Tectonic Archives - gettectonic.com - Page 14
Salesforce Revenue Lifecycle

Salesforce Revenue Lifecycle

Revenue Lifecycle Management (RLM) with Revenue Cloud empowers businesses to drive profitable growth by automating the entire product-to-cash process. From managing product catalogs and pricing to order fulfillment and billing, RLM streamlines operations and accelerates revenue generation. Salesforce Revenue Lifecycle. Here’s how businesses can leverage RLM for efficient revenue lifecycle management: Additionally, RLM is available in Lightning Experience and requires a Revenue Lifecycle Management license in Sales Cloud and Service Cloud for Enterprise, Unlimited, and Developer editions. Key features of RLM include: As businesses navigate the subscription economy, RLM offers a scalable, flexible, and efficient omni-channel platform for transacting revenue. By addressing growth, churn reduction, and profitability gains, RLM empowers organizations to scale and grow, leveraging modular components and APIs to support various sales motions and use cases. With ongoing innovation and industry expertise, Salesforce and partners like Tectonic are committed to helping subscription companies achieve profitable and efficient growth with Revenue Cloud and Revenue Lifecycle Management. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
Salesforce for Manufacturing Operational Efficiency

Salesforce for Manufacturing Operational Efficiency

Shopping with distributors offers a distinct advantage in the form of personalized guidance throughout the purchasing journey, nurturing enduring relationships and encouraging repeat business. While distributors excel in delivering tailored service, digital-native rivals such as Amazon leverage operational efficiency to vie in today’s marketplace. As B2B preferences veer towards remote transactions and omnichannel experiences, distributors lagging in digitalization encounter hurdles. Salesforce for Manufacturing Operational Efficiency Manufacturing operational efficiency, while vital, cannot overshadow the significance of personalized service for customers. Traditional distributors hold a formidable competitive edge in this aspect. However, their reliance on antiquated, non-digitalized processes impedes effective competition. Distributors must confront these inefficiencies and embrace innovative technology to flourish. Conventional distributors often rely on disparate systems like spreadsheets and sticky notes, resulting in disjointed customer experiences. Siloed data leads to quote disparities, communication breakdowns, and a dearth of omnichannel capabilities. To retain competitiveness, distributors must transition to digital platforms that centralize data and streamline operations. Digital transformation in manufacturing is possible with Salesforce Manufacturing Cloud. Salesforce for Manufacturing Operational Efficiency with Manufacutring Cloud Salesforce Manufacturing Cloud emerges as a solution to these challenges. By consolidating all information within a centralized system, Salesforce empowers distributors to deliver seamless customer experiences across various channels. This eradicates confusion, ensures consistent communication, and elevates service quality. With Salesforce, distributors can provide exemplary service, nurturing customer loyalty and outpacing the competition. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
The Growing Role of AI in Cloud Management

The Growing Role of AI in Cloud Management

AI technologies are redefining cloud management by automating IT systems, improving security, optimizing cloud costs, enhancing data management, and streamlining the provisioning of AI services across complex cloud ecosystems. With the surging demand for AI, its ability to address technological complexities makes a unified cloud management strategy indispensable for IT teams. Cloud and security platforms have steadily integrated AI and machine learning to support increasingly autonomous IT operations. The rapid rise of generative AI (GenAI) has further spotlighted these AI capabilities, prompting vendors to prioritize their development and implementation. Adnan Masood, Chief AI Architect at UST, highlights the transformative potential of AI-driven cloud management, emphasizing its ability to oversee vast data centers hosting millions of applications and services with minimal human input. “AI automates tasks such as provisioning, scaling, cost management, monitoring, and data migration,” Masood explains, showcasing its wide-ranging impact. From Reactive to Proactive Cloud Management Traditionally, CloudOps relied heavily on manual intervention and expertise. AI has shifted this paradigm, introducing automation, predictive analytics, and intelligent decision-making. This evolution enables enterprises to transition from reactive, manual management to proactive, self-optimizing cloud environments. Masood underscores that this shift allows cloud systems to self-manage and optimize with minimal human oversight. However, organizations must navigate challenges, including complex data integration, real-time processing limitations, and model accuracy concerns. Business hurdles like implementation costs, uncertain ROI, and maintaining the right balance between AI automation and human oversight also require careful evaluation. AI’s Transformation of Cloud Computing AI has reshaped cloud management into a more proactive and efficient process. Key applications include: “AI enhances efficiency, scalability, and flexibility for IT teams,” says Agustín Huerta, SVP of Digital Innovation at Globant. He views AI as a pivotal enabler of automation and optimization, helping businesses adapt to rapidly changing environments. AI also automates repetitive tasks such as provisioning, performance monitoring, and cost management. More importantly, it strengthens security across cloud infrastructure by detecting misconfigurations, vulnerabilities, and malicious activities. Nick Kramer of SSA & Company highlights how AI-powered natural language interfaces simplify cloud management, transforming it from a technical challenge to a logical one. With conversational AI, business users can manage cloud operations more efficiently, accelerating problem resolution. AI-Enabled Cloud Management Tools Ryan Mallory, COO at Flexential, categorizes AI-powered cloud tools into: The Rise of Self-Healing Cloud Systems AI enables cloud systems to detect, resolve, and optimize issues with minimal human intervention. For instance, AI can identify system failures and trigger automatic remediation, such as restarting services or reallocating resources. Over time, machine learning enhances these systems’ accuracy and reliability. Key Applications of AI in Cloud Management AI’s widespread applications in cloud computing include: Benefits of AI in Cloud Management AI transforms cloud management by enabling autonomous systems capable of 24/7 monitoring, self-healing, and optimization. This boosts system reliability, reduces downtime, and provides businesses with deeper analytical insights. Chris Vogel from S-RM emphasizes that AI’s analytical capabilities go beyond automation, driving strategic business decisions and delivering measurable value. Challenges of AI in Cloud Management Despite its advantages, AI adoption in cloud management presents challenges, including: AI’s Impact on IT Departments AI’s growing influence on cloud management introduces new responsibilities for IT teams, including managing unauthorized AI systems, ensuring data security, and maintaining high-quality data for AI applications. IT departments must provide enterprise-grade AI solutions that are private, governed, and efficient while balancing the costs and benefits of AI integration. Future Trends in AI-Driven Cloud Management Experts anticipate that AI will revolutionize cloud management, much like cloud computing reshaped IT a decade ago. Prasad Sankaran from Cognizant predicts that organizations investing in AI for cloud management will unlock opportunities for faster innovation, streamlined operations, and reduced technical debt. As AI continues to evolve, cloud environments will become increasingly autonomous, driving efficiency, scalability, and innovation across industries. Businesses embracing AI-driven cloud management will be well-positioned to adapt to the complexities of tomorrow’s IT landscape. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
Service Cloud Digital Engagement

Service Cloud Digital Engagement

Salesforce Enhances Service Cloud Digital Engagement for Unified Customer Interactions Salesforce has unveiled new enhancements to Service Cloud Digital Engagement, aimed at unifying unstructured conversational data from various digital channels, departments, and devices within a single platform. Built on the Einstein 1 Platform, these enhancements enable service leaders to gain a more holistic view of customers, enhancing the value delivered in every interaction. Importance of Enhancements Detailed Enhancements Service Cloud Digital Engagement is designed to deliver seamless, personalized conversational experiences across channels at scale. By connecting to Salesforce Data Cloud, which unifies structured and unstructured enterprise and customer data, companies can engage in more meaningful conversations. Key enhancements include: With Service Cloud built on the Einstein 1 Platform, companies can integrate sales, service, and marketing data into one platform, facilitating more relevant customer experiences and driving business growth. Salesforce’s Perspective Kishan Chetan, EVP & GM of Service Cloud, commented, “As customers interact with companies across more touch points and channels, they are looking for more personalization and a higher-touch experience. With Service Cloud built on the Einstein 1 Platform, companies can bring in sales, service, and marketing data on one platform to deliver more relevant customer experiences and drive business growth.” Customer Reactions Olivia Boles, Director of Operations Projects at PenFed, said, “Being able to see all the communication — chat transcripts, emails, phone calls — on the member’s profile page has totally transformed the agent and member experiences.” Availability Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
TEFCA could drive payer-provider interoperability

TEFCA could drive payer-provider interoperability

Bridging the Interoperability Gap: TEFCA’s Role in Payer-Provider Data Exchange The electronic health information exchange (HIE) between healthcare providers has seen significant growth in recent years. However, interoperability between healthcare providers and payers has lagged behind. The Trusted Exchange Framework and Common Agreement (TEFCA) aims to address this gap and enhance data interoperability across the healthcare ecosystem. TEFCA could drive payer-provider interoperability with a little help from the world of technology. TEFCA’s Foundation and Evolution TEFCA was established under the 21st Century Cures Act to improve health data interoperability through a “network of networks” approach. The Office of the National Coordinator for Health Information Technology (ONC) officially launched TEFCA in December 2023, designating five initial Qualified Health Information Networks (QHINs). By February 2024, two additional QHINs had been designated. The Sequoia Project, TEFCA’s recognized coordinating entity, recently released several key documents for stakeholder feedback, including draft standard operating procedures (SOPs) for healthcare operations and payment under TEFCA. During the 2024 WEDI Spring Conference, leaders from three QHINs—eHealth Exchange, Epic Nexus, and Kno2—discussed the future of TEFCA in enhancing provider and payer interoperability. ONC released Version 2.0 of the Common Agreement on April 22, 2024. Common Agreement Version 2.0 updates Common Agreement Version 1.1, published in November 2023, and includes enhancements and updates to require support for Health Level Seven (HL7®) Fast Healthcare Interoperability Resources (FHIR®) based transactions. The Common Agreement includes an exhibit, the Participant and Subparticipant Terms of Participation (ToP), that sets forth the requirements each Participant and Subparticipant must agree to and comply with to participate in TEFCA. The Common Agreement and ToPs incorporate all applicable standard operating procedures (SOPs) and the Qualified Health Information Network Technical Framework (QTF). View the release notes for Common Agreement Version 2.0 The Trusted Exchange Framework and Common AgreementTM (TEFCATM) has 3 goals: (1) to establish a universal governance, policy, and technical floor for nationwide interoperability; (2) to simplify connectivity for organizations to securely exchange information to improve patient care, enhance the welfare of populations, and generate health care value; and (3) to enable individuals to gather their health care information. Challenges in Payer Data Exchange Although the QHINs on the panel have made progress in facilitating payer HIE, they emphasized that TEFCA is not yet fully operational for large-scale payer data exchange. Ryan Bohochik, Vice President of Value-Based Care at Epic, highlighted the complexities of payer-provider data exchange. “We’ve focused on use cases that allow for real-time information sharing between care providers and insurance carriers,” Bohochik said. “However, TEFCA isn’t yet capable of supporting this at the scale required.” Bohochik also pointed out that payer data exchange is complicated by the involvement of third-party contractors. For example, health plans often partner with vendors for tasks like care management or quality measure calculation. This adds layers of complexity to the data exchange process. Catherine Bingman, Vice President of Interoperability Adoption for eHealth Exchange, echoed these concerns, noting that member attribution and patient privacy are critical issues in payer data exchange. “Payers don’t have the right to access everything a patient has paid for themselves,” Bingman said. “This makes providers cautious about sharing data, impacting patient care.” For instance, manual prior authorization processes frequently delay patient access to care. A 2023 AMA survey found that 42% of doctors reported care delays due to prior authorization, with 37% stating that these delays were common. Building Trust Through Use Cases Matt Becker, Vice President of Interoperability at Kno2, stressed the importance of developing specific use cases to establish trust in payer data exchange via TEFCA. “Payment and operations is a broad category that includes HEDIS measures, quality assurance, and provider monitoring,” Becker said. “Each of these requires a high level of trust.” Bohochik agreed, emphasizing that narrowing the scope and focusing on specific, high-value use cases will be essential for TEFCA’s adoption. “We can’t solve everything at once,” Bohochik said. “We need to focus on achieving successful outcomes in targeted areas, which will build momentum and community support.” He also noted that while technical data standards are crucial, building trust in the data exchange process is equally important. “A network is only as good as the trust it inspires,” Bohochik said. “If healthcare systems know that data requests for payment and operations are legitimate and secure, it will drive the scalability of TEFCA.” By focusing on targeted use cases, ensuring rigorous data standards, and building trust, TEFCA has the potential to significantly enhance interoperability between healthcare providers and payers, ultimately improving patient care and operational efficiency. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
Gen AI and Test Automation

Gen AI and Test Automation

Generative AI has brought transformative advancements across industries, and test automation is no exception. By generating code, test scenarios, and even entire suites, Generative AI enables Software Development Engineers in Test (SDETs) to boost efficiency, expand test coverage, and improve reliability. 1. Enhanced Test Case Generation One of the biggest hurdles in test automation is generating diverse, comprehensive test cases. Traditional methods often miss edge cases or diverse scenarios. Generative AI, however, can analyze existing data and automatically generate extensive test cases, including potential edge cases that may not be apparent to human testers. Example: An SDET can use Generative AI to create test cases for a web application by feeding it requirements and user data. This enables the AI to produce hundreds of test cases, capturing diverse user behaviors and interactions that manual testers may overlook. pythonCopy codeimport openai openai.api_key = ‘YOUR_API_KEY’ def generate_test_cases(application_description): response = openai.Completion.create( engine=”text-davinci-003″, prompt=f”Generate comprehensive test cases for the following application: {application_description}”, max_tokens=500 ) return response.choices[0].text app_description = “An e-commerce platform for browsing products, adding to cart, and checking out.” test_cases = generate_test_cases(app_description) print(test_cases) Sample Output: 2. Intelligent Test Script Creation Writing test scripts manually can be labor-intensive and error-prone. Generative AI can simplify this by generating test scripts based on an application’s flow, ensuring consistency and precision. Example: If an SDET needs to automate tests for a mobile app, they can use Generative AI to generate scripts for various scenarios, significantly reducing manual work. pythonCopy codeimport hypothetical_ai_test_tool ui_description = “”” Login Page: – Username field – Password field – Login button Home Page: – Search bar – Product listings – Add to cart buttons “”” test_scripts = hypothetical_ai_test_tool.generate_selenium_scripts(ui_description) Sample Output for test_login.py: pythonCopy codefrom selenium import webdriver from selenium.webdriver.common.keys import Keys def test_login(): driver = webdriver.Chrome() driver.get(“http://example.com/login”) username_field = driver.find_element_by_name(“username”) password_field = driver.find_element_by_name(“password”) login_button = driver.find_element_by_name(“login”) username_field.send_keys(“testuser”) password_field.send_keys(“password”) login_button.click() assert “Home” in driver.title driver.quit() 3. Automated Maintenance of Test Suites As applications evolve, maintaining test suites is critical. Generative AI can monitor app changes and update test cases automatically, keeping test suites accurate and relevant. Example: In a CI/CD pipeline, an SDET can deploy Generative AI to track code changes and update affected test scripts. This minimizes downtime and ensures tests stay aligned with application updates. pythonCopy codeimport hypothetical_ai_maintenance_tool def maintain_test_suite(): changes = hypothetical_ai_maintenance_tool.analyze_code_changes() updated_scripts = hypothetical_ai_maintenance_tool.update_test_scripts(changes) for script_name, script_content in updated_scripts.items(): with open(script_name, ‘w’) as file: file.write(script_content) maintain_test_suite() Sample Output:“Updating test_login.py with new login flow changes… Test scripts updated successfully.” 4. Natural Language Processing for Test Case Design Generative AI with NLP can interpret human language, enabling SDETs to create test cases from plain-language descriptions, enhancing collaboration across technical and non-technical teams. Example: An SDET can use an NLP-powered tool to translate a feature description from a product manager into test cases. This speeds up the process and ensures that test cases reflect intended functionality. pythonCopy codeimport openai openai.api_key = ‘YOUR_API_KEY’ def create_test_cases(description): response = openai.Completion.create( engine=”text-davinci-003″, prompt=f”Create test cases based on this feature description: {description}”, max_tokens=500 ) return response.choices[0].text feature_description = “Allow users to reset passwords via email to regain account access.” test_cases = create_test_cases(feature_description) print(test_cases) Sample Output: 5. Predictive Analytics for Test Prioritization Generative AI can analyze historical data to prioritize high-risk areas, allowing SDETs to focus testing on critical functionalities. Example: An SDET can use predictive analytics to identify areas with frequent bugs, allocating resources more effectively and ensuring robust testing of high-risk components. pythonCopy codeimport hypothetical_ai_predictive_tool def prioritize_tests(): risk_areas = hypothetical_ai_predictive_tool.predict_risk_areas() prioritized_tests = hypothetical_ai_predictive_tool.prioritize_test_cases(risk_areas) return prioritized_tests prioritized_test_cases = prioritize_tests() print(“Prioritized Test Cases:”) for test in prioritized_test_cases: print(test) Sample Output: Gen AI and Test Automation Generative AI has the potential to revolutionize test automation, offering SDETs tools to enhance efficiency, coverage, and reliability. By embracing Generative AI for tasks like test case generation, script creation, suite maintenance, NLP-based design, and predictive prioritization, SDETs can reduce manual effort and focus on strategic tasks, accelerating testing processes and ensuring robust, reliable software systems. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
Salesforce Success Story

Case Study: Salesforce Health Services Modernization

Modernizing Public Health using Salesforce Client is the largest tribally-operated health care system in the United States, with almost 160 full-time providers and over 2,200 health services employees. Committed to improving patient access, this Tribal Nation has invested substantially in technology, building new facilities and expanding existing locations. The primary hospital and nine health centers are dedicated to providing world-class patient care. Salesforce Health Services Modernization “The Gadugi Portal and Salesforce technology is helping us get to our citizens quicker by streamlining the application process, and enabling us to have updated records that are online and easily accessible to our citizens to truly help them recover in their greatest time of need” Client Implemented – Case Study: Salesforce Health Services Modernization The Problem: The Solution: The Results: Ready to explore a Salesforce implementation with Health Cloud, MuleSoft, and Experience Cloud for your government or public sector entity? Contact Tectonic today. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
AI Design Beyond the Chatbot

AI Design Beyond the Chatbot

As AI continues to advance, designers, builders, and creators are confronted with profound questions about the future of applications and how users will engage with digital experiences. AI Design Beyond the Chatbot. Generative AI has opened up vast possibilities, empowering people to utilize AI for tasks such as writing articles, generating marketing materials, building teaching assistants, and summarizing data. However, alongside its benefits, there are challenges. Sometimes, generative AI produces unexpected or biased responses, a phenomenon known as hallucination. In response, approaches like retrieval augmented generation (RAG) have emerged as effective solutions. RAG leverages a vector database, like SingleStore, to retrieve relevant information and provide users with contextually accurate responses. AI Design Beyond the Chatbot Looking ahead, the evolution of AI may lead to a future where users interact with a central LLM operating system, fostering more personalized and ephemeral experiences. Concepts like Mercury OS offer glimpses into this potential future. Moreover, we anticipate the rise of multimodal experiences, including voice and gesture interfaces, making technology more ubiquitous in our lives. Imran Chaudhri’s demonstration of a screen-less future, where humans interact with computers through natural language, exemplifies this trend. However, amidst these exciting prospects, the current state of AI integration in businesses varies. While some are exploring innovative ways to leverage AI, others may simply add AI chat interfaces without considering contextual integration. To harness AI effectively, it’s crucial to identify the right use cases and prioritize user value. AI should enhance experiences by reducing task time, simplifying tasks, or personalizing experiences. Providing contextual assistance is another key aspect. AI models can offer tailored suggestions and recommendations based on user context, enriching the user experience. Notion and Coda exemplify this by seamlessly integrating AI recommendations into user workflows. Furthermore, optimizing for creativity and control ensures users feel empowered in creation experiences. Tools like Adobe Firefly strike a balance between providing creative freedom and offering control over generated content. Building good prompts is essential for obtaining quality results from AI models. Educating users on how to construct effective prompts and managing expectations regarding AI limitations are critical considerations. Ultimately, as AI becomes more integrated into daily workflows, it’s vital to ensure seamless integration into user experiences. Responsible AI design requires ongoing dialogue and exploration to navigate this rapidly evolving landscape effectively. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
Otter AI S-Docs and Salesforce

Otter AI S-Docs and Salesforce

Numerous vendors in the enterprise software market are currently emphasizing their AI capabilities, envisioning a future where AI can address a wide array of global challenges, from healthcare to climate change. While the realization of these claims remains uncertain, the practical and impactful applications of AI in everyday scenarios often go unnoticed. There exists ample opportunity for leveraging AI tools that are readily available and require minimal setup to enhance efficiency. Otter AI S-Docs and Salesforce. One such example is S-Docs, a document automation vendor integrated natively on the Salesforce platform, which is harnessing Otter.ai, an AI transcription service, to revolutionize its sales process and product development. S-Docs is seamlessly integrating Otter.ai into its digital collaboration tools, enabling automatic transcription during sales calls. This not only aids sales representatives in navigating diverse dialects but also streamlines post-call administrative tasks, prompting quicker action. Moreover, the product development team at S-Docs is leveraging Otter.ai to analyze the transcribed content from sales calls and incorporate insights into its product feedback loop. This integration was sparked by S-Docs’ CTO, Anand Narasimhan, who discovered Otter.ai through a LinkedIn connection and recognized its potential value for the business. Initially used during team calls and sprint reviews, Otter.ai’s high transcription accuracy and insightful summaries impressed Narasimhan and his colleague, Keith Bossier, VP of Sales at S-Docs. Subsequently, Otter.ai was adopted by the sales and customer success teams, offering benefits that surpassed those of their previous provider, Gong. For the sales team, Otter.ai significantly reduces the administrative burden by providing real-time transcriptions, catch-all summaries, and key takeaways from meetings. This facilitates quicker follow-ups and enhances the overall customer experience. Buoyed by the success in sales, S-Docs is exploring avenues to expand the use of Otter.ai across its business. Bossier envisions leveraging transcripts from sales calls for onboarding new representatives, while Narasimhan explores integrating the captured content into the product development cycle. Additionally, they are collaborating with Otter.ai to introduce automated action items directly into the S-Docs platform, further streamlining operations and enhancing efficiency. As S-Docs continues to innovate and optimize its processes with Otter.ai, it exemplifies the tangible benefits of leveraging AI in practical business scenarios. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
Einstein Web Recommendations

Einstein Web Recommendations

Einstein Web Recommendations leverage Einstein’s capabilities to analyze user behavior, construct preference profiles, and deliver personalized content tailored to each website visitor. Utilize application scenarios to fine-tune recommendations according to your specific business rules. Web Recommendations are provided through two methods: a JSON response or HTML/JS. While the JSON response is the recommended delivery method due to its flexibility, HTML/JS can be used if the web team is unable to work with JSON. As the JSON method allows for greater flexibility, you are responsible for parsing and styling the recommendations within your web environment. Marketing Cloud Einstein Recommendations enable the creation of product or content recommendations for display on your website. The Einstein Recommendation Engine necessitates a minimum of three active items in your product catalog. Incorporate any catalog field into the web recommendation call, emphasizing a clear understanding of the data driving recommendations during catalog setup. A unique web recommendation call is generated for each page type, with Home, Product, Category, Cart, and Conversion pages recommended as a best practice. However, there is no restriction on the number of pages that can be configured in the UI. Page templates are utilized by Einstein Recommendations, treating a Product Page as a template for building personalized recommendations specific to a product page. Different types of page templates may have distinct scenarios and contexts. For instance, recommendations on a product page may be based on the viewed product, adding context. Conversely, homepage recommendations rely on overall user affinity, lacking specific context. Integrate the Einstein Web Recommendations code into the designated page’s code, incorporating both the JavaScript for the recommendation call and the HTML recommendation zone placeholder provided. Select and configure the content to be included in your web recommendations: Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
MuleSoft B2B and B2C With AI

MuleSoft B2B and B2C With AI

Salesforce yesterday announced new solutions to help streamline and accelerate end-to-end order lifecycle management: MuleSoft’s Anypoint Partner Manager with Intelligent Document Processing (IDP) and MuleSoft Accelerator for Salesforce Order Management. MuleSoft B2B and B2C With AI. Together, these business-to-business (B2B) and business-to-consumer (B2C) integration solutions make it easier to connect essential data across third-party applications, Salesforce OMS, and partner ecosystems – all within MuleSoft. Enhanced with AI, these new solutions help IT teams unify data from multiple data and system sources to achieve end-to-end order visibility, improved efficiency, and customer satisfaction. Why it matters: IT teams are inundated with requests to integrate disparate systems and adopt different technologies. And IT teams in retail, consumer goods, manufacturing, logistics, and healthcare must manage the thousands of daily transactions between suppliers and buyers across the supply chain ecosystem. To add to the complexity, 75% of B2B digital sales occur via standardized Electronic Data Interchange (EDI) and specialized solutions are needed to handle these transactions. Go deeper: Anypoint Partner Manager with IDP is a cloud-native B2B integration solution that accelerates partner onboarding and operational management of both API and EDI-based transactions through the commerce and supply chain lifecycle. It provides visibility tools to accurately monitor the health of partner transactions along with key business and operational insights like overall order frequency and volume, shipment statuses, and more. By utilizing IDP, developers can leverage AI to extract, read, and store unstructured data from documents such as invoice and purchase order PDFs, surfacing it in systems of record and order management systems like Salesforce OMS. IT and business teams can rapidly develop integrations and APIs, monitor and manage their performance, and secure them in compliance with partner requirements, all through a single pane of glass. New capabilities of MuleSoft B2B and B2C With AI include: MuleSoft Accelerator for Salesforce OMS makes it easier and faster to achieve end-to-end order visibility across channels from a centralized hub. The accelerator includes pre-built APIs, connectors, implementation templates, and other technical assets for Anypoint Platform to unify B2B orders with Salesforce OMS and connect all B2B and B2C orders to enterprise resource planning (ERP) systems. By leveraging the available out-of-the-box integration assets, customers can significantly reduce the development time required for integrating systems and accelerate time to market. MuleSoft B2B and B2C With AI. New capabilities of this offering include: Industry Use Cases: Customer perspective: “We were struggling with disjointed technology that was causing order and shipping delays while hampering our ability to innovate across our ecosystem,” said Jeff Blank, VP, Finance & Infrastructure at Jillamy. “MuleSoft’s Anypoint Partner Manager helped accelerate our partner onboarding processes with seamless B2B integration and more efficient management of our EDI transactions.” Salesforce perspective: “B2B and B2C integrations are critical to the success of supply chain management. From getting berries out of the farm or medical devices to hospitals, organizations across the globe are looking for a unified solution to manage and securely monitor their business partner transactions. With Anypoint Partner Manager and MuleSoft Accelerator for OMS, our customers can use our technology to build a composable business ecosystem that meets business partner compliance standards and drives end-to-end supply chain and commerce processes with efficiency, visibility, and speed.” – Andrew Comstock, VP, Product Management With Anypoint Partner Manager and MuleSoft Accelerator for OMS, our customers can use our technology to build a composable business ecosystem that meets business partner compliance standards and drives end-to-end supply chain and commerce processes with efficiency, visibility, and speed. Andrew Comstock, VP, Product Management Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
Public Sector Approval Process Queue

Public Sector Approval Process Queue

Share the workload effectively by establishing queues in Public Sector Solutions to enable reviewers to access ready-to-process applications. This involves creating queues with assigned members based on user roles, such as a queue for application reviewers managing initial approval steps. Multiple queues, like those for compliance officers handling onsite inspections, can be created. During the approval process, the queue takes ownership of the application record, allowing any member to advance the approval steps. In Salesforce, a public sector approval process queue allows multiple approvers to manage a backlog of applications. The queue owns the application record during the approval process, and any member of the queue can take action to complete a step. Here’s a step-by-step guide to creating a queue: To enhance communication, create an email template and enable email approval responses in Setup’s Process Automation Settings. Now, your reps can efficiently manage activities through the Cadences tab, where details and targets for each cadence are visible. Cadences in Salesforce guide reps through prospecting steps, streamlining outreach and ensuring timely logging of activities. To create a branched cadence for varied outreach based on call or email outcomes, utilize the Cadence Builder. This tool enables the addition of email, call, wait periods, or custom steps. Branching is achieved through call or listener branch steps, ensuring tailored outreach steps based on outcomes. Finally, Salesforce users can activate cadences after creation, and both reps and managers can add prospects directly from lead, contact, or person account detail pages. The Sales Engagements component on these pages enhances visibility, allowing reps to act on the next sales step conveniently. In summary, Salesforce’s Cadence Builder Classic streamlines prospecting and opportunity nurturing, while queues optimize workload distribution in Public Sector Solutions. Effective use of cadences and queues contributes to a well-organized and responsive sales process. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

Read More
gettectonic.com