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TrueDialog Adds SMS Application for Salesforce Marketing Cloud Engagement

TrueDialog Adds SMS Application for Salesforce Marketing Cloud Engagement

TrueDialog Unveils SMS Integration for Salesforce Marketing Cloud

TrueDialog, a leader in SMS texting solutions, has expanded its Salesforce offerings with the launch of its SMS integration for Salesforce Marketing Cloud Engagement, complementing its existing Sales Cloud application. Now available on Salesforce AppExchange, this addition enables seamless documentation of SMS activities across the Salesforce Cloud ecosystem.

With TrueDialog’s integration for Salesforce Marketing Cloud, companies can incorporate SMS into customized, targeted marketing journeys. This includes sending promotional messages, geo-targeted offers, triggered alerts, order confirmations, account updates, and more—all as part of a cohesive customer engagement strategy.

TrueDialog’s solution uniquely enables SMS message flow across Salesforce Marketing Cloud, Sales Cloud, Service Cloud, and Education Cloud, ensuring communication continuity across platforms. “TrueDialog is the only provider offering bidirectional SMS integration between Salesforce Marketing Cloud and other Salesforce Clouds, including Sales, Service, Commerce, and Education Clouds,” said John Wright, CEO of TrueDialog. “Our solution records text messages on all relevant Salesforce Clouds, maintaining communication continuity between companies and their customers—something no other provider offers.”

The TrueDialog SMS application is fully native to Salesforce Marketing Cloud, allowing users to work within their regular workflow without switching applications to integrate SMS steps. TrueDialog also offers flexible options for short and long codes, automated 10DLC registration for long codes, and TrueDelivery, a tool for assessing SMS deliverability.

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Scope of Generative AI

Scope of Generative AI

Generative AI has far more to offer your site than simply mimicking a conversational ChatGPT-like experience or providing features like generating cover letters on resume sites. Let’s explore how you can integrate Generative AI with your product in diverse and innovative ways! There are three key perspectives to consider when integrating Generative AI with your features: system scope, spatial relationship, and functional relationship. Each perspective offers a different lens for exploring integration pathways and can spark valuable conversations about melding AI with your product ecosystem. These categories aren’t mutually exclusive; instead, they overlap and provide flexible ways of envisioning AI’s role. 1. System Scope — The Reach of Generative AI in Your System System scope refers to the breadth of integration within your system. By viewing integration from this angle, you can assess the role AI plays in managing your platform’s overall functionality. While these categories may overlap, they are useful in facilitating strategic conversations. 2. Spatial Relationships — Where AI Interacts with Features Spatial relationships describe where AI features sit in relation to your platform’s functionality: 3. Functional Relationships — How AI Interacts with Features Functional relationships determine how AI and platform features work together. This includes how users engage with AI and how AI content updates based on feature interactions: Scope of Generative AI By considering these different perspectives—system scope, spatial, and functional—you can drive more meaningful conversations about how Generative AI can best enhance your product’s capabilities. Each approach offers unique value, and careful thought can help teams choose the integration path that aligns with their needs and goals. Scope of Generative AI conversations with Tectonic can assist in planning the best ROI approach to AI. Contact us 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 Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more 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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Scaling Generative AI

Scaling Generative AI

Many organizations follow a hybrid approach to AI infrastructure, combining public clouds, colocation facilities, and on-prem solutions. Specialized GPU-as-a-service vendors, for instance, are becoming popular for handling high-demand AI computations, helping businesses manage costs without compromising performance. Business process outsourcing company TaskUs, for example, focuses on optimizing compute and data flows as it scales its gen AI deployments, while Cognizant advises that companies distinguish between training and inference needs, each with different latency requirements.

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Cohere-Powered Slack Agents

Cohere-Powered Slack Agents

Salesforce AI and Cohere-Powered Slack Agents: Seamless CRM Data Interaction and Enhanced Productivity Slack agents, powered by Salesforce AI and integrated with Cohere, enable seamless interaction with CRM data within the Slack platform. These agents allow teams to use natural language to surface data insights and take action, simplifying workflows. With Slack’s AI Workflow Builder and support for third-party AI agents, including Cohere, productivity is further enhanced through automated processes and customizable AI assistants. By leveraging these technologies, Slack agents provide users with direct access to CRM data and AI-powered insights, improving efficiency and collaboration. Key Features of Slack Agents: Salesforce AI and Cohere Productivity Enhancements with Slack Agents: Salesforce AI and Cohere AI Agent Capabilities in Slack: Salesforce and Cohere Data Security and Compliance for Slack Agents FAQ What are Slack agents, and how do they integrate with Salesforce AI and Cohere?Slack agents are AI-powered assistants that enable teams to interact with CRM data directly within Slack. Salesforce AI agents allow natural language data interactions, while Cohere’s integration enhances productivity with customizable AI assistants and automated workflows. How do Salesforce AI agents in Slack improve team productivity?Salesforce AI agents enable users to interact with both CRM and conversational data, update records, and analyze opportunities using natural language. This integration improves workflow efficiency, leading to a reported 47% productivity boost. What features does the Cohere integration with Slack AI offer?Cohere integration offers customizable AI assistants that can help generate workflows, summarize channel content, and provide intelligent responses to user queries within Slack. How do Slack agents handle data security and compliance?Slack agents leverage cloud-native DLP solutions, automatically detecting sensitive data across different file types and setting up automated remediation processes for enhanced security and compliance. Can Slack agents work with AI providers beyond Salesforce and Cohere?Yes, Slack supports AI agents from various providers. In addition to Salesforce AI and Cohere, integrations include Adobe Express, Anthropic, Perplexity, IBM, and Amazon Q Business, offering users a wide array of AI-powered capabilities. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Salesforce Government Cloud Premium

Salesforce Government Cloud Premium

Software company Salesforce announced on Monday that it has rolled out a new version of its government cloud that has Top Secret authorization and is geared toward U.S. national security agencies and intelligence organizations.

The new offering, called Government Cloud Premium, is hosted on Amazon Web Services’ Top Secret cloud.

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Google on Google AI

Google on Google AI

As a leading cloud provider, Google Cloud is also a major player in the generative AI market. Google on Google AI provides insights into this new tool. In the past two years, Google has been in a competitive battle with AWS, Microsoft, and OpenAI to gain dominance in the generative AI space. Recently, Google introduced several generative Artificial Intelligence products, including its flagship large language model, Gemini, and the Vertex AI Model Garden. Last week, it also unveiled Audio Overview, a tool that transforms documents into audio discussions. Despite these advancements, Google has faced criticism for lagging in some areas, such as issues with its initial image generation tool, like X’s Grok. However, the company remains committed to driving progress in generative AI. Google’s strategy focuses not only on delivering its proprietary models but also offering a broad selection of third-party models through its Model Garden. Google’s Thoughts on Google AI Warren Barkley, head of product for Google Cloud’s Vertex AI, GenAI, and machine learning, emphasized this approach in a recent episode of the Targeting AI podcast. He noted that a key part of Google’s ongoing effort is ensuring users can easily transition to more advanced models. “A lot of what we did in the early days, and we continue to do now, is make it easy for people to move to the next generation,” Barkley said. “The models we built 18 months ago are a shadow of what we have today. So, providing pathways for people to upgrade and stay on the cutting edge is critical.” Google is also focused on helping users select the right AI models for specific applications. With over 100 closed and open models available in the Model Garden, evaluating them can be challenging for customers. To address this, Google introduced evaluation tools that allow users to test prompts and compare model responses. In addition, Google is exploring advancements in Artificial Intelligence reasoning, which it views as crucial to driving the future of generative AI. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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LLMs and AI

LLMs and AI

Large Language Models (LLMs): Revolutionizing AI and Custom Solutions Large Language Models (LLMs) are transforming artificial intelligence by enabling machines to generate and comprehend human-like text, making them indispensable across numerous industries. The global LLM market is experiencing explosive growth, projected to rise from $1.59 billion in 2023 to $259.8 billion by 2030. This surge is driven by the increasing demand for automated content creation, advances in AI technology, and the need for improved human-machine communication. Several factors are propelling this growth, including advancements in AI and Natural Language Processing (NLP), large datasets, and the rising importance of seamless human-machine interaction. Additionally, private LLMs are gaining traction as businesses seek more control over their data and customization. These private models provide tailored solutions, reduce dependency on third-party providers, and enhance data privacy. This guide will walk you through building your own private LLM, offering valuable insights for both newcomers and seasoned professionals. What are Large Language Models? Large Language Models (LLMs) are advanced AI systems that generate human-like text by processing vast amounts of data using sophisticated neural networks, such as transformers. These models excel in tasks such as content creation, language translation, question answering, and conversation, making them valuable across industries, from customer service to data analysis. LLMs are generally classified into three types: LLMs learn language rules by analyzing vast text datasets, similar to how reading numerous books helps someone understand a language. Once trained, these models can generate content, answer questions, and engage in meaningful conversations. For example, an LLM can write a story about a space mission based on knowledge gained from reading space adventure stories, or it can explain photosynthesis using information drawn from biology texts. Building a Private LLM Data Curation for LLMs Recent LLMs, such as Llama 3 and GPT-4, are trained on massive datasets—Llama 3 on 15 trillion tokens and GPT-4 on 6.5 trillion tokens. These datasets are drawn from diverse sources, including social media (140 trillion tokens), academic texts, and private data, with sizes ranging from hundreds of terabytes to multiple petabytes. This breadth of training enables LLMs to develop a deep understanding of language, covering diverse patterns, vocabularies, and contexts. Common data sources for LLMs include: Data Preprocessing After data collection, the data must be cleaned and structured. Key steps include: LLM Training Loop Key training stages include: Evaluating Your LLM After training, it is crucial to assess the LLM’s performance using industry-standard benchmarks: When fine-tuning LLMs for specific applications, tailor your evaluation metrics to the task. For instance, in healthcare, matching disease descriptions with appropriate codes may be a top priority. Conclusion Building a private LLM provides unmatched customization, enhanced data privacy, and optimized performance. From data curation to model evaluation, this guide has outlined the essential steps to create an LLM tailored to your specific needs. Whether you’re just starting or seeking to refine your skills, building a private LLM can empower your organization with state-of-the-art AI capabilities. For expert guidance or to kickstart your LLM journey, feel free to contact us for a free consultation. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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NYT Issues Cease-and-Desist Letter to Perplexity AI

NYT Issues Cease-and-Desist Letter to Perplexity AI

NYT Issues Cease-and-Desist Letter to Perplexity AI Over Alleged Unauthorized Content Use The New York Times (NYT) has issued a cease-and-desist letter to Perplexity AI, accusing the AI-powered search startup of using its content without permission. This move marks the second time the NYT has confronted a company for allegedly misappropriating its material. According to reports, the Times claims Perplexity is accessing and utilizing its content to generate summaries and other outputs, actions it argues infringe on copyright laws. The startup now has two weeks to respond to the accusations. A Growing Pattern of Tensions Perplexity AI is not the only publisher-facing scrutiny. In June, Forbes threatened legal action against the company, alleging “willful infringement” by using its text and images. In response, Perplexity launched the Perplexity Publishers’ Program, a revenue-sharing initiative that collaborates with publishers like Time, Fortune, and The Texas Tribune. Meanwhile, the NYT remains entangled in a separate lawsuit with OpenAI and its partner Microsoft over alleged misuse of its content. A Strategic Legal Approach The NYT’s decision to issue a cease-and-desist letter instead of pursuing an immediate lawsuit signals a calculated move. “Cease-and-desist approaches are less confrontational, less expensive, and faster,” said Sarah Kreps, a professor at Cornell University. This method also opens the door for negotiation, a pragmatic step given the uncharted legal terrain surrounding generative AI and copyright law. Michael Bennett, a responsible AI expert from Northeastern University, echoed this view, suggesting that the cease-and-desist approach positions the Times to protect its intellectual property while maintaining leverage in ongoing legal battles. If the NYT wins its case against OpenAI, Bennett added, it could compel companies like Perplexity to enter financial agreements for content use. However, if the case doesn’t favor the NYT, the publisher risks losing leverage. The letter also serves as a warning to other AI vendors, signaling the NYT’s determination to safeguard its intellectual property. Perplexity’s Defense: Facts vs. Expression Perplexity AI has countered the NYT’s claims by asserting that its methods adhere to copyright laws. “We aren’t scraping data for building foundation models but rather indexing web pages and surfacing factual content as citations,” the company stated. It emphasized that facts themselves cannot be copyrighted, drawing parallels to how search engines like Google operate. Kreps noted that Perplexity’s approach aligns closely with other AI platforms, which typically index pages to provide factual answers while citing sources. “If Perplexity is culpable, then the entire AI industry could be held accountable,” she said, contrasting Perplexity’s citation-based model with platforms like ChatGPT, which often lack transparency about data sources. The Crux of the Copyright Argument The NYT’s cease-and-desist letter centers on the distinction between facts and the creative expression of facts. While raw facts are not protected under copyright, the NYT claims that its specific interpretation and presentation of those facts are. Vincent Allen, an intellectual property attorney, explained that if Perplexity is scraping data and summarizing articles, it may involve making unauthorized copies of copyrighted content, strengthening the NYT’s claims. “This is a big deal for content providers,” Allen said, “as they want to ensure they’re compensated for their work.” Implications for the AI Industry The outcome of this dispute could set a precedent for how AI platforms handle content generated by publishers. If Perplexity’s practices are deemed infringing, it could reshape the operational models of similar AI vendors. At the heart of the debate is the balance between fostering innovation in AI and protecting intellectual property, a challenge that will likely shape the future of generative AI and its relationship with content creators. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Ambient AI Enhances Patient-Provider Relationship

Ambient AI Enhances Patient-Provider Relationship

How Ambient AI is Enhancing the Patient-Provider Relationship Ambient AI is transforming the patient-provider experience at Ochsner Health by enabling clinicians to focus more on their patients and less on their screens. While some view technology as a barrier to human interaction, Ochsner’s innovation officer, Dr. Jason Hill, believes ambient AI is doing the opposite by fostering stronger connections between patients and providers. Researchers estimate that physicians spend over 40% of consultation time focused on electronic health records (EHRs), limiting face-to-face interactions. “We have highly skilled professionals spending time inputting data instead of caring for patients, and as a result, patients feel disconnected due to the screen barrier,” Hill said. Additionally, increased documentation demands related to quality reporting, patient satisfaction, and reimbursement are straining providers. Ambient AI scribes help relieve this burden by automating clinical documentation, allowing providers to focus on their patients. Using machine learning, these AI tools generate clinical notes in seconds from recorded conversations. Clinicians then review and edit the drafts before finalizing the record. Ochsner began exploring ambient AI several years ago, but only with the advent of advanced language models like OpenAI’s GPT did the technology become scalable and cost-effective for large health systems. “Once the technology became affordable for large-scale deployment, we were immediately interested,” Hill explained. Selecting the Right Vendor Ochsner piloted two ambient AI tools before choosing DeepScribe for an enterprise-wide partnership. After the initial rollout to 60 physicians, the tool achieved a 75% adoption rate and improved patient satisfaction scores by 6%. What set DeepScribe apart were its customization features. “We can create templates for different specialties, but individual doctors retain control over their note outputs based on specific clinical encounters,” Hill said. This flexibility was crucial in gaining physician buy-in. Ochsner also valued DeepScribe’s strong vendor support, which included tailored training modules and direct assistance to clinicians. One example of this support was the development of a software module that allowed Ochsner’s providers to see EHR reminders within the ambient AI app. “DeepScribe built a bridge to bring EHR data into the app, so clinicians could access important information right before the visit,” Hill noted. Ensuring Documentation Quality Ochsner has implemented several safeguards to maintain the accuracy of AI-generated clinical documentation. Providers undergo training before using the ambient AI system, with a focus on reviewing and finalizing all AI-generated notes. Notes created by the AI remain in a “pended” state until the provider signs off. Ochsner also tracks how much text is generated by the AI versus added by the provider, using this as a marker for the level of editing required. Following the successful pilot, Ochsner plans to expand ambient AI to 600 clinicians by the end of the year, with the eventual goal of providing access to all 4,700 physicians. While Hill anticipates widespread adoption, he acknowledges that the technology may not be suitable for all providers. “Some clinicians have different documentation needs, but for the vast majority, this will likely become the standard way we document at Ochsner within a year,” he said. Conclusion By integrating ambient AI, Ochsner Health is not only improving operational efficiency but also strengthening the human connection between patients and providers. As the technology becomes more widespread, it holds the potential to reshape how clinical documentation is handled, freeing up time for more meaningful patient interactions. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Battle of Copilots

Battle of Copilots

Salesforce is directly challenging Microsoft in the growing battle of AI copilots, which are designed to enhance customer experience (CX) across key business functions like sales and support. In this competitive landscape, Salesforce is taking on not only Microsoft but also major AI rivals such as Google Gemini, OpenAI GPT, and IBM watsonx. At the heart of this strategy is Salesforce Agentforce, a platform that leverages autonomous decision-making to meet enterprise demands for data and AI abstraction. Salesforce Dreamforce Highlights One of the most significant takeaways from last month’s Dreamforce conference in San Francisco was the unveiling of autonomous agents, bringing advanced GenAI capabilities to the app development process. CEO Marc Benioff and other Salesforce executives made it clear that Salesforce is positioning itself to compete with Microsoft’s Copilot, rebranding and advancing its own AI assistant, previously known as Einstein AI. Microsoft’s stronghold, however, lies in Copilot’s seamless integration with widely used products like Teams, Outlook, PowerPoint, and Word. Furthermore, Microsoft has established itself as a developer’s favorite, especially with GitHub Copilot and the Azure portfolio, which are integral to app modernization in many enterprises. “Salesforce faces an uphill battle in capturing market share from these established players,” says Charlotte Dunlap, Research Director at GlobalData. “Salesforce’s best chance lies in highlighting the autonomous capabilities of Agentforce—enabling businesses to automate more processes, moving beyond basic chatbot functions, and delivering a personalized customer experience.” This emphasis on autonomy is vital, given that many enterprises are still grappling with the complexities of emerging GenAI technologies. Dunlap points out that DevOps teams are struggling to find third-party expertise that understands how GenAI fits within existing IT systems, particularly around security and governance concerns. Salesforce’s focus on automation, combined with the integration prowess of MuleSoft, positions it as a key player in making GenAI tools more accessible and intuitive for businesses. Elevating AI Abstraction and Automation Salesforce has increasingly focused on the idea of abstracting data and AI, exemplified by its Data Cloud and low-level UI capabilities. Now, with models like the Atlas Reasoning Engine, Salesforce is looking to push beyond traditional AI assistants. These tools are designed to automate complex, previously human-dependent tasks, spanning functions like sales, service, and marketing. Simplifying the Developer Experience The true measure of Salesforce’s success in its GenAI strategy will emerge in the coming months. The company is well aware that its ability to simplify the developer experience is critical. Enterprises are looking for more than just AI innovation—they want thought leadership that can help secure budget and executive support for AI initiatives. Many companies report ongoing struggles in gaining that internal buy-in, further underscoring the importance of strong, strategic partnerships with technology providers like Salesforce. In its pursuit to rival Microsoft Copilot, Salesforce’s future hinges on how effectively it can build on its track record of simplifying the developer experience while promoting the unique autonomous qualities of Agentforce. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Salesforce Success Story

Case Study: Children’s Hospital Use Cases

In need of help to implement requisite configuration updates to establish a usable data model for data segmentation that supports best practices utilization of Marketing Cloud features including Contact Builder, Email Studio and Journey Builder.

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AI Agents, Tech's Next Big Bet

AI Agents, Tech’s Next Big Bet

What Marketers Need to Know About AI Agents, Tech’s Next Big Bet Companies like Salesforce and OpenAI are making significant investments in AI agents, which are digital assistants poised to represent the next evolution of artificial intelligence. These agents promise to autonomously handle a variety of tasks, from making reservations to negotiating business deals. During OpenAI’s DevDay event in San Francisco last week, the company showcased a voice bot that successfully ordered 400 chocolate-covered strawberries from a local delivery service, specifying delivery and payment terms with minimal issues. OpenAI CEO Sam Altman stated, “2025 is when agents will work,” highlighting the potential for these technologies to revolutionize workflows. While this may seem futuristic, companies like Salesforce, HubSpot, and Pactum AI are already implementing their own AI agents, though examples from brands like Qantas Airways remain relatively scarce—a point of discussion at Advertising Week New York. What Are AI Agents? AI agents extend beyond mere chatbots. According to Parasvil Patel, a partner at Radical Ventures, they lack a single unifying definition and encompass a wide range of functionalities, from automating workflows to scheduling meetings. The overarching goal, however, is clear: “The ultimate aim is to execute work autonomously,” Patel explained. Currently, AI agents are in the “co-pilot” phase, handling specific tasks such as summarizing meetings. The true breakthrough will occur when they transition to “autopilot,” managing more complex tasks without human intervention. According to Patel, this shift could take up to 24 months. When Did They Emerge? AI agents first gained attention on social media in early 2023, with various startups, including AutoGPT—an open-source application built on OpenAI’s models—promising autonomous capabilities. However, Patel notes that many of these early experiments were not robust enough to be deployed effectively in production environments. How Are Companies Using AI Agents? The appeal of AI agents lies in their ability to save time, enhance efficiency, and free employees from repetitive tasks. For instance, a large distribution company struggling to manage 100,000 suppliers utilized Pactum’s AI, which deploys autonomous agents for negotiations. Instead of seeing negotiations as a dead end, these AI agents continuously customized payment deals based on the speed of suppliers’ goods. This approach led to price discounts, rebates, and allowances. Salesforce has also seen positive results with its AI agents. Its pilot program, AgentForce, launched with five clients—including OpenTable and global publisher Wiley—and achieved a 40% increase in case resolution compared to its previous chatbot for Wiley. At the firm’s Dreamforce event, Salesforce demonstrated AgentForce with Ask Astro, assisting attendees in planning their schedules by suggesting sessions and making reservations. Salesforce’s chief marketing officer, Ariel Kelman, stated that the company has heavily invested in developing its AI agent platform in response to client demand. “What companies are figuring out with generative AI is how to deliver productivity improvements for employees and provide meaningful interactions with customers,” he noted. What About Roadblocks? The journey to fully functional AI agents is not without challenges. Managing different data formats—text, images, and videos—can be complex, as highlighted by William Chen, director of product management for AI & emerging tech at Agora. “Your system is only as good as your data source,” he said. For Salesforce, the challenge lies in the nascent customer adoption of AI agents, with companies just beginning to explore how to leverage them for productivity, according to Kelman. The key challenge is determining what solutions work best for employees and customers across various use cases. Are Jobs at Risk? Not necessarily. AI agents are unlikely to replace jobs in the immediate future. Instead, they allow employees to focus on more strategic and meaningful tasks. Rand explained, “The role of people will shift to configuring the autopilot, rather than flying the plane, which is a positive change.” For example, a major logistics client of Pactum, which previously relied on human negotiators for managing deals with freight providers, can now use AI agents to negotiate more efficiently. This adaptability allows companies to dynamically shift their business strategies based on market conditions. What’s Next? While early adopters of AI agents are seeing initial successes, there’s much more to discover. Salesforce plans to launch its next AI agent later this month: a Sales Development Representative (SDR) designed to manage early-stage sales interactions. Typically, human SDRs follow up on marketing leads through emails and calls, but this AI agent will qualify leads, providing human salespeople with a targeted list of 50 to 100 prospects eager to engage. “Instead of receiving a list of 500 leads, they’ll get a refined list of those who actually want to talk,” Kelman concluded. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Einstein Copilot for Healthcare

Einstein Copilot for Healthcare

Einstein Copilot for Healthcare – Salesforce has introduced a new AI-powered healthcare assistant within its CRM system, marking its latest move to expand into the healthcare industry. As AI development accelerates, tech giants like Microsoft, Google, Amazon Web Services, and Salesforce are capitalizing on the opportunity to integrate AI and cloud technologies into healthcare to streamline administrative and operational tasks. Salesforce’s healthcare-specific AI tool, Einstein Copilot, is a conversational assistant that leverages an organization’s private data to provide relevant responses. Einstein Copilot enables healthcare providers and care teams to digitally capture and summarize information from both clinical and nonclinical sources, update patient records, and automate manual workflows. Key Features of Einstein Copilot Providers can use Einstein Copilot to generate patient summaries that include medications, diagnoses, social determinants, assessments, clinical service requests, and care gaps. A care manager can also ask the assistant to find an in-network provider based on location, specialty, and insurance coverage, and auto-fill referral forms using natural language prompts. The AI assistant can also trigger workflows for tasks such as sending referrals, scheduling appointments, and updating care plans. Salesforce expects Einstein Copilot to be HIPAA-compliant by summer 2024, with Copilot: Health Actions slated for general availability in winter 2024. Digitizing Health Assessments Salesforce is adding a feature called Assessment Generation that allows healthcare organizations to digitize standardized health assessments. These can be automatically populated into Salesforce Health Cloud, filled out electronically, and tracked for progress over time. Reducing Administrative Waste Salesforce cites research from McKinsey & Co. showing that administrative costs account for nearly a quarter of U.S. healthcare spending, with a potential savings of up to $320 billion. By integrating AI and CRM tools, Salesforce aims to reduce the operational burden on healthcare providers and improve patient outcomes. Amit Khanna, Senior Vice President and General Manager for Health at Salesforce, highlighted the value of these innovations: “These new data, AI, and CRM features reduce the administrative and operational burden for healthcare providers, leading to better outcomes for patients. With Salesforce’s trusted AI, healthcare organizations excited about generative AI—but wary of clinical and security concerns—can confidently integrate these innovations into their workflows.” Early Adopters and Impact Healthcare providers including Baptist Health South Florida and HarmonyCares are already leveraging Salesforce to personalize patient interactions and create unified patient views. HarmonyCares, which operates across 14 states with over 150 primary care providers, has used Salesforce’s AI-driven field service platform to streamline patient scheduling. The company reported a 50% increase in self-scheduling efficiency since adopting the platform and plans to expand its use of Salesforce Health Cloud for care management and engagement. Kristin Darby, Chief Information Officer at HarmonyCares, emphasized the benefits of AI in healthcare: “AI will dramatically improve our ability to quickly synthesize patient needs and preferences, enabling us to offer a more personalized experience with greater accuracy.” However, the integration of AI in healthcare is not without skepticism. A recent survey revealed that 69% of individuals are uncomfortable with AI being used to diagnose them, though more than half are open to its use in nonclinical tasks like scheduling and billing. Salesforce’s Healthcare Journey Salesforce first launched Health Cloud in 2015 to help providers manage patients by aggregating data from electronic medical records, devices, and wearables. In 2022, the company expanded this offering with Customer 360 for Health, a unified platform that combines real-time data from Data Cloud, Einstein AI, and automation tools like Flow to streamline processes such as prior authorizations, intake, and patient scheduling. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Salesforce Powering EVPassport

Salesforce Powering EVPassport

EVPassport, a global leader in EV charging networks, announced an expanded partnership with Salesforce to enhance its customer experience through the deployment of Salesforce Service Cloud. This initiative solidifies EVPassport’s standing as a top provider in the EV charging space, recognized for customer satisfaction, loyalty, and reliability. With Salesforce Service Cloud, EVPassport can deliver more personalized, efficient service and support to its enterprise, commercial customers, and electric vehicle drivers. The platform enables deeper insights into each driver’s journey, resulting in a seamless, tailored experience. Hooman Shahidi, co-founder and CEO of EVPassport, highlighted the significance of Salesforce in driving the company’s next-generation mobility experience, stating, “As we build the mobility experience of tomorrow, having the right partners is crucial. Salesforce’s innovative solutions will help us exceed the evolving needs of our customers, sites, and communities.” By leveraging Salesforce’s AI, data, and CRM capabilities, EVPassport aims to strengthen customer connections and improve operational efficiency, ensuring a forward-thinking approach to EV charging for years to come. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Gen AI to Predict and Automate Discharge

Gen AI to Predict and Automate Discharge

While electronic health records (EHRs) have improved data exchange for care coordination, they have also increased the clinical documentation burden on healthcare providers. Research from 2023 suggests that clinicians may now spend more time on EHRs than on direct patient care. On average, providers spend over 36 minutes on EHR tasks for every 30-minute patient visit. Generative AI, however, holds the potential to transform this. As defined by the Government Accountability Office, generative AI (GenAI) is a technology that can create content—such as text, images, audio, or video—based on user prompts. With the rise of chatbot interfaces like Chat-GPT, health IT vendors and healthcare systems are piloting GenAI tools to streamline clinical documentation. While the technology shows promise in reducing the documentation burden and mitigating clinician burnout, several challenges still hinder widespread adoption. Ambient Clinical Intelligence Ambient clinical intelligence leverages smartphone microphones and GenAI to transcribe patient encounters in real time, producing draft clinical documentation for providers to review within seconds. A 2024 study examined the use of ambient AI scribes by 10,000 physicians and staff at The Permanente Medical Group. The results were promising—providers reported better patient conversations and less after-hours EHR documentation. However, accuracy is critical for patient safety. A 2023 study found that ambient AI tools struggle with non-lexical conversational sounds (NLCSes)—like “mm-hm” and “uh-uh”—which patients and providers use to convey information. For instance, a patient might say “Mm-hm” to confirm they have no allergies to antibiotics. The study found that while the AI tools had a word error rate of 12% for all words, the error rate for NLCSes conveying clinically relevant information was as high as 98.7%. These inaccuracies could lead to patient safety risks, highlighting the importance of provider review. Patient Communication Patient portal messaging has surged since the COVID-19 pandemic, with a 2023 report showing a 157% increase in messages compared to pre-pandemic levels. To manage inbox overload, healthcare systems are exploring generative AI for drafting responses to patient messages. Clinicians review and edit these drafts before sending them to patients. A 2024 study found that primary care physicians rated AI-generated responses higher in communication style and empathy than those written by providers. However, the AI-generated responses were often longer and more complex, posing challenges for patients with lower health or English literacy. There are also potential risks to clinical decision-making. A 2024 simulation study revealed that the content of replies to patient messages changed when physicians used AI assistance, introducing an automation bias that could impact patient outcomes. Although most AI-generated drafts posed minimal safety risks, a small portion, if left unedited, could result in severe harm or death. Although GenAI may reduce the cognitive load of writing replies, it might not significantly decrease the overall time spent on patient communications. A recent study showed that while providers felt less emotional exhaustion when using AI to draft messages, the time spent on replying, reading, and writing messages remained unchanged from pre-pilot levels. Discharge Summaries Generative AI has also been shown to improve the readability of patient discharge summaries. A study published in JAMA Network Open demonstrated that GenAI could lower the reading level of discharge notes from an eleventh-grade to a sixth-grade level, which is more appropriate for diverse health literacy levels. However, accuracy is still a concern. Physician reviews of these AI-generated summaries found that while some were complete, others contained omissions and inaccuracies that raised safety concerns. Balancing AI’s Benefits with Oversight While generative AI shows promise in alleviating the documentation burden and improving patient communication, challenges remain. Issues such as accurately capturing non-verbal cues and ensuring document accuracy underscore the need for careful provider oversight. As AI technologies continue to evolve, ensuring that the benefits are balanced with provider review will be crucial for safe and effective healthcare implementation. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more 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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