App Archives - gettectonic.com - Page 33
Einstein Chatbot
09Aug

Einstein Chatbot

Businesses have increasingly adopted “chatbots” to provide quick answers to customer queries outside regular business hours or to route customers to the appropriate department after answering preliminary questions. While these chatbots can be useful, they often fall short in delivering the same level of value as human interaction, sometimes leading to frustration. Today, chatbots are advancing significantly, with Salesforce’s Einstein Service Agent leading this evolution. This technology offers notable benefits but also presents challenges that businesses must address for effective implementation. Advantages of Einstein Service Agent Seamless Integration with Salesforce: Unlike standalone AI tools, Einstein Service Agent leverages comprehensive customer profiles, purchase histories, and previous interactions to offer personalized responses. Its integration within established Salesforce workflows allows for rapid deployment, reducing both time and cost associated with implementation. Experience has shown that selecting technologies with built-in CRM or ERP integration is a significant advantage over those requiring separate integration efforts. Built on Salesforce’s Trust Layer: Einstein Service Agent ensures secure handling of customer data, adhering to relevant regulations. This enhances trust among businesses and their customers, facilitating smoother adoption. GenAI Capabilities: The AI can manage complex, multi-step tasks like processing returns or refunds, and deliver tailored responses based on specific customer needs, enhancing the overall customer experience. Scalability Across Salesforce Clouds: Einstein Service Agent is adaptable to various business needs and can evolve as those needs change. Whether a company expands, introduces new services, or shifts its customer service strategy, the agent can be scaled and customized to maintain long-term value and utility. Challenges in Implementing AI Agents Data Quality and Integration: The effectiveness of AI tools relies heavily on the quality of the data they access. Incomplete, outdated, or poorly maintained data can lead to inaccurate or ineffective responses. To address this, businesses should prioritize data quality through regular audits and ensure comprehensive and up-to-date customer information. Change Management and Employee Training: The introduction of AI can lead to resistance from employees concerned about job displacement or unfamiliarity with new technology. Businesses should invest in change management strategies, including clear communication about AI as a complement to, not a replacement for, human agents. Training programs should focus on helping employees work alongside AI tools, enhancing skills where human judgment and empathy are crucial. Balancing Customer Service: Over-reliance on AI may diminish the personal touch essential in customer service. AI should handle straightforward and repetitive inquiries, while more complex or sensitive issues should be escalated to human agents who can provide personalized responses. Considerations for a Successful Deployment Customization and Flexibility: Tailoring the AI to fit unique processes and customer service requirements may require additional configuration or custom development to align with the company’s goals and service expectations. Ethical and Bias Concerns: AI systems can unintentionally perpetuate biases present in their training data, leading to unfair interactions. Businesses must actively identify and mitigate biases, ensuring that their AI operates fairly and equitably. This includes regularly reviewing training data for biases, implementing safeguards, and maintaining a commitment to ethical AI practices. Customer Acceptance and User Experience: Some customers may be hesitant to interact with AI or have negative perceptions of automated service. To improve acceptance, businesses should design user-friendly AI interactions, ensure transparency, and provide clear options for escalating issues to human agents. Einstein Chatbot Implementing AI agents like Salesforce’s Einstein Service Agent can significantly enhance customer service efficiency, personalization, and scalability. However, businesses must carefully navigate challenges related to data quality, change management, and maintaining trust. A thoughtful approach to AI deployment can transform customer service operations and drive 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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August 9, 2024in Salesforce Einstein, Salesforce Marketing Automation
Salesforce Bulk API Mappings
09Aug

Salesforce Bulk API Mappings

Using Salesforce Bulk API and Bulk API 2.0 in Mappings and Mapping Tasks The Salesforce Bulk API and Bulk API 2.0 allow you to efficiently process and retrieve large datasets from Salesforce sources with minimal API calls. These APIs are particularly useful for handling extensive data loads in mappings and mapping tasks. Salesforce Bulk API The Salesforce Bulk API processes large volumes of data by dividing it into manageable batches. Key limitations to consider: Salesforce Bulk API 2.0 Bulk API 2.0 offers enhanced scalability and automation for even larger datasets: Recommendations for Bulk API 2.0 To take full advantage of Bulk API 2.0: By leveraging these APIs, you can seamlessly manage and process large-scale Salesforce data, optimizing performance and minimizing API call overhead. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 9, 2024in Data
Demandbase One for Sales iFrame
09Aug

Demandbase One for Sales iFrame

Understanding the Demandbase One for Sales iFrame in Salesforce The Demandbase One for Sales iFrame (formerly known as Sales Intelligence) allows sales teams to access deep, actionable insights directly within Salesforce. This feature provides account-level and people-level details, including engagement data, technographics, intent signals, and even relevant news, social media posts, and email communications. By offering this level of visibility, sales professionals can make informed decisions and take the most effective next steps on accounts. Key Points: Overview of the Demandbase One for Sales iFrame The iFrame is divided into several key sections: Account, People, Engagement, and Insights tabs. Each of these provides critical information to help you better understand and engage with the companies and people you’re researching. Account Tab People Tab Engagement Tab Final Notes: The Demandbase One for Sales iFrame is a powerful tool that provides a complete view of account activity, helping sales teams make informed decisions and drive 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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August 9, 2024in Data
Benefits of Loan Software for Government Agencies on Salesforce
08Aug

Benefits of Loan Software for Government Agencies on Salesforce

The Benefits of Loan Software for Government Agencies on Salesforce Government agencies managing loans face unique challenges in financial administration, and integrating loan software with Salesforce can greatly enhance their efficiency, accuracy, and transparency. This streamlined solution addresses key areas such as document management, borrower engagement, and robust reporting, making processes more efficient and improving service delivery. Centralized Document Management Loan software offers a central hub for managing extensive paperwork, helping government agencies manage loan applications, agreements, and compliance documents. Key benefits include: This centralized approach saves time and helps agencies maintain audit-ready records. Improved Borrower Management Loan software on Salesforce offers a seamless borrower management experience through custom borrower portals. Features include: By improving borrower relationships, agencies can foster trust and boost repayment rates. Powerful Reporting and Dashboards Salesforce’s loan software provides comprehensive reporting tools to support data-driven decision-making: These tools provide insights that help agencies manage loans efficiently while aligning with fiscal goals. Efficient Payment Processing Loan software simplifies payment workflows, providing: This automation improves payment accuracy and speeds up processing times for both agencies and borrowers. Conclusion Integrating loan software with Salesforce offers government agencies significant benefits, from enhanced document management to improved borrower engagement, advanced reporting, and efficient payment processing. These solutions help agencies better serve citizens while maintaining compliance and fiscal responsibility. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 8, 2024in Salesforce Implementation Services, Salesforce Optimization, Salesforce Public Sector Solutions
Autonomous AI Service Agents
07Aug

Autonomous AI Service Agents

Salesforce Set to Launch Autonomous AI Service Agents. Considering Tectonic only first wrote about Agentic AI in late June, its like Christmas in July! Salesforce is gearing up to introduce a new generation of customer service chatbots that leverage advanced AI tools to autonomously navigate through various actions and workflows. These bots, termed “autonomous AI agents,” are currently in pilot testing and are expected to be released later this year. Autonomous AI Service Agents Named Einstein Service Agent, these autonomous AI bots aim to utilize generative AI to understand customer intent, trigger workflows, and initiate actions within a user’s Salesforce environment, according to Ryan Nichols, Service Cloud’s chief product officer. By integrating natural language processing, predictive analytics, and generative AI, Einstein Service Agents will identify scenarios and resolve customer inquiries more efficiently. Traditional bots require programming with rules-based logic to handle specific customer service tasks, such as processing returns, issuing refunds, changing passwords, and renewing subscriptions. In contrast, the new autonomous bots, enhanced by generative AI, can better comprehend customer issues (e.g., interpreting “send back” as “return”) and summarize the steps to resolve them. Einstein Service Agent will operate across platforms like WhatsApp, Apple Messages for Business, Facebook Messenger, and SMS text, and will also process text, images, video, and audio that customers provide. Despite the promise of these new bots, their effectiveness is crucial, emphasized Liz Miller, an analyst at Constellation Research. If these bots fail to perform as expected, they risk wasting even more customer time than current technologies and damaging customer relationships. Miller also noted that successful implementation of autonomous AI agents requires human oversight for instances when the bots encounter confusion or errors. Customers, whether in B2C or B2B contexts, are often frustrated with the limitations of rules-based bots and prefer direct human interaction. It is annoying enough to be on the telephone repeating “live person” over and over again. It would be trafic to have to do it online, too. “It’s essential that these bots can handle complex questions,” Miller stated. “Advancements like this are critical, as they can prevent the bot from malfunctioning when faced with unprogrammed scenarios. However, with significant technological advancements like GenAI, it’s important to remember that human language and thought processes are intricate and challenging to map.” Nichols highlighted that the forthcoming Einstein Service Agent will be simpler to set up, as it reduces the need to manually program thousands of potential customer requests into a conversational decision tree. This new technology, which can understand multiple word permutations behind a service request, could potentially lower the need for extensive developer and data scientist involvement for Salesforce users. The pricing details for the autonomous Einstein Service Agent will be announced at its release. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 7, 2024in Generative AI
Impact of EHR Adoption
07Aug

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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August 7, 2024in Data, Generative AI, Life Sciences Cloud, Salesforce Health and Life Sciences Solutions, Technology
Box Acquires Alphamoon
07Aug

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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August 7, 2024in Generative AI
Salesforce Query Builder
07Aug

Salesforce Query Builder

Salesforce Query Builder Effortlessly Build SOQL Queries for Salesforce Objects with Salesforce Query Builder. The Salesforce Query Builder is a powerful Chrome extension that simplifies the creation of SOQL (Salesforce Object Query Language) queries for administrators, developers, and power users. This tool addresses the common challenge of building complex queries directly within your Salesforce environment, eliminating the need for external tools. Key Features and Benefits Seamless Integration: The Query Builder works directly within your Salesforce tabs, streamlining your workflow by removing the need to switch between apps or browser windows. This integration ensures better productivity without disruption. User-Friendly Interface: Its intuitive design makes query building easy for users at any skill level. A step-by-step process walks you through selecting objects, fields, and applying filters, reducing the complexities of SOQL syntax. Dynamic Object and Field Selection: The extension automatically fetches and displays available Salesforce objects and fields, saving time and minimizing errors by using up-to-date schema information. Real-Time Query Generation: As you choose objects, fields, and filters, the extension generates the SOQL query in real-time. This live feedback helps you understand the structure of the query, allowing for quick adjustments. Secure Authentication: Using your existing Salesforce session, the Query Builder ensures your credentials remain secure. It doesn’t store or transmit sensitive information, maintaining the integrity of your data. Flexible Filtering: Easily add WHERE clauses to filter data based on specific criteria, making it simple to focus on the data subsets you need. Copy to Clipboard: With one click, copy the generated SOQL query to your clipboard for easy use in other tools, development environments, or for sharing with teammates. Field Search: For objects with many fields, the search function helps you quickly locate the fields you need, reducing time spent scrolling. Lightweight and Fast: As a browser extension, the Query Builder is lightweight, requiring no installation on your Salesforce instance, ensuring fast performance without impacting your org. Cross-Domain Support: The tool supports multiple Salesforce domains (salesforce.com, force.com, cloudforce.com), providing a consistent experience across different environments. Why You Should Install It Time-Saving: The Query Builder dramatically reduces the time spent constructing SOQL queries, especially for complex objects or unfamiliar schemas. Error Reduction: By providing a visual interface, the tool minimizes syntax errors that can occur when manually writing SOQL queries. Learning Tool: Ideal for those new to SOQL, the Query Builder helps users understand query structure and best practices through its interactive design. Increased Productivity: With seamless Salesforce integration, you can generate queries quickly without disrupting your workflow. Accessibility: The tool empowers users who may not be comfortable writing SOQL manually, making advanced querying capabilities accessible to a wider range of Salesforce users. Consistency: It encourages consistent query-building practices across teams, making collaboration and sharing of queries easier. No Setup Required: As a browser extension, it requires no changes to your Salesforce org, making it perfect for admins or developers working across multiple orgs or with limited customization permissions. By installing the Salesforce Query Builder, you gain a valuable tool for your daily Salesforce tasks. Whether you’re a developer needing to prototype queries, an admin exploring data relationships, or a business analyst needing custom views, this tool simplifies interacting with your Salesforce data. With its combination of ease of use, security, and powerful features, it’s an essential addition to any Salesforce professional’s toolkit. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 7, 2024in Data, Salesforce
Understanding AI Agents
06Aug

Understanding AI Agents

Understanding AI Agents: A Comprehensive Guide Artificial Intelligence (AI) has come a long way, offering systems that automate tasks and provide intelligent, responsive solutions. One key concept within AI is the AI agent—an autonomous system capable of perceiving its environment and taking actions to achieve specific goals. This guide explores AI agents, their types, working mechanisms, and how to build them using platforms like Microsoft Autogen and Google Vertex AI Agent Builder. It also highlights how companies like LeewayHertz and Markovate can assist in the development of AI agents. What is an AI Agent? AI agents are systems designed to interact with their environment autonomously. They process inputs, make decisions, and execute actions based on predefined rules or learned experiences. These agents range from simple rule-based systems to complex machine learning models that adapt over time. Types of AI Agents AI agents can be classified based on complexity and functionality: How AI Agents Work The working mechanism of an AI agent involves four key components: Architectural Blocks of an Autonomous AI Agent An autonomous AI agent typically includes: Building an AI Agent: The Basics Building an AI agent involves several essential steps: Microsoft Autogen: A Platform Overview Microsoft Autogen is a powerful tool for building AI agents, offering a range of features that simplify the development, training, and deployment process. Its user-friendly interface allows developers to create custom agents quickly. Key Steps to Building AI Agents with Autogen: Benefits of Autogen: Vertex AI Agent Builder: Enabling No-Code AI Development Google’s Vertex AI Agent Builder simplifies AI agent development through a no-code platform, making it accessible to users without extensive programming experience. Its drag-and-drop functionality allows for quick and efficient AI agent creation. Key Features of Vertex AI Agent Builder: Conclusion AI agents play a critical role in automating decision-making and performing tasks independently. Platforms like Microsoft Autogen and Google Vertex AI Agent Builder make the development of these agents more accessible, providing powerful tools for both novice and experienced developers. By leveraging these technologies and partnering with companies like LeewayHertz and Markovate, businesses can build custom AI agents that enhance automation, decision-making, and operational efficiency. Whether you’re starting from scratch or looking to integrate AI capabilities into your existing systems, the right tools can make the process seamless and effective. How do you think these tools stack up next to Salesforce AI Agents? Comment below. 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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August 6, 2024in AI Tools, Artificial Intelligence, Data, Google
July Changes to Preference Center
05Aug

July Changes to Preference Center

Privacy Center Update What’s the July Changes to Preference Center? Starting in July 2024, the Privacy Center app within the core platform now supports retention features. July Changes to Preference Center introduces a new Hyperforce-based retention store, allows for retention testing in sandboxes, and offers the option to mask data during retention. The new Hyperforce-based retention store can be provisioned using the core Privacy Center app, eliminating the need for Heroku or the Privacy Center managed package. The rollout of this new retention capability will be phased across regions, initially launching in Germany, Australia, and America East. You can spin up a retention store once it’s available in your region. For more details, refer to the Privacy Center’s Hyperforce-Based Retention Store FAQ. What action do I need to take? What if I don’t take any action? You can continue using the legacy Privacy Center app (managed package version) for data retention, but it will no longer be enhanced and will remain in maintenance mode. Heroku can still be used for managing data retention policies until the end of your contract. Where can I learn more about this upcoming change? Review the Privacy Center’s Hyperforce-Based Retention Store FAQ for more information. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 5, 2024in Hyperforce
ChatGPT Word Choices
05Aug

ChatGPT Word Choices

Why Does ChatGPT Use the Word “Delve” So Much? Mystery Solved. The mystery behind ChatGPT’s frequent use of the word “delve” (one of the 10 most common words it uses) has finally been unraveled, and the answer is quite unexpected. Why ChatGPT Word Choices are repetitive. While “delve” and other words like “tapestry” aren’t common in everyday conversations, ChatGPT seems to favor them. You may have noticed this tendency in its outputs. The sudden rise in the use of “delve” in medical papers from March 2024, coincides with the first full year of ChatGPT’s widespread use. “Delve,” along with phrases like “as an AI language model…,” has become a hallmark of ChatGPT’s language, almost a giveaway that a text is AI-generated. But why does ChatGPT overuse “delve”? If it’s trained on human data, how did it develop this preference? Is it emergent behavior? And why “delve” specifically? A Guardian article, “How Cheap, Outsourced Labour in Africa is Shaping AI English,” provides a clue. The key lies in how ChatGPT was built. Why “Delve” So Much? The overuse of “delve” suggests ChatGPT’s language might have been influenced after its initial training on internet data. After training on a massive corpus of data, an additional supervised learning step is used to align the AI’s behavior. Human annotators evaluate the AI’s outputs, and their feedback fine-tunes the model. Here’s a summary of the process: This iterative process involves human feedback to improve the AI’s responses, ensuring it stays aligned and useful. However, this feedback is often provided by a workforce in the global south, where English-speaking annotators are more affordable. In Nigeria, “delve” is more commonly used in business English than in the US or UK. Annotators from these regions provided examples using their familiar language, influencing the AI to adopt a slightly African English style. This is an example of poor sampling, where the evaluators’ language differs from that of the target users, introducing a bias in the writing style. This bias likely stems from the RLHF step rather than the initial training. ChatGPT’s writing style, with or without “delve,” is already somewhat robotic and easy to detect. Understanding these potential pitfalls helps us avoid similar issues in future AI development. Making ChatGPT More Human-Like To make ChatGPT sound more human and avoid overused words like “delve,” consider these Prompt Engineering approaches: These methods can be time-consuming. Ideally, a quick, reliable tool, like a Chrome extension, would streamline this process. If you’ve found a solution or a reliable tool for this issue, share it below in the comments. This is a widespread challenge that many users face. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 5, 2024in Salesforce
AI for Consumers and Retailers
02Aug

AI for Consumers and Retailers

Before generative AI became mainstream, tech-savvy retailers had long been leveraging transformative technologies to automate tasks and understand consumer behavior. Insights from consumer and future trends, along with predictive analytics, have long guided retailers in improving customer experiences and enhancing operational efficiency. AI for Consumers and Retailers improved customer experiences. While AI is currently used for personalized recommendations and online customer support, many consumers still harbor distrust towards AI. Salesforce is addressing this concern by promoting trustworthy AI with human oversight and implementing powerful controls that focus on mitigating high-risk AI outcomes. This approach is crucial as many knowledge workers fear losing control over AI. Although people trust AI to handle significant portions of their work, they believe that increased human oversight would bolster their confidence in AI. Building this trust is a challenge retailers must overcome to fully harness AI’s potential as a reliable assistant. So, where does the retail industry stand with AI, and how can retailers build consumer trust while developing AI responsibly? AI for Consumers and Retailers Recent research from Salesforce and the Retail AI Council highlights how AI is reshaping consumer behavior and retailer interactions. AI is now integral to providing personalized deals, suggesting tailored products, and enhancing customer service through chatbots. Retailers are increasingly embedding generative AI into their business operations. A significant majority (93%) of retailers report using generative AI for personalization, enabling customers to find products and make purchases faster through natural language interactions on digital storefronts and messaging apps. For instance, a customer might tell a retailer’s AI assistant about their camping needs, and based on location, preferences, and past purchases, the AI can recommend a suitable tent and provide a direct link for checkout and store collection. As of early 2024, 92% of retailers’ investments were directed towards AI technology. While AI is not new to retail, with 59% of merchants already using it for product recommendations and 55% utilizing digital assistants for online purchases, its applications continue to expand. From demand forecasting to customer sentiment analysis, AI enhances consumer experiences by predicting preferences and optimizing inventory levels, thereby reducing markdowns and improving efficiency. Barriers and Ethical Considerations Despite its promise, integrating generative AI in retail faces significant challenges, particularly regarding bias in AI outputs. The need for clear ethical guidelines in AI use within retail is pressing, underscoring the gap between adoption rates and ethical stewardship. Strategies that emphasize transparency and accountability are vital for fostering responsible AI innovation. Half of the surveyed retailers indicated they could fully comply with stringent data security standards and privacy regulations, demonstrating the industry’s commitment to protecting consumer data amidst evolving regulatory landscapes. Retailers are increasingly aware of the risks associated with AI integration. Concerns about bias top the list, with half of the respondents worried about prejudiced AI outcomes. Additionally, issues like hallucinations (38%) and toxicity (35%) linked to generative AI implementation highlight the need for robust mitigation strategies. A majority (62%) of retailers have established guidelines to address transparency, data security, and privacy concerns related to the ethical deployment of generative AI. These guidelines ensure responsible AI use, emphasizing trustworthy and unbiased outputs that adhere to ethical standards in the retail sector. These insights reveal a dual imperative for retailers: leveraging AI technologies to enhance operational efficiency and customer experiences while maintaining stringent ethical standards and mitigating risks. Consumer Perceptions and the Future of AI in Retail As AI continues to redefine retail, balancing ethical considerations with technological advancements is essential. To combat consumer skepticism, companies should focus on transparent communication about AI usage and emphasize that humans, not technology, are ultimately in control. Whether aiming for top-line growth or bottom-line efficiency, AI is a crucial addition to a retailer’s technology stack. However, to fully embrace AI, retailers must take consumers on the journey and earn their trust. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 2, 2024in Generative AI
Rold of Small Language Models
02Aug

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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August 2, 2024in Salesforce

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