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Pulse for Salesforce

Pulse for Salesforce

Salesforce Unveils Pulse for Salesforce: Integrating Tableau Analytics with CRM to Revolutionize Data-Driven Decision-Making In today’s data heavy business world, where data-driven decision-making is essential for success, the fusion of advanced analytics with customer relationship management (CRM) systems is more crucial than ever. Addressing this need, Salesforce has introduced Pulse for Salesforce, a groundbreaking tool that integrates Tableau’s powerful analytics directly into the Salesforce CRM environment. Meeting the Demand for Actionable Insights This launch aligns with a broader trend in the business intelligence (BI) market, where companies strive to make data analytics more accessible and actionable for non-technical users. Recent studies indicate that while 80% of business leaders view data as critical to decision-making, nearly one-third feel overwhelmed by the sheer volume of information available. Moreover, 91% of these leaders believe their organizations would significantly benefit from generative AI (Gen AI) technologies. Pulse for Salesforce marks a significant milestone in Salesforce’s ongoing strategy following its $15.7 billion acquisition of Tableau in 2019. Tableau, a leader in data visualization and BI since its founding in 2003, has been central to Salesforce’s mission of enhancing customer data management and analysis. The integration of Tableau’s capabilities within Salesforce’s CRM platform represents a major step forward in providing a comprehensive, data-driven solution. Ryan Aytay, President and CEO of Tableau, on the New Integration “Historically, sales leaders and teams have lacked personalized, accessible data insights in their daily flow of work, and analysts often spend considerable time on ad hoc requests and repetitive queries, slowing down decision-making and business growth,” says Ryan Aytay, CEO of Tableau. “By integrating Tableau Pulse’s AI-driven insights into Salesforce, we’re addressing these needs and enhancing data-driven decision-making to help businesses accelerate growth.” Boosting CRM Productivity with Salesforce’s AI Platform Pulse for Salesforce is built on Salesforce’s Einstein 1 AI Platform and leverages Gen AI to provide contextual metrics and insights directly within the Salesforce interface. This seamless integration streamlines decision-making for sales teams by reducing the need for manual data searches or reliance on analysts for ad-hoc queries. Key Features of Pulse for Salesforce Practical Applications and Data Security A practical application of Pulse for Salesforce is performance monitoring. Sales leaders can track team win rate trends directly from their homepage, quickly identifying areas or individuals needing additional support. Similarly, individual sales representatives can monitor their conversion rates and use natural language queries to analyze data by industry, potentially leading to more targeted sales efforts. The integration also addresses data security concerns, a critical issue in the age of AI-powered analytics. Pulse for Salesforce employs the Einstein Trust Layer, a secure AI architecture built into the Einstein 1 Platform, ensuring that customer data remains protected while benefiting from the advanced capabilities of generative AI. Collaboration Salesforce partnered with key industry players and partners to bring this innovative solution to market. With Pulse for Salesforce, organizations can now fully harness the power of integrated analytics and CRM to drive informed decision-making, enhance productivity, and ultimately accelerate business growth. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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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Collabrate With AI

Collabrate With AI

Many artists, writers, musicians, and creators are facing fears that AI is taking over their jobs. On the surface, generative AI tools can replicate work in moments that previously took creators hours to produce—often at a fraction of the cost and with similar quality. This shift has led many businesses to adopt AI for content creation, leaving creators worried about their livelihoods. Yet, there’s another way to view this situation, one that offers hope to creators everywhere. AI, at its core, is a tool of mimicry. When provided with enough data, it can replicate a style or subject with reasonable accuracy. Most of this data has been scraped from the internet, often without explicit consent, to train AI models on a wide variety of creative outputs. If you’re a creator, it’s likely that pieces of your work have contributed to the training of these AI models. Your art, words, and ideas have helped shape what these systems now consider ‘good’ in the realms of art, music, and writing. AI can combine the styles of multiple creators to generate something new, but often these creations fall flat. Why? While image-generating AI can predict pixels, it lacks an understanding of human emotions. It knows what a smile looks like but can’t grasp the underlying feelings of joy, nervousness, or flirtation that make a smile truly meaningful. AI can only generate a superficial replica unless the creator uses extensive prompt engineering to convey the context behind that smile. Emotion is uniquely human, and it’s what makes our creations resonate with others. A single brushstroke from a human artist can convey emotions that might take thousands of words to replicate through an AI prompt. We’ve all heard the saying, “A picture is worth a thousand words.” But generating that picture with AI often takes many more words. Input a short prompt, and the AI will enhance it with more words, often leading to results that stray from your original vision. To achieve a specific outcome, you may need hours of prompt engineering, trial, and error—and even then, the result might not be quite right. Without a human artist to guide the process, these generated works will often remain unimpressive, no matter how advanced the technology becomes. That’s where you, the creator, come in. By introducing your own inputs, such as images or sketches, and using workflows like those in ComfyUI, you can exert more control over the outputs. AI becomes less of a replacement for the artist and more of a tool or collaborator. It can help speed up the creative process but still relies on the artist’s hand to guide it toward a meaningful result. Artists like Martin Nebelong have embraced this approach, treating AI as just another tool in their creative toolbox. Nebelong uses high levels of control in AI-driven workflows to create works imbued with his personal emotional touch. He shares these workflows on platforms like LinkedIn and Twitter, encouraging other creators to explore how AI can speed up their processes while retaining the unique artistry that only humans can provide. Nebelong’s philosophy is clear: “I’m pro-creativity, pro-art, and pro-AI. Our tools change, the scope of what we can do changes. I don’t think creative AI tools or models have found their best form yet; they’re flawed, raw, and difficult to control. But I’m excited for when they find that form and can act as an extension of our hands, our brush, and as an amplifier of our artistic intent.” AI can help bring an artist 80% of the way to a finished product, but it’s the final 20%—the part where human skill and emotional depth come in—that elevates the piece to something truly remarkable. Think about the notorious issues with AI-generated hands. Often, the output features too many fingers or impossible poses, a telltale sign of AI’s limitations. An artist is still needed to refine the details, correct mistakes, and bring the creation in line with reality. While using AI may be faster than organizing a full photoshoot or painting from scratch, the artist’s role has shifted from full authorship to that of a collaborator, guiding AI toward a polished result. Nebelong often starts with his own artwork and integrates AI-generated elements, using them to enhance but never fully replace his vision. He might even use AI to generate 3D models, lighting, or animations, but the result is always driven by his creativity. For him, AI is just another step in the creative journey, not a shortcut or replacement for human effort. However, AI’s ability to replicate the styles of famous artists and public figures raises ethical concerns. With platforms like CIVIT.AI making it easy to train models on any style or subject, questions arise about the legality and morality of using someone else’s likeness or work without permission. As regulations catch up, we may see a future where AI models trained on specific styles or individuals are licensed, allowing creators to retain control over their works in the same way they license their traditional creations today. The future may also see businesses licensing AI models trained on actors, artists, or styles, allowing them to produce campaigns without booking the actual talent. This would lower costs while still benefiting creators through licensing fees. Actors and artists could continue to contribute their talents long after they’ve retired, or even passed on, by licensing their digital likenesses, as seen with CGI performances in movies like Rogue One. In conclusion, AI is pushing creators to learn new skills and adapt to new tools. While this can feel daunting, it’s important to remember that AI is just that—a tool. It doesn’t understand emotion, intent, or meaning, and it never will. That’s where humans come in. By guiding AI with our creativity and emotional depth, we can produce works that resonate with others on a deeper level. For example, you can tell artificial intelligence what an image should look like but not what emotions the image should evoke. Creators, your job isn’t disappearing. It’s

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Salesforce on AI

Salesforce on AI

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

SigFig

SigFig, a leading provider of digital wealth management solutions, has upgraded its Engage platform with advanced AI features designed to enhance the effectiveness of financial advisors. The Engage platform seamlessly combines human expertise with state-of-the-art technology, equipping financial advisors and their teams with a range of tools to boost their efficiency. New AI-driven functionalities in Engage include smart tips that deliver real-time prompts and tailored recommendations during meetings, helping advisors identify new growth opportunities. The platform also proactively presents the most relevant content, tools, paperwork, and workflows to streamline discussions. To further optimize advisors’ workflows, Engage now automates the creation of transcripts, meeting summaries, and post-meeting notes. This automation frees up advisors to focus more on building client relationships rather than managing administrative tasks. Mike Sha, founder and CEO of SigFig, stated, “AI can significantly enhance the productivity and effectiveness of financial advisors. Advisors need to concentrate on developing meaningful relationships with their clients and understanding their goals and challenges. Engage serves as a central hub for creating richer, more personalized client interactions while managing time-consuming administrative duties.” Engage integrates smoothly with popular systems such as Salesforce, Docusign, Microsoft, and Google. Its CRM integration features bi-directional data sync, allowing advisors to access client data within Engage and automatically update Salesforce with meeting notes, client details, and follow-ups. This integration accelerates the sales process and boosts client conversion rates. The platform also fosters a collaborative environment, enabling clients to engage actively in the financial planning process. This collaboration supports clients’ financial well-being and enhances advisors’ understanding of individual financial situations. SigFig has developed various customizable modules for different aspects of financial services, including client prospecting, retirement planning, account opening, and annual reviews. 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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GitHub Copilot Autofix

GitHub Copilot Autofix

On Wednesday, GitHub announced the general availability of Copilot Autofix, an AI-driven tool designed to identify and remediate software vulnerabilities. Originally unveiled in March and tested in public beta, Copilot Autofix integrates GitHub’s CodeQL scanning engine with GPT-4, heuristics, and Copilot APIs to generate code suggestions for developers. The tool provides prompts based on CodeQL analysis and code snippets, allowing users to accept, edit, or reject the suggestions. In a blog post, Mike Hanley, GitHub’s Chief Security Officer and Senior Vice President of Engineering, highlighted the challenges developers and security teams face in addressing existing vulnerabilities. “Code scanning tools can find vulnerabilities, but the real issue is remediation, which requires security expertise and time—both of which are in short supply,” Hanley noted. “The problem isn’t finding vulnerabilities; it’s fixing them.” According to GitHub, the private beta of Copilot Autofix showed that users could respond to a CodeQL alert and automatically remediate a vulnerability in a pull request in just 28 minutes on average, compared to 90 minutes for manual remediation. The tool was even faster for common vulnerabilities like cross-site scripting, with remediation times averaging 22 minutes compared to three hours manually, and SQL injection flaws, which were fixed in 18 minutes on average versus almost four hours manually. Hanley likened the efficiency of Copilot Autofix in fixing vulnerabilities to the speed at which GitHub Copilot, their generative AI coding assistant released in 2022, produces code for developers. However, there have been concerns that GitHub Copilot and similar AI coding assistants could replicate existing vulnerabilities in the codebases they help generate. Industry analyst Katie Norton from IDC noted that while the replication of vulnerabilities is concerning, the rapid pace at which AI coding assistants generate new software could pose a more significant security issue. Chris Wysopal, CTO and co-founder of Veracode, echoed this concern, pointing out that faster coding speeds have led to more software being produced and a larger backlog of vulnerabilities for developers to manage. Norton also emphasized that AI-powered tools like Copilot Autofix could help alleviate the burden on developers by reducing these backlogs and enabling them to fix vulnerabilities without needing to be security experts. Other vendors, including Mobb and Snyk, have also developed AI-powered autoremediation tools. Initially supporting JavaScript, TypeScript, Java, and Python during its public beta, Copilot Autofix now also supports C#, C/C++, Go, Kotlin, Swift, and Ruby. Hanley also highlighted that Copilot Autofix would benefit the open-source software community. GitHub has previously provided open-source maintainers with free access to enterprise security tools for code scanning, secret scanning, and dependency management. Starting in September, Copilot Autofix will also be made available for free to these maintainers. “As the global home of the open-source community, GitHub is uniquely positioned to help maintainers detect and remediate vulnerabilities, making open-source software safer and more reliable for everyone,” Hanley said. Copilot Autofix is now available to all GitHub customers globally. 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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Impact of EHR Adoption

Connected Care Technology

How Connected Care Technology Can Transform the Provider Experience Northwell Health is leveraging advanced connected care technologies, including AI, to alleviate administrative burdens and foster meaningful interactions between providers and patients. While healthcare technology has revolutionized traditional care delivery models, it has also inadvertently created barriers, increasing the administrative workload and distancing providers from their patients. Dr. Michael Oppenheim, Senior Vice President of Clinical Digital Solutions at Northwell Health, highlighted this challenge during the Connected Health 2024 virtual summit, using a poignant illustration published a decade ago in the Journal of the American Medical Association. The image portrays a physician focused on a computer with their back to a patient and family, emphasizing how technology can inadvertently shift attention away from patient care. Reimagining Technology to Enhance Provider-Patient Connections To prevent technology from undermining the patient-provider relationship, healthcare organizations must reduce the administrative burden and enhance connectivity between patients and care teams. Northwell Health exemplifies this approach by implementing innovative solutions aimed at improving access, efficiency, and communication. 1. Expanding Access Without Overloading Providers Connected healthcare technologies can dramatically improve patient access but may strain clinicians managing large patient panels. Dr. Oppenheim illustrated how physicians often need to review extensive patient histories for every interaction, consuming valuable time. Northwell Health addresses this challenge by employing mapping tools, propensity analyses, and matching algorithms to align patients with the most appropriate providers. By connecting patients to specialists who best meet their needs, providers can maximize their time and expertise while ensuring better patient outcomes. 2. Leveraging Generative AI for Chart Summarization Generative AI is proving transformative in managing the immense data volumes clinicians face. AI-driven tools help summarize patient records, extracting clinically relevant details tailored to the provider’s specialty. For instance, in a pilot at Northwell Health, AI successfully summarized complex hospitalizations, capturing the critical elements of care transitions. This “just right” approach ensures providers receive actionable insights without unnecessary data overload. Additionally, ambient listening tools are being used to document clinical consultations seamlessly. By automatically summarizing interactions into structured notes, physicians can focus entirely on their patients during visits, improving care quality while reducing after-hours charting. 3. Streamlining Team-Based Care Effective care delivery often involves a multidisciplinary team, including primary physicians, specialists, nurses, and social workers. Coordinating communication across these groups has historically been challenging. Northwell Health is addressing this issue by adopting EMR systems with integrated team chat functionalities, enabling real-time collaboration among care teams. These tools facilitate better care planning and communication, ensuring patients receive coordinated and consistent treatment. Dr. Oppenheim emphasized the importance of not only uniting clinicians in decision-making but also involving patients in discussions. By presenting clear, viable options, providers can enhance patient engagement and shared decision-making. The Path Forward: Balancing Technology with Provider Needs As healthcare continues its digital transformation, connected care technologies must prioritize clinician satisfaction alongside patient outcomes. Tools that simplify workflows, enhance communication, and reduce administrative burdens are crucial for fostering provider buy-in and ensuring the success of health IT initiatives. Northwell Health’s efforts demonstrate how thoughtfully implemented technologies can empower clinicians, strengthen patient relationships, and create a truly connected healthcare experience. Tectonic is here to help your facility plan. Content updated November 2024. 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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Box Acquires Alphamoon

Box Acquires Alphamoon

Box Inc. has acquired Alphamoon to enhance its intelligent document processing (IDP) capabilities and its enterprise knowledge management AI platform. Now that Box acquires Alphamoon, it will imr improves IDP. Box Acquires Alphamoon IDP goes beyond traditional optical character recognition (OCR) by applying AI to scanned paper documents and unstructured PDFs. While AI technologies like natural language processing (NLP), workflow automation, and document structure recognition have been around for some time, Alphamoon introduces generative AI (GenAI) into the mix, providing advanced capabilities. According to Rand Wacker, Vice President of AI Product Strategy at Box, the integration of GenAI helps not only with summarizing and extracting content from documents but also with recognizing document structures and categorizing them. GenAI works alongside existing OCR and NLP tools, making the digital conversion of paper documents more accurate. Box Acquires Alphamoon – Not LLM Although Box hasn’t acquired a large language model (LLM) outright, it has gained a toolkit that will enhance its Box AI platform. Box AI already uses retrieval-augmented generation to combine a user’s content with external LLMs, ensuring data security while training Box AI to better recognize and categorize documents. Alphamoon’s technology will further refine this process, enabling administrators to create tools more efficiently within the Box ecosystem. “For example, if Alphamoon’s OCR misreads or misextracts something, the system can adjust that specific part and feed it back into the LLM,” Wacker explained. “This approach is powered by an LLM, but it’s specifically trained to understand the documents it encounters, rather than relying on generic content from the internet.” Previewing an upcoming report from Deep Analysis, founder Alan Pelz-Sharpe shared that a survey of 500 enterprises across various industries, including financial services, manufacturing, healthcare, and government, revealed that 53% of enterprise documents still exist on paper. This highlights the need for Box users to have more precise tools to digitize contracts, letters, invoices, faxes, and other paper-based documents. Alphamoon’s generative AI-driven IDP solution allows for human oversight to ensure that attributes are correctly imported from the original documents. Pelz-Sharpe noted that IDP is challenging, but AI has made significant advancements, especially in handling imperfections like crumpled paper, coffee stains, and handwriting. He added that this acquisition addresses a critical gap for Box, which previously relied on partners for these capabilities. Box Buys Alphamoon – Integration Box plans to integrate Alphamoon’s tools into its platform later this year, with deeper integrations expected next year. These will include no-code app-building capabilities related to another acquisition, Crooze, as well as Box Relay’s forms and document generation tools. 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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Rold of Small Language Models

Role of Small Language Models

The Role of Small Language Models (SLMs) in AI While much attention is often given to the capabilities of Large Language Models (LLMs), Small Language Models (SLMs) play a vital role in the AI landscape. Role of Small Language Models. Large vs. Small Language Models LLMs, like GPT-4, excel at managing complex tasks and providing sophisticated responses. However, their substantial computational and energy requirements can make them impractical for smaller organizations and devices with limited processing power. In contrast, SLMs offer a more feasible solution. Designed to be lightweight and resource-efficient, SLMs are ideal for applications operating in constrained computational environments. Their reduced resource demands make them easier and quicker to deploy, while also simplifying maintenance. What are Small Language Models? Small Language Models (SLMs) are neural networks engineered to generate natural language text. The term “small” refers not only to the model’s physical size but also to its parameter count, neural architecture, and the volume of data used during training. Parameters are numeric values that guide a model’s interpretation of inputs and output generation. Models with fewer parameters are inherently simpler, requiring less training data and computational power. Generally, models with fewer than 100 million parameters are classified as small, though some experts consider models with as few as 1 million to 10 million parameters to be small in comparison to today’s large models, which can have hundreds of billions of parameters. How Small Language Models Work SLMs achieve efficiency and effectiveness with a reduced parameter count, typically ranging from tens to hundreds of millions, as opposed to the billions seen in larger models. This design choice enhances computational efficiency and task-specific performance while maintaining strong language comprehension and generation capabilities. Techniques such as model compression, knowledge distillation, and transfer learning are critical for optimizing SLMs. These methods enable SLMs to encapsulate the broad understanding capabilities of larger models into a more concentrated, domain-specific toolset, facilitating precise and effective applications while preserving high performance. Advantages of Small Language Models Applications of Small Language Models Role of Small Language Models is lengthy. SLMs have seen increased adoption due to their ability to produce contextually coherent responses across various applications: Small Language Models vs. Large Language Models Feature LLMs SLMs Training Dataset Broad, diverse internet data Focused, domain-specific data Parameter Count Billions Tens to hundreds of millions Computational Demand High Low Cost Expensive Cost-effective Customization Limited, general-purpose High, tailored to specific needs Latency Higher Lower Security Risk of data exposure through APIs Lower risk, often not open source Maintenance Complex Easier Deployment Requires substantial infrastructure Suitable for limited hardware environments Application Broad, including complex tasks Specific, domain-focused tasks Accuracy in Specific Domains Potentially less accurate due to general training High accuracy with domain-specific training Real-time Application Less ideal due to latency Ideal due to low latency Bias and Errors Higher risk of biases and factual errors Reduced risk due to focused training Development Cycles Slower Faster Conclusion The role of Small Language Models (SLMs) is increasingly significant as they offer a practical and efficient alternative to larger models. By focusing on specific needs and operating within constrained environments, SLMs provide targeted precision, cost savings, improved security, and quick responsiveness. As industries continue to integrate AI solutions, the tailored capabilities of SLMs are set to drive innovation and efficiency across various domains. 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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Generative AI Replaces Legacy Systems

Securing AI for Efficiency and Building Customer Trust

As businesses increasingly adopt AI to enhance automation, decision-making, customer support, and growth, they face crucial security and privacy considerations. The Salesforce Platform, with its integrated Einstein Trust Layer, enables organizations to leverage AI securely by ensuring robust data protection, privacy compliance, transparent AI functionality, strict access controls, and detailed audit trails. Why Secure AI Workflows Matter AI technology empowers systems to mimic human-like behaviors, such as learning and problem-solving, through advanced algorithms and large datasets that leverage machine learning. As the volume of data grows, securing sensitive information used in AI systems becomes more challenging. A recent Salesforce study found that 68% of Analytics and IT teams expect data volumes to increase over the next 12 months, underscoring the need for secure AI implementations. AI for Business: Predictive and Generative Models In business, AI depends on trusted data to provide actionable recommendations. Two primary types of AI models support various business functions: Addressing Key LLM Risks Salesforce’s Einstein Trust Layer addresses common risks associated with large language models (LLMs) and offers guidance for secure Generative AI deployment. This includes ensuring data security, managing access, and maintaining transparency and accountability in AI-driven decisions. Leveraging AI to Boost Efficiency Businesses gain a competitive edge with AI by improving efficiency and customer experience through: Four Strategies for Secure AI Implementation To ensure data protection in AI workflows, businesses should consider: The Einstein Trust Layer: Protecting AI-Driven Data The Einstein Trust Layer in Salesforce safeguards generative AI data by providing: Salesforce’s Einstein Trust Layer addresses the security and privacy challenges of adopting AI in business, offering reliable data security, privacy protection, transparent AI operations, and robust access controls. Through this secure approach, businesses can maximize AI benefits while safeguarding customer trust and meeting compliance requirements. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Lead Generation 101

Lead Generation 101

Lead Generation 101 In today’s world, where people are bombarded with countless messages and offers daily, marketers need to find effective ways to capture attention and generate genuine interest in their products and services. According to the State of the Connected Customer report, customer preferences and expectations are the top influences on digital strategy for Chief Marketing Officers (CMOs). The ultimate goal of lead generation is to build interest over time that leads to successful sales. Here’s a comprehensive guide to understanding lead generation, the role of artificial intelligence (AI), and the steps you need to take to effectively find and nurture leads. What is Lead Generation? Lead generation is the process of creating interest in a product or service and converting that interest into a sale. By focusing on the most promising prospects, lead generation enhances the efficiency of the sales cycle, leading to better customer acquisition and higher conversion rates. Leads are typically categorized into three types: The lead generation process starts with creating awareness and interest. This can be achieved by publishing educational blog posts, engaging users on social media, and capturing leads through sign-ups for email newsletters or “gated” content such as webinars, virtual events, live chats, whitepapers, or ebooks. Once you have leads, you can use their contact information to engage them with personalized communication and targeted promotions. Effective Lead Generation Strategies To successfully move prospects from interest to buyers, focus on the following strategies: How Lead Qualification and Nurturing Work To effectively evaluate and nurture leads, consider the following: Methods for Nurturing Leads Once you’ve established your lead scoring and grading, consider these nurturing methods: Current Trends in Lead Generation AI is increasingly influencing lead generation by offering advanced tools and strategies: Measuring Success in Lead Generation To evaluate the effectiveness of your lead generation efforts, track the following key metrics: Best Practices for Lead Generation To optimize lead generation efforts and build strong customer relationships, follow these best practices: Effective lead generation is essential for building trust and fostering meaningful customer relationships. By implementing these strategies and best practices, you can enhance your lead generation efforts and drive better business results. 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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Einstein Service Agent

Einstein Service Agent

Introducing Agentforce Service Agent: Salesforce’s Autonomous AI to Transform Chatbot Experiences Accelerate case resolutions with an intelligent, conversational interface that uses natural language and is grounded in trusted customer and business data. Deploy in minutes with ready-made templates, Salesforce components, and a large language model (LLM) to autonomously engage customers across any channel, 24/7. Establish clear privacy and security guardrails to ensure trusted responses, and escalate complex cases to human agents as needed. Editor’s Note: Einstein Service Agent is now known as Agentforce Service Agent. Salesforce has launched Agentforce Service Agent, the company’s first fully autonomous AI agent, set to redefine customer service. Unlike traditional chatbots that rely on preprogrammed responses and lack contextual understanding, Agentforce Service Agent is dynamic, capable of independently addressing a wide range of service issues, which enhances customer service efficiency. Built on the Einstein 1 Platform, Agentforce Service Agent interacts with large language models (LLMs) to analyze the context of customer messages and autonomously determine the appropriate actions. Using generative AI, it creates conversational responses based on trusted company data, such as Salesforce CRM, and aligns them with the brand’s voice and tone. This reduces the burden of routine queries, allowing human agents to focus on more complex, high-value tasks. Customers, in turn, receive faster, more accurate responses without waiting for human intervention. Available 24/7, Agentforce Service Agent communicates naturally across self-service portals and messaging channels, performing tasks proactively while adhering to the company’s defined guardrails. When an issue requires human escalation, the transition is seamless, ensuring a smooth handoff. Ease of Setup and Pilot Launch Currently in pilot, Agentforce Service Agent will be generally available later this year. It can be deployed in minutes using pre-built templates, low-code workflows, and user-friendly interfaces. “Salesforce is shaping the future where human and digital agents collaborate to elevate the customer experience,” said Kishan Chetan, General Manager of Service Cloud. “Agentforce Service Agent, our first fully autonomous AI agent, will revolutionize service teams by not only completing tasks autonomously but also augmenting human productivity. We are reimagining customer service for the AI era.” Why It Matters While most companies use chatbots today, 81% of customers would still prefer to speak to a live agent due to unsatisfactory chatbot experiences. However, 61% of customers express a preference for using self-service options for simpler issues, indicating a need for more intelligent, autonomous agents like Agentforce Service Agent that are powered by generative AI. The Future of AI-Driven Customer Service Agentforce Service Agent has the ability to hold fluid, intelligent conversations with customers by analyzing the full context of inquiries. For instance, a customer reaching out to an online retailer for a return can have their issue fully processed by Agentforce, which autonomously handles tasks such as accessing purchase history, checking inventory, and sending follow-up satisfaction surveys. With trusted business data from Salesforce’s Data Cloud, Agentforce generates accurate and personalized responses. For example, a telecommunications customer looking for a new phone will receive tailored recommendations based on data such as purchase history and service interactions. Advanced Guardrails and Quick Setup Agentforce Service Agent leverages the Einstein Trust Layer to ensure data privacy and security, including the masking of personally identifiable information (PII). It can be quickly activated with out-of-the-box templates and pre-existing Salesforce components, allowing companies to equip it with customized skills faster using natural language instructions. Multimodal Innovation Across Channels Agentforce Service Agent supports cross-channel communication, including messaging apps like WhatsApp, Facebook Messenger, and SMS, as well as self-service portals. It even understands and responds to images, video, and audio. For example, if a customer sends a photo of an issue, Agentforce can analyze it to provide troubleshooting steps or even recommend replacement products. Seamless Handoffs to Human Agents If a customer’s inquiry requires human attention, Agentforce seamlessly transfers the conversation to a human agent who will have full context, avoiding the need for the customer to repeat information. For example, a life insurance company might program Agentforce to escalate conversations if a customer mentions sensitive topics like loss or death. Similarly, if a customer requests a return outside of the company’s policy window, Agentforce can recommend that a human agent make an exception. Customer Perspective “Agentforce Service Agent’s speed and accuracy in handling inquiries is promising. It responds like a human, adhering to our diverse, country-specific guidelines. I see it becoming a key part of our service team, freeing human agents to handle higher-value issues.” — George Pokorny, SVP of Global Customer Success, OpenTable. Content updated October 2024. 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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What is Salesforce Health Cloud

Explore Salesforce Health Cloud

Empower Your Healthcare Team with Salesforce Health Cloud Equip your healthcare team with comprehensive 360-degree views that help connect and engage every patient, member, employee, and partner. Explore Salesforce Health Cloud Explore Health Cloud Understanding the capabilities of this platform is the first step to transforming your organization’s patient management. Let’s explore what Health Cloud offers to various types of healthcare organizations. Introducing Salesforce Health Cloud: A CRM Solution for Patient Management Over 600 companies, including industry leaders like Lilly, Pacific Clinics, United Healthcare, Progyny, Stanley Healthcare, and Humana, trust Salesforce Health Cloud for their patient management needs. As the healthcare industry rapidly evolves, effective patient information management is essential. This insight looks into Salesforce Health Cloud’s capabilities, features, integration options, and benefits, including its security architecture. What is Health Cloud? Salesforce Health Cloud is a cloud-based technology designed specifically for the healthcare industry. It centralizes patient information, giving healthcare professionals a complete view of patient records, enabling more effective treatments and better patient care. Key Capabilities of Salesforce Health Cloud Salesforce Health Cloud is a robust platform offering key capabilities such as: Salesforce in the Healthcare Industry Salesforce is increasingly popular among healthcare organizations for several reasons: Salesforce Health Platform Features Salesforce Health Cloud offers three main sets of features: Salesforce Health Cloud Architecture The architecture of Salesforce Health Cloud includes: Salesforce Health Cloud Security Salesforce Health Cloud is designed to securely manage healthcare data, featuring: Revolutionizing Healthcare Delivery with Salesforce Health Cloud Salesforce Health Cloud is designed for healthcare organizations to automate processes and provide personalized patient care. Since its launch in 2016, Health Cloud has evolved to address the complexities of the healthcare industry, including the introduction of Customer 360 for Health, an AI-driven healthcare solution. Why Choose Salesforce Health Cloud? Salesforce Health Cloud connects healthcare teams to ensure that patients receive the right care, supported by multi-layered security to protect sensitive patient data. It integrates clinical and non-clinical patient data, streamlining workflows and enhancing patient satisfaction. Top Features of Salesforce Health Cloud Key features include Patient 360, Care Plans, Care Coordination, Health Timeline, and Einstein Analytics for Healthcare, among others. Salesforce has also introduced AI-powered innovations under the Patient 360 for Health initiative, enhancing patient care and operational efficiency. Integration with MuleSoft Salesforce Health Cloud’s integration with MuleSoft allows organizations to connect with existing healthcare systems, ensuring accurate and up-to-date patient information, unlocking the full potential of their data, and improving decision-making. Conclusion Salesforce Health Cloud is more than just a platform—it’s a comprehensive solution for managing doctor-patient interactions, recordkeeping, and delivering personalized care. By leveraging Health Cloud, healthcare organizations can transform patient experiences, streamline processes, and ensure data security and compliance, positioning themselves for a brighter future in healthcare. 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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AI-Powered Smarter Media

AI-Powered Smarter Media

Transforming Retail Media: Personalization and Faster Monetization with Smarter Media Dentsu, a leading growth and transformation partner, has announced a strategic collaboration with Salesforce, the world’s #1 AI-powered CRM, to launch Smarter Media—an innovative solution designed to accelerate retail media monetization through personalized buying experiences powered by AI. Why Smarter Media Matters With shifting consumer priorities, personalized retail experiences are more critical than ever. Salesforce research highlights that: Smarter Media addresses this growing demand by enabling retailers to quickly adapt, offering tailored buying experiences that strengthen customer loyalty while driving revenue. What is Smarter Media? Smarter Media combines the power of Salesforce’s ecosystem—including Media Cloud, Sales Cloud, and Marketing Cloud Engagement—to deliver an end-to-end retail media solution. The platform assesses a brand’s retail media maturity, identifies gaps, and creates a roadmap to optimize media, technology, and skills. The solution simplifies access to advanced media technology, empowering brands to connect with customers 24/7, expand their customer base, and nurture long-term relationships. Key Features and Benefits 1. Comprehensive Assessment 2. AI-Powered Personalization 3. Built for Retail Media Success 4. Quick and Easy Adoption How Smarter Media Works Smarter Media combines Salesforce Sales Cloud’s leading sales and pipeline management tools with Media Cloud’s Advertising Sales Management application. The result is a solution that seamlessly supports both simple and complex retailer models: Real-World Value Across Retail By addressing challenges like fragmented media strategies and inaccessible technology, Smarter Media delivers transformative value for retailers: Driving Innovation Together Paul Lynch, Integrated Solutions Lead for Commerce and Retail at Dentsu UK&I, shared: “Smarter Media will democratize cutting-edge technology for brands by providing a one-stop solution to create personalized buying experiences. In today’s experience economy, maintaining compelling customer relationships has never been more vital.” Christopher Dean, SVP and GM for Communications, Media & Entertainment at Salesforce, added: “By combining Salesforce Media Cloud’s industry-specific solutions with Dentsu’s creative retail media expertise, we’re making advanced media technology accessible for retailers, helping them thrive in a competitive market.” The Future of Retail Media Smarter Media from Dentsu and Salesforce offers a transformative approach to retail media, empowering brands to deliver personalized experiences, improve customer loyalty, and accelerate revenue growth—all while leveraging cutting-edge AI and automation. With its ability to deliver value in just six months, Smarter Media is the ultimate solution for retailers looking to succeed in today’s fast-paced, customer-centric market. 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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