PHI Archives - gettectonic.com - Page 14
Ethical and Responsible AI
02Apr

Ethical and Responsible AI

Responsible AI and ethical AI are closely connected, with each offering complementary yet distinct principles for the development and use of AI systems. Organizations that aim for success must integrate both frameworks, as they are mutually reinforcing. Responsible AI emphasizes accountability, transparency, and adherence to regulations. Ethical AI—sometimes called AI ethics—focuses on broader moral values like fairness, privacy, and societal impact. In recent discussions, the significance of both has come to the forefront, encouraging organizations to explore the unique advantages of integrating these frameworks. While Responsible AI provides the practical tools for implementation, ethical AI offers the guiding principles. Without clear ethical grounding, responsible AI initiatives can lack purpose, while ethical aspirations cannot be realized without concrete actions. Moreover, ethical AI concerns often shape the regulatory frameworks responsible AI must comply with, showing how deeply interwoven they are. By combining ethical and responsible AI, organizations can build systems that are not only compliant with legal requirements but also aligned with human values, minimizing potential harm. The Need for Ethical AI Ethical AI is about ensuring that AI systems adhere to values and moral expectations. These principles evolve over time and can vary by culture or region. Nonetheless, core principles—like fairness, transparency, and harm reduction—remain consistent across geographies. Many organizations have recognized the importance of ethical AI and have taken initial steps to create ethical frameworks. This is essential, as AI technologies have the potential to disrupt societal norms, potentially necessitating an updated social contract—the implicit understanding of how society functions. Ethical AI helps drive discussions about this evolving social contract, establishing boundaries for acceptable AI use. In fact, many ethical AI frameworks have influenced regulatory efforts, though some regulations are being developed alongside or ahead of these ethical standards. Shaping this landscape requires collaboration among diverse stakeholders: consumers, activists, researchers, lawmakers, and technologists. Power dynamics also play a role, with certain groups exerting more influence over how ethical AI takes shape. Ethical AI vs. Responsible AI Ethical AI is aspirational, considering AI’s long-term impact on society. Many ethical issues have emerged, especially with the rise of generative AI. For instance, machine learning bias—when AI outputs are skewed due to flawed or biased training data—can perpetuate inequalities in high-stakes areas like loan approvals or law enforcement. Other concerns, like AI hallucinations and deepfakes, further underscore the potential risks to human values like safety and equality. Responsible AI, on the other hand, bridges ethical concerns with business realities. It addresses issues like data security, transparency, and regulatory compliance. Responsible AI offers practical methods to embed ethical aspirations into each phase of the AI lifecycle—from development to deployment and beyond. The relationship between the two is akin to a company’s vision versus its operational strategy. Ethical AI defines the high-level values, while responsible AI offers the actionable steps needed to implement those values. Challenges in Practice For modern organizations, efficiency and consistency are key, and standardized processes are the norm. This applies to AI development as well. Ethical AI, while often discussed in the context of broader societal impacts, must be integrated into existing business processes through responsible AI frameworks. These frameworks often include user-friendly checklists, evaluation guides, and templates to help operationalize ethical principles across the organization. Implementing Responsible AI To fully embed ethical AI within responsible AI frameworks, organizations should focus on the following areas: By effectively combining ethical and responsible AI, organizations can create AI systems that are not only technically and legally sound but also morally aligned and socially responsible. Content edited 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 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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April 2, 2024in AI Tools, Data, Generative AI
AI Then and Now
30Mar

AI Then and Now

AI: Transforming User Interactions and Experiences Have you ever been greeted by a waitress who already knows your breakfast order? It’s a relief not to detail every aspect — temperature, how do you want your eggs, what kind of juice, bacon or sausage, etc. This example encapsulates the journey we’re navigating with AI today. AI Then and Now. This article isn’t about ordering breakfast; it’s about the evolution of user interactions, particularly how generative AI might evolve based on past trends in graphical user interfaces (GUIs) and emerging trends in AI interactions. We’ll explore the significance of context bundling, user curation, trust, and ecosystems as key trends in AI user experience in this Tectonic insight. From Commands to Conversations Let’s rewind to the early days of computing when users had to type precise commands in a Command-Line Interface (CLI). Imagine the challenge of remembering the exact command to open a file or copy data. This complexity meant that only a few people could use computers effectively. To reach a broader audience, a shift was necessary. You might think Apple’s creation of the mouse and drop down menues was the pinnacle of success, but truly the evolution predates Apple. Enter ELIZA in 1964, an early natural language processing program that engaged users in basic conversations through keyword recognition and scripted responses. Although groundbreaking, ELIZA’s interactions were far from flexible or scalable. Around the same time, Xerox PARC was developing the Graphical User Interface (GUI), later popularized by Apple in 1984 and Microsoft shortly thereafter. GUIs transformed computing by replacing complex commands with icons, menus, and windows navigable by a mouse. This innovation made computers accessible and intuitive for everyday tasks, laying the groundwork for technology’s universal role in our lives. Not only did it make computing accessible to the masses but it layed the foundation upon which every household would soon have one or more computers! The Evolution of AI Interfaces Just as early computing transitioned from the complexity of CLI to the simplicity of GUIs, we’re witnessing a parallel evolution in generative AI. User prompts are essentially mini-programs crafted in natural language, with the quality of outcomes depending on our prompt engineering skills. We are moving towards bundling complex inputs into simpler, more user-friendly interfaces with the complexity hidden in the background. Context Bundling Context bundling simplifies interactions by combining related information into a single command. This addresses the challenge of conveying complex instructions to achieve desired outcomes, enhancing efficiency and output quality by aligning user intent and machine understanding in one go. We’ve seen context bundling emerge across generative AI tools. For instance, sample prompts in Edge, Google Chrome’s tab manager, and trigger-words in Stable Diffusion fine-tune AI outputs. Context bundling isn’t always about conversation; it’s about achieving user goals efficiently without lengthy interactions. Context bundling is the difference in ordering the eggs versus telling the cook how to crack and prepare it. User Curation Despite advancements, there remains a spectrum of needs where users must refine outputs to achieve specific goals. This is especially true for tasks like researching, brainstorming, creating content, refining images, or editing. As context windows and multi-modal capabilities expand, guiding users through complexity becomes even more crucial. Humans constantly curate their experiences, whether by highlighting text in a book or picking out keywords in a conversation. Similarly, users interacting with ChatGPT often highlight relevant information to guide their next steps. By making it easier for users to curate and refine their outputs, AI tools can offer higher-quality results and enrich user experiences. User creation takes ordering breakfast from a manual conversational process to the click of a button on a vending-like system. Designing for Trust Trust is a significant barrier to the widespread adoption of generative AI. To build trust, we need to consider factors such as previous experiences, risk tolerance, interaction consistency, and social context. Without trust, in AI or your breakfast order, it becomes easier just to do it yourself. Trust is broken if the waitress brings you the wrong items, or if the artificial intelligence fails to meet your reasonable expectations. Context Ecosystems Generative AI has revolutionized productivity by lowering the barrier for users to start tasks, mirroring the benefits and journey of the GUI. However, modern UX has evolved beyond simple interfaces. The future of generative AI lies in creating ecosystems where AI tools collaborate with users in a seamless workflow. We see emergent examples like Edge, Chrome, and Pixel Assistant integrating AI functionality into their software. This integration goes beyond conversational windows, making AI aware of the software context and enhancing productivity. The Future of AI Interaction Generative AI will likely evolve to become a collaborator in our daily tasks. Tools like Grammarly and Github Copilot already show how AI can assist users in creating and refining content. As our comfort with AI grows, we may see generative AI managing both digital and physical aspects of our lives, augmenting reality and redefining productivity. The evolution of generative AI interactions is repeating the history of human-computer interaction. By creating better experiences that bundle context into simpler interactions, empower user curation, and augment known ecosystems, we can make generative AI more trustworthy, accessible, usable, and beneficial for everyone. 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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March 30, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Google, Salesforce, Salesforce Einstein, Technology
Salesforce Success Story
21Mar

Case Study: Healthcare Health Cloud Marketing Cloud Large Childrens Hospital

Large children’s hospital needs a usable data model and enhanced security to deliver excellent patient outcomes. Healthcare Health Cloud Marketing Cloud Large Childrens Hospital. Industry: Healthcare Client is a large children’s hospital with pediatric healthcare offering acute care. Problem: Implemented : Our solution? Results: In order to improve operations, provide physician-facing services, and move data—including PHI and PII—to the cloud, we have assisted healthcare providers in overcoming these obstacles. Salesforce offers all-inclusive solutions specifically designed to meet the demands of payers (insurance companies) and providers (healthcare organizations). Better health outcomes, more operational effectiveness, and increased patient engagement are the goals of these solutions. Salesforce solutions for the health and life sciences are tailored to the particular requirements of the medical industry. Salesforce offers digital transformation technology for health and life sciences industries. If you are considering a Salesforce healthcare implementation, contact Tectonic today. Like2 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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March 21, 2024in Salesforce, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Salesforce Marketing Cloud, Salesforce Sales Cloud, Salesforce Shield, Salesforce Success Stories
Workflow Rules & Process Builder End of Support
07Mar

Workflow Rules And Process Builder End of Support

Publish Date: Mar 5, 2024 Description Salesforce will no longer be supporting Workflow Rules and Process Builder on December 31, 2025, and we recommend that you migrate your automation to Flow Builder by that time. Workflow Rules & Process Builder End of Support You’re also probably wondering why we’re retiring Workflow Rules and Process Builder. Salesforce wants to focus development on a modern, extensible, low-code automation solution in Flow Builder, which led us to retire the previous features. What does this change mean for me? If you have active Workflow Rules or Process Builder processes running after 2025, they will no longer receive customer support or bug fixes. What action can I take? We recommend implementing a plan to migrate any active rules or processes to Flow Builder before the deadline. Depending on the complexity of your org, this migration may take a significant amount of time and testing, so we recommend starting now. To assist in the migration process, we have a Migrate to Flow tool and extensive support resources available. What happens if I don’t take action? After December 31, 2025, Workflow Rules and Process Builder may continue to function and execute existing automation, but customer support will not be available, and bugs will not be fixed. How do I identify affected users? You can identify whether you have active workflow rules by going to Setup | Process Automation | Workflow Rules and sorting the Active column for checkmarks. You can identify whether you have active Process Builder processes by going to Setup | Process Automation | Process Builder and sorting the Status column for Active. If you have more questions, open a case with support via Salesforce Help. To view all current and past retirements, see Salesforce Product & Feature Retirements. To read about the Salesforce approach to retirements, read our Product & Feature Retirement Philosophy. 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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March 7, 2024in Salesforce
Einstein Copilot Studio
02Mar

Einstein Copilot Studio Explained

Einstein Copilot Studio Explained: Crafting and Personalizing a Reliable AI Assistant Enterprises aiming to personalize Einstein Copilot can leverage the newly introduced Einstein Copilot Studio. This platform enables the construction and customization of AI assistants, incorporating pertinent prompts, skills, and AI models tailored for specific sales, service, marketing, commerce, and IT tasks. Beyond the confines of Salesforce applications, companies can seamlessly integrate Einstein Copilot into consumer-facing channels. This extension enhances customer interactions by embedding AI assistants into websites for real-time chat capabilities or integrating with popular messaging platforms such as Slack, WhatsApp, or SMS. Einstein Copilot Studio comprises the following key components: Just as Microsoft has introduced its own Copilot solutions, powered by generative AI, Salesforce is tapping into the power of LLMs to empower sales, marketing, and customer service professionals. Building on Salesforce’s existing range of Einstein AI features, the company announced “Einstein 1” this year – the next generation of the Salesforce platform. Einstein 1 is a comprehensive suite of tools that empowers users to bring AI into their everyday workflows. The Einstein Copilot (Salesforce Copilot) solution is at the core of this solution, alongside the new Copilot studio and the Einstein Trust Layer. Contact Tectonic today to explore the value of Einstein Copilot Studio for your company., Like2 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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March 2, 2024in Einstein 1 Platform
Sales Cloud Innovation Driven by UX Design Principles
29Feb

Sales Cloud Innovation Driven by UX Design Principles

Driving Sales Cloud Innovation Through UX Design Principles: Sales Cloud Innovation Driven by UX Design Principles Enhancing user experiences and driving innovation within Sales Cloud relies on the fundamental principles of UX design. The core philosophy revolves around understanding users’ needs and ensuring simplicity as the default, allowing for increased trust and success. Here’s how three foundational UX design principles guide the product design team at Salesforce: UX Design in Action: The principles of meeting users where they’re at, maintaining low walls and high ceilings, and favoring simplicity are integral to Sales Cloud’s UX design philosophy. By adhering to these principles, Sales Cloud strives to build confidence among users, fostering a collaborative approach to developing innovative and user-friendly products.  Sales Cloud administrators need to operate with the same thought process. Tectonic is proud to introduce our Sales Cloud Implementation Solutions. Content updated May 2024. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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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February 29, 2024in Salesforce Implementation Services, Salesforce Sales Cloud
How Good is Our Data
22Feb

How Good is Our Data?

Generative AI promises to significantly reshape how you manage your customer relationships, but it requires data that is accurate, updated, accessible, and complete. Why is this important? You may do something differently this quarter than you did last quarter, based on the latest data. But if your data is outdated or incorrect, that’s what the AI will use.  Generative AI focuses on creating new and original content, chat responses, designs, synthetic content or even deepfakes. It’s particularly valuable in creative fields and for novel problem-solving, as it can autonomously generate many types of new outputs. Generative Artificial Intelligence models often present inaccurate information as though it were correct. This is often caused by limited information in the system, biases in training data, and issues with the algorithm. These are commonly called ‘hallucinations‘ and they present a huge problem. When training your models for generative AI, you should first ensure high information excellence from top to bottom. To get your information house in order, remove duplicates, outliers, errors, and other things that can negatively affect how you make decisions. Then connect your data sources — marketing, sales, service, commerce – into a single record, updated in real time, so the AI can make the best recommendations.   McKinsey recently wrote, “Companies that have not yet found ways to harmonize and provide ready access to their information will be unable to unlock much of generative AI’s potentially transformative power.” Why is data important in generative AI? Aside from the cost factor, poor information quality can introduce unnecessary and harmful noise into the generative AI systems and models, leading to misleading answers, nonsensical output, or overall lower efficacy. What is high-quality data for AI? High-quality information is essential for AI systems to deliver meaningful results. Data quality possesses several key attributes: Accuracy: High-quality information is free from errors and inaccuracies. Inaccurate information can mislead AI models and produce unreliable outputs. Is AI 100 percent accurate? Because AI will still rely on your data for decision making and accuracy depends on the quality of your information. AI machines must be well-programmed to make sure the machine is making decisions based on the correct, available information. Also, privacy and security of the data are paramount. AI machines need to access information that is encrypted and secure. Understand that Generative AI is most effective at creating new data based on existing patterns and examples, with a focus on text and image data. Generative AI is most suitable for generating new data based on existing patterns and examples. It doesn’t actually think for itself. Yet. Known Limitations Of Generative AI Large language models (LLMs) are prone to “hallucinations” – generating fictitious information, presented as factual or accurate. This can include citations, publications, biographical information, and other information commonly used in research and academic papers. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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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February 22, 2024in Generative AI, Salesforce Einstein
Exploring Google Vertex AI
21Feb

Vertex AI

Exploring Google Vertex AI Conversation — Dialogflow CX with Generative AI, Data Stores, and Generators Vertex AI Conversation, built on Dialogflow and Vertex AI, introduces generative conversational features that utilize large language models (LLMs) for natural language understanding, crafting responses, and managing conversation flow. These advancements streamline agent design and enhance the quality of interactions. With Vertex AI Conversation, you can employ a state machine approach to develop sophisticated, generative AI-powered agents for dynamic conversation design and automation. In this insight, we’ll delve into the cutting-edge Dialogflow CX Generative AI technology, focusing on Data Stores and Generators. Data Stores: The Library of Information for Conversations Imagine Data Stores as an extensive library. When a question is asked, the virtual assistant acts as a librarian, locating relevant information. Dialogflow CX’s Data Store feature makes it easy to create conversations around stored information from various sources: For data preparation guidance, visit Google’s official documentation. Generators: LLM-Enhanced Dynamic Responses Dialogflow CX also enables Generators to use an LLM directly in Dialogflow CX without webhooks. Generators can perform tasks like summarization, parameter extraction, and data manipulation. Sourced from Vertex AI, they create real-time responses based on your prompts. For example, a Generator can be customized to summarize lengthy answers—an invaluable feature for simplifying conversations in chat or voice applications. You can find common Generator configurations in Google Cloud Platform (GCP) documentation. Creating a Chat Application with Vertex AI To start building, go to the Search and Conversation page in Google Cloud, agree to the terms, activate the API, and select “Chat.” Setting Up Your Agent After naming your agent and configuring data sources, like a Cloud Storage bucket with PDF documents, you’ll see your new chat app under Search & Conversation | Apps. Navigate to Dialogflow CX, where you can use your data store by setting up parameters for the agent and configuring responses. Once your agent is ready, you can test it in the Agent simulator. Adding a Generator for Summarization Using the Generator feature, you can further refine responses. Set parameters to target the Generator’s summarization feature, and link it to a specific page for summarized responses. This improves chat flow, providing concise answers for faster interactions. Integrating with Discord If you want to deploy your agent on platforms like Discord, follow Google’s integration guide for Dialogflow and adjust your code as needed. With the integration, responses will include hyperlinks for easy reference. Conclusion Vertex AI Conversation, with Dialogflow CX, enables powerful, human-like chat experiences by combining LLMs, Data Stores, and Generators. Ready to build your own dynamic conversational experiences? Now is the perfect time to experiment with this technology and see where it can take you. 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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February 21, 2024in Data, Generative AI, Google, Technology
Salesforce Pro Suite
20Feb

Salesforce Pro Suite

Revolutionizing CRM: Introducing Salesforce Pro Suite In today’s dynamic business technology landscape, Salesforce has established itself as a leader in customer relationship management (CRM) solutions. The launch of Salesforce Pro Suite marks a significant milestone in their mission to empower businesses with cutting-edge tools designed to optimize operations, enhance customer engagement, and drive growth. This article explores the features, benefits, and potential of Salesforce Pro Suite, showcasing why it stands out as a transformative solution for businesses of all sizes. What is Salesforce Pro Suite? Salesforce Pro Suite is a comprehensive collection of integrated tools and services designed to augment the capabilities of Salesforce’s CRM platform. Tailored for modern businesses—from startups to large enterprises—it incorporates advanced functionalities such as artificial intelligence (AI), automation, and data analytics to boost productivity, foster collaboration, and facilitate informed decision-making. Unlock growth and deepen customer relationships with Pro Suite—the all-in-one CRM suite with marketing, sales, service, and commerce tools that scale with your business. Get the flexibility to automate tasks and customize your CRM to fit your specific needs with Pro Suite. Key Features of Salesforce Pro Suite Benefits of Salesforce Pro Suite Use Cases of Salesforce Pro Suite What Can You Do with Pro Suite? Conclusion Salesforce Pro Suite represents a significant advancement in CRM technology, offering a comprehensive suite of tools that cater to the diverse needs of modern businesses. By harnessing AI, automation, and advanced analytics, Pro Suite empowers organizations to optimize operations, enhance customer engagement, and make informed, data-driven decisions. Whether you’re a small startup or a large enterprise, Salesforce Pro Suite provides the scalability, flexibility, and security required to thrive in today’s competitive landscape. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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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February 20, 2024in Artificial Intelligence, Data, Enterprise, Salesforce, Technology
Generative AI Prompts with Retrieval Augmented Generation
11Feb

Generative AI Cheat Sheets

Wanted to utilize this insight to share a link to some incredible AI cheat sheets compiled by Medium. Generative AI Cheat Sheets. Top 8 Cheat Sheets on AI Whether you need assistance building a Powerpoint Presentation, AI for enterprise, machine learning, podcast enhancement tools, large language models, efficient ChatGPT prompts, efficient use of emojis, journeys, or more. This list is pretty inclusive. Tectonic would like to share one additional tool we have been using internally. Fireflies. Firflies helps teams transcribe, summarize, search, and analyze voice conversations. When ChatGPT made its debut in late 2022, it sparked global recognition of the transformative capabilities of artificial intelligence (AI). This groundbreaking chatbot represents one of the most significant advancements in AI history. Unlike traditional AI systems that analyze or categorize existing data, generative AI has the remarkable ability to create entirely new content, spanning text, images, audio, synthetic data, and more. This innovation is poised to revolutionize human creativity and productivity across industries, including business, science, and society as a whole. From ChatGPT to DALL-E, the latest wave of generative AI applications has emerged from foundation models, sophisticated machine learning systems trained on massive datasets encompassing text, images, audio, or a combination of these data types. Recent advancements now enable companies to develop specialized models for image and language generation based on these foundation models, most of which are large language models (LLMs) trained on natural language. The power of these models lies not only in their scale but also in their adaptability to diverse tasks without the need for task-specific training. Techniques like zero-shot learning and in-context learning allow models to make predictions and generate responses even in domains they haven’t been explicitly trained on. As a result, companies can leverage these models to address a wide range of challenges, from customer service automation to product design. The introduction of pre-trained foundation models with unprecedented adaptability is expected to have profound implications. According to Accenture’s 2023 Technology Vision report, 97% of global executives believe that foundation models will revolutionize how and where AI is applied, enabling seamless connections across different data types. To thrive in this evolving landscape, businesses must leverage the full potential of generative AI. To expedite implementation, organizations can readily access foundation models through APIs. However, customization and fine-tuning are necessary to tailor these models to specific use cases and maximize their effectiveness. By harnessing generative AI, companies can enhance efficiency, drive innovation, and gain a competitive edge in the market. As generative AI continues to evolve, its impact will only multiply. Companies will increasingly rely on these technologies to streamline workflows, optimize processes, and unlock new opportunities for growth and innovation. With the global AI market projected to reach nearly trillion by 2030, the future holds immense potential for companies to leverage generative AI in solving complex problems and driving transformative change. Generative AI encompasses various machine learning techniques, including transformer models, generative adversarial networks (GANs), and variational autoencoders (VAEs). These technologies underpin a wide range of applications, from natural language processing to image generation, enabling businesses to approach tasks in innovative ways. While generative AI presents unprecedented opportunities, it also raises ethical and security concerns. It is essential for companies to adopt responsible AI practices and ensure the safe and ethical use of these technologies. By embracing generative AI and investing in the necessary infrastructure and talent, businesses can unlock its full potential and drive sustainable growth in the digital era. 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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February 11, 2024in Generative AI, Salesforce
Considerations When Implementing PHI
11Feb

Considerations When Implementing PHI

Consumers today express heightened concerns about their data privacy, with 92% of Americans showing apprehension about online privacy. This apprehension extends beyond worries about the security of mobile phones, email, and browsers, particularly in the healthcare sector, where providers face increased scrutiny in safeguarding patients’ Protected Health Information (PHI). PHI is a prime target for cybercriminals due to its sensitivity, but securing it at scale poses significant challenges. Considerations When Implementing PHI. What is PHI and how is it protected? With certain exceptions, the Privacy Rule protects a subset of individually identifiable health information, known as protected health information or PHI, that is held or maintained by covered entities or their business associates acting for the covered entity. HIPAA mandates stringent rules for the protection of healthcare information qualifying as PHI, imposing severe financial and criminal penalties for non-compliance. The HIPAA Privacy Rule specifically oversees PHI, encompassing health or personal information that can identify an individual, including historical, present, or future data related to mental or physical health. Entities handling PHI must adhere to strict requirements for transmitting, storing, and disposing of this data, as patients inherently possess legal rights to the privacy and security of their PHI. Compliance is vital for the protection of PHI, not only to fulfill regulatory obligations but also to mitigate the substantial risks posed by cybercriminals who target this valuable information. The allure for cybercriminals lies in the lucrative market for healthcare data, with records selling for hundreds to thousands of dollars per record on the black market. Given the potential for compromising millions of patient records in a single breach, attackers stand to gain significant sums. In contrast, other personal identifiers like Social Security numbers and credit card information fetch considerably lower prices. What are some of the barriers to implementing HIPAA guidelines in health care organizations? The three main aspects of HIPAA that continue to be a challenge for organizations are privacy, security and breach notification. Ensuring compliance involves both technical and procedural considerations, and practices must implement updated training programs, access controls, secure data disposal methods, encryption measures, and regular security assessments. Compliance extends beyond internal practices, requiring thorough scrutiny of third-party vendors’ adherence to PHI protection regulations. In the broader context of system compliance with PHI regulations, including HIPAA, specific software requirements play a pivotal role. These requirements, such as data encryption, access controls, audit logs, data integrity measures, and breach notification capabilities, collectively ensure the confidentiality, integrity, and availability of PHI. Compliance necessitates an organizational commitment to privacy and security considerations, encompassing technical safeguards, administrative policies, and physical security measures. Various businesses, including hospitals, insurance providers, pharmacies, and psychologists, handle PHI, making its protection challenging yet imperative to adhere to HIPAA standards. Maintain documents containing PHI in locked cabinets or locked rooms when the documents are not in use and after working hours. Establish physical and/or procedural controls (e.g., key or combination access, access authorization levels) that limit access to only those persons who have a need for the information. What’s your responsibility in protecting PHI? This includes implementing HIPAA-required administrative , physical , and technical safeguards with regard to any person, process, application, service, or system used to collect, process, manage, analyze, or store PHI. Like1 Related Posts 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 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 50 Advantages of Salesforce Sales Cloud According to the Salesforce 2017 State of Service report, 85% of executives with service oversight identify customer service as a Read more

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February 11, 2024in Salesforce Data Cloud, Salesforce Health and Life Sciences Solutions
Healthcare Cloud Marketplace
07Feb

Healthcare Cloud Marketplace

Healthcare Cloud Computing Market: A Comprehensive Overview and Future Outlook Vantage Market Research Report: Insights into Healthcare Cloud Computing by 2030 WASHINGTON, D.C., February 6, 2024 /EINPresswire.com/ — The global Healthcare Cloud Marketplace was valued at USD 38.25 billion in 2022 and is projected to grow at a compound annual growth rate (CAGR) of 18.2% from 2023 to 2030, reaching approximately USD 145.86 billion by 2030, according to Vantage Market Research. This technology allows healthcare organizations to utilize cloud-based services for data storage, management, and analysis, providing numerous benefits such as cost efficiency, scalability, flexibility, security, and interoperability. It enhances healthcare delivery by enabling seamless data access and sharing across various locations, devices, and networks. Additionally, cloud computing supports the integration of advanced technologies like artificial intelligence, big data analytics, telehealth, and mobile health, driving progress in disease diagnosis, treatment, and prevention. Market Dynamics The market’s growth is fueled by several key factors, including the increasing demand for healthcare IT solutions, the rising prevalence of chronic diseases, the widespread adoption of electronic health records (EHRs), and evolving payment models and regulatory frameworks. The exponential increase in healthcare data, encompassing patient records, imaging scans, and research findings, necessitates scalable storage and analysis solutions. Cloud computing meets this need by providing flexible and scalable infrastructure, accommodating data growth without overburdening IT systems. The rise of telehealth and remote patient monitoring further boosts the demand for secure, cloud-based platforms that facilitate efficient data exchange. However, stringent data privacy regulations like HIPAA and GDPR require robust security measures, compelling healthcare organizations to seek cloud providers that offer strong compliance and access controls. This need for a balance between agility and security shapes the healthcare cloud computing market’s future trajectory. Leading Companies in the Global Healthcare Cloud Computing Market Market Segmentation By Product: By Deployment: By Component: By Pricing Model: By Service Model: Key Trends and Opportunities The healthcare cloud computing market is witnessing significant trends, including the adoption of hybrid and multi-cloud models, which combine the benefits of both public and private clouds. The integration of artificial intelligence (AI) and machine learning (ML) into cloud-based healthcare applications is opening new avenues for personalized medicine, clinical decision support, and drug discovery. Moreover, blockchain technology is emerging as a solution to enhance data security and patient privacy, addressing critical industry concerns. Key Findings: Opportunities: Healthcare Cloud Marketplace The healthcare cloud computing market is poised for robust growth, driven by the increasing demand for scalable and secure data management solutions. As healthcare organizations navigate challenges related to data privacy and security, robust cloud solutions and supportive government policies will be essential in unlocking the full potential of cloud computing 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 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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February 7, 2024in Salesforce
AI Adoption Not Even Across the Board
07Feb

AI Adoption Not Even Across the Board

Reflecting on AI’s potential and its challenges, McElheran calls for a balanced approach: “To fully harness AI’s benefits, we need a realistic, evidence-based approach that accounts for both the advantages and the societal costs associated with adoption.”

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February 7, 2024in Data, Technology

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