Technology

04Sep

Salesforce to Acquire PredictSpring

Salesforce to Acquire PredictSpring, Enhancing Omnichannel Capabilities Last month, Salesforce finalized an agreement to acquire PredictSpring, a leading provider of point-of-sale (POS) software. PredictSpring, known for its omnichannel commerce solutions, offers a suite of mobile POS systems along with clienteling, inventory management, and order management tools tailored for the retail sector. Insights from Industry Analysts In a recent episode of CX Today’s BIG News Update, key analysts shared their perspectives on the acquisition, highlighting three major points. Filling a Critical Gap Rebecca Wetteman, CEO & Principal Analyst at Valoir, noted that while Salesforce has effectively assisted many B2B clients, such as Fiserv and Peloton, in transitioning to B2C strategies, one crucial component was missing: order management. PredictSpring’s solutions address this gap, enhancing Salesforce’s data strategy and providing a more comprehensive customer view. Wetteman stated, “This addition is a significant move for Salesforce, strengthening their position beyond B2B and bridging the B2B to B2C divide.” Advancing Omnichannel Retail Simon Harrison, Founder & CEO at Actionary, emphasized that the acquisition represents a major step forward in delivering effective omnichannel solutions. PredictSpring’s technology promises to solve challenges associated with integrating in-store and digital experiences, enhancing overall customer interactions. Harrison praised the investment, stating, “This is a smart move, addressing real-world issues and increasing value for both staff and customers in today’s dynamic retail environment.” Expanding Market Reach Martin Schnieder, VP and Principal Analyst at Constellation Research, pointed out that acquiring PredictSpring aligns with Salesforce’s strategy to expand its total addressable market (TAM). He highlighted retail as a sector with unique challenges and opportunities, where Salesforce’s Data Cloud and platform can create impactful vertical-specific solutions. Schnieder noted, “Retail offers a different model with constrained margins, and Salesforce can leverage its platform to provide substantial value.” Michael Fauscette, Founder, CEO, and Chief Analyst at Arion Research, observed that Salesforce is strategically acquiring startups to fill gaps in its vertical offerings. He remarked, “Salesforce’s approach involves identifying startups that address specific needs and integrating them into their ecosystem. This strategy has proven effective and allows Salesforce to go to market directly with these partners, a practice not always seen among enterprise vendors.” Conclusion Salesforce’s acquisition of PredictSpring is a strategic move to enhance its omnichannel capabilities and address key gaps in its offerings. By integrating PredictSpring’s advanced POS solutions, Salesforce aims to strengthen its position in the retail sector and continue its growth trajectory in both B2B and B2C markets. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 4, 2024in Salesforce

04Sep

Autonomous AI Sans Human

Rise of Autonomous AI: Less Human Control and Increasing Adoption A recent Salesforce study reveals that nearly half of employees in Switzerland (46%) are either using or experimenting with AI technologies. While there is a general comfort with AI when it complements human efforts, many employees still prefer human oversight for tasks like training, data security, and onboarding. Despite this, the data indicates that increased investment in education and training could enhance trust in autonomous AI systems. Switzerland’s AI Adoption Compared to Other Countries Switzerland shows a higher openness to AI compared to other nations. In Germany, only 28% of respondents use AI confidently, compared to 46% in Switzerland. The UK (17%) and Ireland (15%) show even more skepticism. Conversely, India has the highest AI confidence, with 40% of respondents showing strong support. In Switzerland, however, 24% of employees are reluctant to use AI at work, and 25% are not keen on Generative AI. Sector-Specific AI Usage Trends The study also highlights significant sector differences. In the communications industry, 69% of employees are willing to use AI tools like ChatGPT and Gemini without hesitation. This contrasts with the life sciences and biotechnology sectors, where 72% of respondents are resistant to AI adoption. In the public sector, while there is general willingness, 56% express reservations due to a lack of expertise and guidelines. Notably, 39% of public sector respondents are completely opposed to using AI tools. Generational Insights on AI Proficiency Among different generations, Millennials and Gen X exhibit the highest proficiency and comfort with AI technology. In contrast, Gen Z appears more critical of AI, with 82% of this group avoiding AI assistants like IBM Watson or Microsoft Copilot. Millennials are more engaged, with 39% actively experimenting with or fully integrating AI assistants into their work routines. Gregory Leproux, Senior Director of Solution Engineering at Salesforce Switzerland, notes, “Our study reflects our customer experience: AI is widely used in Swiss companies, but human intervention remains prevalent. To fully leverage the benefits of AI, there is a need for robust control mechanisms and policies for responsible AI use, allowing for systematic review rather than piecemeal assessment. Thoughtfully designed AI systems can merge human and machine intelligence, marking the beginning of an exciting new era.” The survey, conducted by Salesforce in partnership with YouGov, took place from March 20 to April 3, 2024, with nearly 6,000 full-time employees from various industries and countries, including Switzerland (265 participants). The online survey covered nine countries: the US, UK, Ireland, Australia, France, Germany, India, Singapore, and Switzerland. Source: www.salesforce.com 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

September 4, 2024in Generative AI

03Sep

Salesforce for Public Loan Management

Public Loan Management Solutions: Optimized with Salesforce Technology In the dynamic world of financial services, public loan management has become increasingly important as organizations aim to improve efficiency, transparency, and borrower satisfaction. Leveraging cutting-edge technology is key to achieving these goals, and Salesforce offers a powerful platform for optimizing public loan management. This article explores how Salesforce’s capabilities can streamline and enhance various aspects of public loan management. The Role of Loan Boarding in Public Loan Management Loan boarding is a critical step where approved loans are entered into the system. This process typically includes data entry, document verification, and compliance checks. Salesforce’s customizable objects and automation features simplify this process by automating workflows, ensuring all necessary documents are collected and verified before the loan is boarded. Integrating external systems allows real-time data updates, minimizing errors and reducing manual intervention. Streamlining Handoff and Approval Processes Loan applications often require multiple approvals from stakeholders, which can be time-consuming and prone to delays. Salesforce facilitates efficient communication and collaboration through tools like Chatter, which allows instant messaging and file sharing. This ensures all stakeholders stay informed about application status changes, eliminating the need to switch between different platforms and speeding up the approval process. Disbursement Efficiency Timely fund disbursement is vital for maintaining borrower trust. Salesforce can automate disbursement processes by integrating payment gateways, accelerating fund transfers while providing borrowers with real-time updates on their disbursements. This enhances transparency and improves borrower satisfaction. Effective Management of Amortization Schedules Amortization schedules detail the repayment of loans over time, including both principal and interest. Salesforce’s reporting tools, combined with custom formulas, enable organizations to generate accurate, customized amortization schedules. These schedules can be easily updated if loan terms change, ensuring borrowers and lenders have up-to-date information. Simplifying Repayment Schedules Repayment schedules are essential for managing loan payments. Salesforce’s task management features allow organizations to automate reminders for upcoming payments, while borrowers can access personalized portals to view their schedules, promoting transparency and accountability. Customizable Loan Templates In public lending, creating flexible yet standardized loan templates is essential. Salesforce allows organizations to design customizable templates that meet both organizational policies and borrower needs, reducing onboarding time and improving efficiency. Comprehensive Document Management Managing loan-related documents is often challenging due to regulatory requirements and varying documentation needs. Salesforce’s integrated document management tools, such as Files and Content Libraries, provide secure storage and easy retrieval of documents, ensuring compliance and simplifying audits. Automating Interest Accruals and Invoicing Interest accruals require accurate tracking to ensure transparency for both lenders and borrowers. Salesforce’s invoicing automation ensures that billing cycles align with interest accruals, reducing administrative overhead and improving financial accuracy. Efficient Payment Processing and Waterfall Management Payment processing is essential for collecting repayments and adhering to waterfall structures, which determine how funds are allocated (e.g., to principal vs. interest). Salesforce integrates with NACHA/ACH for seamless payment processing and offers batch import capabilities for external payment records, improving cash flow management. Portfolio Management and Risk Assessment Managing a large portfolio of loans involves monitoring performance and assessing risks. Salesforce’s real-time analytics, reports, and dashboards provide managers with insights into portfolio performance, enabling data-driven decisions regarding portfolio adjustments, repayment patterns, and borrower risk. Enhancing Borrower Communication Consistent, clear communication is vital throughout the loan lifecycle, from initial inquiry to final repayment. Salesforce automates alerts and task assignments to ensure no critical communications are missed, keeping borrowers engaged and informed at every stage. Conclusion Salesforce technology offers a transformative approach to public loan management by enhancing operational efficiency, improving borrower experiences, and streamlining processes. Whether through automating document management, optimizing approval workflows, or managing payment cycles, Salesforce provides public lending organizations with the tools they need to deliver reliable, transparent, and efficient loan services to their constituents. By adopting Salesforce for public loan management, organizations not only improve internal operations but also elevate the borrower experience, ultimately contributing to community development and financial inclusion on a national scale. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

September 3, 2024in Salesforce Financial Services Solutions, Salesforce Optimization

02Sep

2024 AI Glossary

Artificial intelligence (AI) has moved from an emerging technology to a mainstream business imperative, making it essential for leaders across industries to understand and communicate its concepts. To help you unlock the full potential of AI in your organization, this 2024 AI Glossary outlines key terms and phrases that are critical for discussing and implementing AI solutions. Tectonic 2024 AI Glossary Active LearningA blend of supervised and unsupervised learning, active learning allows AI models to identify patterns, determine the next step in learning, and only seek human intervention when necessary. This makes it an efficient approach to developing specialized AI models with greater speed and precision, which is ideal for businesses aiming for reliability and efficiency in AI adoption. AI AlignmentThis subfield focuses on aligning the objectives of AI systems with the goals of their designers or users. It ensures that AI achieves intended outcomes while also integrating ethical standards and values when making decisions. AI HallucinationsThese occur when an AI system generates incorrect or misleading outputs. Hallucinations often stem from biased or insufficient training data or incorrect model assumptions. AI-Powered AutomationAlso known as “intelligent automation,” this refers to the integration of AI with rules-based automation tools like robotic process automation (RPA). By incorporating AI technologies such as machine learning (ML), natural language processing (NLP), and computer vision (CV), AI-powered automation expands the scope of tasks that can be automated, enhancing productivity and customer experience. AI Usage AuditingAn AI usage audit is a comprehensive review that ensures your AI program meets its goals, complies with legal requirements, and adheres to organizational standards. This process helps confirm the ethical and accurate performance of AI systems. Artificial General Intelligence (AGI)AGI refers to a theoretical AI system that matches human cognitive abilities and adaptability. While it remains a future concept, experts predict it may take decades or even centuries to develop true AGI. Artificial Intelligence (AI)AI encompasses computer systems that can perform complex tasks traditionally requiring human intelligence, such as reasoning, decision-making, and problem-solving. BiasBias in AI refers to skewed outcomes that unfairly disadvantage certain ideas, objectives, or groups of people. This often results from insufficient or unrepresentative training data. Confidence ScoreA confidence score is a probability measure indicating how certain an AI model is that it has performed its assigned task correctly. Conversational AIA type of AI designed to simulate human conversation using techniques like NLP and generative AI. It can be further enhanced with capabilities like image recognition. Cost ControlThis is the process of monitoring project progress in real-time, tracking resource usage, analyzing performance metrics, and addressing potential budget issues before they escalate, ensuring projects stay on track. Data Annotation (Data Labeling)The process of labeling data with specific features to help AI models learn and recognize patterns during training. Deep LearningA subset of machine learning that uses multi-layered neural networks to simulate complex human decision-making processes. Enterprise AIAI technology designed specifically to meet organizational needs, including governance, compliance, and security requirements. Foundational ModelsThese models learn from large datasets and can be fine-tuned for specific tasks. Their adaptability makes them cost-effective, reducing the need for separate models for each task. Generative AIA type of AI capable of creating new content such as text, images, audio, and synthetic data. It learns from vast datasets and generates new outputs that resemble but do not replicate the original data. Generative AI Feature GovernanceA set of principles and policies ensuring the responsible use of generative AI technologies throughout an organization, aligning with company values and societal norms. Human in the Loop (HITL)A feedback process where human intervention ensures the accuracy and ethical standards of AI outputs, essential for improving AI training and decision-making. Intelligent Document Processing (IDP)IDP extracts data from a variety of document types using AI techniques like NLP and CV to automate and analyze document-based tasks. Large Language Model (LLM)An AI technology trained on massive datasets to understand and generate text. LLMs are key in language understanding and generation and utilize transformer models for processing sequential data. Machine Learning (ML)A branch of AI that allows systems to learn from data and improve accuracy over time through algorithms. Model AccuracyA measure of how often an AI model performs tasks correctly, typically evaluated using metrics such as the F1 score, which combines precision and recall. Natural Language Processing (NLP)An AI technique that enables machines to understand, interpret, and generate human language through a combination of linguistic and statistical models. Retrieval Augmented Generation (RAG)This technique enhances the reliability of generative AI by incorporating external data to improve the accuracy of generated content. Supervised LearningA machine learning approach that uses labeled datasets to train AI models to make accurate predictions. Unsupervised LearningA type of machine learning that analyzes and groups unlabeled data without human input, often used to discover hidden patterns. By understanding these terms, you can better navigate the AI implementation world and apply its transformative power to drive innovation and efficiency across your organization. Tectonic 2024 AI Glossary Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

September 2, 2024in Generative AI

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 Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

August 31, 2024in Generative AI

29Aug

Salesforce Einstein SDR and Sales Coach Agents

Salesforce Introduces Autonomous AI Sales Agents: Einstein SDR Agent and Einstein Sales Coach Agent Salesforce, the leading CRM for sales, has announced two new fully autonomous AI sales agents: Einstein Sales Development Rep (SDR) Agent and Einstein Sales Coach Agent. These groundbreaking agents, set to be generally available in October, are designed to help sales teams accelerate growth by handling key sales functions autonomously. Built on the Einstein 1 Agentforce Platform, these agents are poised to transform how sales teams operate, allowing them to focus on more complex deals while automating routine tasks. Einstein SDR Agent: Automating Pipeline 24/7 The Einstein SDR Agent autonomously engages with inbound leads, nurturing pipelines around the clock. Unlike traditional chatbots, which can only respond to pre-programmed queries, the Einstein SDR Agent uses advanced AI to make decisions, prioritize actions, and handle various lead interactions. Whether it’s answering product questions, managing objections, or booking meetings, the SDR Agent ensures that every response is trusted, accurate, and personalized, grounded in your company’s CRM and external data. Key features of the Einstein SDR Agent include: Einstein Sales Coach Agent: Enhancing Seller Performance Through AI-Driven Role-Play Einstein Sales Coach Agent takes sales enablement to the next level by autonomously engaging in role-plays with sellers. Whether simulating a buyer during discovery, pitch, or negotiation calls, the Sales Coach Agent uses generative AI to convert text into speech, providing a realistic training environment. This agent helps sellers refine their skills by offering personalized feedback based on real deal contexts. Key features of the Einstein Sales Coach Agent include: Accenture’s Collaboration with Salesforce Accenture, a global leader in business consulting, will leverage these new AI agents to enhance deal team effectiveness, scale support for more deals, and allow their sales teams to concentrate on the most complex transactions. According to Sara Porter, Global Sales Excellence Lead at Accenture, these AI-driven tools will empower their sales practitioners with advanced technology and processes to drive more intelligent customer conversations and accelerate revenue. Salesforce’s Vision for AI in Sales Salesforce sees these autonomous AI agents as a key part of the future of sales. By integrating AI that can generate high-quality pipeline and provide personalized coaching, sales teams can focus on higher-value deals and better prepare for them. Ketan Karkhanis, EVP and GM of Sales Cloud, emphasizes that every AI conversation must translate into ROI, and these new agents are designed to do just that by augmenting human sales teams to accelerate growth. Availability Both Einstein SDR Agent and Einstein Sales Coach Agent will be generally available in October, with additional functionalities expected to be rolled out throughout the year. Learn More: Note: Any unreleased services or features mentioned here are not currently available and may be subject to changes. Customers should base their purchasing decisions from Salesforce on currently available features. 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

August 29, 2024in Agentforce Platform, Generative AI, Salesforce Einstein

29Aug

Key Insights on Navigating AI Compliance

Grammarly’s AI Regulatory Master Class: Key Insights on Navigating AI Compliance On August 27, 2024, Grammarly hosted an AI Regulatory Master Class webinar, featuring Scout Moran, Senior Product Counsel, and Alan Luk, Head of Governance, Risk, and Compliance (GRC). The event provided a comprehensive overview of the current and upcoming AI regulations affecting organizations’ AI strategies, along with guidance on evaluating AI solution providers, including those offering generative AI. While the webinar avoided deep legal analysis and did not serve as legal advice, Moran and Luk spotlighted key regulations emerging from both the U.S. and European Union (EU), highlighting the rapid response of regulatory bodies to AI’s growth. Overview of AI Regulations The AI regulatory landscape is changing quickly. A May 2024 report from law firm Davis & Gilbert noted that nearly 200 AI-related laws have been proposed across various U.S. states. Grammarly’s presentation emphasized the need for organizations to stay updated, as both U.S. and EU regulations are shaping the future of AI governance. The EU AI Act: A New Regulatory Framework The EU AI Act, which took effect on August 2, 2024, applies to AI system providers, importers, distributors, and others connected to the EU market, regardless of where they are based. As Moran pointed out, the Act is designed to ensure AI systems are deployed safely. Unsafe systems may be removed from the market, establishing a regulatory baseline that individual EU countries can strengthen with more stringent measures. However, the Act does not fully define “safety.” Legal experts Hadrien Pouget and Ranj Zuhdi noted that while safety requirements are crucial to the Act, they are currently broad, allowing room for further development of standards. The Act prohibits certain AI practices, such as manipulative systems, those exploiting personal vulnerabilities, and AI used to assess or predict criminal risk. AI systems are categorized into four risk levels: unacceptable, high-risk, limited risk, and minimal risk. High-risk systems—such as those in critical infrastructure or public services—face stricter regulation, while minimal-risk systems like spam filters have fewer requirements. Full enforcement of the Act will begin in 2025. U.S. AI Regulations Unlike the EU, the U.S. focuses more on national security than consumer safety in its AI regulation. The U.S. Executive Order on Safe, Secure, and Trustworthy AI addresses these concerns. At the state level, Moran highlighted trends such as requiring clear disclosure when interacting with AI and giving individuals the right to opt out of having their data used for AI model training. States like California and Utah are leading the way with specific laws (SB-1047 and SB-149, respectively) addressing accountability and disclosure in AI use. Key Considerations When Selecting AI Vendors Moran stressed the importance of thoroughly vetting AI vendors. Organizations should ensure vendors meet cybersecurity standards, such as SOC 2, and clearly define how their data will be used, particularly in training large language models (LLMs). “Eyes off” agreements, which prevent vendor employees from accessing customer data, should also be considered. Martha Buyer, a frequent contributor to No Jitter, emphasized verifying the originality of AI-generated content from providers like Grammarly or Microsoft Copilot. She urged caution in ensuring the ownership and authenticity of AI-assisted outputs. The Importance of Strong Third-Party Agreements Luk highlighted Grammarly’s commitment to data privacy, noting that the company neither sells customer data nor uses it to train models. Additionally, Grammarly enforces agreements preventing its third-party LLM providers from doing so. These contractual protections are crucial for safeguarding customer data. Organizations should also ensure third-party vendors adhere to strict guidelines, including securing customer data, encrypting it, and preventing unauthorized access. Vendors should maintain updated security certifications and manage risks like bias, which, while not entirely avoidable, must be actively addressed. Staying Ahead in a Changing Regulatory Environment Both Moran and Luk stressed the importance of ongoing monitoring. Organizations need to regularly reassess whether their vendors comply with their data-sharing policies and meet evolving regulatory standards. As AI technology and regulations continue to evolve, staying informed and agile will be critical for compliance and risk mitigation. In conclusion, organizations adopting AI-powered solutions must navigate a dynamic regulatory environment. As AI advances and regulations become more comprehensive, remaining vigilant and asking the right questions will be key to ensuring compliance and reducing risks. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

August 29, 2024in Generative AI

28Aug

MCG and Salesforce Health Cloud

Independent Publisher of Evidence-Based Guidance Integrates with Salesforce Health Cloud to Enhance Chronic Disease Care SEATTLE, Aug. 27, 2024 /PRNewswire-PRWeb/ — MCG Health, a member of the Hearst Health network and a leader in evidence-based clinical guidance, announces a new integration with Salesforce Health Cloud. This partnership aims to improve the management of patients with chronic conditions and those transitioning to different care settings, such as ambulatory care, recovery facilities, or home care. The integration combines Salesforce Health Cloud, the leading AI-powered CRM, with MCG Health’s trusted, evidence-based guidelines to support better patient outcomes. “This integration deepens our collaboration with MCG and delivers greater return on investment for our Health Cloud customers by emphasizing patient-focused and evidence-based disease management,” said Amit Khanna, Senior Vice President and General Manager of Health at Salesforce. Enhanced Care Planning with Salesforce Health Cloud Salesforce Health Cloud’s Integrated Care Management (ICM) feature now incorporates MCG Health’s industry-leading, evidence-based guidelines for Chronic Care and Transitions of Care. This interactive integration simplifies and optimizes care planning for patients’ post-acute journeys. The solution includes tools for identifying patient needs related to social determinants of health (SDOH) and offers branching logic tailored to individual patient situations. This enhancement significantly reduces administrative burdens for hospital and health plan staff while supporting evidence-based care management for populations with chronic conditions and those needing transition management. Additionally, patient education materials from MCG Health can now be easily distributed from within Salesforce Health Cloud, providing patients with enhanced information on their diagnosis and treatment. “MCG’s collaboration with Salesforce Health Cloud provides a powerful, evidence-based tool for managing chronic disease,” said Jon Shreve, President and CEO of MCG Health. “Through this new integration, we can help Salesforce’s healthcare customers streamline their care planning and disease management programs. This solution enhances hospitals’ and health plans’ ability to adhere to evidence-based practices, improving clinical workflows and benefiting both healthcare organizations and, most importantly, patients.” A Strategic Partnership for Better Patient Outcomes “Salesforce is excited to partner with MCG to integrate their trusted, evidence-based guidance into Health Cloud, advancing the care of patients with chronic and complex diseases,” said Amit Khanna, Senior Vice President and General Manager of Health at Salesforce. “This integration strengthens our ongoing collaboration with MCG and delivers more value to our Health Cloud customers by focusing on patient-centered and evidence-based disease management.” Interested parties can request a demo from MCG via this link: Schedule a Demo. About MCG Health MCG Health, part of the Hearst Health network, provides unbiased clinical guidance that empowers healthcare organizations to deliver patient-centered care with confidence. MCG’s AI-driven technology, combined with clinical expertise, enables clients to prioritize and simplify their work. MCG’s world-class customer service ensures clients maximize the benefits of MCG solutions, resulting in improved clinical and financial outcomes. For more information, visit MCG Health. Salesforce, Health Cloud, and related marks are trademarks of Salesforce, Inc. 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

August 28, 2024in Salesforce Health Cloud

27Aug

Uplimit AI-Powered ELP

Uplimit Secures $11M in Series A Funding to Enhance AI-Powered Enterprise Learning SAN FRANCISCO, July 24, 2024 /PRNewswire/ — Uplimit, a leading provider of AI-powered enterprise learning solutions, has announced the successful completion of an $11M Series A funding round. This funding, led by Salesforce Ventures with participation from existing investors GSV Ventures, Greylock Partners, and Cowboy Ventures, as well as new investors Translink Capital, Workday Ventures, and Conviction, underscores the growing importance of effective employee upskilling in response to the rapid advancements in Generative AI technology. Uplimit AI-Powered ELP. “Helping employees stay ahead of technological advancements is now a critical priority for the organizations we serve,” said Claudine Emeott, Partner at Salesforce Ventures and Head of the Salesforce Ventures Impact Fund. “AI has the potential to equip both companies and individuals with the necessary skills to thrive, and Uplimit is at the forefront of integrating AI into education and training. We are excited to support their continued growth and look forward to seeing the significant impact they will have in the coming years.” “AI has the potential to equip both companies and individuals with the necessary skills to thrive, and Uplimit is at the forefront of integrating AI into education and training. We are excited to support their continued growth and look forward to seeing the significant impact they will have in the coming years.” Claudine Emeott, Partner at Salesforce Ventures and Head of the Salesforce Ventures Impact Fund Uplimit AI-Powered ELP With this new funding, Uplimit plans to expand its enterprise platform offerings, aiming to provide comprehensive upskilling solutions to more organizations and employees. Traditional education systems often require extensive resources for content creation, personalized feedback, and support, which can hinder scalability. While some scalable solutions exist, they often compromise on quality and outcomes. Uplimit is addressing this challenge with an innovative approach that combines scale and effectiveness. Their AI-driven platform enhances cohort management, learner support, and course authoring, enabling companies to deliver personalized learning experiences at scale. Uplimit’s recent introduction of AI-enabled role-play scenarios provides learners with immediate feedback, revolutionizing training and development for roles such as managers, support teams, and sales professionals. “Quality education has historically been a scarce resource, but AI is changing that,” said Julia Stiglitz, CEO and Co-founder of Uplimit. “AI allows us to create and update educational content rapidly, ensuring that learners receive personalized experiences even in large-scale courses. This is crucial as the demand for new skills, driven by the rapid evolution of AI technologies, continues to grow. Uplimit provides the tools needed for employees to quickly grasp new skills, tailored to their current knowledge and needs.” Uplimit has collaborated with a diverse range of companies, from Fortune 500 giants like GE Healthcare and Kraft Heinz to innovative startups such as Procore. Databricks, a leader in AI-powered data intelligence, was an early adopter of Uplimit’s platform for customer education. “We needed a learning platform that could scale to hundreds of thousands of learners while maintaining high levels of engagement and completion,” said Rochana Golani, VP of Learning and Enablement at Databricks. “Uplimit’s platform offers the perfect blend of real-time human instruction and personalized AI support, along with valuable peer interaction. This approach is set to be transformative for many of our customers.” The new funding will enable Uplimit to further enhance its enterprise and customer education offerings, expanding its AI capabilities to include advanced cohort management tools, rapid course feedback integration, interactive practice and assessment modules, and AI-powered course authoring. Join us on August 14th for our launch event, where we will explore how this funding will accelerate our mission and demonstrate the impact our platform is having on enterprise learning. About Uplimit Uplimit is a comprehensive AI-driven learning platform designed to equip companies with the tools needed to train employees and customers in emerging skills. The platform leverages AI to scale learning programs effectively, offering features such as AI-powered learner support, generative AI for content creation, and live cohort management tools. This approach ensures high engagement and completion rates, significantly surpassing traditional online courses. Uplimit also offers a marketplace of advanced courses in AI, technology, and leadership, taught by industry experts. Founded by Julia Stiglitz, Sourabh Bajaj, and Jake Samuelson, Uplimit is backed by Salesforce Ventures, Greylock Partners, Cowboy Ventures, GSV Ventures, Conviction, Workday Ventures, and Translink Capital, with contributions from the co-founders of OpenAI and DeepMind. Notable customers include GE Healthcare, Kraft Heinz, and Databricks. Uplimit has been featured in leading industry publications such as ATD, Josh Bersin, and Fast Company. 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

August 27, 2024in Generative AI

26Aug

Unlocking Enterprise AI Success

Companies are diving into artificial intelligence. Unlocking enterprise AI success depends on four main factors. Tectonic is here to help you address each. Trust is Important-Trust is Everything Data is everything—it’s reshaping business models and steering the world through health and economic challenges. But data alone isn’t enough; in fact, it can be worse than useless—it’s a risk unless it’s trustworthy. The solution lies in a data trust strategy: one that maximizes data’s potential to create value while minimizing the risks associated with it. Data Trust is Declining, Not Improving Do you believe your company is making its data and data practices more trustworthy? If so, you’re in line with most business leaders. However, there’s a disconnect: consumers don’t share this belief. While 55% of business leaders think consumers trust them with data more than they did two years ago, only 21% of consumers report increased trust in how companies use their data. In fact, 28% say their trust has decreased, and a staggering 76% of global consumers view sharing their data with companies as a “necessary evil.” For companies that manage to build trust in their data, the benefits are substantial. Yet, only 37% of companies with a formal data valuation process involve privacy teams. Integrating privacy is just one aspect of building data trust, but companies that do so are already more than twice as likely as their peers to report returns on investment from key data-driven initiatives, such as developing new products and services, enhancing workforce effectiveness, and optimizing business operations. To truly excel, companies need to create an ongoing system that continually transforms raw information into trusted, business-critical data. Data is the Backbone-Data is the Key Data leaks, as shown below, are a major factor on data trust and quality. As bad as leaked data is to security, data availability is to being a data-driven organization. Extortionist Attack on Costa Rican Government Agencies In an unprecedented event in April 2022, the extortionist group Conti launched a cyberattack on Costa Rican government agencies, demanding a $20 million ransom. The attack crippled much of the country’s IT infrastructure, leading to a declared state of emergency. Lapsus$ Attacks on Okta, Nvidia, Microsoft, Samsung, and Other Companies The Lapsus$ group targeted several major IT companies in 2022, including Okta, Nvidia, Microsoft, and Samsung. Earlier in the year, Okta, known for its account and access management solutions—including multi-factor authentication—was breached. Attack on Swissport International Swissport International, a Swiss provider of air cargo and ground handling services operating at 310 airports across 50 countries, was hit by ransomware. The attack caused numerous flight delays and resulted in the theft of 1.6 TB of data, highlighting the severe consequences of such breaches on global logistics. Attack on Vodafone Portugal Vodafone Portugal, a major telecommunications operator, suffered a cyberattack that disrupted services nationwide, affecting 4G and 5G networks, SMS messaging, and TV services. With over 4 million cellular subscribers and 3.4 million internet users, the impact was widespread across Portugal. Data Leak of Indonesian Citizens In a massive breach, an archive containing data on 105 million Indonesian citizens—about 40% of the country’s population—was put up for sale on a dark web forum. The data, believed to have been stolen from the “General Election Commission,” included full names, birth dates, and other personal information. The Critical Importance of Accurate Data There’s no shortage of maxims emphasizing how data has become one of the most vital resources for businesses and organizations. At Tectonic, we agree that the best decisions are driven by accurate and relevant data. However, we also caution that simply having more data doesn’t necessarily lead to better decision-making. In fact, we argue that data accuracy is far more important than data abundance. Making decisions based on incorrect or irrelevant data is often worse than having too little of the right data. This is why accurate data is crucial, and we’ll explore this concept further in the following sections. Accurate data is information that truly reflects reality or another source of truth. It can be tested against facts or evidence to verify that it represents something as it actually is, such as a person’s contact details or a location’s coordinates. Accuracy is often confused with precision, but they are distinct concepts. Precision refers to how consistent or varied values are relative to one another, typically measured against some other variable. Thus, data can be accurate, precise, both, or neither. Another key factor in data accuracy is the time elapsed between when data is produced and when it is collected and used. The shorter this time frame, the more likely the data is to be accurate. As modern businesses integrate data into more aspects of their operations, they stand to gain significant competitive advantages if done correctly. However, this also means there’s more at stake if the data is inaccurate. The following points will highlight why accurate data is critical to various facets of your company. Ease and speed of access Access speeds are measured in bytes per second (Bps). Slower devices operate in thousands of Bps (kBps), while faster devices can reach millions of Bps (MBps). For example, a hard drive can read and write data at speeds of 300MBps, which is 5,000 times faster than a floppy disk! Fast data refers to data in motion, streaming into applications and computing environments from countless endpoints—ranging from mobile devices and sensor networks to financial transactions, stock tick feeds, logs, retail systems, and telco call routing and authorization systems. Improving data access speeds can significantly enhance operational efficiency by providing timely and accurate data to stakeholders throughout an organization. This can streamline business processes, reduce costs, and boost productivity. However, data access is not just about retrieving information. It plays a crucial role in ensuring data integrity, security, and regulatory compliance. Effective data access strategies help organizations safeguard sensitive information from unauthorized access while making it readily available to those who are authorized. Additionally, the accuracy and availability of data are essential to prevent data

August 26, 2024in Salesforce

25Aug

AI Services and Models Security Shortcomings

Orca Report: AI Services and Models Show Security Shortcomings Recent research by Orca Security reveals significant security vulnerabilities in AI services and models deployed in the cloud. The “2024 State of AI Security Report,” released in 2024, underscores the urgent need for improved security practices as AI technologies advance rapidly. AI Services and Models Security Shortcomings. AI usage is exploding. Gartner predicts that the AI software market will grow19.1% annually, reaching 8 billion by 2027. In many ways, AI is now inthe stage reminiscent of where cloud computing was over a decade ago. Orca’s analysis of cloud assets across major platforms—AWS, Azure, Google Cloud, Oracle Cloud, and Alibaba Cloud—has highlighted troubling risks associated with AI tools and models. Despite the surge in AI adoption, many organizations are neglecting fundamental security measures, potentially exposing themselves to significant threats. The report indicates that while 56% of organizations use their own AI models for various purposes, a substantial portion of these deployments contain at least one known vulnerability. Orca’s findings suggest that although most vulnerabilities are currently classified as low to medium risk, they still pose a serious threat. Notably, 62% of organizations have implemented AI packages with vulnerabilities, which have an average CVSS score of 6.9. Only 0.2% of these vulnerabilities have known public exploits, compared to the industry average of 2.5%. Insecure Configurations and Controls Orca’s research reveals concerning security practices among widely used AI services. For instance, Azure OpenAI, a popular choice for building custom applications, was found to be improperly configured in 27% of cases. This lapse could allow attackers to access or manipulate data transmitted between cloud resources and AI services. The report also criticizes default settings in Amazon SageMaker, a prominent machine learning service. It highlights that 45% of SageMaker buckets use non-randomized default names, and 98% of organizations have not disabled default root access for SageMaker notebook instances. These defaults create vulnerabilities that attackers could exploit to gain unauthorized access and perform actions on the assets. Additionally, the report points out a lack of self-managed encryption keys and encryption protection. For instance, 98% of organizations using Google Vertex have not enabled encryption at rest for their self-managed keys, potentially exposing sensitive data to unauthorized access or alteration. Exposed Access Keys and Platform Risks Security issues extend to popular AI platforms like OpenAI and Hugging Face. Orca’s report found that 20% of organizations using OpenAI and 35% using Hugging Face have exposed access keys, heightening the risk of unauthorized access. This follows recent research by Wiz, which demonstrated vulnerabilities in Hugging Face during Black Hat USA 2024, where sensitive data was compromised. Addressing the Security Challenge Orca co-founder and CEO Gil Geron emphasizes the need for clear roles and responsibilities in managing AI security. He stresses that security practitioners must recognize and address these risks by setting policies and boundaries. According to Geron, while the challenges are not new, the rapid development of AI tools makes it crucial to address security from both engineering and practitioner perspectives. Geron also highlights the importance of reviewing and adjusting default settings to enhance security, advocating for rigorous permission management and network hygiene. As AI technology continues to evolve, organizations must remain vigilant and proactive in safeguarding their systems and data. In conclusion, the Orca report serves as a critical reminder of the security risks associated with AI services and models. Organizations must take concerted action to secure their AI deployments and protect against potential vulnerabilities. Balance Innovation and Security in AI Tectonic notes Salesforce was not included in the sampling. Content updated September 2024. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 25, 2024in AI Tools, Data, Google, Technology

24Aug

Loan Boarding and Approval

Streamlining Loan Boarding and Approval Processes with Salesforce Technology The financial services industry is undergoing a rapid transformation, driven by the need for greater efficiency and improved customer experiences. One area where this shift is particularly evident is in the loan boarding and approval processes. Leveraging Salesforce technology, financial institutions can streamline these essential workflows, enhancing both speed and accuracy while delivering a superior borrower experience. Understanding Loan Boarding Loan boarding is the process of transitioning a loan from its origination phase into servicing. This involves several key steps, including data entry, document management, and compliance checks. Traditionally, this process has been manual, cumbersome, and prone to errors. However, Salesforce offers robust capabilities that allow organizations to automate and optimize these tasks, significantly reducing inefficiencies. Automating Data Entry Salesforce facilitates automated data entry through its customizable forms and integration capabilities. Tools like Salesforce Flow and Apex enable businesses to create workflows that automatically populate fields based on predefined criteria or data extracted from documents using Optical Character Recognition (OCR) technology. This automation reduces manual errors and accelerates the loan boarding process. Efficient Document Management Effective document management is crucial in loan boarding. Salesforce provides a centralized platform for secure storage and easy access to all necessary documents. Features like Salesforce Files enable organizations to manage documentation efficiently, allowing for easy retrieval, sharing, and version control. This streamlined document management ensures that all relevant information is readily available throughout the loan lifecycle. Streamlining Handoff and Approval Processes After a loan is boarded, it must go through a series of approvals before disbursement. The handoff between departments such as underwriting and risk assessment can cause delays if not properly managed. Salesforce’s collaborative tools facilitate seamless communication among stakeholders, ensuring a smooth transition through the approval process. Customizable Approval Workflows Salesforce allows for the creation of customizable approval workflows, enabling organizations to define specific criteria for each stage of loan approval. This flexibility ensures that loans are reviewed by the appropriate personnel based on their complexity or risk profile. Automated alerts notify relevant team members when their input is needed, minimizing bottlenecks and keeping the process moving efficiently. Enhanced Visibility with Real-Time Dashboards One of Salesforce’s standout features is its ability to generate real-time dashboards that provide insights into various stages of the loan process. Stakeholders can monitor key metrics, such as the average time for approvals or the number of loans pending at each stage, through intuitive visualizations. This transparency promotes quicker decision-making and fosters accountability within the team. Seamless Disbursement Process Once loans are approved, the disbursement phase is the next critical step. Salesforce’s integration capabilities with payment processing systems, such as NACHA/ACH solutions, allow organizations to automate fund transfers directly within the platform, streamlining the disbursement process. Automating Payment Processing Automated triggers for payments can be set up within Salesforce, reducing the need for manual intervention. This automation speeds up the disbursement process and minimizes the risk of errors associated with manual data entry during fund transfers, ensuring a smooth and reliable process. Comprehensive Portfolio Management Managing a large loan portfolio requires meticulous tracking of various elements, including amortization schedules, repayments, interest accruals, and fees. Salesforce excels in these areas, offering tools to manage all aspects of a loan portfolio effectively. Dynamic Amortization and Repayment Schedules Salesforce enables the creation of dynamic amortization schedules tailored to individual borrower agreements, easily accessible via custom borrower portals. These portals enhance borrower engagement by providing real-time information about repayment obligations and remaining balances, improving transparency and customer satisfaction. Fee Automation Automating fee calculations within Salesforce reduces administrative burdens and ensures accurate billing according to agreed-upon terms. This feature helps avoid discrepancies and delays, providing a seamless experience for both the lender and the borrower. Risk Management and Collections In today’s volatile economic environment, effective risk management is essential for financial institutions. Salesforce’s advanced analytics and performance rating tools allow organizations to proactively identify potential risks before they escalate, enabling more informed lending decisions. Performance and Risk Ratings By analyzing historical data, Salesforce enables lenders to assign risk ratings based on borrowers’ past behaviors and external market conditions. This data-driven approach supports more accurate and strategic lending decisions, helping to mitigate risk. Effective Collections Strategies For overdue accounts, Salesforce’s task management features automate reminders and follow-ups, ensuring timely communication and effective debt recovery. Maintaining open communication channels with borrowers during the collections process is crucial for preserving relationships and achieving successful outcomes. Conclusion: Embracing Digital Transformation By embracing digital transformation through Salesforce technology, financial institutions can significantly streamline their loan boarding and approval processes. This not only enhances operational efficiency but also positions them competitively in a tech-driven marketplace, delivering the high-quality service that today’s consumers demand. Salesforce’s powerful tools enable institutions to meet the unique needs of their borrowers effectively, ensuring both efficiency and excellence in service delivery. 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

August 24, 2024in Salesforce

24Aug

Pulse for Salesforce

Salesforce Unveils Pulse for Salesforce: Integrating Tableau Analytics with CRM to Revolutionize Data-Driven Decision-Making In today’s data heavy business world, where data-driven decision-making is essential for success, the fusion of advanced analytics with customer relationship management (CRM) systems is more crucial than ever. Addressing this need, Salesforce has introduced Pulse for Salesforce, a groundbreaking tool that integrates Tableau’s powerful analytics directly into the Salesforce CRM environment. Meeting the Demand for Actionable Insights This launch aligns with a broader trend in the business intelligence (BI) market, where companies strive to make data analytics more accessible and actionable for non-technical users. Recent studies indicate that while 80% of business leaders view data as critical to decision-making, nearly one-third feel overwhelmed by the sheer volume of information available. Moreover, 91% of these leaders believe their organizations would significantly benefit from generative AI (Gen AI) technologies. Pulse for Salesforce marks a significant milestone in Salesforce’s ongoing strategy following its $15.7 billion acquisition of Tableau in 2019. Tableau, a leader in data visualization and BI since its founding in 2003, has been central to Salesforce’s mission of enhancing customer data management and analysis. The integration of Tableau’s capabilities within Salesforce’s CRM platform represents a major step forward in providing a comprehensive, data-driven solution. Ryan Aytay, President and CEO of Tableau, on the New Integration “Historically, sales leaders and teams have lacked personalized, accessible data insights in their daily flow of work, and analysts often spend considerable time on ad hoc requests and repetitive queries, slowing down decision-making and business growth,” says Ryan Aytay, CEO of Tableau. “By integrating Tableau Pulse’s AI-driven insights into Salesforce, we’re addressing these needs and enhancing data-driven decision-making to help businesses accelerate growth.” Boosting CRM Productivity with Salesforce’s AI Platform Pulse for Salesforce is built on Salesforce’s Einstein 1 AI Platform and leverages Gen AI to provide contextual metrics and insights directly within the Salesforce interface. This seamless integration streamlines decision-making for sales teams by reducing the need for manual data searches or reliance on analysts for ad-hoc queries. Key Features of Pulse for Salesforce Practical Applications and Data Security A practical application of Pulse for Salesforce is performance monitoring. Sales leaders can track team win rate trends directly from their homepage, quickly identifying areas or individuals needing additional support. Similarly, individual sales representatives can monitor their conversion rates and use natural language queries to analyze data by industry, potentially leading to more targeted sales efforts. The integration also addresses data security concerns, a critical issue in the age of AI-powered analytics. Pulse for Salesforce employs the Einstein Trust Layer, a secure AI architecture built into the Einstein 1 Platform, ensuring that customer data remains protected while benefiting from the advanced capabilities of generative AI. Collaboration Salesforce partnered with key industry players and partners to bring this innovative solution to market. With Pulse for Salesforce, organizations can now fully harness the power of integrated analytics and CRM to drive informed decision-making, enhance productivity, and ultimately accelerate business growth. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

August 24, 2024in Einstein 1 Platform, Einstein Insights, Generative AI, Salesforce Development Services, Salesforce Einstein, Salesforce Optimization, Salesforce Tableau

22Aug

Repayment Schedules With Salesforce

Simplifying Repayment Schedules: Elevating Borrower Experience with Salesforce In the rapidly changing financial services industry, managing loan repayment schedules efficiently is vital for both lenders and borrowers. A well-designed system can significantly enhance the borrower experience, streamline operations, and boost overall efficiency. Salesforce software emerges as a powerful solution that simplifies repayment schedules and fosters better communication between lenders and borrowers. This article explores how Salesforce can revolutionize repayment management with its comprehensive features. Understanding Repayment Schedules Repayment schedules define how a borrower will pay back their loan over time, detailing payment amounts, due dates, interest rates, and the total loan duration. A clear and well-structured repayment schedule not only helps borrowers manage their finances but also ensures that lenders receive timely payments. The complexity of repayment schedules can vary based on factors like loan terms, interest rates, and borrower profiles. Therefore, having an effective system to manage these variables is crucial for maintaining accuracy and transparency throughout the borrowing process. The Role of Salesforce in Loan Management Salesforce offers an extensive suite of tools designed to enhance customer relationship management (CRM) across various industries, including finance. By utilizing Salesforce’s capabilities, lenders can develop customized solutions that address key aspects of loan management, such as: 1. Automated Amortization SchedulesSalesforce enables the automated creation of amortization schedules tailored to individual loans. This feature minimizes manual errors and ensures accurate calculations from the start. Automation allows lenders to provide borrowers with clear payment plans, including details on principal reductions and interest accruals over time. 2. Custom Borrower PortalsOne of Salesforce’s major strengths is the ability to create custom borrower portals. These portals allow clients to access their repayment schedules anytime, view upcoming payments, track their balances in real-time, and even make payments through secure channels. This transparency builds trust between lenders and borrowers, enhancing overall satisfaction. 3. Document ManagementEffective document management is essential for maintaining organized records related to loans and repayments. Salesforce’s document management features enable lenders to securely store important documents—such as contracts, amendments, or communications—within each borrower’s profile. This accessibility simplifies audits and reviews while ensuring compliance with regulatory standards. Streamlined Communication with Automated Alerts A common challenge for borrowers is keeping track of payment deadlines and understanding when payments are due. Salesforce addresses this by offering automated alerts via email or text message, reminding borrowers of upcoming due dates or changes in payment schedules. These notifications help keep borrowers informed about their obligations without overwhelming them, balancing proactive communication with user-friendliness. Enhanced Reporting & Analytics Salesforce provides powerful reporting tools that allow lenders to effectively analyze repayment patterns across different portfolios. By identifying trends related to timely payments, defaults, or late fees, financial institutions can strategically tailor their offerings. Detailed dashboards also present key performance indicators (KPIs) related to collection efficiency, aiding in risk assessment and decision-making processes. Portfolio Management Integration Integrating portfolio management features within Salesforce allows lenders to monitor individual loans and gain insights into overall portfolio health. This includes tracking repayments received versus outstanding balances owed by all clients collectively. This holistic view supports decision-making around refinancing options for struggling clients and identifying growth opportunities based on historical data trends. This integration enhances lender profitability while improving borrower experiences. Conclusion: Transforming the Borrower Experience Integrating Salesforce software into loan repayment scheduling represents a significant advancement in enhancing borrower experiences in the financial services industry. From automating complex amortization calculations to providing personalized customer portals, Salesforce empowers both lenders and borrowers at every stage of the process. By embracing technology like Salesforce, lenders can streamline communication, reduce administrative burdens, and position themselves favorably against competitors. This buildss long-lasting relationships built on trust and reliability, ultimately benefiting all parties involved. Contact Tectonic today to explore lending solutions from Salesforce. 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

August 22, 2024in Salesforce Financial Services Solutions, Salesforce Managed Services, Salesforce Optimization

Why Choose Salesforce as Your Mortgage CRM?

22Aug

Banking Complaints to Profits

Tectonic: Elevating Complaint Management in Banking with Salesforce Customer satisfaction is key in banking, but complaints are unavoidable. Banking Complaints to Profits is not only learning from complaints but increasing revenue by them. Banking complaints also present a unique opportunity. Handled effectively, complaints can offer valuable insights that drive process improvements and ultimately strengthen customer relationships. Banking Complaints to Profits Banks need a robust, strategic complaint management system to capitalize on this opportunity. Such a system must go beyond simply documenting and resolving grievances. It must enable banks to proactively identify trends, assess root causes, and implement targeted solutions that address individual complaints and prevent future issues. Salesforce offers a comprehensive platform that can transform your complaint management process. Let’s explore how its key features align perfectly with the needs of a strategic approach. Streamlining Complaint Intake Salesforce simplifies and customizes the process of collecting customer complaints, aligning with your specific policies and regulatory needs. Its dynamic intake process ensures a smooth and compliant experience for your customers and your team. Efficient Complaint Lifecycle Management Salesforce streamlines the entire complaint management process, ensuring seamless routing to the right teams and individuals for swift resolution. Automated assignments, milestone tracking, and clear follow-up expectations (including Service Level Agreements) guarantee accountability and efficiency at every stage. Automated escalations expedite resolutions when needed, ensuring regulatory compliance and maximizing customer satisfaction. Securing Your Complaint Data Salesforce prioritizes data security with Shield and Financial Services Cloud’s Compliance Data Sharing Model to ensure the confidentiality of sensitive complaint information through robust access controls and permissions. This guarantees that only authorized personnel can view and interact with sensitive data, maintaining the highest levels of privacy and compliance. Centralizing and Unifying Your Data Beyond security, Salesforce eliminates information silos by centralizing complaint data from across your organization. This creates a single source of truth, providing a comprehensive and unified view of customer feedback. This holistic perspective enables deeper analysis, informed decision-making, and a more proactive and practical approach to complaint management. Harnessing Complaint Data for Continuous Improvement Financial Services Cloud’s Case Management and Data Processing Engines can give you a complete view of customer complaints and their lifecycle. By harnessing this case data within CRM Analytics, you can enhance the customer 360, proactively monitor trends, prioritize areas for improvement, and enhance the customer experience while effectively mitigating risk. The Future of Complaint Management: Salesforce as a Strategic Advantage In an increasingly competitive and regulated landscape, banks must be equipped to address customer complaints efficiently and leverage them for continuous improvement. By combining Salesforce’s power with a strategic, customer-centric approach, banks can turn complaints into a catalyst for growth, ensuring a more resilient and customer-focused future. At Tectonic, we’ve watched firsthand how a well-designed complaint management system can transform customer interactions from points of friction into opportunities for improvement. Our experience in the financial services sector has taught us that technology is only part of the equation. A comprehensive approach, encompassing data-driven insights, staff training, and ongoing process optimization, is essential for maximizing the benefits of any system. Chat with our financial services experts to learn how Salesforce can transform your complaint management process to deliver exceptional service and strengthen trusted customer relationships. 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

August 22, 2024in Salesforce Financial Cloud, Salesforce Financial Services Solutions, Salesforce Optimization

Autonomous AI Sans Human

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Salesforce Einstein SDR and Sales Coach Agents

Key Insights on Navigating AI Compliance

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Pulse for Salesforce

Repayment Schedules With Salesforce

Banking Complaints to Profits

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