PHI

16Oct

Third Wave of AI at Salesforce

The Third Wave of AI at Salesforce: How Agentforce is Transforming the Landscape At Dreamforce 2024, Salesforce unveiled several exciting innovations, with Agentforce taking center stage. This post explores the key changes and enhancements designed to improve efficiency and elevate customer interactions. Introducing Agentforce Agentforce is a customizable AI agent builder that empowers organizations to create and manage autonomous agents for various business tasks. But what exactly is an agent? An agent is akin to a chatbot but goes beyond traditional capabilities. While typical chatbots are restricted to scripted responses and predefined questions, Agentforce agents leverage large language models (LLMs) and generative AI to comprehend customer inquiries contextually. This enables them to make independent decisions, whether processing requests or resolving issues using real-time data from your company’s customer relationship management (CRM) system. The Role of Atlas At the heart of Agentforce’s functionality lies the Atlas reasoning engine, which acts as the operational brain. Unlike standard assistive tools, Atlas is an agentic system with the autonomy to act on behalf of the user. Atlas formulates a plan based on necessary actions and can adjust that plan based on evaluations or new information. When it’s time to engage, Atlas knows which business processes to activate and connects with customers or employees via their preferred channels. This sophisticated approach allows Agentforce to significantly enhance operational efficiency. By automating routine inquiries, it frees up your team to focus on more complex tasks, delivering a smoother experience for both staff and customers. Speed to Value One of Agentforce’s standout features is its emphasis on rapid implementation. Many AI projects can be resource-intensive and take months or even years to launch. However, Agentforce enables quick deployment by leveraging existing Salesforce infrastructure, allowing organizations to implement solutions rapidly and with greater control. Salesforce also offers pre-built Agentforce agents tailored to specific business needs—such as Service Agent, Sales Development Representative Agent, Sales Coach, Personal Shopper Agent, and Campaign Agent—all customizable with the Agent Builder. Agentforce for Service and Sales will be generally available starting October 25, 2024, with certain elements of the Atlas Reasoning Engine rolling out in February 2025. Pricing begins at $2 per conversation, with volume discounts available. Transforming Customer Insights with Data Cloud and Marketing Cloud Dreamforce also highlighted enhancements to Data Cloud, Salesforce’s backbone for all cloud products. The platform now supports processing unstructured data, which constitutes up to 90% of company data often overlooked by traditional reporting systems. With new capabilities for analyzing various unstructured formats—like video, audio, sales demos, customer service calls, and voicemails—businesses can derive valuable insights and make informed decisions across Customer 360. Furthermore, Data Cloud One enables organizations to connect siloed Salesforce instances effortlessly, promoting seamless data sharing through a no-code, point-and-click setup. The newly announced Marketing Cloud Advanced edition serves as the “big sister” to Marketing Cloud Growth, equipping larger marketing teams with enhanced features like Path Experiment, which tests different content strategies across channels, and Einstein Engagement Scoring for deeper insights into customer behavior. Together, these enhancements empower companies to engage customers more meaningfully and measurably across all touchpoints. Empowering the Workforce Through Education Salesforce is committed to making AI accessible for all. They recently announced free instructor-led courses and AI certifications available through 2025, aimed at equipping the Salesforce community with essential AI and data management skills. To support this initiative, Salesforce is establishing AI centers in major cities, starting with London, to provide hands-on training and resources, fostering AI expertise. They also launched a global Agentforce World Tour to promote understanding and adoption of the new capabilities introduced at Dreamforce, featuring repackaged sessions from the conference and opportunities for specialists to answer questions. The Bottom Line What does this mean for businesses? With the rollout of Agentforce, along with enhancements to Data Cloud and Marketing Cloud, organizations can operate more efficiently and connect with customers in more meaningful ways. Coupled with a focus on education through free courses and global outreach, getting on board has never been easier. If you’d like to discuss how we can help your business maximize its potential with Salesforce through data and AI, connect with us and schedule a meeting with our team. Legacy systems can create significant gaps between operations and employee needs, slowing lead processes and resulting in siloed, out-of-sync data that hampers business efficiency. Responding to inquiries within five minutes offers a 75% chance of converting leads into customers, emphasizing the need for rapid, effective marketing responses. Salesforce aims to help customers strengthen relationships, enhance productivity, and boost margins through its premier AI CRM for sales, service, marketing, and commerce, while also achieving these goals internally. Recognizing the complexity of its decade-old processes, including lead assignment across three systems and 2 million lines of custom code, Salesforce took on the role of “customer zero,” leveraging Data Cloud to create a unified view of customers known as the “Customer 360 Truth Profile.” This consolidation of disparate data laid the groundwork for enterprise-wide AI and automation, improving marketing automation and reducing lead time by 98%. As Michael Andrew, SVP of Marketing Decision Science at Salesforce, noted, this initiative enabled the company to provide high-quality leads to its sales team with enriched data and AI scoring while accelerating time to market and enhancing data quality. Embracing Customer Zero “Almost exactly a year ago, we set out with a beginner’s mind to transform our lead automation process with a solution that would send the best leads to the right sales teams within minutes of capturing their data and support us for the next decade,” said Andrew. The initial success metric was “speed to lead,” aiming to reduce the handoff time from 20 minutes to less than one minute. The focus was also on integrating customer and lead data to develop a more comprehensive 360-degree profile for each prospect, enhancing lead assignment and sales rep productivity. Another objective was to boost business agility by cutting the average time to implement assignment changes from four weeks to mere days. Accelerating Success with

October 16, 2024in Data, Enterprise, Generative AI, Google, Google Analytics, Marketing Automation, Salesforce

14Oct

Battle of Copilots

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

October 14, 2024in AI Tools, Data, Enterprise, Google, Mulesoft, Salesforce, Salesforce Copilots, Salesforce Optimization, Technology

10Oct

Collaboration Between Humans and AI

The Future of AI: What to Expect in the Next 5 Years In the next five years, AI will accelerate human life, reshape behaviors, and transform industries—these changes are inevitable. Collaboration Between Humans and AI. For much of the early 20th century, AI existed mainly in science fiction, where androids, sentient machines, and futuristic societies intrigued fans of the genre. From films like Metropolis to books like I, Robot, AI was the subject of speculative imagination. AI in fiction often over-dramatized reality and caused us to suspend belief in what was and was not possible. But by the mid-20th century, scientists began working to bring AI into reality. A Brief History of AI’s Impact on Society The 1956 Dartmouth Summer Research Project on Artificial Intelligence marked a key turning point, where John McCarthy coined the term “artificial intelligence” and helped establish a community of AI researchers. Although the initial excitement about AI often outpaced its actual capabilities, significant breakthroughs began emerging by the late 20th century. One such moment was IBM’s Deep Blue defeating chess champion Garry Kasparov in 1997, signaling that machines could perform complex cognitive tasks. The rise of big data and Moore’s Law, which fueled the exponential growth of computational power, enabled AI to process vast amounts of information and tackle tasks previously handled only by humans. By 2022, generative AI models like ChatGPT proved that machine learning could yield highly sophisticated and captivating technologies. AI’s influence is now everywhere. No longer is it only discussed in IT circles. AI is being featured in nearly all new products hitting the market. It is part of if not the creation tool of most commercials. Voice assistants like Alexa, recommendation systems used by Netflix, and autonomous vehicles represent just a glimpse of AI’s current role in society. Yet, over the next five years, AI’s development is poised to introduce far more profound societal changes. How AI Will Shape the Future Industries Most Affected by AI Long-term Risks of Collaboration Between Humans and AI AI’s potential to pose existential risks has long been a topic of concern. However, the more realistic danger lies in human societies voluntarily ceding control to AI systems. Algorithmic trading in finance, for example, demonstrates how human decisions are already being replaced by AI’s ability to operate at unimaginable speeds. Still, fear of AI should not overshadow the opportunities it presents. If organizations shy away from AI out of anxiety, they risk missing out on innovations and efficiency gains. The future of AI depends on a balanced approach that embraces its potential while mitigating its risks. In the coming years, the collaboration between humans and AI will drive profound changes across industries, legal frameworks, and societal norms, creating both challenges and opportunities for the future. Tectonic can help you map your AI journey for the best Collaboration Between Humans and AI. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more 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

October 10, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Laws

10Oct

Fivetrans Hybrid Deployment

Fivetran’s Hybrid Deployment: A Breakthrough in Data Engineering In the data engineering world, balancing efficiency with security has long been a challenge. Fivetran aims to shift this dynamic with its Hybrid Deployment solution, designed to seamlessly move data across any environment while maintaining control and flexibility. Fivetrans Hybrid Deployment. The Hybrid Advantage: Flexibility Meets Control Fivetran’s Hybrid Deployment offers a new approach for enterprises, particularly those handling sensitive data or operating in regulated sectors. Often, these businesses struggle to adopt data-driven practices due to security concerns. Hybrid Deployment changes this by enabling the secure movement of data across cloud and on-premises environments, giving businesses full control over their data while maintaining the agility of the cloud. As George Fraser, Fivetran’s CEO, notes, “Businesses no longer have to choose between managed automation and data control. They can now securely move data from all their critical sources—like Salesforce, Workday, Oracle, SAP—into a data warehouse or data lake, while keeping that data under their own control.” How it Works: A Secure, Streamlined Approach Fivetran’s Hybrid Deployment relies on a lightweight local agent to move data securely within a customer’s environment, while the Fivetran platform handles the management and monitoring. This separation of control and data planes ensures that sensitive information stays within the customer’s secure perimeter. Vinay Kumar Katta, a managing delivery architect at Capgemini, highlights the flexibility this provides, enabling businesses to design pipelines without sacrificing security. Beyond Security: Additional Benefits Hybrid Deployment’s benefits go beyond just security. It also offers: Early adopters are already seeing its value. Troy Fokken, chief architect at phData, praises how it “streamlines data pipeline processes,” especially for customers in regulated industries. AI Agent Architectures: Defining the Future of Autonomous Systems In the rapidly evolving world of AI, a new framework is emerging—AI agents designed to act autonomously, adapt dynamically, and explore digital environments. These AI agents are built on core architectural principles, bringing the next generation of autonomy to AI-driven tasks. What Are AI Agents? AI agents are systems designed to autonomously or semi-autonomously perform tasks, leveraging tools to achieve objectives. For instance, these agents may use APIs, perform web searches, or interact with digital environments. At their core, AI agents use Large Language Models (LLMs) and Foundation Models (FMs) to break down complex tasks, similar to human reasoning. Large Action Models (LAMs) Just as LLMs transformed natural language processing, Large Action Models (LAMs) are revolutionizing how AI agents interact with environments. These models excel at function calling—turning natural language into structured, executable actions, enabling AI agents to perform real-world tasks like scheduling or triggering API calls. Salesforce AI Research, for instance, has open-sourced several LAMs designed to facilitate meaningful actions. LAMs bridge the gap between unstructured inputs and structured outputs, making AI agents more effective in complex environments. Model Orchestration and Small Language Models (SLMs) Model orchestration complements LAMs by utilizing smaller, specialized models (SLMs) for niche tasks. Instead of relying on resource-heavy models, AI agents can call upon these smaller models for specific functions—such as summarizing data or executing commands—creating a more efficient system. SLMs, combined with techniques like Retrieval-Augmented Generation (RAG), allow smaller models to perform comparably to their larger counterparts, enhancing their ability to handle knowledge-intensive tasks. Vision-Enabled Language Models for Digital Exploration AI agents are becoming even more capable with vision-enabled language models, allowing them to interact with digital environments. Projects like Apple’s Ferret-UI and WebVoyager exemplify this, where agents can navigate user interfaces, recognize elements via OCR, and explore websites autonomously. Function Calling: Structured, Actionable Outputs A fundamental shift is happening with function calling in AI agents, moving from unstructured text to structured, actionable outputs. This allows AI agents to interact with systems more efficiently, triggering specific actions like booking meetings or executing API calls. The Role of Tools and Human-in-the-Loop AI agents rely on tools—algorithms, scripts, or even humans-in-the-loop—to perform tasks and guide actions. This approach is particularly valuable in high-stakes industries like healthcare and finance, where precision is crucial. The Future of AI Agents With the advent of Large Action Models, model orchestration, and function calling, AI agents are becoming powerful problem solvers. These agents are evolving to explore, learn, and act within digital ecosystems, bringing us closer to a future where AI mimics human problem-solving processes. As AI agents become more sophisticated, they will redefine how we approach digital tasks and interactions. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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

October 10, 2024in Data, Enterprise, Salesforce

08Oct

Revolution Customer Service with Agentforce

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

October 8, 2024in Data, Enterprise, Generative AI, Salesforce, Unified Knowledge

07Oct

Salesforce Agentforce Integration

The rise of AI-powered solutions is transforming customer service, support, and automation. For organizations such as nonprofits, national associations, and large enterprises, delivering seamless customer experiences has become crucial. Salesforce’s Agentforce, a next-generation conversational AI tool, plays a vital role in this transformation. Designed to elevate customer support and interaction, Agentforce provides an intelligent and scalable solution for integrating AI chatbots into content management systems (CMS) like WordPress, Drupal, and HubSpot. Salesforce Agentforce Integration. In this detailed feature review, we will dive into the extensive capabilities of Salesforce Agentforce, analyzing its role as a conversational tool, its technical requirements, and the benefits it provides for nonprofits, national associations, and businesses. We’ll also compare its applications across various CMS platforms like Drupal, WordPress, and HubSpot, exploring its potential as a powerful AI assistant for website automation and customer interaction. Salesforce Agentforce: A Technical Perspective Salesforce Agentforce is an advanced AI-driven conversational assistant that seamlessly integrates into the Salesforce environment. By tapping into Salesforce CRM’s vast data resources, Agentforce serves as an intelligent interface, automating everything from initial customer inquiries to more personalized interactions. Utilizing natural language processing (NLP) and machine learning, it continually refines responses and scales interactions, making it an indispensable tool for organizations aiming to enhance customer service workflows. Agentforce integrates smoothly with Salesforce Service Cloud, automating both live chat support and chatbot responses. Additionally, it can connect with third-party platforms, including popular CMS solutions like WordPress, Drupal, and HubSpot, allowing organizations to centralize customer service operations in one interface. Core Features and Technical Architecture of Agentforce Natural Language Understanding (NLU) and Processing (NLP) Agentforce’s NLP capabilities are its backbone, allowing it to understand complex human language and respond contextually. This empowers it to manage initial inquiries as well as more sophisticated support requests. Agentforce’s NLU also enables it to work across different languages, dialects, and industry-specific terminology, making it particularly valuable for global organizations and national associations serving diverse audiences. Machine Learning for Continuous Improvement Agentforce’s machine learning feature enhances its ability to improve accuracy and understanding over time. Each interaction helps the chatbot evolve, making it more effective at delivering relevant, real-time responses. This model integrates directly with Salesforce’s data infrastructure, giving Agentforce access to historical data and interactions to offer highly personalized, context-aware answers. Deep Integration with Salesforce CRM As a Salesforce-native tool, Agentforce can harness CRM data in ways other AI tools cannot. By accessing customer histories, purchase data, and previous interactions, it creates personalized experiences that build customer trust. Nonprofits and associations can use this data to improve donor or member interactions, offering targeted support and outreach. Agentforce can also be tailored to retrieve specific datasets, such as an individual’s support history or ongoing service case updates. Cross-Platform Flexibility and API Integration Agentforce offers flexible APIs that enable integration with third-party systems, including CMS platforms like WordPress, Drupal, and HubSpot. This flexibility ensures that AI-powered chatbots can be deployed on organizational websites, providing a seamless experience for customers, donors, and members alike. Whether it’s a nonprofit using Drupal or a business on WordPress, Agentforce acts as the central hub for support and engagement, offering fluid interactions on top of your CMS. HubSpot users can further leverage Agentforce’s marketing features to align lead generation with personalized, chat-based interactions. Use Cases for Agentforce in Nonprofits, National Associations, and Businesses Nonprofit Organizations For nonprofits managing donor, volunteer, and beneficiary relationships, Agentforce offers scalable, automated support: National Associations National associations can use Agentforce to handle high volumes of inquiries from members and professionals: Businesses For service-based enterprises, Agentforce is essential for customer service: Salesforce Agentforce and CMS Integration: WordPress, Drupal, and HubSpot WordPress and Salesforce Agentforce Integration For WordPress users, Agentforce offers customizable chatbot widgets that enhance customer engagement, handle ecommerce inquiries, and integrate with WooCommerce for product support. Drupal and Agentforce Integration Drupal’s modular architecture allows Agentforce to automate membership management, provide multilingual support, and distribute content for nonprofits and associations. HubSpot and Agentforce Integration HubSpot users benefit from Agentforce’s ability to automate lead nurturing, sales and marketing workflows, and customer support, all while keeping HubSpot and Salesforce CRM data synchronized. Tectonic and Salesforce Agentforce Integration At Tectonic, we understand that adopting AI-powered solutions like Salesforce Agentforce is only the first step toward delivering exceptional customer experiences. We specialize in crafting, training, and implementing tailored AI chatbot solutions that enhance engagement, streamline processes, and drive growth, all while seamlessly integrating with your current website or mobile app. As a full-service digital strategy firm, Tectonic excels in integrating advanced tools like Salesforce Agentforce into platforms like WordPress, Drupal, and HubSpot, ensuring your automation strategies are executed with precision. From custom chatbot implementations to comprehensive digital strategy services, our team is dedicated to optimizing your website for engagement and lead generation. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 7, 2024in Agentforce Platform, AI Tools, Data, Enterprise, Salesforce, Salesforce Service Cloud

07Oct

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

To enhance your experience in Salesforce Maps on desktop, the new features currently available in all environments will be auto-enabled in the Winter ’25 release this October. The production rollout for Salesforce Maps will begin the night of October 8th and will be completed with all organizations updated by October 18th. The Enhanced User Experience setting in the admin configuration settings will remain and can be manually disabled until the Spring ‘25 release. Take Action Now To ensure a smooth transition, please take the following actions prior to the production release.In Production: From Setup, in the Quick Find box, enter Remote Site Settings, and then select Remote Site Settings. Find and activate the following remote sites: https://lookup.search.hereapi.com, https://autosuggest.search.hereapi.com, and https://revgeocode.search.hereapi.comFailure to do so may result in disruptions to the Points of Interest Search and Click2Create featuresPrior to the deployment to production, we encourage you to explore the enhanced experience in your sandbox environments. All sandbox environments have been updated with the enhanced experience enabled by default. What to Expect Experience a drastic improvement in performance and rendering, plotting layers and mapping content up to 6x as fast!View Maps with updated styling and designs across many parts of the application, such as modernized marker pop-ups, updated drawing tools, and new cluster styling. In addition, map content along with base maps are displayed with increased detail and clarity.Combine the power of ESRI Living Atlas with CRM data directly inside Salesforce Maps. ESRI provides an evolving collection of ready-to-use global geographic content, such as imagery, base maps, demographics, landscape, and boundary data. Identify new leads and opportunities, analyze key geographical-based data, and gain valuable industry insight with lightning speed. Instructions on visualizing Living Atlas data in Maps can be found here.View plotted records in our redesigned List View, providing new capabilities and features for your users, such as the ability to dynamically build a sublist of data.For a full breakdown, please refer to the Maps Summer ‘24 Release Notes and Maps Winter ‘25 Release Notes.How can I get more information or help? Contact your account team or open a case with Salesforce Customer Support. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 7, 2024in Data, Google, Salesforce

03Oct

Marc Benioff Interviewed

Salesforce, now one of the most influential companies in tech history, was founded by Marc Benioff after his successful tenure at Oracle. Having witnessed the potential of the internet, Benioff realized it would usher in a new era for delivering enterprise software through the cloud. However, this new approach demanded a complete overhaul of traditional business models, marketing strategies, and software development practices. Salesforce would embody this transformation, evolving over the decades into a platform supporting a diverse array of enterprise applications. Today, Salesforce’s flagship initiative is Agentforce, a pioneering wave of AI agents built on the Salesforce platform. This initiative, which I covered in detail earlier this month, exemplifies Benioff’s forward-thinking vision for AI. He passionately argues that Salesforce is well-positioned to lead in this space, and his insights on AI and its potential are as compelling as they are thought-provoking. Before diving into Agentforce and Benioff’s views on AI, it’s important to explore his early life and the founding of Salesforce. Benioff’s path to becoming the CEO of one of the most powerful software companies in the world was shaped by an early interest in technology. His first foray into software development was creating an app called “How to Juggle,” which he sold for $75 to CLOAD Magazine in Goleta, California. This early success marked the beginning of a long career in tech. Benioff went on to establish an Atari game company called Liberty Software and later wrote assembly code for the Mac as part of an internship. Despite his success as a software developer, he transitioned to a leadership role after realizing his interest in the broader business of technology. He credits his time at Oracle, where he learned the intricacies of sales, marketing, and customer communication, as critical to shaping his vision for Salesforce. Oracle, during Larry Ellison’s prime, was an ideal place to hone these business skills, which Benioff would later apply to revolutionize the enterprise software space. With this foundation, Benioff founded Salesforce, a company that has fundamentally changed the way businesses engage with technology. The company’s success lies not just in its cloud-based software, but in its ongoing evolution to address new challenges, including the AI-powered Agentforce initiative, which stands at the forefront of Salesforce’s future growth. Benioff’s journey, from a young programmer to the CEO of a global tech giant, is a testament to his visionary approach and adaptability in an ever-changing tech landscape. Marc Benioff’s early entrepreneurial instincts were evident even before he fully embraced the title of entrepreneur. From a young age, he knew he wanted to build something of his own, though he hadn’t yet honed the full set of skills needed to succeed in business. He had a knack for technology, excelling as a programmer working in assembly code, but his mentors, including university professors and colleagues at Apple, recognized that being an entrepreneur required more than just technical ability. They encouraged him to develop his sales, marketing, and business acumen. His early exposure to these lessons was pivotal in shaping his future trajectory. Benioff’s transition from a programmer to a business leader began when he sought advice from Bill Jost at Apple, who connected him with Gary Gibson, an Oracle executive. After a memorable and unexpected interview, Benioff joined Oracle, starting with a role answering calls at the company’s 800-number. He quickly moved into sales and marketing, eventually working directly with Larry Ellison, who would become a significant mentor over the next 13 years. Benioff’s success at Oracle set the stage for his eventual departure to start Salesforce in 1999. In the mid-1990s, while at Oracle, Benioff had an epiphany while using a web browser. The internet was transforming how software could be delivered, and Benioff saw the potential for a new business model based on cloud computing. At this time, Oracle was focused on the network computer—a vision Benioff did not fully share. After discussing his ideas with Larry Ellison and taking some time off for reflection and research, Benioff recognized that the world was on the cusp of a major shift in technology. He felt compelled to create something new that would leverage the internet, offering software over the web through a subscription model. During his time off, Benioff traveled to Hawaii, reflecting on the future of the tech industry and contemplating ideas. He also experimented with domain names, purchasing You.com and later gifting it to a friend who turned it into a search and AI company. Benioff returned to Larry Ellison to share his vision of a cloud-based business model and later found inspiration from spiritual gurus during a trip to India, who reminded him that success in business should also be grounded in positive values. Benioff’s experience led to the founding of Salesforce with its revolutionary 1-1-1 Model—dedicating 1% of equity, profit, and employee time to philanthropy. Salesforce also embraced emerging technologies such as social, mobile, and AI, and created a new business model through subscription-based services. This combination of technological innovation and a strong sense of social responsibility has set Salesforce apart, attracting both admiration and emulation from other companies. Benioff’s Career Benioff’s career is a testament to the power of surrounding oneself with great people, continuing to learn, and having a clear vision. His leadership at Salesforce has been guided by values he believes are crucial to the company’s success and its ability to make a positive impact on the world. His insights on the Innovator’s Dilemma, a concept popularized by Clayton Christensen, highlight the challenges companies face as they evolve with changing technologies. While Salesforce’s core principles remain intact, the company has continued to adapt to new technology paradigms, ensuring it remains at the forefront of innovation in the software industry. Marc Benioff (MB) recently shared his excitement about the future of AI and its potential to revolutionize business and technology. He explained that, from a business perspective, this is the most thrilling moment in his career. AI, according to him, will transform how enterprises operate, making them more

October 3, 2024in Agentforce Platform, Data, Enterprise, Google, Hyperforce, Salesforce, Salesforce Data Cloud, Snowflake, Technology

AI evolves with tools like Agentforce and Atlas

02Oct

AI Evolves With Agentforce and Atlas

Not long ago, financial services companies were still struggling with the challenge of customer data trapped in silos. Though it feels like a distant issue, this problem remains for many large organizations unable to integrate different divisions that deal separately with the same customers. Salesforce AI evolves with tools like Agentforce and Atlas. The solution is a concept known as a “single source of truth.” This theme took center stage at Dreamforce 2024 in San Francisco, hosted by Salesforce (NYSE). The event showcased Salesforce’s latest AI innovations, including Agentforce, which is set to revolutionize customer engagement through its advanced AI capabilities. Agentforce, which becomes generally available on October 25, enables businesses to deploy autonomous AI agents to manage a wide variety of tasks. These agents differ from earlier Salesforce-based AI tools by leveraging Atlas, a cutting-edge reasoning engine that allows the bots to think like human beings. Unlike generative AI models, which might write an email based on prompts, Agentforce’s AI agents can answer complex, high-order questions such as, “What should I do with all my customers?” The agents break down these queries into actionable steps—whether that’s sending emails, making phone calls, or texting customers—thanks to the deep capabilities of Atlas. Atlas is at the heart of what makes these AI agents so powerful. It combines multiple large language models (LLMs), large action models (LAMs), and retrieval-augmented generation (RAG) modules, along with REST APIs and connectors to various datasets. This robust system processes user queries through multiple layers, checking for validity and then expanding the query into manageable chunks for processing. Once a query passes through the chit-chat detector—which filters out non-relevant inputs—it enters the evaluation phase, where the AI determines if it has enough data to provide a meaningful answer. If not, the system loops back to the user for more information in a process Salesforce calls the agentic loop. The fewer loops required, the more efficient the AI becomes, making the experience seamless for users. Phil Mui, Senior Vice President of Salesforce AI Research, explained that the AI agents created via Agentforce are powered by the Atlas reasoning engine, which makes use of several key tools like a re-ranker, a refiner, and a response synthesizer. These tools ensure that the AI retrieves, ranks, and synthesizes relevant information to generate high-quality, natural language responses for the user. But Salesforce’s AI agents don’t stop at automation—they also emphasize trust. Before responses reach users, they go through additional checks for toxicity detection, bias prevention, and personally identifiable information (PII) masking. This ensures that the output is both accurate and safe. The potential of Agentforce is massive. According to Wedbush, Salesforce’s AI strategy could generate over $4 billion annually by 2025. Wedbush analysts recently increased their price target for Salesforce stock to $325, reflecting the strong customer reception of Agentforce’s AI ecosystem. While some analysts, such as Yiannis Zourmpanos from Seeking Alpha, have expressed caution due to Salesforce’s high valuation and slower revenue growth, the company’s continued focus on AI and multi-cloud solutions places it in a strong position for the future. Robin Fisher, Salesforce’s head of growth markets for Europe, the Middle East, and Africa, highlighted two major takeaways from Dreamforce for African businesses: the Data Cloud and AI. Data Cloud provides a 360-degree view of the customer, consolidating data into a single source of truth without requiring full data migration. Meanwhile, Agentforce’s autonomous AI agents will drive operational efficiency across industries, especially in markets like Africa. Zuko Mdwaba, Salesforce’s managing director for South Africa, added that the company’s decade-long AI journey is culminating in its most advanced AI offerings yet. This new wave of AI, he said, is transforming not just customer engagement but also internal operations, empowering employees to focus on more strategic tasks while AI handles repetitive ones. The future is clear: as AI evolves with tools like Agentforce and Atlas, businesses across sectors, from banking to retail, are poised to harness the transformative power of autonomous technology and data-driven insights, finally breaking free from the silos of the past. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

October 2, 2024in Agentforce Platform, AI Tools, Data, Generative AI, Salesforce, Salesforce Implementation Services, Technology

02Oct

Marketing Cloud and Generative AI

Generative AI and Salesforce: Revolutionizing Digital Marketing with Einstein AI Generative AI is a form of Artificial Intelligence that learns from existing content to generate new, creative outputs. Salesforce has long been at the forefront of AI innovation, primarily through its Einstein assistant, which has evolved to offer increasingly sophisticated solutions over time. Artificial Intelligence: Key Concepts Before diving into Salesforce’s AI capabilities, let’s clarify some foundational concepts. Artificial Intelligence (AI) refers to the creation of intelligent systems that can learn and reason autonomously. Within AI, Machine Learning (ML) plays a crucial role by enabling computers to learn from data and improve over time without explicit programming. ML models fall into two broad categories: Deep Learning and Neural Networks A more advanced subset of ML is Deep Learning, which uses neural networks to process large amounts of data and make autonomous decisions. Deep Learning powers technologies like voice assistants (e.g., Alexa or Siri), which can recognize speech and execute tasks. A specific application within Deep Learning is Generative AI, capable of autonomously creating new content based on learned patterns from vast datasets. Another critical AI system is the Foundational Model, which is trained on enormous amounts of unstructured data from across the web, including text, images, and videos. These models offer a wide range of capabilities, such as generating text, answering questions, creating designs, or solving complex problems. Salesforce Marketing Cloud and AI Salesforce has utilizeded AI through its Einstein platform, which has evolved over time to offer a variety of data-driven tools. For example, Sent Time Optimization uses customer data to determine the best time to send emails to maximize engagement. AI Tools in Salesforce Marketing Cloud Salesforce offers several AI-powered tools for Marketing Cloud to help businesses leverage data for personalization and efficiency: The Einstein Trust Layer: AI in Salesforce CRM Einstein is the first generative AI model integrated into a CRM, and Salesforce refers to its AI process as the Einstein Trust Layer. Here’s how it works: Marketing Applications of Salesforce AI Tools Salesforce’s AI tools can be applied across omnichannel marketing campaigns to hyper-personalize communication, increasing conversion rates and customer engagement. Predictive analytics also allow businesses to optimize cross-selling and upselling, offering tailored product recommendations based on customer behavior. Chatbots powered by AI further enhance productivity by interacting in natural language, collecting leads, suggesting products, and resolving customer inquiries. Salesforce’s Commitment to AI in Digital Marketing Salesforce has been a pioneer in AI, continually expanding its capabilities through Einstein. With the latest AI tools for Marketing Cloud, businesses can now interact with customers more precisely, boost engagement, and optimize purchase predictions—paving the way for a new era in digital marketing. 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

October 2, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Salesforce, Salesforce Marketing Cloud

30Sep

Cortex Framework Integration with Salesforce (SFDC)

Cortex Framework: Integration with Salesforce (SFDC) This insight outlines the process of integrating Salesforce (SFDC) operational workloads into the Cortex Framework Data Foundation. By integrating Salesforce data through Dataflow pipelines into BigQuery, Cloud Composer can schedule and monitor these pipelines, allowing you to gain insights from your Salesforce data. Cortex Framework Integration with Salesforce explained. Prerequisite: Before configuring any workload integration, ensure that the Cortex Framework Data Foundation is deployed. Configuration File The config.json file in the Cortex Framework Data Foundation repository manages settings for transferring data from various sources, including Salesforce. Below is an example of how Salesforce workloads are configured: jsonCopy code”SFDC”: { “deployCDC”: true, “createMappingViews”: true, “createPlaceholders”: true, “datasets”: { “cdc”: “”, “raw”: “”, “reporting”: “REPORTING_SFDC” } } Explanation of Parameters: Parameter Meaning Default Value Description SFDC.deployCDC Deploy CDC true Generates Change Data Capture (CDC) processing scripts to run as DAGs in Cloud Composer. SFDC.createMappingViews Create mapping views true Creates views in the CDC processed dataset to show the “latest version of the truth” from the raw dataset. SFDC.createPlaceholders Create placeholders true Creates empty placeholder tables if they aren’t generated during ingestion, ensuring smooth downstream reporting deployment. SFDC.datasets.raw Raw landing dataset (user-defined) The dataset where replication tools land data from Salesforce. SFDC.datasets.cdc CDC processed dataset (user-defined) Source for reporting views and target for records processed by DAGs. SFDC.datasets.reporting Reporting dataset for SFDC “REPORTING_SFDC” Name of the dataset accessible for end-user reporting, where views and user-facing tables are deployed. Salesforce Data Requirements Table Structure: Loading SFDC Data into BigQuery The Cortex Framework offers several methods for loading Salesforce data into BigQuery: CDC Processing The CDC scripts rely on two key fields: You can adjust the CDC processing to handle different field names or add custom fields to suit your data schema. Configuration of API Integration and CDC To configure Salesforce data integration into BigQuery, Cortex provides the following methods: Example Configuration (settings.yaml): yamlCopy codesalesforce_to_raw_tables: – base_table: accounts raw_table: Accounts api_name: Account load_frequency: “@daily” Data Mapping and Polymorphic Fields Cortex Framework supports mapping data fields to the expected format. For example, a field named unicornId in your source system would be mapped to AccountId in Cortex with the string data type. Polymorphic Fields: Fields whose names vary but have the same structure can be mapped in Cortex using [Field Name]_Type, such as Who_Type for the Who.Type field in the Task object. Modifying DAG Templates You can customize DAG templates as needed for CDC or raw data processing. To disable CDC or raw data processing from API calls, set deployCDC=false in the configuration file. Setting Up the Extraction Module Follow these steps to set up the Salesforce to BigQuery extraction module: Cloud Composer Setup To run Python scripts for replication, install the necessary Python packages depending on your Airflow version. For Airflow 2.x: bashCopy codegcloud composer environments update my-composer-instance –location us-central1 –update-pypi-package apache-airflow-providers-salesforce>=5.2.0 Security and Permissions Ensure Cloud Composer has access to Google Secret Manager for retrieving stored secrets, enhancing the security of sensitive data like passwords and API keys. Conclusion By following these steps, you can successfully integrate Salesforce workloads into Cortex Framework, ensuring a seamless data flow from Salesforce into BigQuery for reporting and analytics. 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

September 30, 2024in Data, Google, Salesforce

30Sep

Predictive Analytics in Salesforce

Predictive Analytics in Salesforce: Enhancing Decision-Making with AI In an ever-changing business environment, companies seek tools to forecast trends and anticipate challenges, enabling them to remain competitive. Predictive analytics, powered by Salesforce’s AI capabilities, offers a cutting-edge solution for these needs. In this guide, we’ll explore how predictive analytics works and how Salesforce empowers businesses to make smarter, data-driven decisions. What is Predictive Analytics? Predictive analytics uses historical data, statistical modeling, and machine learning to forecast future outcomes. With the vast amount of data organizations generate—ranging from transaction logs to multimedia—unifying this information can be challenging due to data silos. These silos hinder the development of accurate predictive models and limit Salesforce’s ability to deliver actionable insights. The result? Missed opportunities, inefficiencies, and impersonal customer experiences. When organizations implement proper integrations and data management practices, predictive analytics can harness this data to uncover patterns and predict future events. Techniques such as logistic regression, linear regression, neural networks, and decision trees help businesses gain actionable insights that enhance planning and decision-making. Einstein Prediction Builder A key component of the Salesforce Einstein Suite, Einstein Prediction Builder enables users to create custom AI models with minimal coding or data science expertise. Using in-house data, businesses can anticipate trends, forecast customer behavior, and predict outcomes with tailored precision. Key Features of Einstein Prediction Builder Note: Einstein Prediction Builder requires an Enterprise or Unlimited Edition subscription to access. Predictive Model Types in Salesforce Salesforce employs various predictive models tailored to specific needs: Building Custom Predictions Salesforce supports custom predictions tailored to unique business needs, such as forecasting regional sales or calculating appointment attendance rates. Tips for Building Predictions Prescriptive Analytics: Turning Predictions into Actions Predictive insights are only as valuable as the actions they inspire. Einstein Next Best Action bridges this gap by providing context-specific recommendations based on predictions. How Einstein Next Best Action Works Data Quality: The Foundation of Accurate Predictions The effectiveness of predictive analytics depends on the quality of your data. Poor data—whether due to errors, duplicates, or inconsistencies—can skew results and undermine trust. Best Practices for Data Quality Modern tools like DataGroomr can automate data validation and cleaning, ensuring that predictions are based on trustworthy information. Empowering Smarter Decisions with Predictive Analytics Salesforce’s AI-driven predictive analytics transforms decision-making by providing actionable insights from historical data. Businesses can anticipate trends, improve operational efficiency, and deliver personalized customer experiences. As predictive analytics continues to evolve, companies leveraging these tools will gain a competitive edge in an increasingly dynamic marketplace. Embrace the power of predictive analytics in Salesforce to make faster, more strategic decisions and drive sustained success. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 30, 2024in Data, Enterprise, Salesforce, Salesforce Einstein

28Sep

AI Beneficial for Mental Healthcare

Nearly half of the participants in a U.S. survey viewed AI as beneficial for mental healthcare, though concerns around incorrect diagnoses and reduced interaction with providers remain significant. A recent study from Columbia University School of Nursing highlighted that, while AI adoption in healthcare is growing, limited research has explored patient perspectives, especially in mental healthcare. Previous studies mainly focused on somatic healthcare issues like perinatal health and radiology, with patient trust hinging on the use case and clinician endorsement. The survey, which included 500 U.S. adults, revealed that 49.3% believed AI could be beneficial in mental healthcare, though opinions varied by demographic. Black respondents and those with lower health literacy were more likely to see the benefits, while women were less inclined to share that view. Major concerns included AI’s accuracy, risk of incorrect diagnoses, potential for inappropriate treatments, and fear of losing personal connection with providers. Additionally, most participants (81.6%) believed that mental health misdiagnoses involving AI would remain the provider’s responsibility. Key values identified by respondents included confidentiality, autonomy, and the ability to understand personal mental health risk factors. The researchers emphasized the need to communicate AI tool accuracy and ensure trust between patients and providers when implementing AI in mental healthcare. Lead researcher Dr. Natalie Benda emphasized the importance of understanding patient perspectives, as AI becomes more prevalent, to ensure safe and effective deployment of AI tools in mental health. 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

September 28, 2024in AI Tools, Salesforce Development Services, Salesforce Health and Life Sciences Solutions, Salesforce Implementation Services

28Sep

Benefits of AI in Banking

Artificial intelligence (AI) is rapidly gaining traction in the banking and finance sector, with generative AI (GenAI) emerging as a transformative force. Financial institutions are increasingly adopting AI technologies to automate processes, cut operational costs, and boost overall productivity, according to Sameer Gupta, North America Financial Services Organization Advanced Analytics Leader at EY. While traditional machine learning (ML) techniques are commonly used for fraud detection, loan approvals, and personalized marketing, banks are now advancing to incorporate more sophisticated technologies, including ML, natural language processing (NLP), and GenAI. Gupta notes that EY is observing a growing trend of banks using ML to enhance credit approvals, improve fraud detection, and refine marketing strategies, leading to greater efficiency and better decision-making. A recent survey by Gartner’s Jasleen Kaur Sindhu reveals that 58% of banking CIOs have either deployed or plan to deploy AI initiatives in 2024, with this number expected to rise to 77% by 2025. “This indicates not only the growing importance of AI but also its fundamental role in shaping how banks operate and deliver value to their customers,” Sindhu said. “AI is becoming essential to the success of banking institutions.” Here are five key benefits of AI applications in banking: Despite the benefits, concerns about AI in banking persist, particularly regarding data privacy, bias, and ethics. AI can inadvertently extract personal information and raise privacy issues. Regulatory challenges and the potential for AI systems to perpetuate biases are also major concerns. As AI technology evolves, banks are investing in robust governance frameworks, continuous monitoring, and adherence to ethical standards to address these risks. Looking ahead, AI is expected to revolutionize banking by delivering personalized services, enhancing customer interactions, and driving productivity. Deloitte forecasts that GenAI could boost productivity by up to 35% in the top 14 global investment banks, generating significant additional revenue per employee by 2026. 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

September 28, 2024in Artificial Intelligence, Data, Generative AI, Technology

28Sep

Salesforce Continues to Push the Boundaries of AI Innovation

In a strategic move to enhance its AI capabilities, Salesforce has announced the acquisition of Zoomin, a leader in unstructured data management solutions. This acquisition, expected to finalize in the fourth quarter of Salesforce’s fiscal year 2025, aligns with the company’s mission to dominate the enterprise AI landscape through its Agentforce platform. The acquisition further highlights Salesforce’s commitment to advancing AI-driven customer experiences and operational efficiency. Financial details of the transaction were not disclosed, but Salesforce confirmed that it would not affect previous earnings guidance. Previously, in discussions around Service Cloud’s push for Unified Knowledge, there were concerns about relying on partners like Zoomin. This acquisition addresses those concerns by filling a critical gap in Salesforce’s product offerings and adding new functionalities to Data Cloud. Strengthening Data Cloud for AI Zoomin’s technology will enhance Salesforce’s Data Cloud by providing improved support for managing unstructured data—a cornerstone of modern AI systems. This is a critical step in Salesforce’s AI strategy, particularly for the Agentforce platform, as it enables more comprehensive, context-aware AI capabilities. Rahul Auradkar, Salesforce’s EVP & GM of Unified Data Services & Einstein, stressed the importance of this acquisition. “Unstructured data is the key to unlocking AI’s full potential in customer interactions,” Auradkar said. “With Zoomin’s technology, we’re not just improving data management—we’re revolutionizing how AI agents understand and use information to deliver personalized experiences.” The integration of Zoomin’s Unified Knowledge technology directly addresses a key challenge in AI: managing and understanding unstructured data to create smarter AI agents. By strengthening its data foundation, Salesforce is positioning itself to deliver more sophisticated AI applications across its platform. Agentforce: A New AI Frontier Salesforce’s recently launched Agentforce platform aims to revolutionize enterprise AI with autonomous AI agents capable of advanced decision-making and task automation. By incorporating Zoomin’s technology, Agentforce will gain the ability to process and utilize unstructured data more effectively, setting it apart from competitors like Microsoft’s Copilot, which often requires significant user input and prompt engineering. The enhanced Agentforce platform will deliver a host of benefits, from improved customer service automation to more accurate sales forecasting and personalized marketing campaigns. By tapping into unstructured data, Salesforce is paving the way for AI-driven insights and actions previously unattainable with traditional approaches. A Natural Progression from Partnership to Acquisition Zoomin’s relationship with Salesforce began in 2018 as an AppExchange partner, followed by an investment from Salesforce Ventures in 2019. This acquisition marks a natural progression in their partnership, promising a smooth integration into Salesforce’s ecosystem. Zoomin CEO Gal Oron shared his enthusiasm: “Joining forces with Salesforce is a natural next step for us. Our shared vision is to make AI truly intelligent by giving it access to the vast amount of unstructured data that exists in enterprises. Together, we’ll help businesses unlock the full potential of their data and AI investments.” Implications Across the Business Spectrum The integration of Zoomin’s technology is expected to have broad implications, especially in customer service, where AI agents can use unstructured data to deliver more personalized and efficient responses. Beyond customer service, this technology is poised to impact sales, marketing, and overall business operations, enabling deeper insights into customer behavior and more targeted campaigns. Kishan Chetan, EVP and GM of Salesforce Service Cloud, highlighted the potential: “With Unified Knowledge, we’re not just improving AI—we’re transforming how businesses understand and serve their customers. Imagine AI agents that can grasp the full context of a customer’s history, preferences, and needs in real time. That’s the power we’re unlocking.” A Strategic Response to the AI Arms Race Salesforce’s acquisition of Zoomin comes amid an increasingly competitive enterprise AI landscape. By bolstering its embedded AI capabilities through strategic acquisitions, Salesforce is solidifying its position as a leader in enterprise AI, while addressing key challenges faced by rivals like Microsoft and Google. Zoomin’s expertise in processing large volumes of technical content and generating insights based on user behavior will be instrumental in helping Salesforce deliver cutting-edge, AI-driven solutions. These advancements will improve everything from customer service to digital transformation initiatives across industries. With this acquisition, Salesforce continues to push the boundaries of AI innovation, cementing its leadership in the rapidly evolving enterprise AI market. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 28, 2024in Agentforce Platform, Data, Enterprise, Google, Salesforce, Salesforce Service Cloud, Technology, Unified Knowledge

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