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AI and Big Data
31Aug

AI and Big Data

Over the past decade, enterprises have accumulated vast amounts of data, capturing everything from business processes to inventory statistics. This surge in data marked the onset of the big data revolution. However, merely storing and managing big data is no longer sufficient to extract its full value. As organizations become adept at handling big data, forward-thinking companies are now leveraging advanced analytics and the latest AI and machine learning techniques to unlock even greater insights. These technologies can identify patterns and provide cognitive capabilities across vast datasets, enabling organizations to elevate their data analytics to new levels. Additionally, the adoption of generative AI systems is on the rise, offering more conversational approaches to data analysis and enhancement. This allows organizations to extract significant insights from information that would otherwise remain untapped in data stores. How Are AI and Big Data Related? Applying machine learning algorithms to big data is a logical progression for companies aiming to maximize the potential of their data. Unlike traditional rules-based approaches that follow explicit instructions, machine learning systems use data-driven algorithms and statistical models to analyze and detect patterns in data. Big data serves as the raw material for these systems, which derive valuable insights from it. Organizations are increasingly recognizing the benefits of integrating big data with machine learning. However, to fully harness the power of both, it’s crucial to understand their individual capabilities. Understanding Big Data Big data involves extracting and analyzing information from large quantities of data, but volume is just one aspect. Other critical “Vs” of big data that enterprises must manage include velocity, variety, veracity, validity, visualization, and value. Understanding Machine Learning Machine learning, the backbone of modern AI, adds significant value to big data applications by deriving deeper insights. These systems learn and adapt over time without the need for explicit programming, using statistical models to analyze and infer patterns from data. Historically, companies relied on complex, rules-based systems for reporting, which often proved inflexible and unable to cope with constant changes. Today, machine learning and deep learning enable systems to learn from big data, enhancing decision-making, business intelligence, and predictive analysis. The strength of machine learning lies in its ability to discover patterns in data. The more data available, the more these algorithms can identify patterns and apply them to future data. Applications range from recommendation systems and anomaly detection to image recognition and natural language processing (NLP). Categories of Machine Learning Algorithms Machine learning algorithms generally fall into three categories: The most powerful large language models (LLMs), which underpin today’s widely used generative AI systems, utilize a combination of these methods, learning from massive datasets. Understanding Generative AI Generative AI models are among the most powerful and popular AI applications, creating new data based on patterns learned from extensive training datasets. These models, which interact with users through conversational interfaces, are trained on vast amounts of internet data, including conversations, interviews, and social media posts. With pre-trained LLMs, users can generate new text, images, audio, and other outputs using natural language prompts, without the need for coding or specialized models. How Does AI Benefit Big Data? AI, combined with big data, is transforming businesses across various sectors. Key benefits include: Big Data and Machine Learning: A Synergistic Relationship Big data and machine learning are not competing concepts; when combined, they deliver remarkable results. Emerging big data techniques offer powerful ways to manage and analyze data, while machine learning models extract valuable insights from it. Successfully handling the various “Vs” of big data enhances the accuracy and power of machine learning models, leading to better business outcomes. The volume of data is expected to grow exponentially, with predictions of over 660 zettabytes of data worldwide by 2030. As data continues to amass, machine learning will become increasingly reliant on big data, and companies that fail to leverage this combination will struggle to keep up. Examples of AI and Big Data in Action Many organizations are already harnessing the power of machine learning-enhanced big data analytics: Conclusion The integration of AI and big data is crucial for organizations seeking to drive digital transformation and gain a competitive edge. As companies continue to combine these technologies, they will unlock new opportunities for personalization, efficiency, and innovation, ensuring they remain at the forefront of their industries. 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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August 31, 2024in Data, Enterprise, Generative AI, Google, Salesforce, Technology
Mission Control Salesforce
30Aug

Mission Control Salesforce

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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August 30, 2024in Salesforce
Detecting the Hot Chatbot
30Aug

Detecting the Hot Chatbot

All the tech giants are eager to prove their chatbot is the hottest in the market. Like wild stallions fighting over the mares, Google, Meta, Microsoft, and OpenAI are competing to show that their AI models have the most momentum. Companies with built-in AI like Salesforce occupy a broader sector. Detecting the Hot Chatbot is the challenge for the consumer. Why Detecting the Hot Chatbot Matters These companies have poured immense resources—both talent and money—into developing their models and adding new features. Now, they’re keen to showcase that these investments are yielding results. What’s Happening In the past few dayss, several major players have released new usage statistics: The Big Picture Generative AI is still in its early stages, and the entire industry faces the challenge of proving that these products deliver real value—whether by capturing market share from the lucrative search industry or by helping companies save money through increased productivity. How are you Detecting the Hot Chatbot. In the short term, however, everyone is eager to show they’re leading the pack. TV commercials for generative AI are now common, with Meta, Google, and Microsoft all airing spots, although the effectiveness of these ads varies. Some companies even boast that their commercials were created using AI—not necessarily the most convincing selling point. Between the Lines The competition isn’t just about consumer popularity; it’s also spilling over into the battle to secure business customers. On Wednesday’s earnings call, Salesforce CEO Marc Benioff made a point of distinguishing Salesforce’s new Agentforce AI sales assistant from Microsoft’s Copilot offerings. “This is not Copilot,” Benioff said. “So many customers are disappointed with what they bought from Microsoft Copilot because they’re not getting the accuracy and response they want. Microsoft has let down many customers with AI.” Microsoft quickly responded in a comment to CNBC. “We are hearing something quite different from our Copilot for Microsoft 365 customers,” said corporate VP Jared Spataro. “When I talk to CIOs directly, and if you look at recent third-party data, organizations are betting on Microsoft for their AI transformation.” The Bottom Line The competition is heating up as tech giants vie to prove they have the upper hand in the AI race and the Hot Chatbot. Customers will ultimately decide. 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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August 30, 2024in Generative AI, Salesforce Financial Services Solutions, Salesforce for Nonprofits, Salesforce Health and Life Sciences Solutions, Salesforce Hospitality Travel and Transportation, Salesforce Implementation Services, Salesforce Manufacturing Automotive Energy, Salesforce Public Sector Solutions
guide to RAG
30Aug

Tectonic Guide to Rag Part 2

The first notable change in the field of language models is the significant expansion of context window sizes and a reduction in token costs. For instance, Anthropic’s largest model, Claude, has a context window exceeding 200,000 tokens, while recent reports indicate that Gemini’s context window can reach up to 10 million tokens. Under such circumstances, Retrieval-Augmented Generation (RAG) may no longer be necessary for many tasks, as all required data can be accommodated within the expanded context window. Several financial and analytical projects have already demonstrated that tasks can be solved without needing a vector database as intermediate storage. This trend of reducing token costs and increasing context window sizes is likely to continue, potentially decreasing the need for external mechanisms in LLMs, although they are still relevant for the time being. If the context window remains insufficient, methods for summarization and context compression have been introduced. LangChain, for example, offers a class called ConversationSummaryMemory to address this challenge. pythonCopy codellm = OpenAI(temperature=0) conversation_with_summary = ConversationChain( llm=llm, memory=ConversationSummaryMemory(llm=OpenAI()), verbose=True ) conversation_with_summary.predict(input=”Hi, what’s up?”) Knowledge Graphs As the volume of data continues to grow, navigating through it efficiently becomes increasingly critical. In certain cases, understanding the structure and attributes of data is essential for effective use. For example, if the data source is a company’s wiki, an LLM might not recognize a phone number unless the structure or metadata indicates that it’s the company’s contact information. Humans can infer meaning from conventions, such as the subdirectory “Company Information,” but standard RAG may miss such connections. This challenge can be addressed by Knowledge Graphs, also known as Knowledge Maps, which provide both raw data and metadata that illustrates how different entities are interconnected. This method is referred to as Graph Retrieval-Augmented Generation (GraphRAG). Graphs are excellent for representing and managing structured, interconnected information. Unlike vector databases, they excel at capturing complex relationships and attributes among diverse data types. Creating a Knowledge Graph The process of creating a knowledge graph typically involves collecting and structuring data, which requires expertise in both the subject matter and graph modeling. However, LLMs can automate a significant portion of this process by analyzing textual data, identifying entities, and recognizing their relationships, which can then be represented in a graph structure. In many cases, an ensemble of vector databases and knowledge graphs can improve accuracy, as discussed previously. For example, search functionality might combine keyword search through a regular database (e.g., Elasticsearch) and graph-based queries. LangChain can also assist in extracting structured data from entities, as demonstrated in this code example: pythonCopy codedocuments = parse_and_load_data_from_wiki_including_metadata() graph_store = NebulaGraphStore( space_name=”Company Wiki”, tags=[“entity”] ) storage_context = StorageContext.from_defaults(graph_store=graph_store) index = KnowledgeGraphIndex.from_documents( documents, max_triplets_per_chunk=2, space_name=space_name, tags=[“entity”] ) query_engine = index.as_query_engine() response = query_engine.query(“Tell me more about our Company”) Here, searching is conducted based on attributes and related entities, instead of similar vectors. If set up correctly, metadata from the company’s wiki, such as its phone number, would be accessible through the graph. Access Control One challenge with this system is that data access may not be uniform. For instance, in a wiki, access could depend on roles and permissions. Similar issues exist in vector databases, leading to the need for access management mechanisms such as Role-Based Access Control (RBAC), Attribute-Based Access Control (ABAC), and Relationship-Based Access Control (ReBAC). These access control methods function by evaluating paths between users and resources within graphs, such as in systems like Active Directory. To ensure the integrity of data during the ingestion phase, metadata related to permissions must be preserved in both the knowledge graph and vector database. Some commercial vector databases already have this functionality built in. Ingestion and Parsing Data needs to be ingested into both graphs and vector databases, but for graphs, formatting is especially critical since it reflects the data’s structure and serves as metadata. One particular challenge is handling complex formats like PDFs, which can contain diverse elements like tables, images, and text. Extracting structured data from such formats can be difficult, and while frameworks like LLama Parse exist, they are not always foolproof. In some cases, Optical Character Recognition (OCR) may be more effective than parsing. Enhancing Answer Quality Several new approaches are emerging to improve the quality of LLM-generated answers: While these advancements in knowledge graphs, access control, and retrieval mechanisms are promising, challenges remain, particularly around data formatting and parsing. However, these methods continue to evolve, enhancing LLM capabilities and efficiency. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

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August 30, 2024in Data
Connected Vehicles
29Aug

Connected Vehicles

Revolutionizing the Automotive Industry: Salesforce’s Connected Car App The automotive industry has always been a beacon of innovation, consistently pushing the boundaries to enhance the driving experience. From the iconic Model T and the assembly line to today’s electric and autonomous vehicles, the evolution of automobiles has been driven by an unyielding pursuit of progress. I actually purchased a new-to-me car today, and with the connected vehicle on the horizon I’m kind of glad I’ll be able to upgrade in a couple years. Bluetooth and back up cameras are great. But a car that can tell the dealership to get me on the horn before some automotive calamity occurs? The future is here, my friends. Connected Vehicles for Better Experiences Now, as digital transformation reshapes industries, a new chapter is emerging in automotive innovation: the connected car. Leading this charge is Salesforce, a global powerhouse in customer relationship management (CRM), with the introduction of its groundbreaking Connected Car App, poised to redefine in-car experiences for both drivers and passengers. From my personal buying experience today, the car business could use some customer relationship management! The Future of In-Car Connectivity Salesforce’s Connected Car App is more than just a technological enhancement; it represents a fundamental shift in how we interact with our vehicles. By leveraging Salesforce’s Customer 360 platform, this app creates personalized, engaging experiences that go far beyond traditional automotive features. The Connected Car App is designed to make every journey more intuitive and efficient, offering real-time insights and services tailored to the unique needs of each driver. Whether it’s maintenance alerts, optimized route suggestions based on traffic, or personalized entertainment options, the app transforms the car into a truly smart companion on the road. A GPS feature? I guess I can plan on deleting Waze off my phone in the near future! Powered by Salesforce Customer 360 At the heart of the Connected Car App is Salesforce’s Customer 360 platform, which delivers a comprehensive, 360-degree view of each customer. This integration ensures that the app provides tailored experiences based on a deep understanding of the driver’s preferences, habits, and history. It isn’t going to just know you by a vehicle loan number, a VIN number, or even just an email address. For instance, a driver who frequently takes long road trips might receive customized recommendations for rest stops, dining options, and attractions along their route. Meanwhile, commuters could benefit from real-time updates on traffic, weather, and parking availability. The app’s ability to anticipate and respond to the driver’s needs in real time distinguishes it from traditional in-car systems. I can just hear my car now, advising me it has been one hour since I stopped for coffee, and she’s worried about my sanity. Enhancing Customer Loyalty and Satisfaction with Connected Vehicles The Connected Car App offers significant potential to boost customer loyalty and satisfaction. By delivering a personalized driving experience, automakers can strengthen relationships with customers, transforming each driving journey into an opportunity to build brand loyalty. If Toyota is suddenly going to treat me like Shannan Hearne instead of customer # xxxxx would be ecstatic. Additionally, the app’s capability to collect and analyze data in real time opens new avenues for automakers to engage with their customers. Predictive maintenance reminders, targeted promotions, and special offers are just a few examples of how the app fosters a deeper connection between the brand and the driver. Oh, yeah. My connected vehicle app is DEFINITELY going to be talking to me about changing my oil (I’m not exactly diligent), how great the latest model of Toyota is (I drove a Corolla for 18 years and have also owned a Tacoma, a Tundra, and a Prius), and if it would add coffee coupons I would be golden. A New Era of Automotive Innovation Salesforce’s Connected Car App marks a pivotal moment in the automotive industry’s digital transformation. As vehicles become increasingly connected, the opportunities for innovation are boundless. Salesforce is at the forefront with a solution that not only enhances the driving experience but also empowers automakers to build stronger, more meaningful relationships with their customers. In a world where customer expectations are constantly growing, the Connected Car App is a game-changer. Customers, even car owners, expect their brands to know them and recognize them. By integrating Salesforce’s CRM capabilities directly into vehicles, the app creates a seamless, personalized experience that stands out. As we look ahead, it’s clear that the Connected Car App is just the beginning of an exciting new era of automotive innovation. As a marketer at heart and a technologist by trade, I’m really excited about the potential here. Connected Vehicle: A Unified Digital Foundation Salesforce’s Connected Vehicle platform provides automakers with a unified, intelligent digital foundation, enabling them to reduce development time and roll out features and updates faster than ever before. This platform allows seamless integration of vehicle, Internet of Things (IoT), driver, and retail data from various sources, including AWS IoT FleetWise and Snapdragon® Car-to-Cloud Connected Services Platform, to enhance driver experiences and ensure smooth vehicle operation. Can you imagine a smart app like the Connected Vehicle talking to your loyalty apps for gas stations, convenience stores, and grocery stores? I would be driving down the interstate and the app will tell me there is a Starbucks ahead AND I have a 10% off coupon. Automakers and mobility leaders like Sony Honda Mobility are already exploring the use of Connected Vehicle to deliver better experiences for their customers. The platform’s ability to access and integrate data from any source in near real time allows automakers to personalize driver experiences, in-car offers, and safety upgrades. Why It Matters By 2030, every new vehicle sold will be connected, and the advanced, tech-driven features they provide will be increasingly important to consumers. A recent Salesforce survey revealed that drivers already consider connected features to be nearly as important as a car’s brand. Connected Vehicle accelerates this evolution, enabling automakers to immediately deliver branded, customized experiences tailored to

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August 29, 2024in Salesforce Implementation Services, Salesforce Managed Services, Salesforce Manufacturing Automotive Energy
Event Monitoring Analytics App
29Aug

Event Monitoring Analytics App

Event Monitoring Analytics App The Event Monitoring Analytics App integrates with your event monitoring and setup audit trail data to provide insights into user and organizational behavior. It’s a built-in tool for exploring your monitoring data within Salesforce, featuring easy setup, prebuilt dashboards, and datasets to help you quickly analyze your data. This app allows you to identify suspicious activities, slow page performance, and user adoption issues efficiently. Required Editions and User Permissions User Permissions Needed: As part of the Event Monitoring service, the Event Monitoring Analytics App allows you to upload and access data included in your subscription. Ensure that users only access the data provided and do not use the app to upload or access other data. Salesforce may monitor such usage. Note that the app is available in English only, and if you have a CRM Analytics Platform license, you can view dashboards within the Event Monitoring Analytics app. Keep in mind that hourly event log file integration is not supported, with data refreshed daily. Although data retention lasts 30 days, Event Monitoring doesn’t guarantee the availability of 30 days’ worth of data per dataset upload into CRM Analytics. The app is subject to CRM Analytics limits. Event Monitoring log files serve as a source of truth but are not a system of record for user activity. Data loss may occur during Salesforce site switches, instance refreshes, or unplanned outages. For instance, a production instance move might create gaps in event log data. Salesforce strives to maintain data integrity and minimize loss by replicating event logs during site switches or instance refreshes. Setting Up Salesforce Permissions for the Event Monitoring Analytics App Creating and Sharing the Event Monitoring Analytics App Once permissions are set, configure the app. You can choose between two configurations: Upgrading the Event Monitoring Analytics App Stay current with the latest features by upgrading to the newest version of the EM Analytics App whenever a new release is available. Scheduling the Daily Dataflow Schedule a daily refresh to update the dataflow, ensuring that your data is current before you begin analysis with Event Monitoring Analytics. Prebuilt Dashboards The app includes prebuilt dashboards for immediate data exploration. Data from your organization is automatically loaded into the app’s datasets as it is generated. Understanding App Terminology Familiarize yourself with the metrics and terms used in the Event Monitoring App to make the most of its 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 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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August 29, 2024in Salesforce
Slack and AI and Time
29Aug

Slack and AI and Time

The longer you stay at an organization, the more Slack channels you find yourself entangled in, making it increasingly difficult to cut through the noise. Even at Fortune, the accumulation of channels and the platform’s familiar clicking notifications are ever-present, with writers engaging in conversations ranging from breaking news to local food tips and football discussions. Managing Slack can feel like a job in itself. However, as Slack incorporates AI to streamline tasks and minimize busy work, users now face the challenge of deciding how to use their newly freed-up time. Slack’s AI introduces a new time dilemma. By simplifying the once time-consuming task of sifting through messages for critical information, the platform’s new “recap” function provides workers with a summary of incoming messages and highlights what’s most important. Companies like OpenAI, Spotify, and Uber are also optimizing their workflows by integrating Slack with other tools, such as the work management app Asana. AI is being utilized in various ways across Europe as well. Slack CEO Denise Dresser, seven months into her role at the Salesforce-owned messaging app, noted a Workforce Labs study showing that U.K. users are leveraging Slack’s writing assistant feature, while French users are primarily using it for summarization. In Germany, 36% of desk workers are employing AI to automate their tasks more regularly than their European counterparts. The initial results are promising. Slack reports that its AI tool has saved users an average of 97 minutes per week in administrative time. However, this has led to a new challenge: many workers are unsure how to best use their extra time. “They were still focusing on the work of work, which means we haven’t yet made the operational and mindset shift to start doing new things,” Dresser shared with Fortune, reflecting on the Workforce Labs findings. This situation highlights the next significant discussion about AI’s impact on work—how it will enhance productivity, replace roles, and potentially create new ones. There’s also ongoing debate about whether AI could render the traditional five-day workweek obsolete, offering people the option to enjoy more leisure time or take on more work. Dresser, for her part, plans to use her freed-up time to engage more with clients and is optimistic about AI’s potential to revolutionize workflows. “I personally think AI is going to be one of the most significant enhancers of human productivity we’ve ever seen, and I believe it will truly unlock human potential,” Dresser says. “It will likely create more jobs. We may see entirely new roles emerge, and I think that’s going to be exciting.” 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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August 29, 2024in Salesforce Slack
Salesforce Climbs on Back of AI
29Aug

Salesforce Climbs on Back of AI

Shares of Salesforce (CRM.N) climbed approximately 4% on Thursday as investors responded positively to the customer relationship management software maker’s strong quarterly performance and its strategic focus on artificial intelligence to fuel growth. Salesforce has been making significant investments to integrate AI technologies into its existing products, including its messaging platform Slack, to enhance functionality and attract more customers. “We continue to see Salesforce as an underappreciated AI winner due to its unique data and early success in developing and deploying GenAI agents,” noted Goldman Sachs analyst Kash Rangan. Despite concerns from Wall Street that reduced cloud spending might impact Salesforce in a challenging economy, the software-as-a-service (SaaS) company exceeded expectations with better-than-anticipated revenue, profit, and margins in the second quarter. Additionally, Salesforce raised its profit forecast for the fiscal year ending January 2025, as margins continue to improve, largely due to restructuring efforts undertaken last year. The stock is currently trading at 24.49 times Wall Street’s profit expectations, compared to 52.11 for SaaS peer ServiceNow and 13.30 for cloud contact center firm Five9 (FIVN.O), according to LSEG data. If the gains hold, Salesforce could add about billion to its market capitalization, bringing its valuation to 8 billion as of Wednesday’s close. However, Barclays analyst Raimo Lenschow cautioned, “We think these results alone are not sufficient to drive a sustainable rally from here. For that, we need additional catalysts, which could come with the new AI solutions,” set to be unveiled at Dreamforce and launched in October. Some analysts suggest that ongoing growth in the coming quarters may be driven by Salesforce’s customer support platform, Agentforce, which has yet to be commercially released. 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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August 29, 2024in Salesforce
360 SMS
29Aug

360 SMS

360 SMS: Revolutionizing Communication on Salesforce Built natively on the Salesforce platform, 360 SMS is a powerful messaging app that enables seamless communication with customers, prospects, and business partners across the globe. Whether sending single or bulk SMS/MMS, this app empowers teams to connect effectively while leveraging Salesforce’s capabilities. Its innovative features, such as automation, SMS templates, and URL shortening, make it a top choice for businesses seeking efficient and cost-effective communication. 360 SMS stands out as the “Top-Ranked Salesforce Messaging App” due to its versatility, ease of use, and advanced features. By enabling businesses to reach more clients with concise, impactful messages rather than long emails, 360 SMS drives engagement and enhances customer interactions. Additionally, businesses can save on SMS costs by utilizing shortened URLs, maximizing the efficiency of their Salesforce messaging campaigns. Build No-Code Salesforce Chatbots with 360 SMS Creating Salesforce chatbots no longer requires coding expertise or reliance on developers. With 360 SMS, businesses can easily configure no-code chatbots to automate tasks such as lead qualification, customer support, and answering FAQs. This streamlined solution helps enhance customer engagement and optimize workflows. The Salesforce chatbot integrates seamlessly with Salesforce, ensuring that all interactions are logged and accessible within the CRM for easy tracking and follow-up. It supports consistent, efficient communication across multiple touchpoints, enabling businesses to scale their customer support and sales operations effortlessly. Beyond Salesforce: No-Code Chatbots for Any CRM or Industry 360 SMS goes beyond Salesforce, offering drag-and-drop tools to create custom texting chatbots for other CRMs and industries. These chatbots support 11 built-in communication channels, allowing businesses to centralize their interactions and automate two-way communication effectively. Easily Configure Salesforce WhatsApp Chatbots WhatsApp, one of the most widely used communication platforms, is now easier than ever to integrate with Salesforce using 360 SMS. This no-code WhatsApp chatbot solution enables businesses to automate tasks such as customer support, lead qualification, and sales inquiries—all directly within the Salesforce ecosystem. By automating routine tasks like answering FAQs or guiding customers through product inquiries, businesses can improve response times and maintain meaningful customer connections. The chatbot logs every conversation in Salesforce, ensuring full visibility for tracking and follow-up. With 360 SMS, setting up a WhatsApp chatbot becomes a hassle-free process, delivering powerful automation without the need for coding expertise. 360 SMS: Your Partner in Smarter, Scalable CommunicationWhether you’re looking to optimize customer support, drive engagement, or enhance sales, 360 SMS empowers businesses with the tools they need to succeed—all while reducing costs and boosting productivity. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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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August 29, 2024in Salesforce
Salesforce Einstein SDR and Sales Coach Agents
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 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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August 29, 2024in Agentforce Platform, Generative AI, Salesforce Einstein
Slack Operating System
29Aug

Slack Operating System

Slack Advances Its Work Operating System with Enhanced Automation Capabilities With 3,000 workflows created this year alone, Rivian relies on Slack’s automation features to save time and boost team productivity. Slack Operating System are making a world of difference. Salesforce has announced new innovations in Slack, making it easier for users to build automations regardless of their technical expertise. Key Updates: Why This Matters: With 71% of business leaders under pressure to increase team productivity, and 70% of IT leaders concerned that rising business demands could stifle innovation, automation is crucial. A recent survey reveals that 77% of users believe automating routine tasks would significantly boost productivity. Companies need user-friendly, no-code automation solutions that enhance productivity without overburdening IT departments. A Closer Look: Slack’s Perspective: Rob Seaman, Chief Product Officer at Slack, stated, “At Slack, one of our product principles is ‘don’t make me think.’ We’re applying that principle to the traditionally technical and time-consuming area of automation, making it an intuitive and delightful productivity driver for everyone. These new features make Slack even more powerful, giving both developers and end users the tools they need to automate any business process across their work apps.” Customer Reaction: “Automation is a core capability that increases productivity and saves time for Rivian employees when doing repetitive work. Workflow Builder allows Slack users to easily create no-code automation at any experience level. Our Slack users created approximately 3,000 workflows in 2024, with heavy adoption in Production, Operation, and Service groups,” said Anoop Narang, Head of Digital Workplace & Solutions at Rivian. Availability: The enhancements to Workflow Builder are now generally available to all customers. Other app updates you might have missed. Slack 4.40.120 August 27, 2024 Bug Fixes Slack 4.39.95 July 29, 2024 Bug Fixes Slack 4.39.93 July 18, 2024 Bug Fixes Slack 4.39.90 July 8, 2024 Bug Fixes Slack 4.39.89 June 25, 2024 Bug Fixes 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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August 29, 2024in Salesforce Slack
Key Insights on Navigating AI Compliance
29Aug

Key Insights on Navigating AI Compliance

Grammarly’s AI Regulatory Master Class: Key Insights on Navigating AI Compliance On August 27, 2024, Grammarly hosted an AI Regulatory Master Class webinar, featuring Scout Moran, Senior Product Counsel, and Alan Luk, Head of Governance, Risk, and Compliance (GRC). The event provided a comprehensive overview of the current and upcoming AI regulations affecting organizations’ AI strategies, along with guidance on evaluating AI solution providers, including those offering generative AI. While the webinar avoided deep legal analysis and did not serve as legal advice, Moran and Luk spotlighted key regulations emerging from both the U.S. and European Union (EU), highlighting the rapid response of regulatory bodies to AI’s growth. Overview of AI Regulations The AI regulatory landscape is changing quickly. A May 2024 report from law firm Davis & Gilbert noted that nearly 200 AI-related laws have been proposed across various U.S. states. Grammarly’s presentation emphasized the need for organizations to stay updated, as both U.S. and EU regulations are shaping the future of AI governance. The EU AI Act: A New Regulatory Framework The EU AI Act, which took effect on August 2, 2024, applies to AI system providers, importers, distributors, and others connected to the EU market, regardless of where they are based. As Moran pointed out, the Act is designed to ensure AI systems are deployed safely. Unsafe systems may be removed from the market, establishing a regulatory baseline that individual EU countries can strengthen with more stringent measures. However, the Act does not fully define “safety.” Legal experts Hadrien Pouget and Ranj Zuhdi noted that while safety requirements are crucial to the Act, they are currently broad, allowing room for further development of standards. The Act prohibits certain AI practices, such as manipulative systems, those exploiting personal vulnerabilities, and AI used to assess or predict criminal risk. AI systems are categorized into four risk levels: unacceptable, high-risk, limited risk, and minimal risk. High-risk systems—such as those in critical infrastructure or public services—face stricter regulation, while minimal-risk systems like spam filters have fewer requirements. Full enforcement of the Act will begin in 2025. U.S. AI Regulations Unlike the EU, the U.S. focuses more on national security than consumer safety in its AI regulation. The U.S. Executive Order on Safe, Secure, and Trustworthy AI addresses these concerns. At the state level, Moran highlighted trends such as requiring clear disclosure when interacting with AI and giving individuals the right to opt out of having their data used for AI model training. States like California and Utah are leading the way with specific laws (SB-1047 and SB-149, respectively) addressing accountability and disclosure in AI use. Key Considerations When Selecting AI Vendors Moran stressed the importance of thoroughly vetting AI vendors. Organizations should ensure vendors meet cybersecurity standards, such as SOC 2, and clearly define how their data will be used, particularly in training large language models (LLMs). “Eyes off” agreements, which prevent vendor employees from accessing customer data, should also be considered. Martha Buyer, a frequent contributor to No Jitter, emphasized verifying the originality of AI-generated content from providers like Grammarly or Microsoft Copilot. She urged caution in ensuring the ownership and authenticity of AI-assisted outputs. The Importance of Strong Third-Party Agreements Luk highlighted Grammarly’s commitment to data privacy, noting that the company neither sells customer data nor uses it to train models. Additionally, Grammarly enforces agreements preventing its third-party LLM providers from doing so. These contractual protections are crucial for safeguarding customer data. Organizations should also ensure third-party vendors adhere to strict guidelines, including securing customer data, encrypting it, and preventing unauthorized access. Vendors should maintain updated security certifications and manage risks like bias, which, while not entirely avoidable, must be actively addressed. Staying Ahead in a Changing Regulatory Environment Both Moran and Luk stressed the importance of ongoing monitoring. Organizations need to regularly reassess whether their vendors comply with their data-sharing policies and meet evolving regulatory standards. As AI technology and regulations continue to evolve, staying informed and agile will be critical for compliance and risk mitigation. In conclusion, organizations adopting AI-powered solutions must navigate a dynamic regulatory environment. As AI advances and regulations become more comprehensive, remaining vigilant and asking the right questions will be key to ensuring compliance and reducing risks. 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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August 29, 2024in Generative AI

When to use Flow

When and Why Should You Use a Flow in Salesforce? Flow is Salesforce’s premier tool for creating configurable automation and guided user experiences. If you need to build a process that doesn’t require the complexity of Apex code, Flow should be your go-to solution. It’s versatile, user-friendly, and equipped to handle a wide range of business automation needs. Legacy tools like Process Builder and Workflow Rules are being phased out, with support ending in December 2025. While you may choose to edit existing automations in these tools temporarily, migrating to Flow should be a top priority for future-proofing your Salesforce org. Capabilities of FlowFlows allow you to: When Should You Avoid Using a Flow?Although Flow is powerful, it’s not the right choice in every scenario. Here are situations where it may not be suitable: Creating a Flow in Salesforce Pro Tips for Flow Building Flow vs. Apex: Which to Choose?Flows are simpler, faster to deploy, and accessible to admins without coding expertise. Apex, on the other hand, is suited for complex use cases requiring advanced logic or integrations. Here’s when Apex should be used instead: Why Flows Are the FutureSalesforce has positioned Flow as the central automation tool by deprecating Workflow Rules and Process Builder. With every release, Flow’s capabilities expand, making it easier to replace tasks traditionally requiring Apex. For instance: Final ThoughtsSalesforce admins should prioritize building and migrating automation to Flow. It’s a scalable and admin-friendly tool that ensures your org stays up-to-date with Salesforce’s evolving ecosystem. Whether you’re automating basic processes or tackling complex workflows, Flow provides the flexibility to meet your needs. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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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August 28, 2024in Data, Salesforce Flow
Salesforce Button Order
28Aug

Salesforce Button Order

The Importance of Button Order in Salesforce Instances How much thought is given to the order of buttons in a Salesforce instance page layout? Or even for specific user groups? It is common to see varying button orders on pages for different objects, which can be quite frustrating. Salesforce Button Order UX ideas! This insight advocates for careful consideration of the placement of these seemingly simple but crucial buttons. Buttons play a significant role in user interactions, so organizing them effectively can reduce mental effort and clicks for colleagues. For clarity, this discussion refers to “buttons,” but depending on the Salesforce context, these may also be known as “actions” or “quick actions.” Regardless of the terminology, this refers to the elements at the top right of a page or within the Chatter feed and Activity Feed that users click to perform actions. Buttons at the top of the page typically appear as square icons. In Chatter and Activities, they might be displayed as tab-like or button-like elements. The Activity Feed, for example, may show buttons as part of a tabbed view or as individual buttons, sometimes stacked under carats for similar actions. Consistent Button Order The order of buttons should be consistent across objects whenever possible. Although objects with additional or unique buttons may necessitate deviations from this principle, maintaining a standard order for basic buttons like Edit, Printable View, Clone, and Delete is beneficial. Standard Action Buttons: For additional standard buttons like Submit for Approval or Change Owner, use the following principles: Custom Buttons Custom buttons add complexity to design choices. Often, custom buttons include emojis, which can affect visual consistency but add a fun element. While custom buttons may have longer labels than standard ones, their placement generally prioritizes visibility. Custom buttons are often placed before the Edit button to ensure prominence, although Edit may remain first if necessary. Example: Custom buttons, such as a “🧮 Request an Invoice” button, might be placed before standard buttons. Display Number of Buttons Consider the number of buttons visible on a page. Avoid relying solely on Salesforce’s default settings. Display four, five, or even six buttons if they are needed and can fit on the screen. Fewer buttons might be preferable to hide less frequently used options like Delete under a dropdown. Keep in mind that users might overlook dropdown options, so training and reminders are important. Note: In the Highlights Panel component settings, consider hiding the Follow/Unfollow button if it is rarely used. Actions in Feeds The same principles apply to buttons in Chatter and Activity Feeds, though there are usually fewer buttons to manage. Controlling Button Order To manage button placement: Take Charge of Your Design With these tools and guidelines, take control of button and action placement on your Salesforce pages to enhance usability and efficiency. 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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August 28, 2024in Salesforce
AI Capabilities for Nonprofit Cloud
28Aug

AI Capabilities for Nonprofit Cloud

Nonprofit organizations must ensure that every dollar they raise is not only accounted for but also allocated in ways that best support their mission. With its ability to streamline processes, optimize outcomes, and enhance data transparency, AI is poised to significantly impact the nonprofit sector. Salesforce has introduced new AI capabilities for Nonprofit Cloud designed to help organizations harness AI-driven decision-making and maximize their impact. AI-Powered Proposals and Summaries The latest AI and data enhancements in Nonprofit Cloud are designed to boost efficiency, personalize donor engagement, and ultimately increase fundraising opportunities. Built on the Einstein 1 Platform, Salesforce has introduced the following features: Closing Thoughts “Every nonprofit wants to provide the best experience for donors, volunteers, board members, staff — and most importantly, the people and causes they serve,” says Lori Freeman, VP & Global GM of Nonprofit at Salesforce. “But they have a lot to accomplish with limited resources. With industry-specific AI and data tools, Salesforce is helping nonprofits boost productivity by augmenting staff with AI, using data more effectively to build deeper relationships with their stakeholders, and ultimately, raising the funds needed to meet their mission.” The nonprofit sector stands to gain significantly from GenAI. By building on its existing Nonprofit Cloud, Salesforce addresses key challenges within the industry. What’s particularly noteworthy about these updates is the duality of outcomes supported by GenAI: it not only simplifies the tracking and sharing of campaign metrics, enabling better-informed decision-making, but it also enhances the personalization of donor engagement. While the ultimate goal is to increase funding, these advancements also underscore GenAI’s flexibility and its potential to rapidly transform organizational operations. Availability 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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August 28, 2024in Einstein 1 Platform, Salesforce for Nonprofits, Salesforce Nonprofit Cloud

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