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Salesforce and OpenAI Advances in AI
19Sep

Salesforce and OpenAI Advances in AI

With investor enthusiasm for AI beginning to fade, Salesforce is shifting focus to its next AI wave, “Agentforce,” which will be showcased at the Dreamforce customer conference. This announcement comes at a time when Salesforce stock has underperformed, with revenue growth slowing and expectations building that AI-related revenue may not materialize until 2025. Salesforce and OpenAI Advances in AI. The Agentforce platform will be featured at Dreamforce, running from Sept. 17 to Sept. 19, and aims to automate routine business tasks while offering real-time insights and guidance. CEO Marc Benioff noted in a Sept. 12 briefing that Agentforce represents the third wave of AI, moving beyond conversational chatbots to more autonomous agents. Early adopters of the platform include Walt Disney, Kaiser Permanente, Fossil, Wiley, and OpenTable. Meanwhile, Salesforce faces stiff competition. Microsoft is hosting its own AI event, Microsoft 365 Copilot Wave 2, which focuses on business productivity features powered by generative AI. Like Salesforce, Microsoft’s AI tools have yet to demonstrate significant revenue impact, as customers are still testing the technologies. Salesforce is pushing Agentforce as an evolution of its previous Einstein copilot, which integrates conversational AI within its apps. Agentforce aims to take this further by reducing human oversight and improving efficiency in sales, marketing, and customer service roles. The product is scheduled for an October rollout, with a pricing model based on usage—potentially $2 per interaction for complex queries. Analysts have mixed opinions on Agentforce’s potential. Truist Securities sees the AI platform driving future subscription growth, while Barclays believes it could gain more traction than previous AI tools due to its fully autonomous nature. However, others, like Monness Crespi Hardt & Co., remain cautious, noting concerns about Salesforce’s slowing revenue growth in a challenging macroeconomic environment. Salesforce Agentforce PlatformIn its second-quarter earnings call, Salesforce shared promising results from an Agentforce trial, where the platform resolved 90% of patient inquiries for a large healthcare customer. Analysts like Morgan Stanley’s Keith Weiss see Agentforce as a key differentiator for Salesforce, enabling customers to leverage AI at scale with reduced complexity and cost. Despite this optimism, Salesforce still faces challenges. Competitors such as Meta’s AI Studio and ServiceNow are also advancing AI agent technologies. ServiceNow, for instance, emphasizes the need for strict human oversight of AI actions, a sentiment echoed by Salesforce’s chief ethical and humane use officer, Paula Goldman. As the tech industry races to enhance AI autonomy, concerns about the technology’s limitations—such as bias, hallucinations, and decision-making risks—remain central. Experts warn that while AI agents hold great potential, they must be carefully regulated to prevent unintended consequences. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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September 19, 2024in Agentforce Platform, Generative AI
GPT-o1 GPT5 Review
15Sep

GPT-o1 GPT5 Review

OpenAI has released its latest model, GPT-5, also known as Project Strawberry or GPT-o1, positioning it as a significant advancement in AI with PhD-level reasoning capabilities. This new series, OpenAI-o1, is designed to enhance problem-solving in fields such as science, coding, and mathematics, and the initial results indicate that it lives up to the anticipation. Key Features of OpenAI-o1 Enhanced Reasoning Capabilities Safety and Alignment Targeted Applications Model Variants Access and Availability The o1 models are available to ChatGPT Plus and Team users, with broader access expected soon for ChatGPT Enterprise users. Developers can access the models through the API, although certain features like function calling are still in development. Free access to o1-mini is expected to be provided in the near future. Reinforcement Learning at the Core The o1 models utilize reinforcement learning to improve their reasoning abilities. This approach focuses on training the models to think more effectively, improving their performance with additional time spent on tasks. OpenAI continues to explore how to scale this approach, though details remain limited. Major Milestones The o1 model has achieved impressive results in several competitive benchmarks: Chain of Thought Reasoning OpenAI’s o1 models employ the “Chain of Thought” prompt engineering technique, which allows the model to think through problems step by step. This method helps the model approach complex problems in a structured way, similar to human reasoning. Key aspects include: While the o1 models show immense promise, there are still some limitations, which have been covered in detail elsewhere. However, based on early tests, the model is performing impressively, and users are hopeful that these capabilities are as robust as advertised, rather than overhyped like previous projects such as SORA or SearchGPT by OpenAI. Like1 Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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September 15, 2024in Enterprise
Impact of EHR Adoption
14Sep

Impact of EHR Adoption

Fueled by the availability of chatbot interfaces like Chat-GPT, generative AI has become a key focus across various industries, including healthcare. Many electronic health record (EHR) vendors are integrating the technology to streamline administrative workflows, allowing clinicians to focus more on patient care. Whether you see EHR adoption as easy or challenging, the Impact of EHR Adoption will be positive. Generative AI and EHR Efficiency As defined by the Government Accountability Office (GAO), generative AI is “a technology that can create content, including text, images, audio, or video, when prompted by a user.” Generative AI systems learn patterns from vast datasets, enabling them to generate new, similar content using machine learning algorithms and statistical models. One of the areas where generative AI shows promise is in automating EHR workflows, which could alleviate the burden on clinicians. Epic’s AI-Driven Innovations Phil Lindemann, vice president of data and analytics at Epic, noted that generative AI is ideal for automating repetitive tasks. One application under testing allows the technology to draft patient portal message responses for clinicians to review and send. This could save time and let doctors spend more time with patients. Another project focuses on summarizing updates to a patient’s record since their last visit, offering a quick synopsis for the provider. Epic is also exploring how generative AI could help patients better understand their health records by translating complex medical terms into more accessible language. Additionally, the system can translate this information into various languages, enhancing patient education across diverse populations. However, Lindemann emphasized that while AI offers valuable tools, it is not a cure-all for healthcare’s challenges. “We see it as a translation tool,” he said, acknowledging the importance of targeted use cases for successful implementation. Oracle Health’s Clinical Digital Assistant Oracle Health is beta-testing a generative AI chatbot aimed at reducing administrative tasks for healthcare professionals. The Clinical Digital Assistant summarizes patient information and generates automated clinical notes by listening to patient-provider conversations. Physicians can interact with the tool during consultations, asking for relevant patient data without breaking eye contact with the patient. The assistant can also suggest actions based on the discussion, which providers must review before finalizing. Oracle plans to make this tool widely available by the second quarter of 2024, with the goal of easing clinician workloads and improving the patient experience. eClinicalWorks and Ambient Listening Technology In partnership with sunoh.ai, eClinicalWorks is utilizing generative AI-powered ambient listening technology to assist with clinical documentation. This tool automatically drafts clinical notes based on patient conversations, which clinicians can then review and edit as necessary. Girish Navani, CEO of eClinicalWorks, highlighted the potential for generative AI to become a personal assistant for doctors, streamlining documentation tasks and reducing cognitive load. The integration is expected to be available to customers in early 2024. MEDITECH’s AI-Powered Discharge Summaries MEDITECH is collaborating with Google to develop a generative AI tool focused on automating hospital discharge summaries. These summaries, which are crucial for care coordination, are often time-consuming for clinicians to create, especially for patients with longer hospital stays. The AI system generates draft summaries that clinicians can review and edit, aiming to speed up discharges and reduce clinician burnout. MEDITECH is working with healthcare organizations to validate the technology before a general release. Helen Waters, executive vice president and COO of MEDITECH, stressed the importance of careful implementation. The goal is to ensure accuracy and build trust among clinicians so that generative AI can be successfully integrated into clinical workflows. The Impact of EHR Adoption EHR systems have transformed healthcare, improving care coordination and decision support. However, EHR-related administrative burdens have also contributed to clinician burnout. A 2019 study found that 40% of physician burnout was linked to EHR use. By automating time-consuming EHR tasks, generative AI could help reduce this burden and improve clinical efficiency. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

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September 14, 2024in Data, Generative AI, Google, Salesforce Health and Life Sciences Solutions, Salesforce Optimization, Technology
E-Commerce Platform Improvement
13Sep

E-Commerce Platform Improvement

Section I: Problem Statement CVS Health is continuously exploring ways to improve its e-commerce platform, cvs.com. One potential enhancement is the implementation of a complementary product bundle recommendation feature on its product description pages (PDPs). For instance, when a customer browses for a toothbrush, they could also see recommendations for related products like toothpaste, dental floss, mouthwash, or teeth whitening kits. A basic version of this is already available on the site through the “Frequently Bought Together” (FBT) section. Traditionally, techniques such as association rule mining or market basket analysis have been used to identify frequently purchased products. While effective, CVS aims to go further by leveraging advanced recommendation system techniques, including Graph Neural Networks (GNN) and generative AI, to create more meaningful and synergistic product bundles. This exploration focuses on expanding the existing FBT feature into FBT Bundles. Unlike the regular FBT, FBT Bundles would offer smaller, highly complementary recommendations (a bundle includes the source product plus two other items). This system would algorithmically create high-quality bundles, such as: This strategy has the potential to enhance both sales and customer satisfaction, fostering greater loyalty. While CVS does not yet have the FBT Bundles feature in production, it is developing a Minimum Viable Product (MVP) to explore this concept. Section II: High-Level Approach The core of this solution is a Graph Neural Network (GNN) architecture. Based on the work of Yan et al. (2022), CVS adapted this GNN framework to its specific needs, incorporating several modifications. The implementation consists of three main components: Section III: In-Depth Methodology Part 1: Product Embeddings Module A: Discovering Product Segment Complementarity Relations Using GPT-4 Embedding plays a critical role in this approach, converting text (like product names) into numerical vectors to help machine learning models understand relationships. CVS uses a GNN to generate embeddings for each product, ensuring that relevant and complementary products are grouped closely in the embedding space. To train this GNN, a product-relation graph is needed. While some methods rely on user interaction data, CVS found that transaction data alone was not sufficient, as customers often purchase unrelated products in the same session. For example: Instead, CVS utilized GPT-4 to identify complementary products at a higher level in the product hierarchy, specifically at the segment level. With approximately 600 distinct product segments, GPT-4 was used to identify the top 10 most complementary segments, streamlining the process. Module B: Evaluating GPT-4 Output To ensure accuracy, CVS implemented a rigorous evaluation process: These results confirmed strong performance in identifying complementary relationships. Module C: Learning Product Embeddings With complementary relationships identified at the segment level, a product-relation graph was built at the SKU level. The GNN was trained to prioritize pairs of products with high co-purchase counts, sales volume, and low price, producing an embedding space where relevant products are closer together. This allowed for initial, non-personalized product recommendations. Part 2: User Embeddings To personalize recommendations, CVS developed user embeddings. The process involves: This framework is currently based on recent purchases, but future enhancements will include demographic and other factors. Part 3: Re-Ranking Scheme To personalize recommendations, CVS introduced a re-ranking step: Section IV: Evaluation of Recommender Output Given that CVS trained the model using unlabeled data, traditional metrics like accuracy were not feasible. Instead, GPT-4 was used to evaluate recommendation bundles, scoring them on: The results showed that the model effectively generated high-quality, complementary product bundles. Section V: Use Cases Section VI: Future Work Future plans include: Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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September 13, 2024in Generative AI
ChatGPT Memory Announced
12Sep

ChatGPT Memory Announced

We’re testing memory with ChatGPT to make your experience more seamless by saving important details across chats, so you won’t have to repeat yourself. This feature helps make future conversations more helpful. You’re fully in control of ChatGPT’s memory. You can ask it to remember something, view what it recalls, and even delete specific memories either conversationally or through settings. Memory can also be turned off completely. This week, we’re rolling out memory to a small group of free and Plus users to gather feedback. Broader rollout plans will be shared soon. How Memory Works As you interact with ChatGPT, it can remember key details from your conversations, improving the quality of future responses. For instance: You’re In Control You can turn memory off at any time (Settings > Personalization > Memory). With memory off, ChatGPT won’t store or use any memories. To delete specific memories, simply ask ChatGPT to forget or manage them in settings. Memory works across interactions, meaning deleting a chat doesn’t erase its associated memory—you’ll need to delete the memory itself. ChatGPT may use the content you provide, including memories, to improve its models for everyone, unless you opt out through Data Controls. Note that content from Team and Enterprise accounts won’t be used to train models. Temporary Chat for No Memory If you’d prefer a conversation without memory, use temporary chat. These conversations won’t appear in history, won’t store memories, and won’t contribute to model training. Custom Instructions and Memory Custom Instructions let you guide ChatGPT on how to respond, while memory captures information shared in conversations. This combination allows ChatGPT to become more personalized and responsive over time. Privacy and Safety Standards We’re evolving our privacy and safety protocols to address memory’s impact. ChatGPT is designed to avoid remembering sensitive information, like health data, unless explicitly requested. Memory for Team and Enterprise Users For Team and Enterprise users, memory helps increase efficiency by learning individual preferences and reducing the need for repetitive instructions. For example, ChatGPT can remember your preferred tone and structure for content or your preferred coding languages for programming tasks. Memory in Team and Enterprise accounts remains secure and excluded from model training, with full control over how and when memories are used. Account owners can disable memory for the organization at any time. Memory for GPTs GPTs, too, will have distinct memories. Builders can choose to enable memory, and each GPT will store its own memories. For example, a book recommendation GPT can remember your favorite genres for tailored suggestions. To interact with memory-enabled GPTs, you’ll need memory on. Each GPT will have its own separate memory, so details shared with ChatGPT won’t carry over unless re-entered. Memory is now available to ChatGPT Free, Plus, Team, and Enterprise users. Based on user feedback, ChatGPT will notify you when a memory is updated, and you can easily review or delete those updates by accessing the “Manage memories” option in settings. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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September 12, 2024in Salesforce
Salesforce Org Merge Risks
10Sep

Salesforce Org Merge Risks

Managing Multiple Salesforce Instances: Challenges and Solutions For growing enterprises, managing multiple Salesforce instances can be a significant challenge. Each instance may house critical business data and processes, which often need to be consolidated, particularly during mergers, acquisitions, or different stages of Salesforce adoption. This consolidation is essential to reduce operating costs and enhance efficiency. Salesforce Org Merge Risks. Salesforce Org Merge Risks Overview Salesforce consolidation involves merging several instances into a single Salesforce organization. This process aims to improve operational efficiency, data visibility, and process standardization while minimizing the total cost of ownership. It may require setting up a new Salesforce organization to facilitate the merger. Typical Salesforce Consolidation Plan A comprehensive consolidation plan typically includes the following steps: Complexity and Benefits of Salesforce Consolidation While Salesforce consolidation offers significant benefits, such as improved efficiency and reduced costs, it is a complex process requiring careful planning and execution. Many companies partner with Salesforce experts, like Tectonic, to navigate the intricacies of consolidation successfully. Salesforce Org Merge Risks Risk 1: Under-Scoping Data Mapping, Migration, and Merging Risk 2: Overlooking Metrics, Measurements, and Reports Risk 3: Limiting Stakeholder Engagement and Change Management Conclusion While meticulous planning cannot guarantee a flawless Salesforce migration, it fosters communication among Salesforce, data, and business leaders, making challenges more manageable. Although managing and consolidating systems might seem straightforward, guiding people, processes, and data through the consolidation process is inherently complex and demanding. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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September 10, 2024in Salesforce Sales Cloud, Salesforce Service Cloud
AI-Powered Field Service
09Sep

AI-Powered Field Service

Salesforce has introduced new AI-powered field service capabilities designed to streamline operations for dispatchers, technicians, and field service leaders. Leveraging the Salesforce platform and Data Cloud, these innovations aim to expedite time-consuming processes and enhance customer satisfaction by making field service operations more proactive and efficient. Why it matters: Field service teams currently spend only 32% of their time interacting with customers, with the remaining 68% consumed by administrative tasks like manually entering case notes. With 78% of field service workers in AI-enabled organizations reporting that AI helps save time, Salesforce’s new tools address these inefficiencies head-on. Key AI-driven innovations for Field Service: Availability: Paul Whitelam, GM & SVP of Salesforce Field Service, notes, “The future of field service lies in the seamless integration of AI, data, and human expertise. Our new capabilities set new standards for efficiency and service delivery.” Rudi Khoury, Chief Digital Officer at Fisher & Paykel, adds, “With Salesforce Field Service, we’re not just embracing AI and data-driven insights — we’re advancing into the future of field service, achieving unprecedented efficiency and exceptional service.” Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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September 9, 2024in Einstein Insights, Salesforce Development Services, Salesforce Manufacturing Automotive Energy, Salesforce Service Cloud
US Comprehensive AI Legislation
09Sep

US Comprehensive AI Legislation

U.S. policymakers have yet to pass comprehensive AI legislation through Congress, but several AI-related bills are now making their way to the Senate floor, presenting new opportunities for regulation. In late July, the U.S. Senate Committee on Commerce, Science, and Transportation advanced eight AI-focused bills aimed at enhancing the transparency and safety of AI systems. These bills also target AI-generated deepfakes—false images, audio, and videos. Since the launch of OpenAI’s ChatGPT in late 2022, regulating AI has become a key issue at both federal and state levels. This week, California lawmakers advanced SB 1047, a bill requiring safety testing for AI models, which is awaiting Governor Gavin Newsom’s signature. Most of the bills before the Senate center on innovation, research, and safety, with only one— the Artificial Intelligence Research, Innovation, and Accountability Act—introducing penalties for non-compliance. “Voluntary guidance and standards can help companies develop safer, more responsible AI, but without binding requirements, the real impact is unlikely,” said Enza Iannopollo, an analyst at Forrester Research. However, Hodan Omaar, a senior policy manager at the Center for Data Innovation, praised the Senate’s emphasis on AI research and innovation, expressing optimism about the progress being made. Here’s a look at the key AI bills up for consideration after Congress returns from summer recess: Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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September 9, 2024in Salesforce
Strong AI Scalability
06Sep

Strong AI Scalability

The rapid pace of digital transformation has made scalability essential for any business looking to remain competitive. The stakes are high—without the ability to scale, businesses risk falling behind as customer demands and market conditions shift. So, what does it take to build a scalable business that can grow without compromising performance or customer satisfaction? In this Tectonic insight, we’ll cover key steps to future-proof your operations, avoid common pitfalls, and ensure your business doesn’t just keep pace with the market, but leads it. Master Scalability with Scale Center Scalability doesn’t have to be overwhelming. Salesforce’s Scale Center, available on Trailhead, provides a comprehensive learning path to help you optimize your scalability strategy. Why Scalability Is a Must-Have Scalability is critical to long-term success. As your business grows, so will the demands on your applications, infrastructure, and resources. If your systems aren’t prepared, you risk performance issues, outages, lost revenue, and dissatisfied customers. Unexpected spikes in demand—from increased customer activity or internal changes like onboarding large numbers of employees—can push systems to their limits, leading to overloads or downtime. A strong scalability plan helps prevent these issues. Here are three best practices to help scale your operations smoothly and sustainably. 1. Prioritize Proactive Scale Testing Scale testing should be a key part of your application lifecycle. Many businesses wait until performance issues arise before addressing them, which can result in maintenance headaches, poor user experiences, and challenges in supporting growth. Proactive steps to take: 2. Use the Right Tools for Seamless Scalability Choosing the right technology is crucial when scaling your business. Equip your team with tools that support growth management, and follow these tips for success: By integrating the right tools and technologies, you’ll not only stay ahead of the curve but also build a culture ready to scale. 3. Focus on Sustainable Growth Strategies Scaling requires a long-term approach. From development to deployment, a strategy that emphasizes scalability from the outset can help you avoid costly fixes down the road. Key practices include: DevOps Done Right Building secure, scalable AI applications and agents requires bridging the gap between tools and skills. Focus on crafting a thoughtful DevOps strategy that supports scalability. Scalability: A Marathon, Not a Sprint Scaling effectively is an ongoing process. Customer needs and market conditions will continue to change, so your strategies should evolve as well. Scalability is about more than just handling increased demand—it’s about ensuring stability and performance across the board. Consider these steps to enhance your approach: Committing to Scalability Scalability isn’t a one-time achievement—it’s a continuous commitment to growing smarter and stronger across all areas of your business. By embedding best practices into your day-to-day operations, you’ll ensure that your systems meet demand and prepare your business for future breakthroughs. As you develop your scalability strategy, remember that customer experience and trust should always guide your decisions. Tackling scalability proactively ensures your business can thrive no matter how market conditions change. It’s more than just a bonus feature—it’s a critical element of a smoother user experience, reduced costs, and the flexibility to pivot when necessary. By embracing these strategies, you’ll not only avoid potential challenges but also build lasting trust with your customers. In a world where loyalty is earned through exceptional experiences, a strong scalability plan is your key to long-term success. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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September 6, 2024in Data, Hyperforce, Salesforce, Technology
AI Senate Bill 1047
06Sep

AI Senate Bill 1047

California’s new AI bill has sparked intense debate, with proponents viewing it as necessary regulation and critics warning it could stifle innovation, particularly for small businesses. Senate Bill 1047, known as the Safe and Secure Innovation for Frontier Artificial Intelligence Models Act, mandates that developers of advanced AI systems costing at least $100 million to train must test their models for potential harm and put safeguards in place. It also offers whistleblower protections for employees at large AI firms and establishes CalCompute, a public cloud computing resource aimed at startups and researchers. The bill is awaiting Governor Gavin Newsom’s signature by Sept. 30 to become law. Prominent AI experts, including Geoffrey Hinton and Yoshua Bengio, support the bill. However, it has met resistance from various quarters, including Rep. Nancy Pelosi and OpenAI, who argue it could hinder innovation and the startup ecosystem. Pelosi and others have expressed concerns that the bill’s requirements might burden smaller businesses and harm California’s leadership in tech innovation. Gartner analyst Avivah Litan acknowledged the dilemma, stating that while regulation is critical for AI, the bill’s requirements might negatively impact small businesses. “Some regulation is better than none,” she said, but added that thresholds could be challenging for smaller firms. Steve Carlin, CEO of AiFi, criticized the bill for its vague language and complex demands on AI developers, including unclear guidance on enforcing the rules. He suggested that instead of focusing on AI models, legislation should address the risks and applications of AI, as seen with the EU AI Act. Despite concerns, some experts like Forrester Research’s Alla Valente support the bill’s safety testing and whistleblower protections. Valente argued that safeguarding AI models is essential across industries, though she acknowledged that the costs of compliance could be higher for small businesses. Still, she emphasized that the long-term costs of not implementing safeguards could be greater, with risks including customer lawsuits and regulatory penalties. California’s approach to AI regulation adds to the growing patchwork of state-level AI laws in the U.S. Colorado and Connecticut have also introduced AI legislation, and cities like New York have tackled issues like algorithmic bias. Carlin warned that a fragmented state-by-state regulatory framework could create a costly and complex environment for developers, calling for a unified federal standard instead. While federal legislation has been proposed, none has passed, and Valente pointed out that relying on Congress for action is a slow process. In the meantime, states like California are pushing ahead with their own AI regulations, creating both opportunities and challenges for the AI industry. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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September 6, 2024in Salesforce
Exploring Large Action Models
05Sep

Exploring Large Action Models

Exploring Large Action Models (LAMs) for Automated Workflow Processes While large language models (LLMs) are effective in generating text and media, Large Action Models (LAMs) push beyond simple generation—they perform complex tasks autonomously. Imagine an AI that not only generates content but also takes direct actions in workflows, such as managing customer relationship management (CRM) tasks, sending emails, or making real-time decisions. LAMs are engineered to execute tasks across various environments by seamlessly integrating with tools, data, and systems. They adapt to user commands, making them ideal for applications in industries like marketing, customer service, and beyond. Key Capabilities of LAMs A standout feature of LAMs is their ability to perform function-calling tasks, such as selecting the appropriate APIs to meet user requirements. Salesforce’s xLAM models are designed to optimize these tasks, achieving high performance with lower resource demands—ideal for both mobile applications and high-performance environments. The fc series models are specifically tuned for function-calling, enabling fast, precise, and structured responses by selecting the best APIs based on input queries. Practical Examples Using Salesforce LAMs In this article, we’ll explore: Implementation: Setting Up the Model and API Start by installing the necessary libraries: pythonCopy code! pip install transformers==4.41.0 datasets==2.19.1 tokenizers==0.19.1 flask==2.2.5 Next, load the xLAM model and tokenizer: pythonCopy codeimport json import torch from transformers import AutoModelForCausalLM, AutoTokenizer model_name = “Salesforce/xLAM-7b-fc-r” model = AutoModelForCausalLM.from_pretrained(model_name, device_map=”auto”, torch_dtype=”auto”, trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(model_name) Now, define instructions and available functions. Task Instructions: The model will use function calls where applicable, based on user questions and available tools. Format Example: jsonCopy code{ “tool_calls”: [ {“name”: “func_name1”, “arguments”: {“argument1”: “value1”, “argument2”: “value2”}} ] } Define available APIs: pythonCopy codeget_weather_api = { “name”: “get_weather”, “description”: “Retrieve weather details”, “parameters”: {“location”: “string”, “unit”: “string”} } search_api = { “name”: “search”, “description”: “Search for online information”, “parameters”: {“query”: “string”} } Creating Flask APIs for Business Logic We can use Flask to create APIs to replicate business processes. pythonCopy codefrom flask import Flask, request, jsonify app = Flask(__name__) @app.route(“/customer”, methods=[‘GET’]) def get_customer(): customer_id = request.args.get(‘customer_id’) # Return dummy customer data return jsonify({“customer_id”: customer_id, “status”: “active”}) @app.route(“/send_email”, methods=[‘GET’]) def send_email(): email = request.args.get(’email’) # Return dummy response for email send status return jsonify({“status”: “sent”}) Testing the LAM Model and Flask APIs Define queries to test LAM’s function-calling capabilities: pythonCopy codequery = “What’s the weather like in New York in fahrenheit?” print(custom_func_def(query)) # Expected: {“tool_calls”: [{“name”: “get_weather”, “arguments”: {“location”: “New York”, “unit”: “fahrenheit”}}]} Function-Calling Models in Action Using base_call_api, LAMs can determine the correct API to call and manage workflow processes autonomously. pythonCopy codedef base_call_api(query): “””Calls APIs based on LAM recommendations.””” base_url = “http://localhost:5000/” json_response = json.loads(custom_func_def(query)) api_url = json_response[“tool_calls”][0][“name”] params = json_response[“tool_calls”][0][“arguments”] response = requests.get(base_url + api_url, params=params) return response.json() With LAMs, businesses can automate and streamline tasks in complex workflows, maximizing efficiency and empowering teams to focus on strategic initiatives. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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September 5, 2024in Data, Salesforce, Technology, Third Party Apps
Enhanced AppExchange Trials
03Sep

Enhanced AppExchange Trials

🚀 Exciting Update: Enhanced AppExchange App Trials! 🚀 Found an app on AppExchange that checks all the boxes—great features, flexible pricing, and stellar reviews—but still unsure if it’s the right fit? At AppExchange, we understand the importance of trying before buying! Tectonic is excited to introduce new and improved features that make testing apps faster and easier than ever. Here’s what’s new: 🔑 A Streamlined Entry Point for App Trials The Try It Free button is now prominently displayed at the top of app listings. Simply click to view all available trial options, including sandbox installations, test drives, and Trialforce trials. 🔍 Compare with Confidence With multiple trial options, choosing the right one can be challenging. Now, each option comes with a summary of key features, presented side-by-side for easy comparison. ⚡ Faster, More Responsive, and Accessible Salesforce has optimized the trial experience by minimizing clicks and reducing load times. Plus, Trialforce trials now feature autogenerated usernames for quicker setup. The entire process is now more accessible and responsive, ensuring a seamless experience on any device. Interested in checking out a new solution on the AppExchange? Now it is easier than ever. Enhanced AppExchange Trials Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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September 3, 2024in Salesforce
Converting 15-Character IDs to 18-Character in Salesforce
02Sep

Converting 15-Character IDs to 18-Character in Salesforce

In Salesforce, every record is assigned a unique Record ID, which is essential for managing data, writing formulas, and referencing records as an admin or developer. There are two types of Record IDs: a 15-character version and an 18-character version, each suited for different scenarios. Converting 15-character IDs to 18-character ones can be time-consuming when done manually, but several tools and methods can simplify the process, allowing for instant conversion with just a click. Understanding Salesforce Record IDs 15-Character Record ID The 15-character Record ID is case-sensitive and typically used in Salesforce’s user interface for tasks like editing records and generating reports. However, its case sensitivity can create issues with systems that do not recognize differences between uppercase and lowercase letters. 18-Character Record ID To mitigate case sensitivity issues, Salesforce offers an 18-character ID, which is used in APIs and tools such as Data Loader. This ID adds three additional characters to the 15-character version and is always returned by these tools during data exports. When to Use Each ID For consistency, the 18-character ID is preferable, especially when working with external systems. It’s best practice to use the 18-character ID in formulas, API calls, or any data comparisons to avoid errors caused by case sensitivity. Converting IDs Using a Formula Field in Salesforce Salesforce recommends creating a formula field with the CASESAFEID(Id) function to convert the 15-character ID to an 18-character ID. Here are some key points to consider: Implementation Steps: Once completed, this formula field will display the 18-character ID on relevant records. APIs and Software DevelopmentIf you need a more scalable or efficient solution, consider using Salesforce APIs or third-party tools for ID conversion. While online tools may suffice for small tasks, they can become unwieldy when handling hundreds or thousands of records in a CSV or Excel file. Streamlining ID Conversion with Xappex Tools Imagine the frustration of manually copying and pasting IDs! That’s where the XL-Connector and G-Connector from Xappex come into play. These tools work directly in Excel or Google Sheets, simplifying the ID conversion process. Instead of juggling multiple tools or navigating complex processes, you can seamlessly convert Salesforce IDs within your spreadsheet, saving significant time and effort. Using XL-Connector for ID Conversion in Excel Using G-Connector (Google Sheets) for ID Conversion G-Connector is Xappex’s integration tool for Google Sheets and Salesforce. If you haven’t installed it yet, do so and log in to your Salesforce org. The sheet will automatically update with the new 18-character IDs and provide links to open the records directly in Salesforce. Conclusion In summary, managing Salesforce Record IDs doesn’t have to be a hassle. While converting 15-character IDs to 18-character IDs is crucial for consistency, doing it manually can be tedious. With XL-Connector and G-Connector, you can streamline ID conversion with just a click in Excel or Google Sheets, making your workflow much more efficient. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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September 2, 2024in Data, Google, Salesforce
AI and Big Data
31Aug

AI and Big Data

Over the past decade, enterprises have accumulated vast amounts of data, capturing everything from business processes to inventory statistics. This surge in data marked the onset of the big data revolution. However, merely storing and managing big data is no longer sufficient to extract its full value. As organizations become adept at handling big data, forward-thinking companies are now leveraging advanced analytics and the latest AI and machine learning techniques to unlock even greater insights. These technologies can identify patterns and provide cognitive capabilities across vast datasets, enabling organizations to elevate their data analytics to new levels. Additionally, the adoption of generative AI systems is on the rise, offering more conversational approaches to data analysis and enhancement. This allows organizations to extract significant insights from information that would otherwise remain untapped in data stores. How Are AI and Big Data Related? Applying machine learning algorithms to big data is a logical progression for companies aiming to maximize the potential of their data. Unlike traditional rules-based approaches that follow explicit instructions, machine learning systems use data-driven algorithms and statistical models to analyze and detect patterns in data. Big data serves as the raw material for these systems, which derive valuable insights from it. Organizations are increasingly recognizing the benefits of integrating big data with machine learning. However, to fully harness the power of both, it’s crucial to understand their individual capabilities. Understanding Big Data Big data involves extracting and analyzing information from large quantities of data, but volume is just one aspect. Other critical “Vs” of big data that enterprises must manage include velocity, variety, veracity, validity, visualization, and value. Understanding Machine Learning Machine learning, the backbone of modern AI, adds significant value to big data applications by deriving deeper insights. These systems learn and adapt over time without the need for explicit programming, using statistical models to analyze and infer patterns from data. Historically, companies relied on complex, rules-based systems for reporting, which often proved inflexible and unable to cope with constant changes. Today, machine learning and deep learning enable systems to learn from big data, enhancing decision-making, business intelligence, and predictive analysis. The strength of machine learning lies in its ability to discover patterns in data. The more data available, the more these algorithms can identify patterns and apply them to future data. Applications range from recommendation systems and anomaly detection to image recognition and natural language processing (NLP). Categories of Machine Learning Algorithms Machine learning algorithms generally fall into three categories: The most powerful large language models (LLMs), which underpin today’s widely used generative AI systems, utilize a combination of these methods, learning from massive datasets. Understanding Generative AI Generative AI models are among the most powerful and popular AI applications, creating new data based on patterns learned from extensive training datasets. These models, which interact with users through conversational interfaces, are trained on vast amounts of internet data, including conversations, interviews, and social media posts. With pre-trained LLMs, users can generate new text, images, audio, and other outputs using natural language prompts, without the need for coding or specialized models. How Does AI Benefit Big Data? AI, combined with big data, is transforming businesses across various sectors. Key benefits include: Big Data and Machine Learning: A Synergistic Relationship Big data and machine learning are not competing concepts; when combined, they deliver remarkable results. Emerging big data techniques offer powerful ways to manage and analyze data, while machine learning models extract valuable insights from it. Successfully handling the various “Vs” of big data enhances the accuracy and power of machine learning models, leading to better business outcomes. The volume of data is expected to grow exponentially, with predictions of over 660 zettabytes of data worldwide by 2030. As data continues to amass, machine learning will become increasingly reliant on big data, and companies that fail to leverage this combination will struggle to keep up. Examples of AI and Big Data in Action Many organizations are already harnessing the power of machine learning-enhanced big data analytics: Conclusion The integration of AI and big data is crucial for organizations seeking to drive digital transformation and gain a competitive edge. As companies continue to combine these technologies, they will unlock new opportunities for personalization, efficiency, and innovation, ensuring they remain at the forefront of their industries. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

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August 31, 2024in Data, Enterprise, Generative AI, Google, Salesforce, Technology

Agentforce in Healthcare

The healthcare industry is on the brink of a digital revolution, and in this era, Salesforce Agentforce—a new AI technology in healthcare—is set to play a pivotal role in providing innovative solutions that enhance AI-driven patient engagement, streamline processes, and improve overall AI services in healthcare. Salesforce Agentforce AI integration in healthcare will lead to significant cost reductions and improved operational efficiency. Healthcare organizations that leverage Agentforce have reported substantial improvements in productivity by automating administrative tasks and streamlining workflows. In this insight, we’ll explore Salesforce Agentforce—the new AI technology in healthcare—the role of conversational AI in healthcare, and what the future holds for AI-driven medical solutions. What is Agentforce and How Does It Support Healthcare? Salesforce’s Agentforce is designed to provide healthcare organizations with fully tailored AI-powered agents that can operate various tasks. These AI agents can handle customer support enquiries, assist in sales processes, and even provide real-time coaching for healthcare professionals. Agentforce AI agents can manage appointment scheduling, follow-ups, and patient enquiries efficiently. The agents also integrate robust data protection protocols to ensure all patient information is securely handled, helping healthcare providers streamline administrative processes while maintaining high standards of security and compliance. By leveraging these AI services in healthcare, organizations can optimize workflows and improve overall efficiency. Challenges in Healthcare AI Integration Healthcare organisations can face several challenges when integrating new AI technology in healthcare. These challenges include data security concerns, the complexity of managing diverse patient needs, and ensuring workflow efficiency. One of the key obstacles is gaining a unified, up-to-date view of patients, providers, and payers while still ensuring multi-channel communication. Data security is a top priority for healthcare institutions, as they must safeguard patient information while complying with stringent regulations. Salesforce Agentforce tackles these challenges by providing advanced security measures, automated workflow optimisation, and real-time decision-making support. Pre-Built Use Cases for Healthcare Salesforce’s Agentforce includes multiple out-of-the-box AI capabilities that enhance AI patient engagement and improve healthcare operations. Some of its key applications include: • Intelligent Document Processing: AI-driven data extraction from medical records reduces administrative workload, allowing healthcare professionals to focus on patient care. • Medical History Summarization: AI agents summarize patient medical histories, prescriptions, andfactors that influence health and well-being, enabling caregivers to make more informed decisions. • Automated Appointment Scheduling: AI-driven systems manage patient appointments, reducing wait times and improving the overall patient experience. • Claims Processing Optimization: AI consolidates data for claims processing, reducing delays and enhancing revenue cycle management. • Trial Candidate Matching: AI-driven algorithms identify suitable candidates for clinical trials by analyzing demographic and medical data. Custom AI Agents for Healthcare Agentforce also enables healthcare organizations to create custom AI agents designed for specific operational needs. These AI agents help automate repetitive tasks, improve AI patient engagement, and optimize care management processes. • Patient Services: AI-driven chatbots summarize medical histories, generate personalized emails, and assist with healthcare assessments. • Provider Network Management: AI optimizes provider networks by assessing provider details and minimizing patient wait times. • Program and Patient Outcome Management: AI offers real-time insights into patient adherence to programs and provides recommendations to improve satisfaction rates. • Benefits Verification: AI streamlines benefits verification through automated call scripts and pharmacy benefits summaries. The Future of AI in Healthcare As AI technology continues to evolve, we can expect deeper integration of AI services in healthcare, particularly in robotic-assisted surgeries, automated patient monitoring, and virtual healthcare assistants. These innovations will not only enhance patient engagement but also reduce the burden on healthcare professionals by automating time-consuming administrative tasks. Conversational AI in healthcare is set to play an even bigger role in the industry. AI-powered chatbots and virtual assistants will become more sophisticated, offering more accurate diagnoses, providing mental health support, and assisting in real-time patient interactions. This will lead to a more seamless, accessible, and efficient healthcare system. With these developments, healthcare providers will need to continue prioritising AI ethics, data security, and compliance to ensure that AI implementation remains both safe and effective. By leveraging trusted AI solutions like Agentforce, healthcare organizations can stay ahead of the curve, delivering cutting-edge care while maintaining operational efficiency. Implementing Agentforce with Tectonic AI Consulting and Integration Specialists – Partnering with the right AI implementation experts ensures smooth integration and better alignment with your healthcare operational goals. Organizations working with Tectonic, a trusted AI and Salesforce implementation partner, can accelerate the deployment of Agentforce, ensuring optimal efficiency and productivity. Tectonic specializes in integrating new AI technology in healthcare to enhance operational efficiency and improve AI patient engagement. Our Salesforce team has helped multiple healthcare organizations seamlessly implement Agentforce, ensuring that AI-driven workflows are tailored to meet the specific needs of their providers and patients. By working with Tectonics, healthcare organizations can leverage: • Seamless AI Integration: Ensuring that Agentforce aligns with existing systems and operational workflows. • Custom AI Development: Designing AI-powered solutions that address key industry challenges. • Ongoing Support and Optimization: Continuous monitoring and improvement to maximise the effectiveness of conversational AI in healthcare. Healthcare providers who collaborate with Tectonic can unlock the full potential of Agentforce, leading to enhanced patient experiences and streamlined operations. The adoption of new AI technology in healthcare will reshape the industry. Agentforce addresses key challenges in healthcare by offering conversational AI in healthcare, AI-powered automation, and data-driven decision-making support. As AI continues to evolve, Agentforce will play an increasingly vital role in optimising AI services in healthcare, empowering medical professionals, and ensuring better patient outcomes. Contact Tectonic today to learn more about implementing Agentforce and modernising your healthcare services. Content updated February 2025. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored

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August 30, 2024in Data, Salesforce, Technology
gettectonic.com