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Salesforce Agentforce Platform
08Jul

Salesforce Agentforce Platform

Salesforce CEO Marc Benioff has hinted at the upcoming launch of a new AI and automation platform geared towards customer support. Salesforce Agentforce Platform. In anticipation of September’s Dreamforce event, Benioff used X, formerly Twitter, to offer a sneak peek into the platform, specifically highlighting its customer-facing “Einstein Service Agent.” In his post, Benioff underscores the essential features of the virtual agent, emphasizing how it will seamlessly blend digital and human-assisted support to expedite case resolutions. This blended experience appears to build upon the technology integrated into Salesforce following its acquisition of Airkit.ai in 2023. Airkit.ai provides a low- to no-code bot-building platform that empowers businesses to orchestrate AI-driven conversational experiences. Primarily used for service automation, Airkit enables firms to design and automate complex cross-platform workflows, enhancing automated customer interactions across Salesforce applications. The Einstein Service Agent, positioned within Salesforce’s broader Einstein 1 vision, leverages Data Cloud to create a unified, cross-functional data ecosystem. Benioff hints at the agent’s capability to span service, sales, marketing, and commerce functionalities, underscoring its pivotal role within the Salesforce ecosystem. Expanding on Airkit.ai’s foundational capabilities, Salesforce has integrated Mulesoft to enable insights triggering cross-platform automations, alongside real-time data harmonization within Data Cloud. Moreover, the platform now incorporates generative AI (GenAI), enhancing its ability to interpret text, images, and audio/video for diverse customer engagement scenarios. Despite these advancements, Benioff acknowledges that no virtual agent, even with GenAI augmentation, can resolve every customer query alone. Salesforce ensures omnichannel transfers and human handoffs to address more complex issues effectively. In an era where many vendors offer similar conversational AI capabilities, Salesforce distinguishes itself through ecosystem integration and orchestration. Benioff emphasizes the value of low- to no-code workflow orchestration, democratizing AI technology for non-programmers and fostering an inclusive AI revolution. Expect Benioff to elaborate further on these themes and unveil more details about the Agentforce Platform during his keynote at the 2024 Dreamforce event. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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July 8, 2024in Generative AI, Mulesoft
AI Agents in Line at HR
03Jul

AI Agents in Line at HR

AI Agents in Line at HR may only be a satirical cartoon for a very short time. Sorry, Farside, but your AI bits may not be able to keep up with AI. July, 2034 — A new software unicorn has just emerged inbehind a bar in a pub in East London. Unicorn, by the way, descibes a startup company valued at over $1 billion, not necessarily with a billion dollar concept. Back to East London behind the soggy bar. Hey, its our fantasy. Besides if Amazon can start in a garage, isn’t anything possible? The CEO logs in as usual and gathers daily updates from the team. The Chief Technology Officer is suggesting a new feature to deploy. The Chief Product Officer wants to redesign the CRM (or whatever CRM has evolved to) integration. The Chief Revenue Officer is showing off the new pipeline, forecast by Accountant in a Box. The Chief Customer Officer is discussing the latest customer levitation tools and product feedback. The Chief Information Security Officer has found a new privacy conflict, which they are addressing with a newly-revised infrastructure set-up. And the Head of HR is fretting about the latest round of IT candidates. This sounds like every software business you’ve ever heard of. But the difference is that the CEO’s teammates are entirely AI, not human: The CTO is Lovable. The CPO is Cogna. The CCO is Gradient Labs. The CRO is 11x. The CISO is Zylon. Back to 2024: The Rise of AI Agents In 2024, the hottest topic in software is AI agents, or Agentic AI. Founders are rapidly standing up agentic applications that can solve specific needs in functions like sales and customer services — without a human required. Software buyers, seeing real opportunities to quickly improve their P&L, are swiftly building or purchasing these agentic products. Investors have poured hundreds of millions of dollars into startups in this space in recent months. Even Salesforce wasn’t launched with a silver AI spoon in its mouth. Salesforce began investing in artificial intelligence (AI) in 2014, when the company started acquiring machine learning startups and announced its Customer Success Platform. In 2016, Salesforce launched Einstein, its AI platform that supports several of its cloud services. Einstein is built into Salesforce products and includes features like natural language processing, machine learning, and predictive analytics. It helps organizations automate processes, make decisions based on insights, and improve the customer experience. YouTube How To Increase Revenue Using AI for CRM: Salesforce … Feb 12, 2024 — What is Salesforce Einstein? Salesforce Einstein is the first trusted artifici… TechForce Services How does Salesforce Use AI for Business Growth? Jan 31, 2024 — Powered by technologies like Machine Learning, Natural Language Processing, im… saasguru · LinkedIn · 7mo History of Salesforce AI From Predictive to Generative – LinkedIn Published Nov 27, 2023. In 2014, Salesforce, under the visionary leadership of… Twistellar AI in Salesforce: History, Present State and Prospects Organizations generate tons of data on marketing and sales, and surely your sales managers… Wikipedia Salesforce – Wikipedia In October 2014, Salesforce announced the development of its Customer Success Platform. Less than ten years ago, folks. Salesforce’s large database of data has helped the company address AI challenges quickly and with quality. The company’s data cloud offering provides AI with the right information at the right time, which can reduce friction and improve the customer experience.  Salesforce’s AI-powered solutions include: To catalyze this evolution, Salesforce strategically acquired RelateIQ in 2014. This move injected machine learning into the Salesforce ecosystem, capturing workplace communications data and providing valuable insights. Europe is home to many of these exciting companies. For example, H, a French AI agent startup, raised a $220 million seed round in May. Beyond RPA: The New Wave of AI Agents AI agents represent a significant step-change from Robotic Process Automation (RPA) bots, which, as explored last year, have several limitations due to their deterministic nature. Next-generation AI agents are non-deterministic, meaning that instead of stopping at a “dead end,” they can learn from mistakes and adjust their series of tasks. Not entirely unlock the mouse running the same maze over and over for the cheese. Eventually Mr. Squeakers learns which paths are dead ends and avoids them by making better choices at intersections. In AI Agents this makes them suited to complex and unstructured tasks and means they can transform the journey from intent to implementation in software development. They can deliver “pure work,” rather than acting only as a helpful co-pilot. The rise of AI agents is not only an opportunity to expand automation beyond what is possible with RPA but also to broadly redefine how knowledge work is performed. And by who. And even how is it defined. Given the right guardrails, next-generation AI agents have the potential to effectively and safely replace knowledge workers in many business scenarios. AI Agents in Action These agents are about to revolutionize the world of work as we know it and are already getting started. For example, Klarna recently revealed that its AI agent system handled two-thirds of customer chats in its first month in operation. While HR may not be swamped with AI CVs yet, it is certainly fathomable. One would suppose those candidates would have to be reviewed and interviewed by IT, not just HR. Here’s another deep thought. The internet of things (IoT) first appeared in a speech by Peter T. Lewis in September 1985. The Internet of Things (IoT) is a network of physical devices that can collect and transmit data over the internet using sensors, software, and other technologies. IoT devices can communicate with each other and with the cloud, and can even perform data analysis and be controlled remotely. The IoT concept was smart homes, health care environments, office spaces, and transportation. Only recently have we begun to think of the IoT as including the actual computers, or AI, in addition to sensored devices. It isn’t exactly a chicken and the egg question, but more of a

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July 3, 2024in Generative AI
Best Practices for RAG LLM
02Jul

Best Practices for RAG LLM

Best Practices for Building a RAG LLM Chatbot In today’s competitive landscape, a robust Retrieval-Augmented Generation (RAG) Large Language Model (LLM) chatbot can provide a significant edge. However, implementing such a system involves multiple steps and challenges. Below is a guide to help you navigate this journey effectively. 1. Define Clear Objectives Start by identifying your goals: A clear purpose will drive your design and implementation decisions. 2. Prepare Your Data Your chatbot’s success relies heavily on the quality and structure of your data. Assess Your Data Format: Enrich Your Data: 3. Select the Right Platform Choose a platform based on your goals and data type. Common architectures include: Standard RAG with Vector Databases: Relational Databases: Hybrid Text Search: Graph Databases: 4. Fine-Tune the Model While RAG often enriches data without altering the base model, fine-tuning may be essential in specific scenarios, such as: You can fine-tune open-source models directly or use OpenAI’s APIs for custom training. OpenAI also offers domain-specific GPTs, which can save development time. 5. Master Prompt Engineering Crafting precise prompts is critical to guide LLM responses. Prompts generally include: Example for Elasticsearch: javascriptCopy codeYour task is to create a valid Elasticsearch DSL query. Given the mapping: “`{mapping}“`, translate the query: “`{query}“` into JSON. – Use only fields from the mapping. – Ensure case-insensitivity and support fuzzy matches. – Compress the JSON output, removing spaces. For advanced prompt strategies, see Modern Advances in Prompt Engineering. 6. Iterative Testing Testing machine learning models is inherently complex. Key strategies: 7. Optimize the Front-End Collaborate with UI/UX teams to integrate LLM capabilities seamlessly. Consider: 8. Avoid Common Pitfalls Looking Ahead The rise of LLMs has transformed how we think about search and chatbot applications. While we’re in the early stages, the possibilities are immense. Businesses that adapt and strategically integrate these technologies will unlock innovative opportunities and thrive in the evolving landscape. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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July 2, 2024in Data
Salesforce AI Journey
02Jul

Salesforce AI Journey

“We’re on an Incredible Journey”: Why Salesforce’s AI Push is Just Beginning This article originally appeared in TechRadar by Mike Moore. Salesforce AI Journey. When it comes to leveraging AI to enhance global workforces, some companies are leading the charge, particularly when their technology is at the forefront. Salesforce, known for its robust AI tools, is one such company pushing the boundaries. At its recent World Tour London event, Salesforce emphasized its commitment to AI, showcasing how its Einstein tools are already benefiting customers worldwide. TechRadar Pro spoke with Paul O’Sullivan, SVP, Solution Engineering & UKI, CTO, Salesforce, about the company’s vision for enhancing efficiency and productivity across all markets, particularly in the UK. A Wave of Innovation “We’re on an incredible journey,” O’Sullivan stated, referencing Salesforce’s $4 billion investment in the UK and Ireland in 2023. “We’re well-positioned in the UK to maximize AI’s potential and help our customers achieve true value.” This ambition is epitomized by Salesforce’s new AI center in London. The 40,000 square foot facility is set to be a hub for AI collaboration and development, addressing the growing demand for AI technology. O’Sullivan hinted that this is just the beginning. “We’re an innovation-led company—always looking ahead,” he said, highlighting the UK’s history of driving innovation as a positive indicator for AI’s future in the capital. Growing Demand and Education As demand for AI tools and services increases among businesses of all sizes, O’Sullivan acknowledged the rapid pace of change in the AI landscape. “It starts with education—at all levels,” he noted, recognizing the varying degrees of AI knowledge among business leaders. O’Sullivan compared the current AI momentum to past technological revolutions like cloud computing, websites, and ecommerce. Companies had to adapt quickly to avoid falling behind, and he noted that the window for catching up with AI might be even smaller. He predicted a “steady wave of innovation” in AI before it becomes ubiquitous in the business world, with various models and platforms vying for dominance. “It feels like everyone is in a race for AI,” he added, “and there’s a collective agreement that AI will enhance productivity and efficiency, benefiting both the bottom and top lines of big enterprises.” Human Jobs and AI Looking forward, O’Sullivan dismissed concerns that AI would replace human jobs. He suggested that AI would instead create new opportunities for human workers. “I think human nature is inherently curious,” he said. “We will continue to explore new ways of doing things and offer different levels of connection and service.” Drawing parallels to the industrial revolution, O’Sullivan pointed out that machines didn’t eliminate jobs; they increased productivity and efficiency. He believes AI will have a similar impact. “We’re going to see a new level of productivity and efficiency with AI, just as we did with the industrial revolution,” he concluded. Salesforce’s AI journey is only just beginning, promising exciting advancements and opportunities for businesses and workers alike. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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July 2, 2024in Salesforce, Salesforce Einstein
Hubspot Hacked
01Jul

Hubspot Hacked

HubSpot recently disclosed a “security incident” where unauthorized access was attempted on several customer accounts. HubSpot is an American software company that provides tools for inbound marketing, sales, and customer service. It was founded in 2006 by Brian Halligan and Dharmesh Shah, and is today best-known for its all-in-one growth platform that helps businesses attract visitors, convert leads, and close customers.. The CRM company detected the incident on June 22, though it was publicly acknowledged six days later by Alyssa Robinson, Chief Information Security Officer at HubSpot. HubSpot seems to have suffered a data breach, but claims to have everything in hand – for now. Robinson stated that the incident involved bad actors targeting a limited number of HubSpot customers, aiming to gain unauthorized access to their accounts. Upon detection, HubSpot promptly activated its incident response procedures and has since been in contact with affected customers, taking necessary steps to revoke unauthorized access and safeguard customer data. HubSpot Hacked With how the statement was worded, it would seem that the attackers, whoever they are, tried to break into the account – but not necessarily succeeded. Still, the company proceeded with the usual practice in case of a cyberattack: “HubSpot triggered our incident response procedures, and since June 22 we have been contacting impacted customers and taking necessary steps to revoke the unauthorized access and protect our customers and their data,” said Robinson. As of Friday, June 28, HubSpot has not disclosed any communication from the hacking group, nor has it specified the full scope of the incident or the exact number of impacted customers. Despite having over 100,000 paying customers and achieving significant financial milestones, such as breaking the $1 billion annual recurring revenue (ARR) mark, HubSpot’s stock price remained stable amid the news, which surfaced through TechCrunch. Ironically, this incident follows HubSpot’s recent announcement of new data protection capabilities for its Smart CRM users. However, it underscores the ongoing challenges faced by major enterprise tech providers regarding cybersecurity. HubSpot says fewer than 50 customer accounts were victims of a breach in late June, all impacted customers were notified and all has been quiet since the initial incident. As of May 2024, HubSpot had more than 216,000 customers, so an incident that impacts fewer than 50 doesn’t seem like a big deal, unless of course you’re one of the accounts involved. What we know:  The company is not releasing many details about the incident other than the basic facts. The company said in a June 28 release that it detected a security incident on June 22, 2004, where bad actors were attempting to gain access to customer accounts without authorization. HubSpot’s detection of the breach triggered its incident response procedures and the company notified impacted accounts. On June 28 and again on July 1, 2024, the company reported no further signs of a problem. What’s not known at this time is whether the attack was targeting a specific group of HubSpot customers. Back in March 2022, fewer than 30 HubSpot customers were impacted by a data breach, but all of the impacted customers were in the cryptocurrency business. HubSpot joins a growing list of enterprise tech firms experiencing cybersecurity incidents. While recent arrests, such as that of the alleged ringleader behind attacks on Twilio, LastPass, and Mailchimp, offer some hope, cybersecurity threats continue to evolve with the proliferation of digital devices and AI accessibility. This trend poses new risks, including the misuse of AI technologies like deepfakes, as highlighted by concerns raised by organizations like OpenAI. As businesses expand their digital presence and adopt new technologies, they must remain vigilant against evolving cybersecurity threats to protect sensitive information and maintain customer trust. HubSpot is an American software company that provides tools for inbound marketing, sales, and customer service. It was founded in 2006 and is today best-known for its all-in-one growth platform that helps businesses attract visitors, convert leads, and close customers. Impact for Marketers As marketers, our martech stacks are heavily reliant on cloud-based SaaS applications (like HubSpot) and cloud-based data storage from vendors like Amazon’s AWS and Google Cloud. Even on-premise applications and data are a security risk. The applications running in the cloud and the data stored there are at arm’s length from your data security professionals. More than 80% of the data breaches recorded in 2023 involved data stored in the cloud, according to the Harvard Business Review. Big breaches impacting millions of consumers get a great deal of attention, like those that struck Sony or Target in years past. But smaller, targeted attacks can be devastating to the businesses that have their data exposed, though they fly under the radar of the national press. The number of reported data breaches increased 78% from 2022 to 2023. The cost of the average breach surpassed $4 million in 2023 and is up 15% since 2020. How secure is HubSpot? Is my data secure with HubSpot? All communications between a web client and HubSpot servers are protected using TLS (1.0, 1.1, 1.2) protocol encryption using 2048 bit keys. We also provide customers with the ability to enable Two-Phase Authentication (2FA) to prevent unauthorized use of their portals. Another July Hack One of the most significant data leaks in recent history is reported to have occurred on July 4. The leak, dubbed RockYou2024 by the original poster, “ObamaCare”, on a leading hacking forum, compiled 9,948,575,739 unique passwords into plain text. This means close to ten billion passwords were leaked. That said, the RockYou2024 is primarily a compilation of all previous password leaks and is built on a prior RockYou2021 compilation of 8.4 billion passwords. That means between RockYou2021 and RockYou2024, about 1.5 billion passwords were added to the list. Further, according to the hacker, at least a few of these passwords were cracked using RTX 4090, a tactic that was warned about earlier. According to Cybernews researchers, “In its essence, the RockYou2024 leak is a compilation of real-world passwords used by individuals all over the world. Revealing that

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July 1, 2024in Salesforce Shield
Salesforce Flow Efficiency and Automation
27Jun

Salesforce Flow Efficiency and Automation

Salesforce Flow: For Efficiency with Automation Salesforce Flow enables businesses to create very sophisticated solutions without the need for extensive coding, using a simple point-and-click interface. This capability is particularly beneficial for Salesforce Admins, offering functionalities akin to those of Salesforce developers. In this insight we will explore Salesforce Flow: Understanding Developer and Admin Contributions. Salesforce Flow Efficiency and Automation. Salesforce Flow, originally known as Visual Flow, has evolved significantly with each Salesforce release, culminating in the intuitive Flow Builder interface available today. Its applications are expansive and continually expanding. Key Capabilities of Salesforce Flow Mass Updates: Easily handle batch processing to update thousands of records simultaneously based on specific criteria, significantly saving time and effort. Automated Workflows: Construct intricate workflows with multiple steps and decision points, ensuring consistency and efficiency across business processes. User-Friendly Interface: Designed to be intuitive, Flow Builder allows users of varying technical skill levels to create and manage workflows effortlessly. Integration Capabilities: Seamlessly integrates with Salesforce products and third-party applications, enabling comprehensive solutions leveraging diverse data sources. Continuous Improvement: With each Salesforce update, Flow receives new features and enhancements, continually enhancing its versatility and power. Salesforce Flow serves as a pivotal tool for enhancing productivity and streamlining complex operations, making it indispensable for Salesforce Admins striving to optimize workflows. Understanding Salesforce Flow in Detail What is Salesforce Flow? Salesforce Flow Builder is a robust tool within the Salesforce ecosystem, enabling users to automate workflows and processes. These workflows encompass tasks such as sending emails, updating records, triggering other flows, executing Apex actions, and sending notifications. Flows can be initiated by various events, including user actions, record changes, and scheduled times. Flows comprise elements such as actions, conditions, variables, and screens. The visual, drag-and-drop interface of Salesforce Flow Builder ensures accessibility for users without extensive coding knowledge while offering advanced capabilities for technical experts. Types of Salesforce Flow Screen Flows: Provide a step-by-step user interface to automate tasks, collect data, and guide users through processes. Ideal for systematically capturing and qualifying leads, Screen Flows are straightforward to set up and manage. Record-Triggered Flows: Automate actions based on changes to Salesforce records, like creating, updating, or deleting records. These flows replace older tools like Workflow Rules and Process Builder, offering flexibility and ease of management. Scheduled Flows: Run at specified times or intervals to automate routine tasks or periodic updates. Useful for scenarios such as sending reminders or performing batch operations. Platform Event-Triggered Flows: Respond to events within the Salesforce platform in real-time, enabling instant automation based on critical business events. Requires technical proficiency in integrations and platform events. Autolaunched Flows: Initiated by other processes or external systems without user interaction, making them essential for automating backend processes like updating records based on external triggers. The Role of Salesforce Administrators Salesforce Administrators play a major role in designing, implementing, and managing flows. Their responsibilities span from creating automated workflows to ensuring optimal flow performance and providing user training. Administrators leverage Flow to automate data entry, streamline approval processes, and set up notifications for critical events. Collaboration Between Admins and Developers Effective collaboration between Salesforce Administrators and Developers is important for creating efficient and robust flows. While Administrators focus on designing and implementing simpler flows, Developers enhance capabilities by integrating custom Apex code, performing advanced data manipulations, and optimizing flow performance. This collaboration ensures comprehensive solutions that meet both business requirements and technical standards. Final Thoughts Salesforce Flow closees the gap between manual operations and automated efficiency, enabling businesses to enhance accuracy, reduce operational bottlenecks, and adapt swiftly to market changes. By understanding the distinct contributions of Administrators and Developers and fostering a collaborative environment, organizations can design and implement innovative workflows that drive success and growth. Embracing Salesforce Flow not only optimizes business processes but also positions organizations to thrive in dynamic market landscapes. Staying abreast of Salesforce automation advancements and best practices ensures sustained competitiveness and growth. 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 27, 2024in Salesforce
Slack Integrating AI Into Platform
23Jun

Slack Integrating AI Into Platform

Slack’s CEO Denise Dresser announced that AI will soon be integrated into every aspect of the platform, enabling users to manage tasks and launch new projects without leaving the application. This announcement highlights a significant shift towards enhancing productivity and collaboration within Slack using advanced AI capabilities. Slack Integrating AI Into Platform. During a media session following her keynote at Salesforce’s World Tour event in Boston, Dresser outlined her vision for AI in Slack. Having taken on her role six months ago after years with Salesforce, she emphasized the integration of Slack with Salesforce’s Einstein Copilot. Acquired by Salesforce in late 2020, Slack aims to provide a unified experience for users by leveraging AI to manage both structured and unstructured data. The goal is to help users quickly find key conversations and turn them into actionable tasks and projects. Dresser noted the challenges in navigating chat histories and identifying important moments, which AI integration aims to address. Slack Integrating AI Into Platform “AI can significantly drive productivity,” Dresser said. “With Slack AI Search, Slack becomes your organization’s long-term memory. It allows users to easily find what they need through generative summaries, which was a major breakthrough for us.” Dresser highlighted the rapid adoption of AI and its integration into Slack’s functionality, leading to an evolution in skills such as prompt engineering and natural language processing. These advancements enable tasks like software creation without traditional coding methods. She pointed out the rapid growth in AI adoption, comparing it to the adoption rates of ChatGPT, mobile phones, and Facebook. Dresser believes this trend will continue as people experience productivity improvements with AI. AI will be embedded in various Slack features, including Canvas, Workflow, and Huddle, providing seamless assistance within the application. Users may not even realize they are interacting with AI, as it will naturally enhance Slack’s functionality. For instance, instead of manually searching through messages, AI will highlight the most important summaries. Dresser also mentioned the newly launched Slack Lists feature, which automatically captures and surfaces key parts of channel conversations. She stressed the importance of reducing the need to switch between different applications, which can drain time and productivity. “We have millions of people working in Slack; why leave Slack?” she said. “We wanted to integrate capabilities for tasks, lists, and projects directly into Slack, starting right within conversations.” In the future, Slack will also suggest relevant chat channels for project purposes, providing users with powerful insights and capabilities. Dresser noted that while only about a third of employees currently use AI-powered platforms, those who do report an average 81% increase in productivity by eliminating mundane tasks. As AI continues to be embedded into Slack and Salesforce tools, Dresser acknowledged the challenge of maintaining the platform’s beloved feel and integrity. “We’ve already integrated Slack, Sales Elevate, and Salesforce. Copilot’s integration will be excellent,” she said. “We have focused on preserving the unique Slack experience, even while enhancing it with new architectural integrations. Our goal is to ensure that Slack remains efficient and productive while staying true to its core identity.” Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 23, 2024in Salesforce Einstein, Salesforce Slack
Requirements Engineering
21Jun

Requirements Engineering

Every project needs clear requirements. No exceptions. Without them, a project turns into a group of people standing around, unsure of what to do, essentially making things up as they go. This scenario may sound familiar to anyone who has been involved in disorganized projects. What are requirements? According to the Association for Project Management (APM), “Requirements are the wants and needs of stakeholders clearly defined with acceptance criteria.” Requirements engineering is the process for managing the entire lifecycle of these needs and involves five key stages: Let’s dive deeper into these stages: 1. Requirements Elicitation Sometimes, the term “requirements capture” is used, as if stakeholders’ needs are floating around, waiting to be caught. However, requirements are not passively waiting; they must be actively elicited. Elicitation Methods: Eliciting requirements involves interpreting genuine needs, not just compiling a wish list of requested features. 2. Requirements Analysis Once you’ve gathered a set of requirements, it’s time for analysis to ensure they are comprehensive, feasible, and aligned with the project’s objectives. This phase is crucial because 80% of project errors occur during the requirements phase, yet it often receives less than 20% of a project’s time. Key steps include: 3. Requirements Documentation After analyzing requirements, document them clearly to communicate with stakeholders and developers. A good requirements document typically includes: One popular method for documenting requirements is through user stories, which frame requirements from the user’s perspective: User stories focus on meeting user needs rather than prescribing technical specifications. 4. Requirements Validation The next step is validating your documented requirements. This ensures they accurately represent what users and stakeholders need. Validation methods include: Validation is essential to ensure requirements are complete, realistic, and verifiable. 5. Requirements Management The final phase involves tracking and managing changes to requirements throughout the project. Key Concepts: Agile frameworks often rely on iterative approaches, where product owners manage changes during sprint reviews and retrospectives. Summary Requirements engineering consists of five interdependent stages: elicitation, analysis, documentation, validation, and management. While these concepts may seem detailed, they offer a structured framework that’s essential for delivering high-quality solutions. By following this approach, even smaller, lower-risk digital projects can benefit from clear and actionable requirements. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 21, 2024in Salesforce
21Jun

AI and Consumer Goods Cloud

Salesforce’s latest “rolling thunder” of AI enhancements brings significant innovations to Consumer Goods Cloud, leveraging the power of the Einstein AI platform already integrated into Sales Cloud and Service Cloud. These enhancements are designed to optimize planning and execution for consumer goods companies. Salesforce Consumer Goods Cloud is an industry-specific solution that helps consumer goods companies streamline their route-to-market processes. By unifying trade promotion management and retail execution capabilities on a single platform, it enables seamless collaboration between headquarters and field teams. Utilizing Salesforce’s core CRM functionality and the Einstein AI platform, Consumer Goods Cloud empowers companies with data-driven insights and intelligent automation to drive profitable growth. “Consumer goods companies are laser-focused on profitable growth. With the latest Salesforce innovations for Consumer Goods Cloud, they can unify consumer and customer data to plan promotions precisely, equip every field rep with tools to increase sales and reduce downtime, and integrate trusted AI into every service agent’s workflow to solve problems and upsell more frequently,” explained Rob Garf, VP and GM of Retail and Consumer Goods at Salesforce. “In short, every consumer goods company can now transform into an AI Enterprise.” What’s New in Consumer Goods Cloud The latest updates in Consumer Goods Cloud focus on integrating Salesforce’s Data Cloud with Einstein generative AI capabilities, enhancing three key areas: Data Cloud for Consumer Goods: Account managers can now unify account and industry data to build rich customer profiles, segment accounts to the individual store level, and design hyper-localized assortment and promotion plans. For instance, a soft drink distributor can identify which citrus-flavored sodas are most popular in specific Mexican convenience stores and optimize replenishment accordingly. Einstein Copilot Account Summarization: Within the service console, agents can access AI-generated account summaries, eliminating the need to switch between screens and knowledge articles. Summaries include last interactions, order history, satisfaction scores, and promotion details, enabling agents to resolve inquiries quickly and upsell intelligently. Consumer Goods Cloud Einstein 1 for Sales: This AI-powered enhancement package provides sales managers, field reps, merchandisers, and delivery drivers with productivity and revenue-boosting insights. Real-time notifications and recommendations on stock levels, replenishment, special handling needs, and payment collection keep field teams responsive and effective. The Salesforce Embedded AI Difference Salesforce’s strategy of embedding AI via a unified Einstein platform offers several advantages: Consistency: With Einstein already integrated into Sales and Service Clouds, Salesforce can efficiently extend proven AI tools to industry-specific use cases, benefiting users with familiar interfaces and interaction paradigms. Completeness: Embedding AI at the platform level allows Salesforce to enhance the entire workflow from planning to execution. Consumer goods companies can apply intelligent insights to both back-office processes like promotion management and field activities like stock checks and payment collection. Continuous Innovation: The Einstein platform enables rapid deployment of Salesforce’s latest generative AI advancements across all clouds, ensuring customers always have access to state-of-the-art capabilities. Mars Snacking, one of the world’s largest consumer goods companies, is already benefiting from Salesforce’s AI-powered industry cloud. “At Mars Snacking, we are on an ambitious journey to rewire and almost double the size of our business by 2030,” said Bartek Kononiuk, Global Head of Product – Trade Promotion Management. “Consumer Goods Cloud and Trade Promotion Management will enable us to improve our business processes, data availability, and user experience in critical growth-enabling areas.” AI Innovation Comes at a Cost As the consumer goods industry strives to meet rapidly evolving buyer expectations, Salesforce’s embedded AI solutions for Consumer Goods Cloud offer timely advantages. By democratizing access to generative AI and data management capabilities, Salesforce enables companies of all sizes to optimize decision-making, boost field productivity, and drive profitable growth. However, these advanced functionalities come with significant costs. Salesforce’s Einstein AI enhancements often have substantial per-user surcharges, sometimes exceeding $100 per month. For large deployments involving thousands of employees, these expenses can quickly escalate. Consumer goods companies must carefully evaluate the productivity and revenue gains against the added licensing costs. Additionally, while Salesforce is leading the way in enterprise generative AI, the technology is still maturing. Early adopters may encounter instances where the AI delivers suboptimal results. Salesforce’s Trust Layer aims to mitigate these risks, but companies should approach generative AI with a clear understanding of its current limitations. The ongoing enhancements in Salesforce’s Einstein portfolio present a promising yet costly opportunity for customers to evolve into full-fledged AI Enterprises. As the costs and benefits become clearer, consumer goods companies will need to strategically decide where and how aggressively to deploy these advanced capabilities. Those that find the right balance could gain a significant competitive edge in the rapidly changing digital landscape. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 21, 2024in Einstein 1 Platform, Generative AI, Salesforce
The AI Tech Stack
15Jun

The AI Tech Stack

The AI tech stack integrates various tools, libraries, and solutions to enable applications with generative AI capabilities, such as image and text generation. Key components of the AI stack include programming languages, model providers, large language model (LLM) frameworks, vector databases, operational databases, monitoring and evaluation tools, and deployment solutions. The infrastructure of the AI tech stack leverages both parametric knowledge from foundational models and non-parametric knowledge from information sources (PDFs, databases, search engines) to perform generative AI functions like text and image generation, text summarization, and LLM-powered chat interfaces. This insight explores each component of the AI tech stack, aiming to provide a comprehensive understanding of the current state of generative AI applications, including the tools and technologies used to develop them. For those with a technical background, this Tectonic insight includes familiar tools and introduces new players within the AI ecosystem. For those without a technical background, the article serves as a guide to understanding the AI stack, emphasizing its importance across business roles and departments. Content Overview: The AI layer is a vital part of the modern application tech stack, integrating across various layers to enhance software applications. This layer brings intelligence to the stack through capabilities such as data visualization, generative AI (image and text generation), predictive analysis (behavior and trends), and process automation and optimization. In the backend layer, responsible for orchestrating user requests, the AI layer introduces techniques like semantic routing, which allocates user requests based on the meaning and intent of the tasks, leveraging LLMs. As the AI layer becomes more effective, it also reduces the roles and responsibilities of the application and data layers, potentially blurring the boundaries between them. Nevertheless, the AI stack is becoming a cornerstone of software development. Components of the AI Stack in the Generative AI Landscape: The emphasis on model providers over specific models highlights the importance of a provider’s ability to update models, offer support, and engage with the community, making switching models as simple as changing a name in the code. Programming Languages Programming languages are crucial in shaping the AI stack, influencing the selection of other components and ultimately determining the development of AI applications. The choice of a programming language is especially important for modern AI applications, where security, latency, and maintainability are critical factors. Among the options for AI application development, Python, JavaScript, and TypeScript (a superset of JavaScript) are the primary choices. Python holds a significant market share among data scientists, machine learning, and AI engineers, largely due to its extensive library support, including TensorFlow, PyTorch, and Scikit-learn. Python’s readable syntax and flexibility, accommodating both simple scripts and full applications with an object-oriented approach, contribute to its popularity. According to the February 2024 update on the PYPL (PopularitY of Programming Language) Index, Python leads with a 28.11% share, reflecting a growth trend of +0.6% over the previous year. The PYPL Index tracks the popularity of programming languages based on the frequency of tutorial searches on Google, indicating broader usage and interest. JavaScript, with an 8.57% share in the PYPL Index, has traditionally dominated web application development and is now increasingly used in the AI domain. This is facilitated by libraries and frameworks that integrate AI functionalities into web environments, enabling web developers to leverage AI infrastructure directly. Notable AI frameworks like LlamaIndex and LangChain offer implementations in both Python and JavaScript/TypeScript. The GitHub 2023 State of Open Source report highlights JavaScript’s dominance among developers, with Python showing a 22.5% year-over-year increase in usage on the platform. This growth reflects Python’s versatility in applications ranging from web development to data-driven systems and machine learning. The programming language component of the AI stack is more established and predictable compared to other components, with Python and JavaScript/TypeScript solidifying their positions among software engineers, web developers, data scientists, and AI engineers. Model Providers Model providers are a pivotal component of the AI stack, offering access to powerful large language models (LLMs) and other AI models. These providers, ranging from small organizations to large corporations, make available various types of models, including embedding models, fine-tuned models, and foundational models, for integration into generative AI applications. The AI landscape features a wide array of models that support capabilities like predictive analytics, image generation, and text completion. These models can be broadly classified into closed-source and open-source categories. Closed-Source Models have privatized internal configurations, architectures, and algorithms, which are not shared with the model consumers. Key information about the training processes and data is withheld, limiting the extent to which users can modify the model’s behavior. Access to these models is typically provided through an API or a web interface. Examples of closed-source models include: Open-Source Models offer a different approach, with varying levels of openness regarding their internal architecture, training data, weights, and parameters. These models foster collaboration and transparency in the AI community. The levels of open-source models include: Open-source models democratize access to advanced AI technology, reducing the entry barriers for developers to experiment with niche use cases. Examples of open-source models include: Open-Source vs. Closed-Source Large Language Models (LLMs) AI engineers and machine learning practitioners often face the critical decision of whether to use open- or closed-source LLMs in their AI stacks. This choice significantly impacts the development process, scalability, ethical considerations, and the application’s utility and commercial flexibility. Key Considerations for Selecting LLMs and Their Providers Resource Availability The decision between open- and closed-source models often hinges on the availability of compute resources and team expertise. Closed-source model providers simplify the development, training, and management of LLMs, albeit at the cost of potentially using consumer data for training or relinquishing control of private data access. Utilizing closed-source models allows teams to focus on other aspects of the stack, such as user interface design and data integrity. In contrast, open-source models offer more control and privacy but require significant resources to fine-tune, maintain, and deploy. Project Requirements The scale and technical requirements of a project are crucial in deciding whether to use open-

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June 15, 2024in Generative AI
HCG Providing Patient-Focused Personalized Care With Salesforce
15Jun

HCG Providing Patient-Focused Personalized Care With Salesforce

HCG Delivers Personalized Patient Care with Salesforce Read more at: CXO Today HCG Providing Patient-Focused Personalized Care With Salesforce Healthcare Global Enterprises Ltd (HCG), a renowned network of 25 hospitals, is elevating patient care through Salesforce. With a 360-degree patient view, HCG connects with patients on a more personal level, delivering proactive support at every step of their journey. HCG’s commitment to value-based, precision medicine is clear, attracting over 200,000 patients annually. Unified Patient View for Personalized Care Previously, HCG faced challenges with siloed patient information and a rudimentary CRM system. “Without a single source of truth on patients, it was difficult for our medical, surgical, and support teams to collaborate,” says Vineesh Ghei, Chief Sales Officer, HCG. Today, Salesforce provides a unified patient view, capturing every case history, treatment plan, and patient preference, enabling teams to personalize patient care effectively. Enhanced Efficiency with Automation Salesforce Sales Cloud captures initial patient interactions, streamlining responses and tracking conversations. “Our response time to patient enquiries has reduced from 45 minutes to 23 minutes,” notes Ghei. Sales productivity has also improved with automated task prioritization and activity tracking. Additionally, partner management processes have been automated, enhancing engagement. Swift Service Query Resolution Patient journeys are orchestrated on Salesforce Service Cloud, integrating outpatient consultations, inpatient processes, and discharge planning. Integration with HCG’s appointment booking and cloud telephony systems ensures patient details are readily available to agents, enabling resolution of over 90% of queries in under five minutes. This comprehensive view allows service agents to provide proactive, personalized support. Contextual Communications for Long-Term Wellness Using Salesforce Marketing Cloud Engagement, HCG segments patients and delivers personalized communications relevant to their treatment stages. “Every patient’s journey is deeply personal,” says Stuti Jain, Head of Brand, Digital, and Communications, HCG. “Our communications are prioritized to add value to their journey.” HCG maintains connection with patients post-visit, supporting them through recovery. Data-Driven Decision Making By integrating data from Health Information Systems, Electronic Medical Records, and other platforms, HCG leverages operational intelligence to enhance decision-making. Insights from dashboards and reports improve patient satisfaction and care quality. Marketing Cloud Intelligence helps optimize campaigns, engagement channels, and team performance. Expanding Digital Transformation HCG plans to unlock additional Salesforce features, such as AI-driven call center operations with Einstein. Further integrations with lab and radiology systems and patient care apps will expand the Patient 360 view. “Our goal is to become an oncology knowledge company, up-to-date on treatment protocols and patient needs,” says Dey. Jain adds, “When people think of cancer care, they should think of HCG.” HCG’s integration of Salesforce is setting a new standard in personalized cancer care, ensuring patients receive the best possible support and treatment throughout their 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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 15, 2024in Salesforce Health and Life Sciences Solutions, Salesforce Implementation Services, Salesforce Marketing Cloud, Salesforce Sales Cloud, Salesforce Service Cloud
AI Yes Gen AI No
12Jun

AI Yes Gen AI No

The race in generative AI may conclude sooner than anticipated, despite it barely starting for most. Consider the role of generative AI as a supportive assistant, aiding users across dashboards, messaging platforms, and datasets by summarizing documents, chats, and answering queries in natural language. AI Yes Gen AI No. At PegaWorld, significant attention was drawn to Pega’s Knowledge Buddy, an assistant integrated with LLM, specifically OpenAI on Azure, tailored to organization-specific data. CTO Don Schuerman emphasized the practicality of Pega’s solution: “Knowledge Buddy solves many enterprise problems, but it’s not the only RAG-based product out there. Everyone’s got one.” Indeed, major companies each boast their AI assistants: Adobe with AI Assistant, Salesforce with Einstein Copilot, Microsoft with Copilot, HubSpot with various AI assistants, Oracle’s Digital Assistant, and SAP’s Joule. Possessing an AI assistant is now a necessity, not a differentiator, as it has become standard across competitors. Generative AI tools like text and image generators have garnered public interest due to their accessibility. For instance, tools like Google Gemini enable anyone to create, blurring the lines between creator roles. The prevalence of generative AI across over 14,000 martech products is notable, exemplified by MarTechBot, which leverages AI to answer queries and generate images based on MarTech’s vast archive. While text and image generation capabilities rapidly advance, offering these tools is becoming a norm rather than a novelty. Soon, lacking these capabilities will be akin to a supermarket not selling eggs. Does this signify the end of the AI arms race? While generative AI will continue to evolve, it is becoming ubiquitous as a fundamental requirement. However, it’s crucial to distinguish the generative AI arms race from the broader AI landscape. Non-generative AI, such as predictive analytics and classification AI used in digital asset management systems, plays a critical role. This statistical AI analyzes data at scale to derive insights, recommend products, or guide customers through complex journeys. Pega exemplifies this with its AI-driven decisioning and workflow automation, predicting optimal actions for specific challenges, which is distinct from the generative AI focus seen in competitors like Salesforce, Adobe, and Oracle. Looking forward, while generative AI will permeate everyday applications, the true transformative AI for enterprises might lie in refined predictive AI or fully autonomous AI capable of unsupervised business decision-making. In conclusion, while text and image generation will become commonplace, their revolutionary impact may wane compared to the potential of other AI applications poised to redefine enterprise capabilities. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 12, 2024in Generative AI
Democratize Data with Einstein Copilot for Tableau
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 Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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June 10, 2024in Einstein 1 Platform, Salesforce Tableau

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