible across application surfaces, enabling reactions to these insights,” Krishnaprasad said. “This unlocks a comprehensive customer journey.” For instance, when a customer views an ad and visits the website, salespeople know what they’re interested in, service personnel understand their concerns, and analysts have the information needed for business insights. These capabilities enhance customer engagement. “Couple this with generative AI, and we enable a lot of self-service,” Krishnaprasad added. “We aim to provide accurate answers, elevating data to create a unified model and powering a unified experience across the entire customer journey.” 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

July 25, 2024in Einstein 1 Platform, Generative AI, Salesforce Data Cloud, Salesforce Einstein

24Jul

Salesforce Workday Partnership

Salesforce and Workday Partner to Launch AI-Powered Employee Service Agent Salesforce (NYSE: CRM), the leading AI CRM platform, and Workday, Inc. (NASDAQ: WDAY), a leader in enterprise cloud applications for finance and HR, today announced a strategic partnership to develop a new AI-powered employee service agent. This solution will enhance employee experiences by automating routine tasks, providing personalized support, and delivering data-driven insights across Salesforce and Workday platforms. A Unified Data Foundation for Enhanced Employee Services The partnership will integrate HR and financial data from Workday with CRM data from Salesforce, creating a unified data foundation. This integration will enable the development of AI-driven use cases that increase productivity, reduce costs, and improve the employee experience. A key feature will be the seamless incorporation of Workday into Slack, allowing for enhanced automation and collaboration around HR and financial records, using AI. The new AI employee service agent, built on Salesforce’s Agentforce Platform and Einstein AI, alongside Workday AI, will cater to various employee service needs, such as onboarding, health benefits management, and career development. This agent will utilize a company’s data to interact with employees in natural language, offering personalized support and executing tasks based on trusted business rules and permissions. Enhancing Employee and Customer Success “The AI opportunity lies in augmenting employees and delivering exceptional customer experiences. Our collaboration with Workday will empower businesses to create remarkable experiences using generative and autonomous AI, allowing employees to efficiently find answers, learn new skills, solve problems, and take actions.” Marc Benioff, Chair and CEO of Salesforce Carl Eschenbach, CEO of Workday, highlighted the integration’s benefits: “By combining our platforms, data, and AI capabilities, we empower customers to deliver unmatched AI-powered employee experiences, leading to happier customers and substantial business value.” Key Features of the Partnership Benefits for Employees and Employers For Employees: For Employers: Sal Companieh, Chief Digital and Information Officer at Cushman & Wakefield, noted the strategic advantage: “The integration of Workday and Salesforce will streamline workflows and deliver more personalized, AI-powered employee experiences, significantly enhancing our operational efficiency.” “The shared data foundation between Workday and Salesforce will enable these partners to deliver transformative AI capabilities, enhancing employee experiences and driving business performance.” R “Ray” Wang, CEO of Constellation Research, Inc. About Workday Workday is a leading enterprise platform that helps organizations manage their most important assets – their people and money. The Workday platform is built with AI at the core to help customers elevate people, supercharge work, and move their business forever forward. Workday is used by more than 10,500 organizations around the world and across industries – from medium-sized businesses to more than 60% of the Fortune 500. For more information about Workday, visit workday.com. 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

July 24, 2024in Einstein 1 Platform, Salesforce Data Cloud, Salesforce Einstein, Salesforce Implementation Services, Salesforce Slack

22Jul

Einstein Code Generation and Amazon SageMaker

Salesforce and the Evolution of AI-Driven CRM Solutions Salesforce, Inc., headquartered in San Francisco, California, is a leading American cloud-based software company specializing in customer relationship management (CRM) software and applications. Their offerings include sales, customer service, marketing automation, e-commerce, analytics, and application development. Salesforce is at the forefront of integrating artificial general intelligence (AGI) into its services, enhancing its flagship SaaS CRM platform with predictive and generative AI capabilities and advanced automation features. Einstein Code Generation and Amazon SageMaker. Salesforce Einstein: Pioneering AI in Business Applications Salesforce Einstein represents a suite of AI technologies embedded within Salesforce’s Customer Success Platform, designed to enhance productivity and client engagement. With over 60 features available across different pricing tiers, Einstein’s capabilities are categorized into machine learning (ML), natural language processing (NLP), computer vision, and automatic speech recognition. These tools empower businesses to deliver personalized and predictive customer experiences across various functions, such as sales and customer service. Key components include out-of-the-box AI features like sales email generation in Sales Cloud and service replies in Service Cloud, along with tools like Copilot, Prompt, and Model Builder within Einstein 1 Studio for custom AI development. The Salesforce Einstein AI Platform Team: Enhancing AI Capabilities The Salesforce Einstein AI Platform team is responsible for the ongoing development and enhancement of Einstein’s AI applications. They focus on advancing large language models (LLMs) to support a wide range of business applications, aiming to provide cutting-edge NLP capabilities. By partnering with leading technology providers and leveraging open-source communities and cloud services like AWS, the team ensures Salesforce customers have access to the latest AI technologies. Optimizing LLM Performance with Amazon SageMaker In early 2023, the Einstein team sought a solution to host CodeGen, Salesforce’s in-house open-source LLM for code understanding and generation. CodeGen enables translation from natural language to programming languages like Python and is particularly tuned for the Apex programming language, integral to Salesforce’s CRM functionality. The team required a hosting solution that could handle a high volume of inference requests and multiple concurrent sessions while meeting strict throughput and latency requirements for their EinsteinGPT for Developers tool, which aids in code generation and review. After evaluating various hosting solutions, the team selected Amazon SageMaker for its robust GPU access, scalability, flexibility, and performance optimization features. SageMaker’s specialized deep learning containers (DLCs), including the Large Model Inference (LMI) containers, provided a comprehensive solution for efficient LLM hosting and deployment. Key features included advanced batching strategies, efficient request routing, and access to high-end GPUs, which significantly enhanced the model’s performance. Key Achievements and Learnings Einstein Code Generation and Amazon SageMaker The integration of SageMaker resulted in a dramatic improvement in the performance of the CodeGen model, boosting throughput by over 6,500% and reducing latency significantly. The use of SageMaker’s tools and resources enabled the team to optimize their models, streamline deployment, and effectively manage resource use, setting a benchmark for future projects. Conclusion and Future Directions Salesforce’s experience with SageMaker highlights the critical importance of leveraging advanced tools and strategies in AI model optimization. The successful collaboration underscores the need for continuous innovation and adaptation in AI technologies, ensuring that Salesforce remains at the cutting edge of CRM solutions. For those interested in deploying their LLMs on SageMaker, Salesforce’s experience serves as a valuable case study, demonstrating the platform’s capabilities in enhancing AI performance and scalability. To begin hosting your own LLMs on SageMaker, consider exploring their detailed guides and resources. 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

July 22, 2024in Generative AI, Salesforce Einstein

19Jul

Can We Customize Manufacturing Cloud For Our Business?

Yes, Salesforce Manufacturing Cloud Can Be Customized to Meet Your Business Needs Salesforce Manufacturing Cloud is designed to be highly customizable, allowing manufacturing organizations to tailor it to their unique business requirements. Whether it’s adapting the platform to fit specific workflows, integrating with third-party systems, or enhancing reporting capabilities, Salesforce provides robust customization options to meet the specific needs of manufacturers. Here are key ways Salesforce Manufacturing Cloud can be customized: 1. Custom Data Models and Objects Salesforce allows you to create custom objects and fields to track data beyond the standard model. This flexibility enables businesses to manage unique production metrics or product configurations seamlessly within the platform. Customization Options: 2. Sales Agreement Customization Sales Agreements in Salesforce Manufacturing Cloud can be tailored to reflect your business’s specific contract terms and pricing models. You can adjust agreement structures, including the customization of terms, conditions, and rebate tracking. Customization Options: 3. Custom Workflows and Automation Salesforce offers tools like Flow Builder and Process Builder, allowing manufacturers to automate routine tasks and create custom workflows that streamline operations. Customization Options: 4. Integration with Third-Party Systems Salesforce Manufacturing Cloud can integrate seamlessly with ERP systems (like SAP or Oracle), inventory management platforms, and IoT devices to ensure a smooth data flow across departments. Integration Options: 5. Custom Reports and Dashboards With Salesforce’s robust reporting tools, you can create custom reports and dashboards that provide real-time insights into key performance indicators (KPIs) relevant to your manufacturing operations. Customization Options: 6. Custom User Interfaces Salesforce Lightning allows you to customize user interfaces to meet the needs of different roles within your organization, such as production managers or sales teams. This ensures users have quick access to relevant data. Customization Options: Conclusion Salesforce Manufacturing Cloud provides a wide range of customization options to suit the unique needs of your manufacturing business. Whether it’s adjusting data models, automating processes, or integrating with external systems, Manufacturing Cloud can be tailored to meet your operational goals. By leveraging these customizations, manufacturers can optimize their operations, improve data accuracy, and gain real-time insights to boost efficiency. If you need help customizing Salesforce Manufacturing Cloud, Service Cloud, or Sales Cloud for your business, our Salesforce Manufacturing Cloud Services team is here to assist. 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

July 19, 2024in Data, Salesforce, Salesforce Einstein, Salesforce Manufacturing Cloud

05Jul

Salesforce Tiny Giant LLM

‘On-device Agentic AI is Here!’: Salesforce Announces the ‘Tiny Giant’ LLM Salesforce CEO Marc Benioff is excited about the company’s latest innovation in AI, introducing the ‘Tiny Giant’ LLM, which he claims is the world’s top-performing “micro-model” for function-calling. Salesforce’s new slimline “Tiny Giant” LLM reportedly outperforms larger models, marking a significant advancement in on-device AI. According to a paper published on Arxiv by Salesforce’s AI Research department, the xLAM-7B LLM model ranked sixth among 46 models, including those from OpenAI and Google, in a competition testing function-calling (execution of tasks or functions through API calls). The xLAM-7B LLM was trained on just seven billion parameters, a small fraction compared to the 1.7 trillion parameters rumored to be used by GPT-4. However, Salesforce highlights the xLAM-1B, a smaller model, as its true star. Despite being trained on just one billion parameters, the xLAM-1B model finished 24th, surpassing GPT-3.5-Turbo and Claude-3 Haiku in performance. CEO Marc Benioff proudly shared these results on X (formerly Twitter), stating: “Meet Salesforce Einstein ‘Tiny Giant.’ Our 1B parameter model xLAM-1B is now the best micro-model for function-calling, outperforming models 7x its size… On-device agentic AI is here. Congrats Salesforce Research!” Salesforce’s research emphasizes that function-calling agents represent a significant advancement in AI and LLMs. Models like GPT-4, Gemini, and Mistral already execute API calls based on natural language prompts, enabling dynamic interactions with various digital services and applications. While many popular models are large and resource-intensive, requiring cloud data centers and extensive infrastructure, Salesforce’s new models demonstrate that smaller, more efficient models can achieve state-of-the-art performance. To test function-calling LLMs, Salesforce developed APIGen, an “Automated Pipeline for Generating verifiable and diverse function-calling datasets,” to synthesize data for AI training. Salesforce’s findings indicate that models trained on relatively small datasets can outperform those trained on larger datasets. “Models trained with our curated datasets, even with only seven billion parameters, can achieve state-of-the-art performance… outperforming multiple GPT-4 models,” the paper states. The ultimate goal is to create agentic AI models capable of function-calling and task execution on devices, minimizing the need for extensive external infrastructure and enabling self-sufficient operations. Dr. Eli David, Co-Founder of the cybersecurity firm Deep Instinct, commented on X, “Smaller, more efficient models are the way to go for widespread deployment of LLMs.” 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

July 5, 2024in Salesforce Einstein

10Jun

Democratize Data with Einstein Copilot for Tableau

Most workers today recognize the importance of rich data analytics in their jobs. However, 33% struggle to understand and generate insights from data. To address this, Salesforce has introduced Einstein Copilot for Tableau, which allows users of all skill levels to create complex data visualizations without extensive learning or coding. Democratize Data with Einstein Copilot for Tableau. Launched in April 2024, the beta version of this AI assistant features a user-friendly interface that simplifies the process with questions or simple commands. This facilitates the quick creation of comprehensive data presentations, including reports, dashboards, and various charts. Democratize Data with Einstein Copilot for Tableau Einstein Copilot for Tableau leverages a combination of AI technologies—natural language processing (NLP), machine learning (ML), and generative AI—to provide actionable insights. NLP enables conversational and intuitive interactions, while ML models process user queries and analyze data. Generative AI drives cognitive reasoning, planning, and creates insights, recommendations, and diagrams based on user inputs. By integrating with Tableau Cloud, Einstein Copilot accesses historical proprietary data, enables advanced data analysis, and translates user intent into actionable insights. It relies on Tableau’s analytics infrastructure to execute code and displays results through user-friendly visualizations and dashboards. Additionally, the Einstein Trust Layer secures and protects private data in Einstein Copilot. It authorizes inbound requests, ensuring users have necessary permissions to access specific data and safeguards model outputs to prevent the disclosure of confidential information. How Einstein Copilot for Tableau Transforms Requests into Insights To understand how Einstein Copilot for Tableau turns requests into actionable insights, let’s walk through each step of the interaction process: Einstein Copilot for Tableau democratizes access to data analytics, enabling all users to harness the power of data without needing extensive technical knowledge. 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

June 10, 2024in Einstein 1 Platform, Salesforce Tableau

05Jun

Five9 Salesforce AI Integration

Five9 and Salesforce Enhance AI-Powered Solutions for Superior Customer Experiences Five9 (NASDAQ: FIVN), a provider of the Intelligent CX Platform, today announced the next step in its collaboration with Salesforce. Five9 Salesforce AI Integration. This partnership aims to deliver AI-powered solutions to enhance customer experiences (CX) in contact centers. The latest release, Five9 for Service Cloud Voice with Partner Telephony, integrates Salesforce Einstein with Five9’s suite of AI solutions. This empowers agents to better service customer requests, improves management’s understanding of contact center operations, and delivers customer resolutions that exceed expectations. Using Five9’s open APIs and Five9 TranscriptStream, the Einstein AI engine identifies opportunities to provide real-time solutions for agents, prompting ‘Next Best Action’ guidance. The solution also offers real-time transcription of customer conversations, ensures call recordings’ accuracy and relevance, and integrates with Salesforce Einstein Conversation Insights to enhance conversation intelligence. “Five9 understands the power of elevating the customer experience through innovative technology and seamless integrations,” said Dan Burkland, President of Five9. “Our collaboration with Salesforce pushes the boundaries of what is possible. Infusing Einstein’s AI insights into the contact center and CRM eliminates repetitive tasks while guiding agents with the next best actions to help them be more effective.” A Long-Standing Partnership The Salesforce-Five9 collaboration, now over 15 years strong, recently introduced Five9 call dispositions for agents within the Salesforce Omni-Channel widget or Voice Call page. This allows organizations to automatically update call dispositions in the Five9 call database, ensuring accurate reporting across the integration. Both companies are meeting the growing demand for AI solutions to enhance customer engagement throughout the customer journey. “Five9’s deeper integration with Salesforce Einstein offers a new level of choice for customers seeking AI capabilities that best match their contact center needs and existing technology investments,” said Sheila McGee-Smith, President & Principal Analyst at McGee-Smith Analytics. “Coupled with features like Five9 TranscriptStream, organizations can significantly reduce an agent’s workload while enhancing the customer’s overall experience. This next step in the Salesforce-Five9 relationship demonstrates each company’s commitment to their joint customer base, enabling them to leverage the latest AI innovations easily.” “Service Cloud Voice with Five9 uses AI to deliver a better customer experience,” said Ryan Nichols, Chief Product Officer of Service Cloud, Salesforce. “Our collaboration focuses on more than just a ‘single pane of glass’– we’re bringing together customer data, knowledge, and real-time conversation transcripts to help make agents more productive and delight customers.” Availability and Further Information These new enhancements to Five9 for Service Cloud Voice with Partner Telephony will be available starting June 30. For a deeper look into the Five9 integration with Service Cloud Voice and to explore common use cases, register for the webinar “Unlock Efficiency with the Power of AI: Five9 and Salesforce Service Cloud Voice” on Tuesday, July 23. An on-demand playback of the December 2023 Five9 and Salesforce joint webinar is also available, covering topics such as using data for personalization, best practices for leveraging engagement data to improve experiences, and how companies can become more customer-centric. Salesforce, Einstein, and other related marks are trademarks of Salesforce, Inc. 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

June 5, 2024in Salesforce, Salesforce Einstein, Salesforce Service Cloud

04Jun

Trust Einstein Copilot for Tableau

Are you prepared to utilize the capabilities of Einstein Copilot to expand your organization’s analytical advantages? This robust tool facilitates data exploration, insights generation, and visualization development at an unprecedented pace. However, before immersing yourself in its capabilities, it’s crucial to grasp how Einstein Copilot upholds Tableau and Salesforce’s core value: Trust. Let’s discover how the Einstein Trust Layer safeguards your data, ensures result accuracy, and facilitates auditing, addressing common questions and concerns raised by our customers.Trust Einstein Copilot for Tableau. What is Einstein Copilot for Tableau? Using generative AI and statistical analysis, Einstein Copilot for Tableau is able to understand the context of your data to create and suggest relevant business questions to help kickstart your analysis. A smart, conversational assistant for Tableau users, Einstein Copilot for Tableau automates data curation—the organization and integration of data collected from various sources—by generating calculations and metadata descriptions. Einstein Copilot for Tableau can fill data gaps and enhance analysis by creating synthetic datasets where real data is limited. Einstein Copilot helps you anticipate outcomes with predictive analytics that simulate diverse scenarios and uncover hidden correlations. Additionally, generative models can increase data privacy by producing non-traceable data for analysis. Fulfilling the promise of generative AI, Einstein Copilot for Tableau presents an efficient, insightful, and ethical approach to data analytics. Think of it as an intelligent assistant integrated into the Tableau suite of products to make everyone successful in their analysis workflow—whether they’re an experienced data analyst or a data explorer. As your intelligent analytics AI assistant, Einstein Copilot for Tableau guides you through the process of creating data visualizations in Tableau by assisting you with recommended questions, conversational data exploration, guided calculation creation, and more. Understanding the Einstein Trust Layer The Einstein Trust Layer constitutes a secure AI architecture embedded within the Salesforce platform. Comprising agreements, security technology, and data privacy controls, it ensures the safety of your data while exploring generative AI solutions. Built upon the Einstein Trust Layer, Einstein Copilot for Tableau and other Tableau AI features inherit its security, governance, and Trust capabilities. The Einstein Trust Layer is a secure AI architecture, built into the Salesforce platform. It is a set of agreements, security technology, and data and privacy controls used to keep your company safe while you explore generative AI solutions. Tableau has been on the journey to help people see and understand their data for over two decades. Thanks to data analysts, this mission has been a success and will continue to be a success. Data analysts are the backbone of organizations that champion data culture, capture business requirements, prep data, and create data content for end users. Data Access and Privacy Who Accesses Your Data? A primary concern among our customers revolves around data access. Rest assured, the Einstein Trust Layer enforces strict policies to safeguard your organization’s data. Third-party LLM providers, including Open AI and Azure Open AI, adhere to a zero data retention policy. This means that data sent to LLMs isn’t stored; once processed, both the prompt and response are promptly forgotten. Additionally, each Einstein Copilot for Tableau customer receives their own Data Cloud instance, securely storing prompts and responses for auditing purposes. Data Residency and Access Control Einstein Copilot for Tableau respects permissions, row-level security, and data policies within Tableau Cloud, ensuring that only authorized personnel within your organization access specific data. Whether using Einstein Copilot or not, data access is restricted based on organizational roles and permissions. Data Handling and Processing Data Sent Outside of Tableau Cloud Site Einstein Copilot for Tableau operates within the confines of your Tableau site, scanning connected data sources to create a summary context. This summarized data is sent to third-party LLM providers for vectorization, enabling accurate interpretation of user queries. Importantly, the zero data retention policy ensures that summarized data is forgotten post-vectorization. Personally Identifiable Information (PII) Data To enhance data privacy, Einstein Copilot for Tableau employs data masking for PII data. This technique replaces sensitive information with placeholder text, ensuring privacy without sacrificing context. While our detection models strive for accuracy, continuous evaluation and refinement are paramount to maintain trust. Result Trustworthiness Ensuring Safe and Accurate Results Einstein Copilot for Tableau employs Toxicity Confidence Scoring to identify harmful inputs and responses. By combining rule-based filters and AI models, potentially harmful content is filtered and flagged for review. Furthermore, accuracy benchmarks ensure that generated results align closely with human-authored ones, bolstering trust in the platform. Future Trust Enhancements Trust remains an ongoing focus for our teams. Initiatives such as a BYO LLM solution and improved disambiguation capabilities are underway to further enhance trustworthiness. Continuous feedback, testing, and iteration drive our efforts to maintain your trust in Einstein Copilot for Tableau and the Einstein Trust Layer. Data analysis and data-driven decision-making have been part of the vocabulary in organizations over the years. And, while data analysis is one of the most in-demand tech skills sought by employers today, not everyone in an organization has “analyst” in their job title—myself included. Yet, so many of us use data daily to make informed decisions. The rise of generative AI presents a significant opportunity for us to bring transformative benefits to analytics. Businesses are eager to embrace generative AI because it can help save time, provide faster insights, and empower analysts to be even more productive with an AI assistant—freeing analysts to focus on delivering high-quality, data-driven insights. Is Tableau replacing Einstein analytics? Einstein Analytics has a new name. Say hello to Tableau CRM. Everything about how it works stays the same, just with that snazzy new name. When Tableau joined the Salesforce family, we brought together analytics capabilities of incredible depth and power. What is the difference between Einstein analytics and Tableau? If you’re only planning on analyzing Salesforce data, Einstein Analytics would probably make the most sense for you. However, if you need to analyze information that is coming from all over the place, Tableau will give your users more options. Tableau GPT infuses automation in every part of analytics – from preparation to communicating

June 4, 2024in Einstein 1 Platform, Salesforce Tableau

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