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Data Cloud Model
08Oct

Data Cloud Model

While many Salesforce products like Sales Cloud, Service Cloud, Education Cloud, Health Cloud, and various industry clouds are built on a common ‘core’ platform that shares the same datastore (an Oracle relational database), Salesforce Data Cloud operates on (Data Cloud Model) a distinct architecture and technology stack. This insight explores the data models and related concepts that underpin the platform. Data Cloud Architecture and Storage Starting with the storage layer, Salesforce Data Cloud integrates multiple services: This combination enables Data Cloud to provide a petabyte-scale data store, overcoming the scalability and performance constraints of traditional relational databases. The physical architecture of Data Cloud is represented as a set of data objects, which form the foundation for how data is ingested, harmonized, and activated within the platform. Key Data Cloud Objects Data Source A Data Source is the initial data layer in Data Cloud, representing an external platform or system from which your data originates. Data Sources can include: Data Stream A Data Stream is an entity extracted from a Data Source, such as ‘Orders’ from Commerce Cloud, ‘Contacts’ from Sales Cloud, or ‘Subscribers’ from Marketing Cloud. Once a Data Source is connected to Data Cloud, Data Streams provide access to these entities and require assignment to a category: Profile, Engagement, or Other. A single Data Source can contain multiple Data Streams. Data Source Object (DSO) Data Streams are ingested into Data Source Objects (DSOs), which serve as temporary staging stores containing data in its raw, native format (e.g., CSV files). Minor transformations can be applied to fields during data ingestion. Data Lake Object (DLO) The next step in the data flow is the Data Lake Object (DLO). DLOs are typed, schema-based, materialized views that reside in the data lake (Amazon S3) and are generally stored as Apache Parquet files. This format, along with Apache Iceberg’s abstraction layer, supports efficient data storage and retrieval. DLOs are the first objects available for inspection and preparation of data, including field mapping and additional transformations. Data Model Object (DMO) Unlike DSOs and DLOs, which use a physical data store, Data Model Objects (DMOs) provide a virtual, non-materialized view into the data lake. Queries against a DMO return results based on the current data snapshot in DLOs, without storing them. DMOs can aggregate attributes from different Data Streams, Calculated Insights, and other sources. DMOs inherit their category from the first DLO mapped to them, and only DLOs with the same category can be mapped to the DMO. Like Salesforce objects, DMOs offer a canonical data model with pre-defined attributes (standard objects), and you can also create custom DMOs (custom objects). DMOs can have one-to-one or many-to-one relationships with other DMOs. Currently, there are 89 standard DMOs in Data Cloud, and this number is growing to support more use cases. DMOs are organized into various subject areas, including: Data Spaces Data Spaces offer logical partitions, allowing data to be segregated between different brands, regions, or departments, without the need for multiple Data Cloud instances. They also align with a Software Development Lifecycle (SDLC), enabling you to stage and test Data Objects in separate environments without affecting production data. Data Sources, Data Streams, and DLOs can be shared across Data Spaces, while DMOs and other platform features are isolated based on user permissions. Final Thoughts With the drop in storage costs and advancements in technology, companies now have vast datasets at their disposal. Every customer interaction, whether it’s a purchase, an email open, or a web page view, can be captured and stored. When properly organized, this data allows businesses to understand customers better, predict their needs, personalize interactions, and much more. However, one of Salesforce’s challenges is that its original platform, built over 20 years ago, was designed for a different era and struggles with handling voluminous, “big” data. Salesforce Data Cloud addresses these limitations with a modern architecture that overcomes the constraints of relational databases. Data Cloud is poised to become the backbone of the Salesforce platform, supporting the data needs of customers and products for the next two decades. If you’re interested in learning how to implement and integrate Salesforce Data Cloud in your organization, following best practices, reach out today. 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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October 8, 2024in Salesforce Data Cloud, Unified Knowledge
Revolution Customer Service with Agentforce
08Oct

Revolution Customer Service with Agentforce

Agentforce stole the spotlight at Dreamforce, but it’s not just about replacing human workers. Equally significant for Service Cloud was the focus on how AI can be leveraged to make agents, dispatchers, and field service technicians more productive and proactive. Join a conversation to unpack the latest Sales Cloud innovations, with a spotlight on Agentforce for sales followed by a Q&A with Salesblazers. During the Dreamforce Service Cloud keynote, GM Kishan Chetan emphasized the dramatic shift over the past year, with AI moving from theoretical to practical applications. He challenged customer service leaders to embrace AI agents, highlighting that AI-driven solutions can transform customer service from delivering “good” benefits to achieving exponential growth. He noted that AI agents are capable of handling common customer requests like tech support, scheduling, and general inquiries, as well as more complex tasks such as de-escalation, billing inquiries, and even cross-selling and upselling. In practice, research by Valoir shows that most Service Cloud customers are still in the early stages of AI adoption, particularly with generative AI. While progress has accelerated recently, most companies are only seeing incremental gains in individual productivity rather than the exponential improvements highlighted at Dreamforce. To achieve those higher-level returns, customers must move beyond simple automation and summarization to AI-driven transformation, powered by Agentforce. Chetan and his team outlined four key steps to make this transition. “Agentforce represents the Third Wave of AI—advancing beyond copilots to a new era of highly accurate, low-hallucination intelligent agents that actively drive customer success. Unlike other platforms, Agentforce is a revolutionary and trusted solution that seamlessly integrates AI across every workflow, embedding itself deeply into the heart of the customer journey. This means anticipating needs, strengthening relationships, driving growth, and taking proactive action at every touchpoint,” said Marc Benioff, Chair and CEO, Salesforce. “While others require you to DIY your AI, Agentforce offers a fully tailored, enterprise-ready platform designed for immediate impact and scalability. With advanced security features, compliance with industry standards, and unmatched flexibility. Our vision is bold: to empower one billion agents with Agentforce by the end of 2025. This is what AI is meant to be.” In contrast to now-outdated copilots and chatbots that rely on human requests and strugglewith complex or multi-step tasks, Agentforce offers a new level of sophistication by operating autonomously, retrieving the right data on demand, building action plans for any task, and executing these plans without requiring human intervention. Like a self-driving car, Agentforce uses real-time data to adapt to changing conditions and operates independently within an organizations’ customized guardrails, ensuring every customer interaction is informed, relevant, and valuable. And when desired, Agentforce seamlessly hands off to human employees with a summary of the interaction, an overview of the customer’s details, and recommendations for what to do next. Deploy AI agents across channelsAgentforce Service Agent is more than a chatbot—it’s an autonomous AI agent capable of handling both simple and complex requests, understanding text, video, and audio. Customers were invited to build their own Service Agents during Dreamforce, and many took up the challenge. Service-related agents are a natural fit, as research shows Service Cloud customers are generally more prepared for AI adoption due to the volume and quality of customer data available in their CRM systems. Turn insights into actionLaunching in October 2024, Customer Experience Intelligence provides an omnichannel supervisor Wall Board that allows supervisors to monitor conversations in real time, complete with sentiment scores and organized metrics by topics and regions. Supervisors can then instruct Service Agent to dive into root causes, suggest proactive messaging, or even offer discounts. This development represents the next stage of Service Intelligence, combining Data Cloud, Tableau, and Einstein Conversation Mining to give supervisors real-time insights. It mirrors capabilities offered by traditional contact center vendors like Verint, which also blend interaction, sentiment, and other data in real time—highlighting the convergence of contact centers and Service Cloud service operations. Empower teams to become trusted advisorsSalesforce continues to navigate the delicate balance between digital and human agents, especially within Service Cloud. The key lies in the intelligent handoff of customer data when escalating from a digital agent to a human agent. Service Planner guides agents step-by-step through issue resolution, powered by Unified Knowledge. The demo also showcased how Service Agent can merge Commerce and Service by suggesting agents offer complimentary items from a customer’s shopping cart. Enable field teams to be proactiveSalesforce also announced improvements in field service, designed to help dispatchers and field service agents operate more proactively and efficiently. Agentforce for Dispatchers enhances the ability to address urgent appointments quickly. Asset Service Prediction leverages AI to forecast asset failures and upcoming service needs, while AI-generated prework briefs provide field techs with asset health scores and critical information before they arrive on site. Setting a clear roadmap for adopting Agentforce across these four areas is an essential step toward helping customers realize more than just incremental gains in their service operations. Equally important will be helping customers develop a data strategy that harnesses the power of Data Cloud and Salesforce’s partner ecosystem, enabling a truly data-driven service experience. Investments in capabilities like My Service Journeys will also be critical in guiding customers through the process of identifying which AI features will deliver the greatest returns for their specific needs. Agentforce leverages Salesforce’s generative AI, like Einstein GPT, to automate routine tasks, provide real-time insights, and offer personalized recommendations, enhancing efficiency and enabling agents to deliver exceptional customer experiences. Agentforce is not just another traditional chatbot; it is a next-generation, AI-powered solution that understands complex queries and acts autonomously to enhance operational efficiency. Unlike conventional chatbots, Agentforce is intelligent and adaptive, capable of managing a wide range of customer issues with precision. It offers 24/7 support, responds in a natural, human-like manner, and seamlessly escalates to human agents when needed and redefining customer service by delivering faster, smarter, and more effective support experiences. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM

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October 8, 2024in Data, Enterprise, Generative AI, Salesforce, Unified Knowledge
Weekly Opportunity Report
08Oct

Weekly Opportunity Report

Building a Weekly Opportunity Report with Different Criteria for Various Opportunity Types An individual was attempting to create a weekly opportunity report using complex filter logic to accommodate different criteria for various types of opportunities. Initially, they tried using the standard “Gifts with Payments” report type but faced challenges due to the nature of the opportunities. Some opportunities needed to be included based on the Close Date, while others were based on the GL Post Date. Since some opportunities have multiple payments, they wanted to use the GL Post Date from the payment record. However, not all opportunities have payment records, such as in-kind gifts. When they filtered by the Payment GL Post Date, it excluded all in-kind gifts, even though this filter was not relevant to them. The in-kind gifts matched the logic for the Close Date (e.g., 2 AND NOT 4 AND NOT 6 AND 8), but they were still being excluded. Here’s the challenge they faced: They also highlighted that the General Ledger Post Date and Payment GL Post Date are custom fields populated manually. The individual is looking for a solution to resolve this issue, allowing them to generate a report that properly includes opportunities based on the necessary criteria while keeping in-kind gifts in the report. 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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October 8, 2024in Salesforce
Coveo and Salesforce Data Cloud
07Oct

Coveo and Salesforce Data Cloud

Coveo Integrates with Salesforce Data Cloud for Enhanced Data Access Coveo has officially integrated with Salesforce Data Cloud, providing companies with improved access to a broad range of data across the Salesforce ecosystem. This integration enhances the capabilities of Salesforce’s Service Cloud, Commerce Cloud, and Experience Cloud by offering secure access to a wide array of enterprise data sources and engagement data from Coveo-powered search experiences. By leveraging Coveo’s advanced indexing and security features, the integration enables enterprises to seamlessly inject external content into Salesforce experiences through Data Cloud. It also delivers comprehensive analytics across both platforms, allowing businesses to track interactions and touchpoints more effectively. “Coveo has worked closely with Salesforce on a global scale for over a decade,” said Laurent Simoneau, president and CTO of Coveo. “The integration of Coveo with Salesforce creates a powerful synergy, particularly beneficial for large enterprises with complex content ecosystems. Coveo’s robust indexing and relevance capabilities, combined with Salesforce Data Cloud, provide secure access to external data sources, boosting performance and delivering added value across all Salesforce Clouds. This integration sets a new standard for unifying enterprise data and elevating the customer experience.” Kishan Chetan, executive vice president and general manager of Service Cloud at Salesforce, emphasized the impact on service interactions: “Providing precise answers during service interactions is crucial for reducing service costs and improving customer satisfaction, especially for large enterprises managing vast amounts of content and diverse customer bases. Coveo’s ability to connect to extensive content sources and enhance relevance is a key enabler for enterprises with complex search needs in Service Cloud.” 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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October 7, 2024in Salesforce Data Cloud
Salesforce Agentforce Integration
07Oct

Salesforce Agentforce Integration

The rise of AI-powered solutions is transforming customer service, support, and automation. For organizations such as nonprofits, national associations, and large enterprises, delivering seamless customer experiences has become crucial. Salesforce’s Agentforce, a next-generation conversational AI tool, plays a vital role in this transformation. Designed to elevate customer support and interaction, Agentforce provides an intelligent and scalable solution for integrating AI chatbots into content management systems (CMS) like WordPress, Drupal, and HubSpot. Salesforce Agentforce Integration. In this detailed feature review, we will dive into the extensive capabilities of Salesforce Agentforce, analyzing its role as a conversational tool, its technical requirements, and the benefits it provides for nonprofits, national associations, and businesses. We’ll also compare its applications across various CMS platforms like Drupal, WordPress, and HubSpot, exploring its potential as a powerful AI assistant for website automation and customer interaction. Salesforce Agentforce: A Technical Perspective Salesforce Agentforce is an advanced AI-driven conversational assistant that seamlessly integrates into the Salesforce environment. By tapping into Salesforce CRM’s vast data resources, Agentforce serves as an intelligent interface, automating everything from initial customer inquiries to more personalized interactions. Utilizing natural language processing (NLP) and machine learning, it continually refines responses and scales interactions, making it an indispensable tool for organizations aiming to enhance customer service workflows. Agentforce integrates smoothly with Salesforce Service Cloud, automating both live chat support and chatbot responses. Additionally, it can connect with third-party platforms, including popular CMS solutions like WordPress, Drupal, and HubSpot, allowing organizations to centralize customer service operations in one interface. Core Features and Technical Architecture of Agentforce Natural Language Understanding (NLU) and Processing (NLP) Agentforce’s NLP capabilities are its backbone, allowing it to understand complex human language and respond contextually. This empowers it to manage initial inquiries as well as more sophisticated support requests. Agentforce’s NLU also enables it to work across different languages, dialects, and industry-specific terminology, making it particularly valuable for global organizations and national associations serving diverse audiences. Machine Learning for Continuous Improvement Agentforce’s machine learning feature enhances its ability to improve accuracy and understanding over time. Each interaction helps the chatbot evolve, making it more effective at delivering relevant, real-time responses. This model integrates directly with Salesforce’s data infrastructure, giving Agentforce access to historical data and interactions to offer highly personalized, context-aware answers. Deep Integration with Salesforce CRM As a Salesforce-native tool, Agentforce can harness CRM data in ways other AI tools cannot. By accessing customer histories, purchase data, and previous interactions, it creates personalized experiences that build customer trust. Nonprofits and associations can use this data to improve donor or member interactions, offering targeted support and outreach. Agentforce can also be tailored to retrieve specific datasets, such as an individual’s support history or ongoing service case updates. Cross-Platform Flexibility and API Integration Agentforce offers flexible APIs that enable integration with third-party systems, including CMS platforms like WordPress, Drupal, and HubSpot. This flexibility ensures that AI-powered chatbots can be deployed on organizational websites, providing a seamless experience for customers, donors, and members alike. Whether it’s a nonprofit using Drupal or a business on WordPress, Agentforce acts as the central hub for support and engagement, offering fluid interactions on top of your CMS. HubSpot users can further leverage Agentforce’s marketing features to align lead generation with personalized, chat-based interactions. Use Cases for Agentforce in Nonprofits, National Associations, and Businesses Nonprofit Organizations For nonprofits managing donor, volunteer, and beneficiary relationships, Agentforce offers scalable, automated support: National Associations National associations can use Agentforce to handle high volumes of inquiries from members and professionals: Businesses For service-based enterprises, Agentforce is essential for customer service: Salesforce Agentforce and CMS Integration: WordPress, Drupal, and HubSpot WordPress and Salesforce Agentforce Integration For WordPress users, Agentforce offers customizable chatbot widgets that enhance customer engagement, handle ecommerce inquiries, and integrate with WooCommerce for product support. Drupal and Agentforce Integration Drupal’s modular architecture allows Agentforce to automate membership management, provide multilingual support, and distribute content for nonprofits and associations. HubSpot and Agentforce Integration HubSpot users benefit from Agentforce’s ability to automate lead nurturing, sales and marketing workflows, and customer support, all while keeping HubSpot and Salesforce CRM data synchronized. Tectonic and Salesforce Agentforce Integration At Tectonic, we understand that adopting AI-powered solutions like Salesforce Agentforce is only the first step toward delivering exceptional customer experiences. We specialize in crafting, training, and implementing tailored AI chatbot solutions that enhance engagement, streamline processes, and drive growth, all while seamlessly integrating with your current website or mobile app. As a full-service digital strategy firm, Tectonic excels in integrating advanced tools like Salesforce Agentforce into platforms like WordPress, Drupal, and HubSpot, ensuring your automation strategies are executed with precision. From custom chatbot implementations to comprehensive digital strategy services, our team is dedicated to optimizing your website for engagement and lead generation. 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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October 7, 2024in Agentforce Platform, AI Tools, Data, Enterprise, Salesforce, Salesforce Service Cloud
Tableau Einstein Alliance
07Oct

Tableau Einstein Alliance

We’re expanding our commitment to the future of data and analytics with the launch of the Tableau Einstein Alliance, an initiative designed to foster an ecosystem of visionary and innovative partners. These partners will help integrate Agentforce across every facet of analytics, positioning our customers to harness the full potential of AI. The future of data-driven insights is here, and our partners play a vital role in this journey. —Ryan Aytay, CEO, Tableau Salesforce Introduces Tableau Einstein Alliance Salesforce recently unveiled the Tableau Einstein Alliance, a new partner community for its Tableau Einstein users. This initiative aims to empower partners to excel in the “agent era” by offering exclusive benefits to support their development and implementation of AI-driven solutions and analytics agents. Members of the Alliance will gain access to product roadmaps, in-house expertise, and dedicated marketing support, as well as opportunities for co-selling. Moreover, Salesforce is offering these partners the ability to leverage the Alliance for building AI agents, apps, and solutions that maximize their clients’ investments in AI and data. What is Tableau Einstein? Launched in September, Tableau Einstein is an AI-powered visual analytics platform designed to scale and enhance data-driven workflows. It seamlessly integrates with Salesforce tools like Agentforce and its privacy framework, providing data professionals with the ability to create semantic models using real-time customer data. Tableau Einstein also features a marketplace where organizations can share analytical assets, and its APIs facilitate seamless collaboration. Teams can easily work together on data models, visualizations, and dashboards in a unified, drag-and-drop interface. Why Tableau Einstein Matters In discussing the platform, Ryan Aytay highlighted its transformative capabilities: “By leveraging high-performance AI to connect data, actions, and humans, autonomous and assistive agents are redefining business efficiency. They ensure that the data foundation you’re already using will continue to support your needs into the future. You no longer need to sift through data silos or be a specialist to access critical insights—data is now accessible to everyone.” With Tableau Einstein, Salesforce is setting a new standard for how businesses can achieve success with AI-powered, data-driven insights. 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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October 7, 2024in Data, Salesforce, Salesforce Tableau
Salesforce LlamaRank
07Oct

Salesforce LlamaRank

Document ranking remains a critical challenge in information retrieval and natural language processing. Effective document retrieval and ranking are crucial for enhancing the performance of search engines, question-answering systems, and Retrieval-Augmented Generation (RAG) systems. Traditional ranking models often struggle to balance result precision with computational efficiency, especially when dealing with large datasets and diverse query types. This challenge underscores the growing need for advanced models that can provide accurate, contextually relevant results in real-time from continuous data streams and increasingly complex queries. Salesforce AI Research has introduced a cutting-edge reranker named LlamaRank, designed to significantly enhance document ranking and code search tasks across various datasets. Built on the Llama3-8B-Instruct architecture, LlamaRank integrates advanced linear and calibrated scoring mechanisms, achieving both speed and interpretability. The Salesforce AI Research team developed LlamaRank as a specialized tool for document relevancy ranking. Enhanced by iterative feedback from their dedicated RLHF data annotation team, LlamaRank outperforms many leading APIs in general document ranking and sets a new standard for code search performance. The model’s training data includes high-quality synthesized data from Llama3-70B and Llama3-405B, along with human-labeled annotations, covering a broad range of domains from topic-based search and document QA to code QA. In RAG systems, LlamaRank plays a crucial role. Initially, a query is processed using a less precise but cost-effective method, such as semantic search with embeddings, to generate a list of potential documents. The reranker then refines this list to identify the most relevant documents, ensuring that the language model is fine-tuned with only the most pertinent information, thereby improving accuracy and coherence in the output responses. LlamaRank’s architecture, based on Llama3-8B-Instruct, leverages a diverse training corpus of synthetic and human-labeled data. This extensive dataset enables LlamaRank to excel in various tasks, from general document retrieval to specialized code searches. The model underwent multiple feedback cycles from Salesforce’s data annotation team to achieve optimal accuracy and relevance in its scoring predictions. During inference, LlamaRank predicts token probabilities and calculates a numeric relevance score, facilitating efficient reranking. Demonstrated on several public datasets, LlamaRank has shown impressive performance. For instance, on the SQuAD dataset for question answering, LlamaRank achieved a hit rate of 99.3%. It posted a hit rate of 92.0% on the TriviaQA dataset. In code search benchmarks, LlamaRank recorded a hit rate of 81.8% on the Neural Code Search dataset and 98.6% on the TrailheadQA dataset. These results highlight LlamaRank’s versatility and efficiency across various document types and query scenarios. LlamaRank’s technical specifications further emphasize its advantages. Supporting up to 8,000 tokens per document, it significantly outperforms competitors like Cohere’s reranker. It delivers low-latency performance, ranking 64 documents in under 200 ms with a single H100 GPU, compared to approximately 3.13 seconds on Cohere’s serverless API. Additionally, LlamaRank features linear scoring calibration, offering clear and interpretable relevance scores. While LlamaRank’s size of 8 billion parameters contributes to its high performance, it is approaching the upper limits of reranking model size. Future research may focus on optimizing model size to balance quality and efficiency. Overall, LlamaRank from Salesforce AI Research marks a significant advancement in reranking technology, promising to greatly enhance RAG systems’ effectiveness across a wide range of applications. With its powerful performance, efficiency, and clear scoring, LlamaRank represents a major step forward in document retrieval and search accuracy. The community eagerly anticipates its broader adoption and further development. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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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October 7, 2024in Salesforce
AI All Grown Up
07Oct

AI All Grown Up

If you thought Salesforce had fully embraced AI, think again. The company has much more in store. AI All Grown Up and Salesforce is the educator! Alongside the announcement of the new Agentforce platform, Salesforce has teased plans to offer free premium instructor-led courses and AI certifications throughout 2025, reflecting a bold commitment to fostering AI skills and expertise. We’ve talked quite a bit over the last year about the need for AI education, and lo and behold here comes Salesforce to the rescue! AI All Grown Up Ah, they grow up so fast. Once just a baby cradeled in our arms with endless possibilities and potential. It was just like a year or so ago we heard of ChatGPT. Prior to that most people’s main exposure to artificial intelligence was their smart phones, which today we realize weren’t reall that smart. Generative, predictive and agentic AI have barreled down the pipeline increasing our vocabulary, and understanding, of what artificial intelligence can do. From generative content to sounds and images, AI continued to amaze us. Then predictive AI did our calculations faster than we could have imagined. Then agentic AI did nearly everything imaginable. AI All Grown Up. Like a very proud mentor of the process, I want to talk about Salesforce’s major contribution. Addressing the AI Skills Gap: Salesforce’s $50 Million Investment As the veritable plethora of AI tools rapidly expands, Salesforce is taking proactive steps to address the growing AI skills gap by investing $50 million into workforce upskilling initiatives. The company aims to ensure that businesses and individuals are prepared to utilize their new wave of AI tools effectively. While the full details have yet to be released, Salesforce has revealed that its premium AI courses and certifications will be made available for free via Trailhead by the end of 2025. This could mean certifications such as AI Associate and AI Specialist, which currently cost $75 and $200 respectively, may soon be offered at no cost. Gratis. Free, Salesforce has also mentioned “premium instructor-led training,” sparking speculation that AI-focused, instructor-led Trailhead Academy courses could become accessible to everyone in the Salesforce ecosystem. Expanding AI Education with Global AI Centers Salesforce’s AI upskilling push is part of a broader initiative to establish “AI Centers” across the globe. Following the opening of its first center in London in June, Salesforce is planning to launch additional AI hubs in cities like Chicago, Tokyo, Sydney, and even a pop-up center in San Francisco. These centers will host in-person premium courses and serve as gathering spaces for industry experts, partners, and customers. This initiative benefits not only the Salesforce ecosystem by increasing AI knowledge where expertise is scarce, but also aligns with Salesforce’s strategy of bringing AI-driven solutions to market through new products like Copilot Studio, Data Cloud, and the newly launched Agentforce platform. Agentforce: Salesforce’s Third Wave of AI On August 28, 2024, Salesforce introduced Agentforce, a suite of autonomous AI agents that marks a significant leap in how businesses engage with customers. Described as the “Third Wave of AI,” Agentforce goes beyond traditional chatbots, providing intelligent agents capable of driving customer success with minimal human intervention. What is Agentforce? Agentforce is a comprehensive platform designed for organizations to build, customize, and deploy autonomous AI agents across various business functions, such as customer service, sales, marketing, and commerce. These agents operate independently, accessing data, crafting action plans, and executing tasks without needing constant human oversight. It is like Artificial Intelligence just graduated highschool and is off to a world of new adventures and growth opportunities at college or university! Key Features of Agentforce: The Technology Behind Agentforce At the core of Agentforce is the Atlas Reasoning Engine, a system designed to mimic human reasoning. Here’s how it works: Customization Tools: Agent Builder Agentforce provides tools like Agent Builder, a low-code platform for customizing out-of-the-box agents or creating new ones for specific business needs. With this tool, users can: The Agentforce Partner Network Salesforce’s partner ecosystem plays a key role in Agentforce’s versatility, with contributions from companies like AWS, Google, IBM, and Workday. Together, they’ve developed over 20 agent actions available through the Salesforce AppExchange. As proud parents we watch our Artificial Intelligence child venture into the world making friends along the way. Learning social skills. Benefits and Impact of Agentforce Early Adopters and Success Stories Several companies are already benefiting from Agentforce: Availability and Pricing of Salesforce’s AI All Grown Up Agentforce for Service and Sales will be generally available on October 25, 2024, with some components of the Atlas Reasoning Engine launching in February 2025. Pricing starts at $2 per conversation, with volume discounts available. The Future of AI and Work Salesforce’s ambitious vision is to empower one billion AI agents with Agentforce by the end of 2025. This reflects their belief that the future of work will involve a hybrid workforce, where humans and AI agents collaborate to drive customer success. AI All Grown Up and We Couldn’t Be Prouder Our amazing AI child has graduated college and ventured out into the workforce. Agentforce vs. Einstein Bots: What’s the Difference? Conclusion Agentforce represents a major leap forward in AI-powered customer engagement. By providing autonomous, intelligent agents capable of managing complex tasks, Salesforce is positioning itself at the forefront of AI innovation. As businesses continue to explore ways to improve efficiency and customer satisfaction, Agentforce could redefine how organizations interact with customers and streamline their operations. If this is the Third Wave of AI, what will the fourth wave bring? Written by Tectonic’s Solutions Architect, Shannan Hearne Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business

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October 7, 2024in Agentforce Platform, AI Tools, Data, Google, Mulesoft, Salesforce, Technology
New Salesforce Maps Experience Auto-Enabled in Winter ‘25 (October) Release
07Oct

New Salesforce Maps Experience Auto-Enabled in Winter ‘25 (October) Release

To enhance your experience in Salesforce Maps on desktop, the new features currently available in all environments will be auto-enabled in the Winter ’25 release this October. The production rollout for Salesforce Maps will begin the night of October 8th and will be completed with all organizations updated by October 18th. The Enhanced User Experience setting in the admin configuration settings will remain and can be manually disabled until the Spring ‘25 release. Take Action Now To ensure a smooth transition, please take the following actions prior to the production release.In Production: From Setup, in the Quick Find box, enter Remote Site Settings, and then select Remote Site Settings. Find and activate the following remote sites: https://lookup.search.hereapi.com, https://autosuggest.search.hereapi.com, and https://revgeocode.search.hereapi.comFailure to do so may result in disruptions to the Points of Interest Search and Click2Create featuresPrior to the deployment to production, we encourage you to explore the enhanced experience in your sandbox environments. All sandbox environments have been updated with the enhanced experience enabled by default. What to Expect Experience a drastic improvement in performance and rendering, plotting layers and mapping content up to 6x as fast!View Maps with updated styling and designs across many parts of the application, such as modernized marker pop-ups, updated drawing tools, and new cluster styling. In addition, map content along with base maps are displayed with increased detail and clarity.Combine the power of ESRI Living Atlas with CRM data directly inside Salesforce Maps. ESRI provides an evolving collection of ready-to-use global geographic content, such as imagery, base maps, demographics, landscape, and boundary data. Identify new leads and opportunities, analyze key geographical-based data, and gain valuable industry insight with lightning speed. Instructions on visualizing Living Atlas data in Maps can be found here.View plotted records in our redesigned List View, providing new capabilities and features for your users, such as the ability to dynamically build a sublist of data.For a full breakdown, please refer to the Maps Summer ‘24 Release Notes and Maps Winter ‘25 Release Notes.How can I get more information or help? Contact your account team or open a case with Salesforce Customer Support. 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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October 7, 2024in Data, Google, Salesforce
Generative AI and Patient Engagement
07Oct

Generative AI and Patient Engagement

The healthcare industry is undergoing a significant digital transformation, with generative AI and chatbots playing a prominent role in various patient engagement applications. Technologies such as online symptom checkers, appointment scheduling, patient navigation tools, medical search engines, and patient portal messaging are prime examples of how AI is enhancing patient-facing interactions. These advancements aim to alleviate staff workload while improving the overall patient experience, according to industry experts. However, even these patient-centric applications face challenges, such as the risk of generating medical misinformation or biased outcomes. As healthcare professionals explore the potential of generative AI and chatbots, they must also implement safeguards to prevent the spread of false information and mitigate disparities in care. Online Symptom Checkers Online symptom checkers allow patients to input their symptoms and receive a list of potential diagnoses, helping them decide the appropriate level of care, whether it’s urgent care or self-care at home. These tools hold promise for improving patient experiences and operational efficiency, reducing unnecessary healthcare visits. For healthcare providers, they help triage patients, ensuring those who need critical care receive it. However, the effectiveness of online symptom checkers is mixed. A 2022 literature review revealed that diagnostic accuracy ranged between 19% and 37.9%, while triage accuracy was higher, between 48.9% and 90%. Patient reception to these tools has been lukewarm as well, with some expressing dissatisfaction with the COVID-19 symptom checkers during the pandemic, mainly when the tools did not emulate human interaction. Moreover, studies have indicated that these tools might exacerbate health inequities, as users tend to be younger, female, and more digitally literate. To mitigate this, developers must ensure that chatbots can communicate in multiple languages, replicate human interactions, and escalate to human providers when needed. Self-Scheduling and Patient Navigation Generative AI and conversational AI have shown promise in addressing lower-level patient inquiries, such as appointment scheduling and navigation, reducing the strain on healthcare staff. AI-driven scheduling systems help fill gaps in navigation by assisting patients with appointment bookings and answering logistical questions, like parking or directions. A December 2023 review noted that AI-optimized patient scheduling reduces provider time burdens and improves patient satisfaction. However, barriers such as health equity, access to broadband, and patient trust must be addressed to ensure effective implementation. While organizations need to ensure these systems are accessible to all, AI is a valuable tool for managing routine patient requests, freeing staff to focus on more complex issues. Online Medical Research AI tools like ChatGPT are expanding on the “Dr. Google” phenomenon, offering patients a way to search for medical information. Despite initial concerns from clinicians about online medical searches, recent studies show that generative AI tools can provide accurate and understandable information. For instance, ChatGPT accurately answered breast cancer screening questions 88% of the time in one 2023 study and offered adequate colonoscopy preparation information in another. However, patients remain cautious about AI-generated medical advice. A 2023 survey revealed that nearly half of respondents were concerned about potential misinformation, and many were unsure about the sources AI tools use. Addressing these concerns by validating source material and providing supplementary educational resources will be crucial for building patient trust. Patient Portal Messaging and Provider Communication Generative AI is also finding its place in patient portal messaging, where it can generate responses to patient inquiries, helping to alleviate clinician burnout. In a 2024 study, AI-generated responses within a patient portal were often indistinguishable from those written by clinicians, requiring human editing in only 58% of cases. While chatbot-generated messages have been found to be more empathetic than those written by overworked providers, it’s important to ensure AI-generated responses are always reviewed by healthcare professionals to catch any potential errors. In addition to patient engagement, generative AI is being used in clinical decision support and ambient documentation, showcasing its potential to improve healthcare efficiency. However, developers and healthcare organizations must remain vigilant about preventing algorithmic bias and other AI-related 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 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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October 7, 2024in AI Tools, Generative AI, Google, Google Analytics, Salesforce Development Services, Salesforce Health and Life Sciences Solutions, Salesforce Implementation Services, Salesforce Optimization, Technology
Natural Language Processing Explained
06Oct

Natural Language Processing Explained

What is Natural Language Processing (NLP)? Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that enables computers to interpret, analyze, and generate human language. By leveraging machine learning, computational linguistics, and deep learning, NLP helps machines understand written and spoken words, making communication between humans and computers more seamless. I apologize folks. I am feeling like the unicorn who missed the Ark. Tectonic has been providing you with tons of great material on artificial intelligence, but we left out a basic building block. Without further ado, Natural Language Processing Explained. Like a lot of components of AI, we often are using it without knowing we are using it. NLP is widely used in everyday applications such as: How Does NLP Work? Natural Language Processing combines several techniques, including computational linguistics, machine learning, and deep learning. It works by breaking down language into smaller components, analyzing these components, and then drawing conclusions based on patterns. If you have ever read a first grader’s reading primer it is the same thing. Learn a little three letter word. Recognize the meaning of the word. Understand it in the greater context of the sentence. Key NLP preprocessing steps include: Why Is NLP Important? NLP plays a vital role in automating and improving human-computer interactions by enabling systems to interpret, process, and respond to vast amounts of textual and spoken data. By automating tasks like sentiment analysis, content classification, and question answering, NLP boosts efficiency and accuracy across industries. For example: Key Use Cases of NLP in Business NLP Tasks NLP enables machines to handle various language tasks, including: Approaches to NLP Future of NLP NLP is becoming more integral in daily life as technology improves. From customer service chatbots to medical record summarization, NLP continues to evolve, but challenges remain, including improving coherence and reducing biases in machine-generated text. Essentially, NLP transforms the way machines and humans interact, making technology more intuitive and accessible across a range of industries. By Tectonic Solutions Architect – Shannan Hearne Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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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October 6, 2024in Artificial Intelligence, Data, Google, Technology
Promising Patient Engagement Use Cases for GenAI and Chatbots
06Oct

Promising Patient Engagement Use Cases for GenAI and Chatbots

Promising Patient Engagement Use Cases for GenAI and Chatbots Generative AI (GenAI) is showing great potential in enhancing patient engagement by easing the burden on healthcare staff and clinicians while streamlining the overall patient experience. As healthcare undergoes its digital transformation, various patient engagement applications for GenAI and chatbots are emerging as promising tools. Let’s look at Promising Patient Engagement Use Cases for GenAI and Chatbots. Key applications of GenAI and patient-facing chatbots include online symptom checkers, appointment scheduling, patient navigation, medical search engines, and even patient portal messaging. These technologies aim to alleviate staff workloads while improving the patient journey, according to some experts. However, patient-facing AI applications are not without challenges, such as the risk of generating medical misinformation or exacerbating healthcare disparities through biased algorithms. As healthcare professionals explore the potential of GenAI and chatbots for patient engagement, they must also ensure safeguards are in place to prevent the spread of inaccuracies and avoid creating health inequities. Online Symptom Checkers Online symptom checkers allow healthcare organizations to assess patients’ medical concerns without requiring an in-person visit. Patients can input their symptoms, and the AI-powered chatbot will generate a list of possible diagnoses, helping them decide whether to seek urgent care, visit the emergency department, or manage symptoms at home. These tools promise to improve both patient experience and operational efficiency by directing patients to the right care setting, thus reducing unnecessary visits. For healthcare providers, symptom checkers can help triage patients and ensure high-acuity areas are available for those needing critical care. Despite their potential, studies show mixed results regarding the diagnostic accuracy of online symptom checkers. A 2022 literature review found that diagnostic accuracy for these tools ranged from 19% to 37.9%. However, triage accuracy—referring patients to the correct care setting—was better, ranging between 48.9% and 90%. Patient reception to symptom checkers has also been varied. For example, during the COVID-19 pandemic, symptom checkers were designed to help patients assess whether their symptoms were virus-related. While patients appreciated the tools, they preferred chatbots that displayed human-like qualities and competence. Tools perceived as similar in quality to human interactions were favored. Furthermore, some studies indicate that online symptom checkers could deepen health inequities, as users tend to be younger, female, and more digitally literate. To mitigate this, AI developers must create chatbots that can communicate in multiple languages, mimic human interaction, and easily escalate issues to human professionals when needed. Self-Scheduling and Patient Navigation GenAI and conversational AI are proving valuable in addressing routine patient queries, like appointment scheduling and patient navigation, tasks that typically fall on healthcare staff. With a strained medical workforce, using AI for lower-level inquiries allows clinicians to focus on more complex tasks. AI-enhanced appointment scheduling systems, for example, not only help patients book visits but also answer logistical questions like parking directions or department locations within a clinic. A December 2023 literature review highlighted that AI-optimized scheduling could reduce provider workload, increase patient satisfaction, and make healthcare more patient-centered. However, key considerations for AI integration include ensuring health equity, broadband access, and patient trust. While AI can manage routine requests, healthcare organizations need to ensure their tools are accessible and functional for diverse populations. Online Medical Research GenAI tools like ChatGPT are contributing to the “Dr. Google” phenomenon, where patients search online for medical information before seeing a healthcare provider. While some clinicians have been cautious about these tools, research suggests they can effectively provide accurate medical information. For instance, an April 2023 study showed that ChatGPT answered 88% of breast cancer screening questions correctly. Another study in May 2023 demonstrated that the tool could adequately educate patients on colonoscopy preparation. In both cases, the information was presented in an easy-to-understand format, essential for improving health literacy. However, GenAI is not without flaws. Patients express concern about the reliability of AI-generated information, with a 2023 Wolters Kluwer survey showing that 49% of respondents worry about false information from GenAI. Additionally, many are uneasy about the unknown sources and validation processes behind the information. To build patient trust, AI developers must ensure the accuracy of their source material and provide supplementary authoritative resources like patient education materials. Patient Portal Messaging and Provider Communication Generative AI has also found use in patient portal messaging, where it can draft responses on behalf of healthcare providers. This feature has the potential to reduce clinician burnout by handling routine inquiries. A study conducted at Mass General Brigham in April 2024 revealed that a large language model embedded in a secure messaging tool could generate acceptable responses to patient questions. In 58% of cases, chatbot-generated messages required human editing. Promising Patient Engagement Use Cases for GenAI and Chatbots Interestingly, other research has found that AI-generated responses in patient portals are often more empathetic than those written by overworked healthcare providers. Nevertheless, AI responses should always be reviewed by a clinician to ensure accuracy before being sent to patients. Generative AI is also making strides in clinical decision support and ambient documentation, further boosting healthcare efficiency. However, as healthcare organizations adopt these technologies, they must address concerns around algorithmic bias and ensure patient safety remains a top priority. 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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October 6, 2024in Agentforce Platform, AI Tools, Generative AI, Google, Laws, Life Sciences Cloud, Salesforce Development Services, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud
Real-World AI
06Oct

Real-World AI

Nearly two years after the widespread adoption of generative AI with the launch of ChatGPT, the technology is shifting from experimental phases to real-world implementation. A recent survey by TechTarget’s Enterprise Strategy Group highlights this growing trend, revealing that generative AI adoption has significantly increased over the past year. The firm surveyed 832 professionals globally and found that the use of generative AI is expanding across sectors like software development, research, IT operations, and customer service. “We’re in the acceleration phase,” noted Mark Beccue, an analyst at Enterprise Strategy Group and author of the survey, during an appearance on the Targeting AI podcast. According to the survey, there is no singular use case driving the adoption of generative AI. Instead, organizations are exploring multiple applications while facing challenges, such as the need for enhanced infrastructure. “Organizations feel infrastructure changes are necessary before fully proceeding with generative AI,” Beccue said. This may involve investing in enterprise-level platforms or new development tools, all aimed at facilitating AI application development. Additionally, there’s no clear consensus on which AI models—open or closed source—best suit organizational needs. “It’s likely a combination of both,” Beccue explained. “Companies are realizing no one model meets all their needs, so they’re evaluating what works best in specific scenarios.” Companies that have seen early success with generative AI are those that invested in AI technologies well before ChatGPT made waves. Beccue pointed to companies like Adobe, ServiceNow, and Zoom, which had already been leveraging machine learning, natural language understanding, and process automation for years. “They recognized the potential for AI to enhance their operations and were well-prepared when generative AI gained mainstream attention,” Beccue added. How can Tectonic help you AI? Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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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October 6, 2024in Enterprise, Generative AI, Technology
Ethical AI Implementation
06Oct

Ethical AI Implementation

AI technologies are rapidly evolving, becoming a practical solution to support essential business operations. However, creating true business value from AI requires a well-balanced approach that considers people, processes, and technology. Ethical AI Implementation. AI encompasses various forms, including machine learning, deep learning, predictive analytics, natural language processing, computer vision, and automation. To leverage AI’s competitive advantages, companies need a strong foundation and a realistic strategy aligned with their business goals. “Artificial intelligence is multifaceted,” said John Carey, managing director at AArete, a business management consultancy. “There’s often hype and, at times, exaggeration about how ‘intelligent’ AI truly is.” Business Advantages of AI Adoption Recent advancements in generative AI, such as ChatGPT and Dall-E, have showcased AI’s significant impact on businesses. According to a McKinsey Global Survey, global AI adoption surged from around 50% over the past six years to 72% in 2024. Some key benefits of adopting AI include: Prerequisites for AI Implementation Successfully implementing AI can be complex. A detailed understanding of the following prerequisites is crucial for achieving positive results: 13 Steps for Successful AI Implementation Common AI Implementation Mistakes Organizations often stumble by: Key Challenges in Ethical AI Implementation Human-related challenges often present the biggest hurdles. To overcome them, organizations must foster data literacy and build trust among stakeholders. Additionally, challenges around data management, model governance, system integration, and intellectual property need to be addressed. Ensuring Ethical AI Implementation To ensure responsible AI use, companies should: Ethical AI implementation requires a continuous commitment to transparency, fairness, and inclusivity across all levels of the organization. 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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October 6, 2024in Generative AI
Telemynd CharmHealth and Salesforce
05Oct

Telemynd CharmHealth and Salesforce

Telemynd Integrates CharmHealth EHR with Salesforce to Scale Nationwide Care Telemynd, a leading behavioral health practice, has integrated its CharmHealth electronic health record (EHR) system with the Salesforce CRM platform to expand its services nationwide. This integrated tech stack enhances patient experiences, personalizes care, and streamlines clinical workflows across the organization. At the 2024 CharmHealth user conference, Charmalot2024, Roger Murray, Vice President of Product & Marketing at Telemynd, and Venky Chellappa, Vice President of Sales & Marketing at CharmHealth, discussed the partnership’s impact on scaling Telemynd’s operations. Key Takeaways: Partnership Rooted in Pandemic Response The partnership between Telemynd and CharmHealth began during the COVID-19 pandemic, when Telemynd needed to quickly expand its telehealth services. CharmHealth’s technology facilitated this growth, allowing Telemynd to deliver mental health services to thousands of patients across the U.S. through telehealth. “We were figuring it [telehealth] out together,” said Murray, reflecting on how both teams adapted to the rapid changes. Today, Telemynd delivers over 60,000 hours of care annually, serving both military and civilian patients nationwide. Seamless Integration: CharmHealth and Salesforce CharmHealth’s adaptable EHR platform was designed with integration in mind, allowing Telemynd to combine the strengths of Salesforce and CharmHealth. “Salesforce helps us maintain a positive user experience for patients and providers,” explained Murray. “But we wanted to continue using our CharmHealth EHR. We worked to make the interfaces between the two systems bidirectional for a seamless experience.” All clinical activities, including charting, notes, and revenue cycle management (RCM), take place in CharmHealth, while patient engagement, follow-ups, and outcomes reporting happen through Salesforce. The integration was achieved by enabling the necessary APIs for a smooth flow of information. Chellappa emphasized the collaboration’s success: “We made a commitment to Roger and his team to support them. When they succeed, we succeed.” Mutual Growth Through Partnership This collaboration has extended beyond Salesforce integration, with many solutions developed for Telemynd influencing CharmHealth’s broader product development. Though the API functionality remains proprietary to Telemynd, the insights gained have driven improvements to CharmHealth’s EHR platform. Murray highlighted the strength of the partnership, noting, “The same engineering team at CharmHealth has worked with us for years. Their commitment to our growth has been invaluable.” As a result of this partnership, Telemynd has rapidly expanded its reach, showcasing how mental health services can be scaled effectively. Both companies credit their ongoing collaboration and open communication as key drivers of their mutual success. 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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October 5, 2024in Salesforce Health and Life Sciences Solutions, Salesforce Implementation Services, Salesforce Optimization

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