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New Technology Risks
27Sep

New Technology Risks

Organizations have always needed to manage the risks that come with adopting new technologies, and implementing artificial intelligence (AI) is no different. Many of the risks associated with AI are similar to those encountered with any new technology: poor alignment with business goals, insufficient skills to support the initiatives, and a lack of organizational buy-in. To address these challenges, executives should rely on best practices that have guided the successful adoption of other technologies, according to management consultants and AI experts. When it comes to AI, this includes: However, AI presents unique risks that executives must recognize and address proactively. Below are 15 areas of risk that organizations may encounter as they implement and use AI technologies: Managing AI Risks While the risks associated with AI cannot be entirely eliminated, they can be managed. Organizations must first recognize and understand these risks and then implement policies to mitigate them. This includes ensuring high-quality data for AI training, testing for biases, and continuous monitoring of AI systems to catch unintended consequences. Ethical frameworks are also crucial to ensure AI systems produce fair, transparent, and unbiased results. Involving the board and C-suite in AI governance is essential, as managing AI risk is not just an IT issue but a broader organizational challenge. 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

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September 27, 2024in Generative AI
Exploring Emerging LLM
27Sep

Exploring Emerging LLM

Exploring Emerging LLM Agent Types and Architectures The Evolution Beyond ReAct AgentsThe shortcomings of first-generation ReAct agents have paved the way for a new era of LLM agents, bringing innovative architectures and possibilities. In 2024, agents have taken center stage in the AI landscape. Companies globally are developing chatbot agents, tools like MultiOn are bridging agents to external websites, and frameworks like LangGraph and LlamaIndex Workflows are helping developers build more structured, capable agents. However, despite their rising popularity within the AI community, agents are yet to see widespread adoption among consumers or enterprises. This leaves businesses wondering: How do we navigate these emerging frameworks and architectures? Which tools should we leverage for our next application? Having recently developed a sophisticated agent as a product copilot, we share key insights to guide you through the evolving agent ecosystem. What Are LLM-Based Agents? At their core, LLM-based agents are software systems designed to execute complex tasks by chaining together multiple processing steps, including LLM calls. These agents: The Rise and Fall of ReAct Agents ReAct (reason, act) agents marked the first wave of LLM-powered tools. Promising broad functionality through abstraction, they fell short due to their limited utility and overgeneralized design. These challenges spurred the emergence of second-generation agents, emphasizing structure and specificity. The Second Generation: Structured, Scalable Agents Modern agents are defined by smaller solution spaces, offering narrower but more reliable capabilities. Instead of open-ended design, these agents map out defined paths for actions, improving precision and performance. Key characteristics of second-gen agents include: Common Agent Architectures Agent Development Frameworks Several frameworks are now available to simplify and streamline agent development: While frameworks can impose best practices and tooling, they may introduce limitations for highly complex applications. Many developers still prefer code-driven solutions for greater control. Should You Build an Agent? Before investing in agent development, consider these criteria: If you answered “yes,” an agent may be a suitable choice. Challenges and Solutions in Agent Development Common Issues: Strategies to Address Challenges: Conclusion The generative AI landscape is brimming with new frameworks and fervent innovation. Before diving into development, evaluate your application needs and consider whether agent frameworks align with your objectives. By thoughtfully assessing the tools and architectures available, you can create agents that deliver measurable value while avoiding unnecessary complexity. 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

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September 27, 2024in Data, Enterprise, Generative AI
Salesforce Einstein Copilot Security
27Sep

Salesforce Einstein Copilot Security

Salesforce Einstein Copilot Security: How It Works and Key Risks to Mitigate for a Safe Rollout With the official rollout of Salesforce Einstein Copilot, this conversational AI assistant is set to transform how sales, marketing, and customer service teams interact with both customers and internal documentation. Einstein Copilot understands natural language queries, streamlining daily tasks such as answering questions, generating insights, and performing actions across Salesforce to boost productivity. Salesforce Einstein Copilot Security However, alongside the productivity gains, it’s essential to address potential risks and ensure a secure implementation. This Tectonic insight covers: Einstein Copilot Use Cases Einstein Copilot enables users to: All of these actions can be performed with simple, natural language prompts, improving efficiency and outcomes. How Einstein Copilot Works Here’s a simplified breakdown of how Einstein Copilot processes prompts: The Einstein Trust Layer Salesforce has built the Einstein Trust Layer to ensure customer data is secure. Customer data processed by Einstein Copilot is encrypted, and no data is retained on the backend. Sensitive data, such as PII (Personally Identifiable Information), PCI (Payment Card Information), and PHI (Protected Health Information), is masked to ensure privacy. Additionally, the Trust Layer reduces biased, toxic, and unethical outputs by leveraging toxic language detection. Importantly, Salesforce guarantees that customer data will not be used to train the AI models behind Einstein Copilot or be shared with third parties. The Shared Responsibility Model Salesforce’s security approach is based on a shared responsibility model: This collaborative model ensures a higher level of security and trust between Salesforce and its customers. Best Practices for Securing Einstein Copilot Rollout Prepare Your Salesforce Org for Einstein Copilot To ensure a smooth rollout, it’s critical to assess your Salesforce security posture and ready your data. Tools like Salesforce Shield can help organizations by: By following these steps, you can utilize the power of Einstein Copilot while ensuring the security and integrity of your data. 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

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September 27, 2024in Data, Enterprise, Salesforce, Salesforce Data Cloud, Salesforce Einstein
Mapping Data Salesforce to Canva
26Sep

Mapping Data Salesforce to Canva

Mapping Data Fields in Salesforce for Canva Integration Salesforce administrators can map data fields from a brand template to Salesforce objects, enabling data from Salesforce to automatically populate placeholders in Canva designs. This feature is available exclusively for Canva Enterprise users and integrates with Salesforce Professional, Enterprise, or Unlimited editions. Mapping Data Salesforce to Canva. Steps for Mapping Data Fields in Salesforce: Pre-requisites: The following are the steps to set up field mapping using the Canva for Salesforce app. Step 1: Sync Brand Templates Before mapping fields, you need to sync brand templates from Canva to Salesforce. Here’s how: Step 2: Create a Template Mapping Template mapping connects data fields from a Salesforce object to placeholders in a Canva brand template, allowing Salesforce data to autofill the design. You need to create a separate template mapping for each Salesforce object. Unmapped Fields: You don’t have to map every field. If a field is unmapped, the placeholder in the Canva template will remain unchanged in the final design. Additional Information: Connecting Data Source Apps to Canva for Autofill You can connect data sources like Salesforce to Canva to autofill elements in your designs. Here’s a brief overview of how to connect and use Salesforce data: Creating Brand Templates for Salesforce To use Canva for Salesforce to generate sales collateral, brand designers must first create and publish a brand template. These templates include data fields that act as placeholders for Salesforce data. Mapping Data Salesforce to Canva With this setup, Salesforce admins can easily map data fields and auto-generate designs based on Salesforce data. 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

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September 26, 2024in Data, Enterprise, Salesforce
Open AI Update
23Sep

Open AI Update

OpenAI has established itself as a leading force in the generative AI space, with its ChatGPT being one of the most widely recognized AI tools. Powered by the GPT series of large language models (LLMs), as of September 2024, ChatGPT primarily uses GPT-4o and GPT-3.5. This insight provides an Open AI Update. In August and September 2024, rumors circulated about a new model from OpenAI, codenamed “Strawberry.” Initially, it was unclear if this model would be a successor to GPT-4o or something entirely different. On September 12, 2024, the mystery was resolved with the official launch of OpenAI’s o1 models, including o1-preview and o1-mini. What is OpenAI o1? OpenAI o1 is a new family of LLMs optimized for advanced reasoning tasks. Unlike earlier models, o1 is designed to improve problem-solving by reasoning through queries rather than just generating quick responses. This deeper processing aims to produce more accurate answers to complex questions, particularly in fields like STEM (science, technology, engineering, and mathematics). The o1 models, currently available in preview form, are intended to provide a new type of LLM experience beyond what GPT-4o offers. Like all OpenAI LLMs, the o1 series is built on transformer architecture and can be used for tasks such as content summarization, new content generation, question answering, and writing code. Key Features of OpenAI o1 The standout feature of the o1 models is their ability to engage in multistep reasoning. By adopting a “chain-of-thought” approach, o1 models break down complex problems and reason through them iteratively. This makes them particularly adept at handling intricate queries that require a more thoughtful response. The initial September 2024 launch included two models: Use Cases for OpenAI o1 The o1 models can perform many of the same functions as GPT-4o, such as answering questions, summarizing content, and generating text. However, they are particularly suited for tasks that benefit from enhanced reasoning, including: Availability and Access The o1-preview and o1-mini models are available to users of ChatGPT Plus and Team as of September 12, 2024. OpenAI plans to extend access to ChatGPT Enterprise and Education users starting September 19, 2024. While free ChatGPT users do not have access to these models at launch, OpenAI intends to introduce o1-mini to free users in the future. Developers can also access the models through OpenAI’s API, and third-party platforms such as Microsoft Azure AI Studio and GitHub Models offer integration. Limitations of OpenAI o1 As preview models, o1 comes with certain limitations: Enhancing Safety with OpenAI o1 To ensure safety, OpenAI released a System Card that outlines how the o1 models were evaluated for risks like cybersecurity threats, persuasion, and model autonomy. The o1 models improve safety through: GPT-4o vs. OpenAI o1 Here’s a quick comparison between GPT-4o and OpenAI’s new o1 models: Feature GPT-4o o1 Models Release Date May 13, 2024 Sept. 12, 2024 Model Variants Single model Two variants: o1-preview and o1-mini Reasoning Capabilities Good Enhanced, especially for STEM fields Mathematics Olympiad Score 13% 83% Context Window 128K tokens 128K tokens Speed Faster Slower due to in-depth reasoning Cost (per million tokens) Input: $5; Output: $15 o1-preview: $15 input, $60 output; o1-mini: $3 input, $12 output Safety and Alignment Standard Enhanced safety, better jailbreak resistance OpenAI’s o1 models bring a new level of reasoning and accuracy, making them a promising advancement in generative AI. 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 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

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September 23, 2024in AI Tools, Data, Generative AI, Technology
chatGPT open ai 01
21Sep

ChatGPT Open AI o1

OpenAI has firmly established itself as a leader in the generative AI space, with its ChatGPT being one of the most well-known applications of AI today. Powered by the GPT family of large language models (LLMs), ChatGPT’s primary models, as of September 2024, are GPT-4o and GPT-3.5. In August and September 2024, rumors surfaced about a new model from OpenAI, codenamed “Strawberry.” Speculation grew as to whether this was a successor to GPT-4o or something else entirely. The mystery was resolved on September 12, 2024, when OpenAI launched its new o1 models, including o1-preview and o1-mini. What Is OpenAI o1? The OpenAI o1 family is a series of large language models optimized for enhanced reasoning capabilities. Unlike GPT-4o, the o1 models are designed to offer a different type of user experience, focusing more on multistep reasoning and complex problem-solving. As with all OpenAI models, o1 is a transformer-based architecture that excels in tasks such as content summarization, content generation, coding, and answering questions. What sets o1 apart is its improved reasoning ability. Instead of prioritizing speed, the o1 models spend more time “thinking” about the best approach to solve a problem, making them better suited for complex queries. The o1 models use chain-of-thought prompting, reasoning step by step through a problem, and employ reinforcement learning techniques to enhance performance. Initial Launch On September 12, 2024, OpenAI introduced two versions of the o1 models: Key Capabilities of OpenAI o1 OpenAI o1 can handle a variety of tasks, but it is particularly well-suited for certain use cases due to its advanced reasoning functionality: How to Use OpenAI o1 There are several ways to access the o1 models: Limitations of OpenAI o1 As an early iteration, the o1 models have several limitations: How OpenAI o1 Enhances Safety OpenAI released a System Card alongside the o1 models, detailing the safety and risk assessments conducted during their development. This includes evaluations in areas like cybersecurity, persuasion, and model autonomy. The o1 models incorporate several key safety features: GPT-4o vs. OpenAI o1: A Comparison Here’s a side-by-side comparison of GPT-4o and OpenAI o1: Feature GPT-4o o1 Models Release Date May 13, 2024 Sept. 12, 2024 Model Variants Single Model Two: o1-preview and o1-mini Reasoning Capabilities Good Enhanced, especially in STEM fields Performance Benchmarks 13% on Math Olympiad 83% on Math Olympiad, PhD-level accuracy in STEM Multimodal Capabilities Text, images, audio, video Primarily text, with developing image capabilities Context Window 128K tokens 128K tokens Speed Fast Slower due to more reasoning processes Cost (per million tokens) Input: $5; Output: $15 o1-preview: $15 input, $60 output; o1-mini: $3 input, $12 output Availability Widely available Limited to specific users Features Includes web browsing, file uploads Lacks some features from GPT-4o, like web browsing Safety and Alignment Focus on safety Improved safety, better resistance to jailbreaking ChatGPT Open AI o1 OpenAI o1 marks a significant advancement in reasoning capabilities, setting a new standard for complex problem-solving with LLMs. With enhanced safety features and the ability to tackle intricate tasks, o1 models offer a distinct upgrade over their predecessors. 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

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September 21, 2024in Data, Enterprise, Generative AI
Agentforce and Thinking AI
21Sep

Agentforce and Thinking AI

Agentforce is how humans with AI drive customer success together, equips organizations with autonomous agents that boost scale, efficiency, and satisfaction across service, sales, marketing, commerce, and more New Agentforce Atlas Reasoning Engine autonomously analyzes data, makes decisions, and completes tasks, providing reliable and accurate results With Agentforce, any organization can build, customize, and deploy their own agents quickly and easily, with low-code tools New Agentforce Partner Network allows customers to deploy pre-built agents and use agent actions from partners like Amazon Web Services, Google, IBM, Workday, and more Customers like OpenTable, Saks, and Wiley are turning to Agentforce because it is integrated with their apps, works across customer channels, augments their employees, and scales capacity for business needs SAN FRANCISCO — September 12, 2024 – Salesforce (NYSE: CRM), the world’s #1 AI CRM, today unveiled Agentforce, a groundbreaking suite of autonomous AI agents that augment employees and handle tasks in service, sales, marketing, and commerce, driving unprecedented efficiency and customer satisfaction. Agentforce enables companies to scale their workforces on demand with a few clicks. Agentforce’s limitless digital workforce of AI agents can analyze data, make decisions, and take action on tasks like answering customer service inquiries, qualifying sales leads, and optimizing marketing campaigns. With Agentforce, any organization can easily build, customize, and deploy their own agents for any use case across any industry. The future of AI is agents, and it’s here. Our vision is bold: to empower one billion agents with Agentforce by the end of 2025. This is what AI is meant to be.” MARC BENIOFF, CHAIR, CEO & CO-FOUNDER, SALESFORCE “Agentforce represents the Third Wave of AI—advancing beyond copilots to a new era of highly accurate, low-hallucination intelligent agents that actively drive customer success. Unlike other platforms, Agentforce is a revolutionary and trusted solution that seamlessly integrates AI across every workflow, embedding itself deeply into the heart of the customer journey. This means anticipating needs, strengthening relationships, driving growth, and taking proactive action at every touchpoint,” said Marc Benioff, Chair and CEO, Salesforce. “While others require you to DIY your AI, Agentforce offers a fully tailored, enterprise-ready platform designed for immediate impact and scalability. With advanced security features, compliance with industry standards, and unmatched flexibility. Our vision is bold: to empower one billion agents with Agentforce by the end of 2025. This is what AI is meant to be.” In contrast to now-outdated copilots and chatbots that rely on human requests and struggle with complex or multi-step tasks, Agentforce offers a new level of sophistication by operating autonomously, retrieving the right data on demand, building action plans for any task, and executing these plans without requiring human intervention. Like a self-driving car, Agentforce uses real-time data to adapt to changing conditions and operates independently within an organizations’ customized guardrails, ensuring every customer interaction is informed, relevant, and valuable. And when desired, Agentforce seamlessly hands off to human employees with a summary of the interaction, an overview of the customer’s details, and recommendations for what to do next. Industry leaders like OpenTable, Saks, and Wiley are already experiencing the transformative power of Agentforce. For example, Agentforce is helping organizations like Wiley provide customers with dynamic, conversational self-service. Agentforce is configured to answer questions using Wiley’s knowledge base already built into Salesforce so it can automatically resolve account access. It also triages registration and payment issues, directing customers to the appropriate resources. With Agentforce handling routine inquiries, Wiley has seen an over 40% increase in case resolution, outperforming their old chatbot and giving their human agents more time to focus on complex cases. Why it Matters An estimated 41% of employee time is spent on repetitive, low-impact work, and 65% of desk workers believe generative AI will allow them to be more strategic, according to the Salesforce Trends in AI Report. Every company has more jobs to be done than the resources available to do them. As a result, many jobs go unaddressed or uncompleted. Agentforce provides relief to overstretched teams with its ability to scale capacity on demand so humans can focus on higher-touch, higher-value, and more strategic outcomes. The future of work is a hybrid workforce composed of humans with agents, enabling companies to compete in an ever-changing world. Supporting Customer Quotes “Piloting Agentforce has made a noticeable difference during one of our busiest periods — back-to-school season. It’s been exciting to go live with our first agent thanks to the no-code builder, and we’ve seen a more than 40% increase in case resolution, outperforming our old bot. Agentforce helps to manage routine responsibilities and free up our service teams for more complex cases.” – Kevin Quigley, Senior Manager, Continuous Improvement, Wiley “Every interaction that restaurants and diners have with our support team must be accurate, fast, and reflective of the hospitality that restaurants show their guests. Agentforce has incredible potential to help us deliver that high touch attentiveness and support while significantly freeing up our team to address more complex needs.” – George Pokorny, SVP Customer Success, OpenTable “As we advance our personalization strategy, we believe Agentforce and its AI-powered capabilities have the potential to make a real impact on our approach to customer engagement, raising the bar in luxury retail. Agentforce will improve our effectiveness across customer touchpoints, empowering our employees and augmenting their ability to deliver the elevated and more individualized shopping experiences for which Saks is known.” – Mike Hite, Chief Technology Officer, Saks Global 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

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September 21, 2024in Data, Enterprise, Generative AI, Google, Salesforce, Technology
Slack Personality Quiz
15Sep

Slack Personality Quiz

Slack has introduced a personality quiz, inspired by BuzzFeed-style formats, to understand how modern office workers interact with AI tools. Based on a survey of 5,000 full-time desk workers across six countries, the quiz categorizes users into five distinct groups based on their engagement with generative AI. From “The Rebel” to “The Maximalist,” these personas are designed to help business leaders organize teams more effectively by understanding how employees perceive and utilize AI. According to Christina Janzer, Slack’s SVP of research and analytics, the personas reflect the diverse ways workers are engaging with AI, emphasizing that there isn’t a one-size-fits-all approach. Interestingly, Slack’s recent survey revealed that while executive leadership is pushing AI adoption, two-thirds of workers have yet to use AI tools in their daily tasks. Among AI users, there’s a split between “Maximalists” (30% of respondents), who actively use AI and advocate for its benefits, and “The Underground” (20%), who also use AI regularly but more discreetly. For non-users, the personas include “Rebels” (19%), who avoid AI and remain skeptical, “Superfans” (16%), who admire AI advancements but haven’t adopted it themselves, and “Observers” (16%), who cautiously watch from the sidelines. Janzer noted demographic differences, such as a higher proportion of women and older individuals in the Rebel category, while Maximalists and Underground users tend to be younger men. Janzer emphasized that while these personas highlight current attitudes, they aren’t permanent. Businesses should take these insights into account when rolling out AI projects, ensuring they address the varied sentiments across their workforce. 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

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September 15, 2024in Generative AI, Salesforce Slack
Impact of EHR Adoption
14Sep

Impact of EHR Adoption

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

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September 14, 2024in Data, Generative AI, Google, Salesforce Health and Life Sciences Solutions, Salesforce Optimization, Technology
Healthcare Cloud Computing
14Sep

Healthcare Cloud Computing

Cloud Computing in Healthcare: Ensuring HIPAA Compliance Amid Growing Adoption As healthcare organizations increasingly turn to cloud computing for scalable and accessible IT services, ensuring HIPAA compliance remains a top priority. The global healthcare cloud computing market is projected to grow from $53.8 billion in 2024 to $120.6 billion by 2029, according to a MarketsandMarkets report. A 2023 Forrester report also highlighted that healthcare organizations are spending an average of .5 million annually on cloud services, with public cloud adoption on the rise. While cloud computing offers benefits like enhanced data mobility and cost efficiency, maintaining a HIPAA-compliant relationship with cloud service providers (CSPs) requires careful attention to regulations, establishing business associate agreements (BAAs), and proactively addressing cloud security risks. Understanding HIPAA’s Role in Cloud Computing The National Institute of Standards and Technology (NIST) defines cloud computing as a model that provides on-demand access to shared computing resources. Based on this framework, the U.S. Department of Health and Human Services (HHS) Office for Civil Rights (OCR) has issued guidance on how HIPAA’s Security, Privacy, and Breach Notification Rules apply to cloud computing. Under the HIPAA Security Rule, CSPs classified as business associates must adhere to specific standards for safeguarding protected health information (PHI). This includes mitigating the risks of unauthorized access to administrative tools and implementing internal controls to restrict access to critical operations like storage and memory. HIPAA’s Privacy Rule further restricts the use or disclosure of PHI by CSPs, even in cases where they offer “no-view services.” CSPs cannot block a covered entity’s access to PHI, even in the event of a payment dispute. Additionally, the Breach Notification Rule requires business associates, including CSPs, to promptly report any breach of unsecured PHI. Healthcare organizations engaging with CSPs should consult legal counsel and follow standard procedures for establishing HIPAA-compliant vendor relationships. The Importance of Business Associate Agreements (BAAs) A BAA is essential for ensuring that a CSP is contractually bound to comply with HIPAA. OCR emphasizes that when a covered entity engages a CSP to create, receive, or transmit electronic PHI (ePHI), the CSP becomes a business associate under HIPAA. Even if the CSP cannot access encrypted PHI, it is still classified as a business associate due to its involvement in storing and processing PHI. In 2016, the absence of a BAA led to a $2.7 million settlement between Oregon Health & Science University and OCR after the university stored the PHI of over 3,000 individuals on a cloud server without the required agreement. BAAs play a crucial role in defining the permitted uses of PHI and ensure that both the healthcare organization and CSP understand their responsibilities under HIPAA. They also outline protocols for breach notifications and security measures, ensuring both parties are aligned on handling potential security incidents. Key Cloud Security Considerations Despite the protections of a BAA, there are inherent risks in partnering with any new vendor. Staying informed on cloud security threats is vital for mitigating potential risks proactively. In a 2024 report, the Cloud Security Alliance (CSA) identified misconfiguration, inadequate change control, and identity management as the top threats to cloud computing. The report also pointed to the rising sophistication of cyberattacks, supply chain risks, and the proliferation of ransomware-as-a-service as growing concerns. By understanding these risks and establishing clear security policies with CSPs, healthcare organizations can better safeguard their data. Prioritizing security, establishing robust BAAs, and ensuring HIPAA compliance will allow healthcare organizations to fully leverage the advantages of cloud computing while maintaining the privacy and security of patient information. 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

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September 14, 2024in Salesforce
Acceptable AI Use Policies
14Sep

Acceptable AI Use Policies

With great power comes—when it comes to generative AI—significant security and compliance risks. Discover how AI acceptable use policies can safeguard your organization while leveraging this transformative technology. AI has become integral across various industries, driving digital operations and organizational infrastructure. However, its widespread adoption brings substantial risks, particularly concerning cybersecurity. A crucial aspect of managing these risks and ensuring the security of sensitive data is implementing an AI acceptable use policy. This policy defines how an organization handles AI risks and sets guidelines for AI system usage. Why an AI Acceptable Use Policy Matters Generative AI systems and large language models are potent tools capable of processing and analyzing data at unprecedented speeds. Yet, this power comes with risks. The same features that enhance AI efficiency can be misused for malicious purposes, such as generating phishing content, creating malware, producing deepfakes, or automating cyberattacks. An AI acceptable use policy is essential for several reasons: Crafting an Effective AI Acceptable Use Policy An AI acceptable use policy should be tailored to your organization’s needs and context. Here’s a general guide for creating one: Essential Elements of an AI Acceptable Use Policy A robust AI acceptable use policy should include: An AI acceptable use policy is not just a document but a dynamic framework guiding safe and responsible AI use within an organization. By developing and enforcing this policy, organizations can harness AI’s power while mitigating its risks to cybersecurity and data integrity, balancing innovation with risk management as AI continues to evolve and integrate into our digital landscapes. 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

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September 14, 2024in Generative AI
E-Commerce Platform Improvement
13Sep

E-Commerce Platform Improvement

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

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September 13, 2024in Generative AI
Challenges for Rural Healthcare Providers
12Sep

Challenges for Rural Healthcare Providers

Rural healthcare providers have long grappled with challenges due to their geographic isolation and limited financial resources. The advent of digital health transformation, however, has introduced a new set of IT-related obstacles for these providers. EHR Adoption and New IT Challenges While federal legislation has successfully promoted Electronic Health Record (EHR) adoption across both rural and urban healthcare organizations, implementing an EHR system is only one component of a comprehensive health IT strategy. Rural healthcare facilities encounter numerous IT barriers, including inadequate infrastructure, interoperability issues, constrained resources, workforce shortages, and data security concerns. Limited Broadband Access Broadband connectivity is essential for leveraging health IT effectively. However, there is a significant disparity in broadband access between rural and urban areas. According to a Federal Communications Commission (FCC) report, approximately 96% of the U.S. population had access to broadband at the FCC’s minimum speed benchmark in 2019, compared to just 73.6% of rural Americans. The lack of broadband infrastructure hampers rural organizations’ ability to utilize IT features that enhance care delivery, such as electronic health information exchange (HIE) and virtual care. Rural facilities, in particular, rely heavily on HIE and telehealth to bridge gaps in their services. For instance, HIE facilitates data sharing between smaller ambulatory centers and larger academic medical centers, while telehealth allows rural clinicians to consult with specialists in urban centers. Additionally, telehealth can help patients in rural areas avoid long travel distances for care. However, without adequate broadband access, these services remain impractical. Despite persistent disparities, the rural-urban broadband gap has narrowed in recent years. Data from the FCC indicates that since 2016, the number of people in rural areas without access to 25/3 Mbps service has decreased by more than 46%. Various programs, including the FCC’s Rural Health Care Program and USDA funding initiatives, aim to expand broadband access in rural regions. Interoperability Challenges While HIE adoption is rising nationally, rural healthcare organizations lag behind their urban counterparts in terms of interoperability capabilities, as noted in a 2023 GAO report. Data from a 2021 American Hospital Association survey revealed that rural hospitals are less likely to engage in national or regional HIE networks compared to medium and large hospitals. Rural providers often lack the economic and technological resources to participate in electronic HIE networks, leading them to rely on manual data exchange methods such as fax or mail. Additionally, rural providers are less likely to join EHR vendor networks for data exchange, partly due to the fact that they often use different systems from those in other local settings, complicating health data exchange. Federal initiatives like TEFCA aim to improve interoperability through a network of networks approach, allowing organizations to connect to multiple HIEs through a single connection. However, TEFCA’s voluntary participation model and persistent barriers such as IT staffing shortages and broadband gaps still pose challenges for rural providers. Financial Constraints Rural hospitals often operate with slim profit margins due to lower patient volumes and higher rates of uninsured or underinsured patients. The financial strain is exacerbated by declining Medicare and Medicaid reimbursements. According to KFF, the median operating margin for rural hospitals was 1.5% in 2019, compared to 5.2% for other hospitals. With limited budgets, rural healthcare organizations struggle to invest in advanced health IT systems and the necessary training and maintenance. Many small rural hospitals are turning to cloud-based EHR platforms as a cost-effective solution. Cloud-based EHRs reduce the need for substantial upfront hardware investments and offer monthly subscription fees, some as low as $100 per month. Workforce Challenges The healthcare sector is facing widespread staff shortages, including a lack of skilled health IT professionals. Rural areas are disproportionately affected by these shortages. An insufficient number of IT specialists can impede the adoption and effective use of health IT in these regions. To address workforce gaps, the ONC suggests strategies such as cross-training multiple staff members in health IT functions and offering additional training opportunities. Some networks, like OCHIN, have secured grants to develop workforce programs, but limited broadband access can hinder participation in virtual training programs, highlighting the need for expanded broadband infrastructure. Data Security Concerns Healthcare data breaches have surged, with a 256% increase in large breaches reported to the Office for Civil Rights (OCR) over the past five years. Rural healthcare organizations, often operating with constrained budgets, may lack the resources and staff to implement robust data security measures, leaving them vulnerable to cyber threats. A cyberattack on a rural healthcare organization can disrupt patient care, as patients may need to travel significant distances to reach alternative facilities. To address cybersecurity challenges, recent legislative efforts like the Rural Hospital Cybersecurity Enhancement Act aim to develop comprehensive strategies for rural hospital cybersecurity and provide educational resources for staff training. In the interim, rural healthcare organizations can use free resources such as the Health Industry Cybersecurity Practices (HICP) publication to guide their cybersecurity strategies, including recommendations for managing vulnerabilities and protecting email systems. Does your practice need help meeting these challenges? Contact Tectonic today. 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

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September 12, 2024in Salesforce Health and Life Sciences Solutions, Salesforce Managed Services, Salesforce Optimization
Salesforce Healthcare and AI
11Sep

Salesforce Healthcare and AI

The Healthcare Industry’s Digital Transformation: An Opportunity Unveiled – Salesforce Healthcare and AI Historically, the healthcare sector has lagged behind in technology adoption, particularly software. It consistently invests less in IT and software compared to other industries, relying heavily on manual processes and outdated tools like faxes and phone calls. Unlike other sectors where platforms like Salesforce, Slack, JIRA, and Notion dominate, healthcare has yet to see similar technological integration. Salesforce Healthcare and AI Future While this low adoption of software has previously been seen as a drawback, it now presents a significant opportunity. Unlike industries burdened by extensive investments in legacy systems, healthcare is not encumbered by sunk costs. This freedom allows it to embrace cutting-edge AI innovations without the hesitation of overhauling existing, expensive software infrastructures. Addressing the Staffing Crisis The healthcare industry is grappling with a severe staffing crisis, with a shortfall of over 100,000 doctors and nurses projected over the next five years. The increasing complexity of medical care, driven by advancements in diagnostics, continuous monitoring, and new treatments, contributes to an overwhelming amount of information for clinicians. To manage this, healthcare requires new tools capable of processing complex data in real-time to support critical decisions for an aging population with more complex health needs. The most valuable asset in healthcare is clinical judgment, which is currently exclusive to human practitioners. A major challenge is to extend this clinical judgment beyond the existing workforce and physical locations, making it accessible to all who need it. Additionally, ensuring that every clinician performs at the highest level is crucial. The Role of Administrative and Clinical AI Administrative AI is essential for reducing the overhead of healthcare delivery, allowing for better resource management and efficiency. Clinical AI products, though challenging to develop due to their high-stakes nature, are uniquely positioned to address these needs. They must integrate seamlessly into existing environments, adding a layer of sophistication to healthcare processes. Regulatory Advantages for Clinical AI One of healthcare’s advantages in adopting AI is its well-established regulatory framework. The FDA has approved numerous clinical AI products and is developing processes to keep pace with advancements in machine learning and generative AI. This rigorous approval process ensures that only the most reliable and clinically sound products make it to market, creating a higher barrier to entry but also a stronger competitive advantage for those that succeed. The Scale of Opportunity The healthcare industry is a massive $4 trillion+ market, predominantly driven by human labor rather than technology. Historically, enterprise software companies have struggled to penetrate this sector, as IT budgets represent just 3.5% of revenue—less than half of that in financial services. However, with AI tools advancing rapidly, they are increasingly seen as “AI staff” rather than mere software. This shift opens up opportunities not just in software but in transforming service delivery, potentially disrupting a market valued in trillions rather than billions. The scale of this opportunity far exceeds past software ventures, as reflected in the significant capital and valuations flowing into AI-driven healthcare companies. Whether you’re launching a new clinic, developing infrastructure for the healthcare system, or creating innovative payment or insurance models, now is an unprecedented time to enter the healthcare space. The transformative power of AI is poised to redefine how healthcare companies are built, scaled, and brought to market. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 11, 2024in Generative AI, Life Sciences Cloud, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud, Salesforce Managed Services, Salesforce Optimization
Salesforce and Tenyx
10Sep

Salesforce and Tenyx

Salesforce has announced its acquisition of AI voice agent firm Tenyx, with the deal expected to close in the third quarter. While the financial terms have not been disclosed, Tenyx’s co-founders, CEO Itamar Arel and CTO Adam Earle, along with their team, will join Salesforce as part of the acquisition. This move comes after Salesforce, under pressure from activist investors, previously shifted away from acquisitions and increased its share buybacks following the dissolution of its mergers and acquisitions committee. However, the company is now pursuing strategic acquisitions to boost revenue growth. Conversational AI forthe Enterprise Tenyx Voice is an Interactive Virtual Agent (IVA) built from the ground up leveraging today’s modern AI stack. Built by a team with a proven track record in voice AI, and leveraging a unique core AI and voice platform, Tenyx promises to redefine customer interactions for the enterprise. Tenyx Voice is an Interactive Virtual Agent (IVA) built from the ground up leveraging today’s modern AI stack. Built by a team with a proven track record in voice AI, and leveraging a unique core AI and voice platform, Tenyx promises to redefine customer interactions for the enterprise. Industries and Use Cases If 2023 was the year of large language models (LLMs), 2024 is shaping up to be the year of voice agents. When ChatGPT made waves globally, startups, tech firms, and entrepreneurs rushed to discover business use cases for the new technology. The ideal applications targeted tasks that are costly, time-consuming, and hard to scale. Voice agents and automated customer service systems quickly emerged as one of the most promising solutions. However, many companies deploying these systems aren’t fully considering their impact on customers. That’s why Tenyx is launching its inaugural Voice AI Consumer Report. We surveyed hundreds of Americans across different age groups, races, geographies, and genders to better understand their preferences and experiences with AI-powered voice agents. Here are the key findings: What this means: Frustrating Calls Hurt Your Brand Imagine calling customer service for a quick solution, only to be met by an automated voice agent that can’t understand your request or handle complex issues. It’s a common and frustrating experience. Our data shows that nearly 7 in 10 people express frustration or annoyance with today’s automated voice agents—sentiments that can severely damage customer loyalty and business outcomes. “Our report highlights a major disconnect between consumer expectations and the performance of current automated voice agents,” says Itamar Arel, CEO of Tenyx. “While these systems promise efficiency and cost savings, they often fall short when it comes to addressing consumers’ nuanced needs.” Incomplete AI Systems Drive Customer Churn Subpar AI systems are driving customers away. Two-thirds of respondents said they wouldn’t return to a company after a negative experience with its AI voice agent. In fact, 67% still prefer interacting with human agents over automated ones. Why? Current AI voice agents struggle with complex issues and fail to provide the empathy and problem-solving skills that human agents, or more advanced AI systems, offer. Selective Deployment and Industry-Specific Agents Matter Our data shows that consumers are more accepting of voice agents in certain industries than others. Sectors like healthcare, restaurants, and telecoms saw the highest satisfaction with AI voice agents, while airlines, banking, and hotels ranked the lowest. This highlights the importance of selective deployment and tailoring voice agents for specific industries to better meet customer needs. Looking Ahead: The Promise of Perfect Automation Despite the skepticism, there’s hope. Two-thirds of respondents indicated they’d embrace automated voice agents if these systems could match the performance of human agents. This is exactly what we’re working on at Tenyx—building scalable, reliable AI agents that serve businesses and customers globally. “As leaders in voice AI technology, Tenyx is dedicated to closing the gap between consumer expectations and technological capabilities,” Arel says. “Our mission is to equip businesses with AI solutions that not only streamline operations but also boost customer satisfaction.” 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

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September 10, 2024in Salesforce

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