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Ethical AI Implementation
06Oct

Ethical AI Implementation

AI technologies are rapidly evolving, becoming a practical solution to support essential business operations. However, creating true business value from AI requires a well-balanced approach that considers people, processes, and technology. Ethical AI Implementation. AI encompasses various forms, including machine learning, deep learning, predictive analytics, natural language processing, computer vision, and automation. To leverage AI’s competitive advantages, companies need a strong foundation and a realistic strategy aligned with their business goals. “Artificial intelligence is multifaceted,” said John Carey, managing director at AArete, a business management consultancy. “There’s often hype and, at times, exaggeration about how ‘intelligent’ AI truly is.” Business Advantages of AI Adoption Recent advancements in generative AI, such as ChatGPT and Dall-E, have showcased AI’s significant impact on businesses. According to a McKinsey Global Survey, global AI adoption surged from around 50% over the past six years to 72% in 2024. Some key benefits of adopting AI include: Prerequisites for AI Implementation Successfully implementing AI can be complex. A detailed understanding of the following prerequisites is crucial for achieving positive results: 13 Steps for Successful AI Implementation Common AI Implementation Mistakes Organizations often stumble by: Key Challenges in Ethical AI Implementation Human-related challenges often present the biggest hurdles. To overcome them, organizations must foster data literacy and build trust among stakeholders. Additionally, challenges around data management, model governance, system integration, and intellectual property need to be addressed. Ensuring Ethical AI Implementation To ensure responsible AI use, companies should: Ethical AI implementation requires a continuous commitment to transparency, fairness, and inclusivity across all levels of the organization. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 6, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Salesforce, Technology
Telemynd CharmHealth and Salesforce
05Oct

Telemynd CharmHealth and Salesforce

Telemynd Integrates CharmHealth EHR with Salesforce to Scale Nationwide Care Telemynd, a leading behavioral health practice, has integrated its CharmHealth electronic health record (EHR) system with the Salesforce CRM platform to expand its services nationwide. This integrated tech stack enhances patient experiences, personalizes care, and streamlines clinical workflows across the organization. At the 2024 CharmHealth user conference, Charmalot2024, Roger Murray, Vice President of Product & Marketing at Telemynd, and Venky Chellappa, Vice President of Sales & Marketing at CharmHealth, discussed the partnership’s impact on scaling Telemynd’s operations. Key Takeaways: Partnership Rooted in Pandemic Response The partnership between Telemynd and CharmHealth began during the COVID-19 pandemic, when Telemynd needed to quickly expand its telehealth services. CharmHealth’s technology facilitated this growth, allowing Telemynd to deliver mental health services to thousands of patients across the U.S. through telehealth. “We were figuring it [telehealth] out together,” said Murray, reflecting on how both teams adapted to the rapid changes. Today, Telemynd delivers over 60,000 hours of care annually, serving both military and civilian patients nationwide. Seamless Integration: CharmHealth and Salesforce CharmHealth’s adaptable EHR platform was designed with integration in mind, allowing Telemynd to combine the strengths of Salesforce and CharmHealth. “Salesforce helps us maintain a positive user experience for patients and providers,” explained Murray. “But we wanted to continue using our CharmHealth EHR. We worked to make the interfaces between the two systems bidirectional for a seamless experience.” All clinical activities, including charting, notes, and revenue cycle management (RCM), take place in CharmHealth, while patient engagement, follow-ups, and outcomes reporting happen through Salesforce. The integration was achieved by enabling the necessary APIs for a smooth flow of information. Chellappa emphasized the collaboration’s success: “We made a commitment to Roger and his team to support them. When they succeed, we succeed.” Mutual Growth Through Partnership This collaboration has extended beyond Salesforce integration, with many solutions developed for Telemynd influencing CharmHealth’s broader product development. Though the API functionality remains proprietary to Telemynd, the insights gained have driven improvements to CharmHealth’s EHR platform. Murray highlighted the strength of the partnership, noting, “The same engineering team at CharmHealth has worked with us for years. Their commitment to our growth has been invaluable.” As a result of this partnership, Telemynd has rapidly expanded its reach, showcasing how mental health services can be scaled effectively. Both companies credit their ongoing collaboration and open communication as key drivers of their mutual success. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 5, 2024in Salesforce Health and Life Sciences Solutions, Salesforce Implementation Services, Salesforce Optimization
Salesforce Einstein Conversation Mining
05Oct

Salesforce Einstein Conversation Mining

What Is Salesforce Einstein Conversation Mining? Imagine truly understanding your customers—knowing what drives their satisfaction, common reasons for support requests, and more. That’s the power of Einstein Conversation Mining (ECM). This AI-powered tool leverages customer interactions—via chats, emails, or calls—to uncover valuable insights. By analyzing these conversations, ECM helps businesses identify patterns, track sentiment, and prioritize what matters most to their customers. Take Your Salesforce Flows to the Next Level Einstein Conversation Mining employs advanced natural language processing (NLP) and machine learning to: Far from being tech for tech’s sake, ECM provides actionable insights that empower service and sales teams to: Key Features and Benefits Einstein Conversation Mining transforms customer conversations into strategic insights. Here’s how: 1. Automatic Call Transcriptions Converts spoken interactions into text, eliminating manual note-taking. These transcripts are analyzed to ensure critical details are captured and actionable. 2. Sentiment Analysis Automatically detects customer emotions (positive, negative, or neutral), enabling teams to address frustrations or identify upsell opportunities. 3. Topic Identification Highlights key topics from interactions, allowing teams to focus on areas of interest or concern and prioritize impactful actions. 4. Actionable Insights Provides AI-driven recommendations for the next steps, enabling more personalized and proactive customer interactions. 5. Trend Analysis Identifies recurring issues or successful strategies, helping teams refine processes and maintain effective practices. 6. Conversation Summarization Generates concise summaries of calls, streamlining the review process and saving time. 7. Customizable Dashboards Tailored reporting ensures teams can focus on the metrics that matter most, driving data-informed decisions. How Does Einstein Conversation Mining Work? Here’s an example of how ECM transforms customer interactions into insights: Scenario: Rescheduling an Appointment Setting Up Einstein Conversation Mining ECM is available on Performance, Unlimited, and Developer Editions of Salesforce. Reporting and Dashboards To generate actionable reports: Considerations and Best Practices Before implementing ECM, keep these in mind: ECM vs. Einstein Conversation Insights (ECI) Why Einstein Conversation Mining Matters In today’s competitive landscape, personalized customer service is critical. Einstein Conversation Mining equips teams to: Despite limitations, ECM’s AI-driven insights enable businesses to work smarter, improve processes, and deliver exceptional customer experiences. Transform Your Customer Interactions Today Embrace Einstein Conversation Mining to turn customer conversations into your greatest asset! Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 5, 2024in Data, Salesforce Einstein, Salesforce Flow
05Oct

Agentforce Autonomous Agents

Agentforce: Transforming Business Operations with Autonomous Agents Agentforce empowers organizations to create and manage autonomous agents that streamline tasks across various business departments. These include Sales Agents, Service Agents, Marketing Agents, Commerce Agents, and Platform Agents—truly delivering on the vision of “an Agentforce in every app.” But how does Agentforce work, and what are the building blocks for configuring these agents? Salesforce emphasizes that Agentforce is built with clicks, not code, making it highly accessible to users. This claim was validated by many attendees at the ‘Agentforce Launchpad’ during Dreamforce, who noted that the tool is as declarative and user-friendly as Salesforce promised. The Building Blocks of Agentforce 1. Agent Builder The journey begins with the Agent Builder within Agentforce Studio. This configuration tool allows users to define their agent’s attributes, such as the avatar, name, and description, using natural language inputs—essentially describing the agent in conversational terms. Salesforce describes it as: “If you can dream it, Agentforce can do it.” The Agent Builder interface comprises: Salesforce also provides out-of-the-box agents, such as Sales Agents, which can be enabled via guided setup. 2. Agent Topics Topics are the foundational building blocks that determine an agent’s scope of work. For example, a topic like “Order Management” grants the agent access to data such as order histories and product specifications. In the Dreamforce keynote, Saks’ service agent demonstrated the importance of topics by resolving customer queries tied to its assigned topics. However, queries outside the defined topics were flagged as “guardrails,” ensuring the agent stayed within its designated scope. 3. Topic Actions Actions, tied to topics, define what an agent can do. These actions are often flows, such as querying a CRM database or triggering automated processes. Users can assign existing actions or create new ones by referencing Apex, Flow, prompts, or MuleSoft APIs. For example, integrating external data sources requires defining a new Agentforce action tied to a MuleSoft API. This allows the agent to query data just as human users would. Testing Agents with the Atlas Reasoning Engine Agentforce’s Atlas Reasoning Engine powers agents with advanced capabilities. Users can test agents within the Agent Builder interface, following the reasoning process step-by-step: Once configured, agents are ready to operate across their assigned communication channels (e.g., email, WhatsApp, voice). Omni Supervisor: Real-Time Agent Monitoring Omni Supervisor, originally a Service Cloud feature, now extends to monitoring agents. It provides insights into overall trends, allows real-time oversight of interactions, and even enables listening to recent conversations. The Role of Data Cloud in Agentforce Data powers Agentforce, enabling agents to provide highly contextual responses. The Data Cloud processes both structured data (e.g., Salesforce records) and unstructured data (e.g., emails, voice memos) using its Vector Database for advanced processing. 1. Retrieval Augmented Generation (RAG) Salesforce employs RAG to enhance the accuracy of agent responses. RAG integrates the Atlas Reasoning Engine with Data Cloud, creating a feedback loop. Data Cloud enriches user prompts by retrieving relevant data, making agent responses more contextual and informed. 2. New Data Streams To enhance Agentforce capabilities, data can be ingested into the platform in three ways: For instance, connecting an order management system like Snowflake is streamlined via Salesforce’s prebuilt connectors. 3. Data Graphs Data Graphs visualize relationships between Data Model Objects (DMOs), enabling users to ensure all necessary data is available for optimal agent performance. Real-time Data Graphs enhance identity resolution, segmentation, and action execution for seamless data flow. Inside Prompt Builder Prompt Builder allows users to create or refine prompts that power Agentforce actions. Low-code tools guide users through the process, offering features such as previewing results and assessing feedback toxicity ratings. Search Index in RAG The Search Index is a critical component of RAG. It retrieves relevant data from Data Cloud to enhance agent reasoning. Search parameters can be configured in three ways: Tectonic’s Thoughts Agentforce, powered by Data Cloud and advanced AI tools like the Atlas Reasoning Engine, represents a new era of automation and efficiency for businesses. Whether through Sales, Service, or Marketing Agents, organizations can leverage this technology to streamline operations, personalize customer experiences, and achieve better outcomes. With over 5,200 customers implementing Agentforce in their sandboxes within the first two days of Dreamforce, the platform is already proving its transformative potential. By 2025 over a billion agents had been created! Agentforce isn’t just about improving efficiency; it’s about redefining what’s possible for business operations. Content updated January 2025. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 5, 2024in AI Tools, Data, Mulesoft, Salesforce, Snowflake, Technology
AI Era Demands an Intelligent Data Infrastructure
05Oct

AI Era Demands an Intelligent Data Infrastructure

Why the AI Era Demands an Intelligent Data Infrastructure NetApp aims to bridge the gap between cloud-based AI models and on-premises data management systems, addressing a major challenge many organizations face as they explore emerging AI capabilities, such as generative AI. AI Era Demands an Intelligent Data Infrastructure. While the most advanced AI tools often reside in hyperscale clouds, the key data of most organizations remains on-premises. As companies move from AI experimentation to scaling their initiatives, they face the complex task of closing the divide between their data and AI. At first glance, the solution seems simple: Organizations can integrate their proprietary data with cloud-based AI services to enhance large language models (LLMs) and other AI tools, leveraging their industry-specific expertise to generate unique insights and drive value. In fact, a recent Enterprise Strategy Group (ESG) survey found that 84% of respondents believed incorporating enterprise data was critical to supporting their generative AI efforts. However, achieving this at scale is far more difficult than it appears. Many organizations are reluctant to send their valuable data to public clouds due to concerns about security, such as exposing sensitive intellectual property or personal information. Additionally, managing the process of transferring large datasets, creating data copies, updating models with fresh data, and keeping track of everything over time adds cost and complexity. Beyond these logistical issues, many organizations struggle with a fundamental challenge: poor data management. Rapid data growth, fragmentation, and a lack of understanding of their own data forces business users and data scientists to spend excessive time “data wrangling”—the process of identifying, gathering, and preparing data for AI models. This inefficiency delays training and deploying models, pushing back the point of inference, which is the key to unlocking value from AI. Increased latency also raises the risk that inferencing is based on outdated data, leading to repeated cycles and further inefficiency. This challenge has made data management a critical obstacle in AI adoption. ESG research identified poor data quality as the top challenge organizations face when implementing AI. Inadequate data is rapidly becoming a roadblock that hampers AI initiatives across industries. To address these challenges, NetApp has shifted its focus toward building an “intelligent data infrastructure” for the AI era. At the company’s Insight 2024 conference, NetApp outlined a strategy to support AI success by bridging the gap between cloud-based AI models and on-premises data environments. The company’s vision includes simplifying, automating, and mitigating risks in the data management workflow needed to scale AI in the enterprise. NetApp’s approach includes innovations like a global metadata namespace and enhancements to its OnTap software, which enable exploration, classification, and management of data across the NetApp ecosystem. These capabilities integrate directly into AI data pipelines, enabling scalable searches and retrieval-augmented generation inferencing. Additionally, NetApp’s new “disaggregated” storage architecture will support more cost-effective scaling for compute-heavy AI tasks such as model training. NetApp’s strategy also extends to the public cloud, where it already has strong partnerships with all three hyperscalers, offering NetApp services as first-party options in their clouds. The company is expanding this by developing additional cloud-native capabilities for AI, such as integrating Azure NetApp Files with Microsoft Azure AI services, FSx for NetApp OnTap with Amazon Bedrock, and Google Cloud NetApp Volumes with Google Vertex AI and BigQuery. These integrations will allow organizations to securely enrich public cloud-based AI models with their on-premises data, creating a scalable and secure environment for AI development. While some aspects of NetApp’s strategy will be delivered over the next year, many cloud integrations will be available sooner. NetApp is also building a partner ecosystem, including hardware partners like Lenovo and Nvidia, software ISVs, and service providers like Domino Data Labs, in addition to its cloud alliances. With this vision for intelligent data infrastructure, NetApp is positioning itself as a key enabler of AI-driven innovation, and it will be intriguing to watch how this strategy unfolds in the coming months. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 5, 2024in AI Tools, Data, Enterprise, Generative AI, Google
AI Revolution in Government
05Oct

AI Revolution in Government

The AI Revolution in Government: Unlocking Efficiency and Public Trust As the AI boom accelerates, it’s essential to explore how artificial intelligence can streamline operations for government and public sector organizations. From enhancing data processing to bolstering cybersecurity and improving public planning, AI has the potential to make government services more efficient and effective for both agencies and constituents. AI Revolution in Government. The Role of AI in Public Sector Efficiency AI presents significant opportunities for government agencies to optimize their operations. By integrating AI-driven tools, public agencies can improve service delivery, boost efficiency, and foster greater trust between the public and private sectors. However, with these advancements comes the challenge of bridging the AI skills gap — a pressing concern as organizations ramp up investments in AI without enough trained professionals to support its deployment. According to a survey by SAS, 63% of decision-makers across various sectors, including government, believe they lack the AI and machine learning resources necessary to keep pace with the growing demand. This skills gap, combined with rapid AI adoption, has many workers concerned about the future of their jobs. Predictions from Goldman Sachs suggest that AI could replace 300 million full-time jobs globally, affecting nearly one-fifth of the workforce, particularly in fields traditionally considered automation-proof, such as administrative and legal professions. Despite concerns about job displacement, AI is also expected to create new roles. The World Economic Forum’s Future of Jobs Report estimates that 75% of companies plan to adopt AI, with 50% anticipating job growth. This presents a crucial opportunity for government organizations to upskill their workforce and ensure they are prepared for the changes AI will bring. Preparing for an AI-Driven Future in Government To fully harness the benefits of AI, public sector organizations must first modernize their data infrastructure. Data modernization is a key step in setting up a future-ready organization, allowing AI to operate effectively by leveraging accurate, connected, and real-time data. As AI automates lower-level tasks, government workers need to transition into more strategic roles, making it essential to invest in AI training and upskilling programs. AI Applications in GovernmentAI is already transforming various government functions, improving operations, and meeting the needs of citizens more effectively. The possibilities are vast: While AI holds immense potential, its successful adoption depends on having a digital-ready workforce capable of managing these applications. Yet, many government employees lack the data science and AI expertise needed to manage large citizen data sets and develop AI models that can improve service delivery. Upskilling the Government Workforce for AI Investing in AI education is critical to ensuring that government employees can meet the demands of the future. Countries like Finland and Singapore have already launched national AI training programs to prepare their populations for the AI-driven economy. For example, Finland’s “Elements of AI” program introduced AI basics to the public and has been completed by over a million people worldwide. Similarly, AI Singapore’s “AI for Everyone” initiative equips individuals and organizations with AI skills for social good. In the U.S., legislation is being considered to create an AI training program for federal supervisors and management officials, helping government leaders navigate the risks and benefits of AI in alignment with agency missions. The Importance of Trust and Data Security As public sector organizations embrace AI, trust is a critical factor. AI tools are only as effective as the data they rely on, and ensuring data integrity, security, and ethical use is paramount. The rise of the Chief Data Officer highlights the growing importance of managing and protecting government data. These roles not only oversee data management but also ensure that AI technologies are used responsibly, maintaining public trust and safeguarding privacy. By modernizing data systems and equipping employees with AI skills, government organizations can unlock the full potential of AI and automation. This transformation will help agencies better serve their communities, enhance efficiency, and build lasting trust with the people they serve. The Future of AI in Government The future of AI in government is bright, but organizations must take proactive steps to prepare for it. By unifying and securing their data, investing in AI training, and focusing on ethical AI deployment, public sector agencies can harness AI’s power to drive meaningful change. Ultimately, this is an opportunity for the public sector to improve service delivery, support their workforce, and build stronger connections with citizens. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 5, 2024in Salesforce Government Cloud, Salesforce Optimization, Salesforce Public Sector Solutions
Generative AI for Match Commentary
05Oct

Generative AI for Match Commentary

SAN FRANCISCO (KGO) — Companies are exploring the use of artificial intelligence for sports commentary, showcasing one of the many innovative applications of this technology in the sports arena. ABC7 reporter J.R. Stone recently got a firsthand look at IBM’s integration of Generative AI to analyze and enhance playing abilities during a demonstration at Dreamforce 2024 in San Francisco. This same technology has also been implemented at prestigious events like Wimbledon and the US Open. “This year marks the introduction of Generative AI for match commentary, which utilizes data collected during the games to create real-time analysis and match summaries,” explained Nick Otto from IBM. In a related segment, Salesforce CEO Marc Benioff revealed a new AI system called “Agent Force,” while Senator Scott Wiener introduced a bill focused on AI safety. The AI tracks various metrics, including average ball and swing speeds, as well as performance on forehand and backhand shots. To put the technology to the test, Stone faced off against Otto in a ping-pong match, where Otto emerged victorious with a score of 11-7. After the match, the AI generated an entertaining summary: “Nick’s arm must have felt like a whirlwind, spinning the ball at an average speed of 8.45 mph. J.R. tried to keep up, but his 30 forehand shots and 5.56 mph swing speed were no match.” While the advancements in AI are exciting, UCLA Professor Ramesh Srinivasan emphasizes the need for caution. “This technology is both incredible and concerning because it raises questions about the future of human journalists and commentators,” he noted. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 5, 2024in Artificial Intelligence, Data, Generative AI, Salesforce, Technology
Salesforce for K-12 and Higher Education
04Oct

Technology to Showcase the Value of Education

How Can Technology Convince Students of the Value of Higher Education? With fewer high school graduates choosing college, technology has a unique role in reigniting students’ belief in higher education. Imagine a high school student eagerly checking the mail and finding an acceptance letter from their dream college, ready to start a journey filled with opportunities, lifelong friends, and a promising future. Just a couple of decades ago, that was a common story. Today, many high schoolers aren’t looking for acceptance letters at all, uncertain if college is the best or even most practical path to success. Higher education now faces a new challenge: proving its worth to students who are increasingly weighing their options. Universities no longer simply wait for students to apply—they need to actively demonstrate that the investment will pay off. Enrollment Data Signals a Shift Away from College Once seen as a distinctive achievement, college attendance has become less of a given. In 1980, only 49% of high school graduates went on to higher education. By 2009, that number had surged to over 70%, but has since declined; by 2022, just 62% of graduates were heading straight to college. Now, with the “enrollment cliff”—a projected decrease in college-aged students due to lower birth rates—looming, colleges face intense competition to attract students. Personalization Is Key to Connecting with Students The days of “Dear applicant” are over. Today’s digital-native students want a personalized approach that speaks directly to them. If they don’t feel personally addressed through email, text, video, or even traditional mail, they may tune out and explore other options. Universities must build meaningful connections to engage students and keep their attention through every stage of the student journey. Student lifecycle management platforms, like Salesforce’s Education Cloud, have become essential tools for higher education institutions. By tracking and analyzing a student’s data—academic performance, extracurricular interests, and social behaviors—these platforms create personalized experiences that engage students from admission to graduation. Salesforce Education Cloud, for example, uses AI and robust data analytics to create a comprehensive student profile, enabling colleges to send tailored communications, schedule regular check-ins, and even reach out to parents. This personalized approach fosters a sense of connection that encourages students to enroll and stay engaged throughout their academic journey. Comprehensive Lifecycle Management and Student Support Beyond admissions, student lifecycle platforms offer extensive features that address other critical areas, from helping students who are academically struggling to managing alumni relationships and fundraising. With years of experience in supporting institutions nationwide, CDW Education partners with colleges to implement these technologies, strengthening their ability to attract, engage, and retain students. In an era when students have more educational choices than ever, colleges must actively communicate the value of a college degree and make that message resonate with each individual. By investing in technology that personalizes the student experience, higher education institutions can create a compelling case for the unique value they offer. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 4, 2024in Data, Salesforce, Salesforce Education Cloud, Technology
Databricks Tools
04Oct

Databricks Tools

Databricks recently introduced Databricks Apps, a toolkit designed to simplify AI and data application development. By integrating native development platforms and offering automatic provisioning of serverless compute, the toolkit enables customers to more easily develop and deploy applications. Databricks Apps builds on the existing capabilities of Mosaic AI, which allows users to integrate large language models (LLMs) with their enterprise’s proprietary data. However, the ability to develop interactive AI applications, such as generative AI chatbots, was previously missing. Databricks Apps addresses this gap, allowing developers to build and deploy custom applications entirely within the secure Databricks environment. According to Donald Farmer, founder and principal of TreeHive Strategy, Databricks Apps removes obstacles like the need to set up separate infrastructure for development and deployment, making the process easier and more efficient. The new features allow companies to go beyond implementing AI/ML models and create differentiated applications that leverage their unique data sets. Kevin Petrie, an analyst at BARC U.S., highlighted the significance of Databricks Apps in helping companies develop custom AI applications, which are essential for maintaining a competitive edge. Databricks, founded in 2013, was one of the pioneers of the data lakehouse storage format, and over the last two years, it has expanded its platform to focus on AI and machine learning (ML) capabilities. The company’s $1.3 billion acquisition of MosaicML in June 2023 was a key milestone in building its AI environment. Databricks has since launched DBRX, its own large language model, and introduced further functionalities through product development. Databricks Apps, now available in public preview on AWS and Azure, advances these AI development capabilities, simplifying the process of building applications within a single platform. Developers can use frameworks like Dash, Flask, Gradio, Shiny, and Streamlit, or opt for integrated development environments (IDEs) like Visual Studio Code or PyCharm. The toolkit also provides prebuilt Python templates to accelerate development. Additionally, applications can be deployed and managed directly in Databricks, eliminating the need for external infrastructures. Databricks Apps includes security features such as access control and data lineage through the Unity Catalog. Farmer noted that the support for popular developer frameworks and the automatic provisioning of serverless compute could significantly impact the AI development landscape by reducing the complexity of deploying data architectures. While competitors like AWS, Google Cloud, Microsoft, and Snowflake have also made AI a key focus, Farmer pointed out that Databricks’ integration of AI tools into a unified platform sets it apart. Databricks Apps further enhances this competitive advantage. Despite the added capabilities of Databricks Apps, Petrie cautioned that developing generative AI applications still requires a level of expertise in data, AI, and the business domain. While Databricks aims to make AI more accessible, users will still need substantial knowledge to effectively leverage these tools. Databricks’ vice president of product management, Shanku Niyogi, explained that the new features in Databricks Apps were driven by customer feedback. As enterprise interest in AI grows, customers sought easier ways to develop and deploy internal data applications in a secure environment. Looking ahead, Databricks plans to continue investing in simplifying AI application development, with a focus on enhancing Mosaic AI and expanding its collaborative AI partner ecosystem. Farmer suggested that the company should focus on supporting nontechnical users and emerging AI technologies like multimodal models, which will become increasingly important in the coming years. The introduction of Databricks Apps marks a significant step forward in Databricks’ AI and machine learning strategy, offering users a more streamlined approach to building and deploying AI applications. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 4, 2024in AI Tools, Data, Enterprise, Generative AI, Google, Snowflake
Commerce Cloud and Agentic AI
04Oct

Commerce Cloud and Agentic AI

Recognizing the demand from both B2B and B2C buyers for seamless, consistent commerce experiences across online and offline channels, Salesforce has introduced an AI-powered, unified commerce version of its Commerce Cloud platform. Salesforce, a leader in merging ecommerce and CRM software, has taken a significant step toward unified commerce with this next-generation update to Salesforce Commerce Cloud. This move aligns with the expectations of both B2B buyers and consumers, who increasingly seek integrated and personalized interactions. The company states that Commerce Cloud now “natively connects all aspects of commerce—B2C, direct-to-consumer, and B2B commerce; order management; and payments—with sales, service, and marketing, all on a single platform.” This integration offers businesses a complete view of the customer journey through a shared catalog and user profile. By unifying elements like catalogs, pricing, orders, and marketing segments, companies can deliver personalized interactions, boost customer loyalty, and drive revenue across all touchpoints. Unified Commerce: A $1.5 Trillion Opportunity Salesforce cites research from Adyen, which indicates that adopting unified commerce strategies could present a $1.5 trillion opportunity for retailers globally. In North America, 76 of the top 2000 online retailers use Salesforce’s ecommerce platform. In 2023, these retailers generated over 6 billion in web sales. Salesforce’s B2B clients include major companies such as Siemens, Schneider Electric, GE Renewable Energy, and Chambers Gasket. AI-Powered Commerce Cloud Salesforce emphasizes that AI powers key aspects of its next-generation Commerce Cloud, enabling the platform to autonomously manage tasks like product recommendations and order lookups by leveraging data from digital and in-store interactions, orders, inventory levels, customer reviews, unified profiles, and CRM information. The AI-backed “Agentforce” agents are designed to assist employees in delivering personalized interactions, strengthening customer relationships, and improving profit margins. According to Justin Racine, Principal of Unified Commerce at Perficient, Salesforce’s efforts to unify the commerce experience across its broad range of products align with the needs of both B2B buyers and consumers. He notes that modern buyers expect brands to connect and communicate with them based on their previous behaviors, preferences, and purchases. Unlocking Revenue with Agentforce Michael Affronti, Senior Vice President and General Manager of Commerce Cloud, highlights that this new version embodies unified commerce by providing businesses with a single, integrated platform. The platform consolidates the entire commerce journey, with AI-powered Agentforce agents unlocking new revenue streams and delivering personalized experiences across every channel. Furniture designer and manufacturer MillerKnoll has already benefited from the unified platform. Frank DeMaria, Vice President of Digital Engineering & Platforms, mentions that the integration of sales, service, marketing, and other functions has helped the company offer personalized experiences and improve online sales and customer satisfaction across its portfolio of brands, including HermanMiller. Key Features of the New Commerce Cloud Racine adds that Salesforce’s new release unifies its product suite under a cohesive platform, providing marketers and business users with a comprehensive 360-degree view of the customer. This enables brands to build experiences and ordering workflows that are predictive rather than reactive. The integration of Agentforce represents a breakthrough, blending AI with brand interactions to unlock potential gains for merchandisers and buyers, and Racine is excited to see how these technologies enhance revenue and customer loyalty. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 4, 2024in Data, Salesforce Commerce Cloud
AI-Ready Text Data
04Oct

AI-Ready Text Data

Large language models (LLMs) are powerful tools for processing text data from various sources. Common tasks include editing, summarizing, translating, and extracting text. However, one of the key challenges in utilizing LLMs effectively is ensuring that your data is AI-ready. This insight will explain what it means to have AI-Ready Text Data and present a few no-code solutions to help you achieve this. What Does AI-Ready Mean? We are surrounded by vast amounts of unstructured text data—web pages, PDFs, emails, organizational documents, and more. These unstructured documents hold valuable information, but they can be difficult to process using LLMs without proper preparation. Many users simply copy and paste text into a prompt, but this method is not always effective. Consider the following challenges: To be AI-ready, your data should be formatted in a way that LLMs can easily interpret, such as plain text or Markdown. This ensures efficient and accurate text processing. Plain Text vs. Markdown Plain text (.txt) is the most basic file type, containing only raw characters without any stylization. Markdown files (.md) are a type of plain text but include special characters to format the text, such as using asterisks for italics or bolding. LLMs are adept at processing Markdown because it provides both content and structure, enhancing the model’s ability to understand and organize information. Markdown’s simple syntax for headers, lists, and links allows LLMs to extract additional meaning from the document’s structure, leading to more accurate interpretations. Markdown is widely supported across various platforms (e.g., Slack, Discord, GitHub, Google Docs), making it a versatile option for preparing AI-ready text. Tools for AI-Ready Data Here are some essential tools to help you manage Markdown and integrate it into your LLM workflows: Recommended Tools for Managing AI-Ready Data Obsidian: Save and Store Plain Text Obsidian is a great tool for saving and organizing Markdown files. It’s a free text editor that supports plain-text workflows, making it an excellent choice for storing content extracted from PDFs or web pages. Jina AI Reader: Convert Web Pages to Markdown Jina AI Reader is an easy-to-use tool for converting web pages into Markdown. Simply add https://r.jina.ai/ before a webpage URL, and it will return the content in Markdown format. This method streamlines the process of extracting relevant text without the clutter of formatting. LlamaParse: Extract Plain Text from Documents Highly formatted documents like PDFs can present unique challenges when working with LLMs. LlamaParse, part of LlamaIndex’s suite, helps strip away formatting to focus on the content. By using LlamaParse, you can extract plain text or Markdown from documents and ensure only the relevant sections are processed. Our Thoughts Preparing text data for AI involves strategies to convert, store, and process content efficiently. While this may seem daunting at first, using the right tools will streamline your workflow and allow you to maximize the power of LLMs for your specific tasks. Tectonic is ready to assist. Contact us today. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 4, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Google
Discharge Planning
03Oct

Discharge Planning

Discharge planning is crucial for smoothly transitioning patients from hospital care to the next stage of their recovery. This process requires collaboration among patients, caregivers, and providers to create a personalized plan that ensures continuity of care after hospitalization. Effective discharge planning must consider the patient’s care needs, preferences, and concerns. When done well, it helps prevent readmissions and alleviates strain on both patients and hospitals. However, balancing clinical judgment with patient data can challenge care teams already burdened with heavy workloads. Jean Halpin, COO at Grant Medical Center, shared how the organization has integrated AI tools to predict discharge dates and automate parts of the discharge planning process, helping to streamline patient care. Challenges of Effective Discharge Planning Halpin emphasized that a streamlined discharge process is essential for reducing wait times and improving patient engagement. Yet, various factors influence how quickly patients are discharged, particularly in emergency rooms where delays can affect overall patient flow. “Most of the wait time we experience as patients boils down to a lengthy discharge process that isn’t effectively moving patients,” Halpin explained. “It’s a domino effect. Someone waiting in the ER for a bed is delayed because another patient hasn’t been discharged when they should have been.” To address these inefficiencies, Grant Medical Center implemented the Qventus Inpatient Solution. This tool integrates with electronic health records (EHRs) to analyze patient data—such as clinical notes, history, and labs—and provides recommendations on discharge timing. These insights have helped reduce ER wait times and improved patient flow. Integrating AI into Clinical Workflows Adopting AI in healthcare comes with integration challenges, particularly ensuring that tools enhance, rather than hinder, clinicians’ workflows. Halpin noted that the Qventus tool minimizes disruptions by seamlessly pulling EHR data to generate an estimated discharge date, allowing care teams to focus on patient care without extra administrative burdens. “As a patient’s health changes, the [discharge] date can fluctuate, but AI uses its data to predict the most accurate day based on similar cases,” Halpin explained. “The care teams can then review the date and determine whether they agree, without having to sift through records to develop their own recommendation.” Halpin also highlighted the value of AI in reducing the administrative load. Tasks like coordinating discharges to rehab facilities, ordering tests, and prescribing medication consume significant time, and automating these functions allows care teams to focus more on direct patient care. Embracing AI to Alleviate Healthcare Worker Burdens For healthcare systems adopting AI, accurately assessing its impact is critical. At Grant Medical Center, leadership is measuring success by evaluating employee satisfaction, patient outcomes, and administrative improvements—such as time and cost savings. “By improving our patient flow, we reduced unnecessary stays by nearly 1,400 days. Patients are happy to go home on time, and our care teams can focus on working at the top of their license,” said Halpin. Despite the benefits, Halpin stressed that implementing AI requires thoughtful onboarding to ensure staff are comfortable with the new tools. Training and support are key to making the transition seamless and enabling teams to see how AI can enhance their workflows. “Health system leaders should embrace advancements that help alleviate burdens for workers,” she said. “Once teams understand the tool, they can prioritize patient care while AI handles the time-consuming admin tasks.” Halpin concluded that embracing AI in discharge planning not only improves operational efficiency but also empowers healthcare teams to deliver better, more focused care. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 3, 2024in AI Tools, Data, Salesforce Einstein, Salesforce Health and Life Sciences Solutions, Salesforce Optimization, Technology
Sales Incentives Can Boost Morale and Performance
03Oct

Sales Incentives Can Boost Morale and Performance

Showing up to work is one thing, but bringing genuine enthusiasm to the job is another. How can you motivate your team to meet and exceed their goals? Sales incentives are a powerful tool to ignite motivation, but they aren’t one-size-fits-all. Figuring out the best structure for your team can lead to more energized, results-driven reps. In this Tectonic insight, we’ll explore different types of sales incentives, how they motivate teams, and best practices for implementing an effective incentive program that drives real results. What are Sales Incentives? Sales incentives are rewards given to sales reps, in addition to their base compensation, for exceptional performance. This often means hitting or exceeding sales targets. While financial bonuses are the most common, incentives can come in many forms, all designed to motivate specific behaviors or outcomes. Types of Sales Incentives Here are five common types of sales incentives to consider: How Sales Incentives Motivate Your Team Sales incentives help reinforce desired behaviors, offering a clear path to rewards. They provide a mutual win: your company increases sales, while reps enjoy additional rewards. Many sales professionals are naturally driven by competition, but incentives give everyone, competitive or not, something tangible to work toward. Incentives also boost employee satisfaction, reduce turnover, and show that you value hard work. This can save your organization the cost and hassle of recruiting and onboarding new talent. Sales Incentives That Actually Work While money is the most popular reward, mixing in creative incentives can add excitement to your program. Consider these options: Best Practices for Implementing Sales Incentives To create an effective sales incentive program, keep these points in mind: Measuring the Impact of Sales Incentives on Performance To assess the effectiveness of your incentive program, track key performance indicators (KPIs) such as: Sales Incentives Can Boost Morale and Performance The key to a successful sales incentive program is simplicity and transparency. By crafting a plan that’s easy to understand and aligned with your team’s motivations, you can drive better performance and improve job satisfaction at the same time. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 3, 2024in Data, Technology
Salesforce 2024 outage
03Oct

October 2024 Outage

On October 1st, 2024, Salesforce experienced a significant outage impacting over 80 instances, causing core services to slow to a crawl. The outage, which began around 6:30 UTC, remained unresolved over 8.5 hours later, with the root cause still unknown at that time. The outage has been particularly damaging, as Salesforce emphasizes trust as one of its core values, promoting transparency and reliability in its service delivery. While the Salesforce Trust website, which provides real-time system status updates, remained operational, some users reported difficulty accessing it when they needed outage details, adding to their frustration. This disruption has been a major blow to organizations that depend on Salesforce for essential business functions such as customer relationship management (CRM), sales pipelines, and workflow management. Key Facts About the Outage Am I Affected by the Salesforce Downtime? If you’re unsure whether your instance is affected, you can check the real-time list of impacted cases on the Salesforce Trust website. To do this, identify your organization’s instance (e.g., NA54, EU13) and compare it to the affected list. However, the Trust site itself has experienced intermittent outages during the incident. Customers have reported issues such as: If you’re encountering these problems, your Salesforce instance is likely impacted. How Salesforce is Responding Salesforce has initiated an emergency release in phases, with the first phase nearly complete. Afterward, validation checks will be performed to assess system stability. While some customers have reported that their instances have recovered, others continue to experience severe issues. Salesforce Support has provided general troubleshooting advice, such as using alternative browsers or clearing caches, but many users have found this ineffective, as the root cause lies within Salesforce’s infrastructure, not user-side configurations. What to Do Now Looking Ahead Although the root cause of the outage remains undetermined, Salesforce has committed to providing regular updates. Customers are encouraged to monitor the Salesforce Trust website and communicate with support teams. Once the cause is identified, Salesforce plans to release a detailed report on the incident and how they will prevent future occurrences. Conclusion Salesforce’s outage on October 1st has caused widespread disruption for businesses globally. While emergency updates are underway and some instances are recovering, full resolution may take time. In the meantime, staying updated and preparing for intermittent issues is critial for those who rely on Salesforce for their daily operations. Stay tuned for further updates as Salesforce continues working to restore full functionality across all affected instances. Like Related Posts AI Automated Offers with Marketing Cloud Personalization AI-Powered Offers Elevate the relevance of each customer interaction on your website and app through Einstein Decisions. Driven by a Read more 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

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October 3, 2024in Salesforce

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