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Large and Small Language Models
13Sep

Large and Small Language Models

Understanding Language Models in AI Language models are sophisticated AI systems designed to generate natural human language, a task that is far from simple. These models operate as probabilistic machine learning systems, predicting the likelihood of word sequences to emulate human-like intelligence. In the scientific realm, the focus of language models has been twofold: While today’s cutting-edge AI models in Natural Language Processing (NLP) are impressive, they have not yet fully passed the Turing Test—a benchmark where a machine’s communication is indistinguishable from that of a human. The Emergence of Language Models We are approaching this milestone with advancements in Large Language Models (LLMs) and the promising but less discussed Small Language Models (SLMs). Large Language Models compared to Small Language Models LLMs like ChatGPT have garnered significant attention due to their ability to handle complex interactions and provide insightful responses. These models distill vast amounts of internet data into concise and relevant information, offering an alternative to traditional search methods. Conversely, SLMs, such as Mistral 7B, while less flashy, are valuable for specific applications. They typically contain fewer parameters and focus on specialized domains, providing targeted expertise without the broad capabilities of LLMs. How LLMs Work Comparing LLMs and SLMs Choosing the Right Language Model The decision between LLMs and SLMs depends on your specific needs and available resources. LLMs are well-suited for broad applications like chatbots and customer support. In contrast, SLMs are ideal for specialized tasks in fields such as medicine, law, and finance, where domain-specific knowledge is crucial. Large and Small Language Models’ Roles Language models are powerful tools that, depending on their size and focus, can either provide broad capabilities or specialized expertise. Understanding their strengths and limitations helps in selecting the right model for your use case. 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 13, 2024in Salesforce
Agentforce to the Team
12Sep

Agentforce to the Team

Salesforce has introduced the Agentforce Atlas Reasoning Engine, a platform designed to perform tasks autonomously with minimal human intervention. Agentforce to the Team changes everything about AI. Businesses can feed the engine data, assign tasks, and step away, as the system is capable of completing work independently. This launch closely follows OpenAI’s recent advancements in artificial intelligence, highlighting the ongoing collaboration between Salesforce and Sam Altman’s firm. Agentforce to the Team-makes me hear “Honey, I’m home”, coming from the front door. The Agentforce Atlas Reasoning Engine is designed to analyze data, make decisions, and execute tasks with high reliability and accuracy, echoing the features of OpenAI’s latest AI model. Salesforce positions this as part of the “Third Wave of AI,” where intelligent agents go beyond assisting humans to actively driving business outcomes without frequent oversight. According to Salesforce CEO Marc Benioff, these agents are deeply integrated into customer workflows, anticipating needs and improving growth by taking proactive action at every touchpoint. Benioff emphasized the revolutionary nature of Agentforce, which he claims will surpass existing AI platforms by offering highly accurate, low-hallucination results. It integrates seamlessly across Salesforce’s ecosystem, benefiting users from industries such as financial services, healthcare, and government. Early adopters, such as Wiley, report a 40% increase in case resolution, with Agentforce handling routine customer service tasks more efficiently than previous chatbots. Disney also saw improved results, noting that Atlas delivered twice the accuracy of other AI tools they had benchmarked. However, the autonomous nature of these agents raises concerns about job displacement, particularly for workers involved in repetitive, low-impact tasks. While Salesforce advocates for reskilling workers to transition into higher-value roles, many organizations struggle to effectively implement such initiatives. The time required to upskill workers may not align with the rapid adoption of AI technologies like Agentforce. Agentforce aims to address common enterprise challenges by offering out-of-the-box solutions for sales, marketing, and customer service roles. The low-code platform allows businesses to customize their AI agents without extensive technical expertise, ensuring that they can scale capacity and improve efficiency. Salesforce plans to showcase Agentforce at its upcoming Dreamforce conference, aiming to onboard 1,000 customers to the platform. The launch signifies Salesforce’s strategic push to dominate the enterprise AI landscape, leveraging its vast data and platform to deliver more value to its customers. Despite its potential, Agentforce introduces new risks, especially in areas like data privacy and ethical AI deployment. Salesforce emphasizes its commitment to addressing these issues by incorporating ethical guardrails, such as toxicity filters. Industry analysts remain cautiously optimistic, noting that while the technology holds promise, the real test will come as more organizations adopt it and integrate it into their workflows. In summary, Salesforce’s Agentforce Atlas Reasoning Engine represents a significant leap in enterprise AI, moving beyond basic AI copilots to fully autonomous agents. While it offers substantial benefits in productivity and efficiency, its impact on the workforce and the challenges of widespread AI adoption will require ongoing attention. By Tectonic’s Shannan Hearne, Solutions Architect 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
AI-Driven Chatbots in Education
11Sep

AI-Driven Chatbots in Education

As AI-driven chatbots enter college courses, the potential to offer students 24/7 support is game-changing. However, there’s a critical caveat: when we customize chatbots by uploading documents, we don’t just add knowledge — we introduce biases. The documents we choose influence chatbot responses, subtly shaping how students interact with course material and, ultimately, how they think. So, how can we ensure that AI chatbots promote critical thinking rather than merely serving to reinforce our own viewpoints? How Course Chatbots Differ from Administrative Chatbots Chatbot teaching assistants have been around for some time in education, but low-cost access to large language models (LLMs) and accessible tools now make it easy for instructors to create customized course chatbots. Unlike chatbots used in administrative settings that rely on a defined “ground truth” (e.g., policy), educational chatbots often cover nuanced and debated topics. While instructors typically bring specific theories or perspectives to the table, a chatbot trained with tailored content can either reinforce a single view or introduce a range of academic perspectives. With tools like ChatGPT, Claude, Gemini, or Copilot, instructors can upload specific documents to fine-tune chatbot responses. This customization allows a chatbot to provide nuanced responses, often aligned with course-specific materials. But, unlike administrative chatbots that reference well-defined facts, course chatbots require ethical responsibility due to the subjective nature of academic content. Curating Content for Classroom Chatbots Having a 24/7 teaching assistant can be a powerful resource, and today’s tools make it easy to upload course documents and adapt LLMs to specific curricula. Options like OpenAI’s GPT Assistant, IBL’s AI Mentor, and Druid’s Conversational AI allow instructors to shape the knowledge base of course-specific chatbots. However, curating documents goes beyond technical ease — the content chosen affects not only what students learn but also how they think. The documents you select will significantly shape, though not dictate, chatbot responses. Combined with the LLM’s base model, chatbot instructions, and the conversation context, the curated content influences chatbot output — for better or worse — depending on your instructional goals. Curating for Critical Thinking vs. Reinforcing Bias A key educational principle is teaching students “how to think, not what to think.” However, some educators may, even inadvertently, lean toward dictating specific viewpoints when curating content. It’s critical to recognize the potential for biases that could influence students’ engagement with the material. Here are some common biases to be mindful of when curating chatbot content: While this list isn’t exhaustive, it highlights the complexities of curating content for educational chatbots. It’s important to recognize that adding data shifts — not erases — inherent biases in the LLM’s responses. Few academic disciplines offer a single, undisputed “truth.” AI-Driven Chatbots in Education. Tips for Ethical and Thoughtful Chatbot Curation Here are some practical tips to help you create an ethically balanced course chatbot: This approach helps prevent a chatbot from merely reflecting a single perspective, instead guiding students toward a broader understanding of the material. Ethical Obligations As educators, our ethical obligations extend to ensuring transparency about curated materials and explaining our selection choices. If some documents represent what you consider “ground truth” (e.g., on climate change), it’s still crucial to include alternative views and equip students to evaluate the chatbot’s outputs critically. Equity Customizing chatbots for educational use is powerful but requires deliberate consideration of potential biases. By curating diverse perspectives, being transparent in choices, and refining chatbot content, instructors can foster critical thinking and more meaningful student engagement. AI-Driven Chatbots in Education AI-powered chatbots are interactive tools that can help educational institutions streamline communication and improve the learning experience. They can be used for a variety of purposes, including: Some examples of AI chatbots in education include: While AI chatbots can be a strategic move for educational institutions, it’s important to balance innovation with the privacy and security of student 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 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 11, 2024in Artificial Intelligence, Data, Einstein 1 Platform, Generative AI, Salesforce Education Cloud, Salesforce Education Solutions
Oracle Fusion Cloud
11Sep

Oracle Fusion Cloud

Oracle has unveiled over 50 role-based AI agents in the Oracle Fusion Cloud Applications Suite as of Wednesday. This suite offers a range of applications designed to help enterprises manage various functions. The newly introduced AI agents aim to assist employees and managers by automating business processes. According to Oracle executives at the CloudWorld 2024 conference in Las Vegas, these agents are tailored to improve efficiency across different functions. In Oracle Fusion Cloud Human Capital Management, the AI agents support shift scheduling, assist with hiring, manage requests to fill or create new positions, and help employees understand their benefits. In Oracle Fusion Cloud Supply Chain, Manufacturing AI Agents provide contextual insights and recommendations for handling order requests and suggest maintenance and repair actions for various assets. The AI agents within Oracle Fusion Cloud Customer Experience assist with planning and research tasks, automate contract workflow and approval processes, and facilitate communication with sales representatives. Oracle has yet to announce the release date for these AI agents. The Next Stage of GenAIThe introduction of AI agents represents an evolution of generative AI, moving beyond chatbots to technology that performs tasks autonomously. “These AI agents are engineered to automate routine tasks and offer personalized insights and recommendations,” noted Sid Nag, Gartner Research analyst. This development underscores a shift in the generative AI market from ideation to practical implementation. “These are very pragmatic and practical ideas,” said Mark Beccue, an analyst at TechTarget’s Enterprise Strategy Group. “It’s a use case we’ve been anticipating, where AI helps complete tasks effectively.” Oracle’s AI Agents for its Fusion Cloud Applications Suite align with the vision for enterprise software vendors, Beccue added. ServiceNow AI AgentsOracle is not alone in embedding AI agents into business applications. On September 10, ServiceNow announced plans to integrate agentic workflows into its platform. The initial AI Agent applications from ServiceNow will focus on Customer Service Management and IT Service Management. These agents are designed to identify and resolve issues independently while still being overseen by human operators. ServiceNow’s AI Agents are expected to launch in November 2024 as part of a limited release. The company also introduced the Now Assist Skill Kit, enabling enterprises to develop custom generative AI skills tailored to specific business needs. Single Task vs. Multitask AgentsA key consideration with AI agents is whether they can handle single tasks or multitask across multiple applications. Mark Beccue suggests that the ability to perform tasks across various applications could lead to a new user interface where AI agents manage tasks seamlessly across different systems. “It’s a vision for the future of AI agents,” Beccue remarked. It remains to be seen how these AI agents will address industry-specific regulations and compliance requirements, particularly in highly regulated sectors such as finance. Additional AI FeaturesOracle has also introduced new AI capabilities in other applications. Oracle Cloud ERP now includes predictive cash forecasting, narrative reporting, and automated transaction records within Oracle Fusion Cloud Sustainability. In Oracle Cloud CX, new features include assisted authoring to help sales teams engage buyers with AI-generated content and advanced AI capabilities in Oracle CX Unity for detecting signals based on role, title, and topic engagement. 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
Salesforce AI Agents Explained
09Sep

Salesforce AI Agents Explained

Salesforce’s AI Agents: Revolutionizing Enterprise Sales and Service for the Future In the rapidly evolving landscape of artificial intelligence (AI), Salesforce continues to lead the charge, transforming enterprise operations with cutting-edge AI agents. With the introduction of Agentforce, Salesforce is not just enhancing sales and service departments but reshaping business processes across sectors. This comprehensive exploration highlights how Salesforce’s AI agents are changing the game, offering enterprise-level executives insights into their revolutionary potential. Salesforce AI Agents Explained. AI Agents: Beyond Autonomous Vehicles A fitting analogy to grasp the progression of AI agents is the evolution of autonomous vehicles. Just as self-driving cars advance from basic driver assistance to full autonomy, AI agents evolve from simple automation to more complex decision-making. Salesforce’s Chief Product Officer, David Schmaier, draws this comparison: “In the autonomous driving world, we have levels of autonomy, from level zero to level five. AI agents for enterprises follow a similar path.” At the core of this evolution is what Salesforce defines as the “agentic” phase of AI. Unlike generative AI that follows instructions to create content, agentic AI autonomously determines and takes actions based on broader goals. Schmaier notes, “We’re at the point where AI not only creates content but takes strategic actions. It’s like having an infinite pool of interns handling mundane tasks so human employees can focus on higher-value activities.” Agentforce: Salesforce’s Next-Generation AI Platform Agentforce is the latest addition to Salesforce’s AI arsenal, unveiled during their Q2 ’25 earnings call and now positioned as a significant milestone in AI development. With Agentforce, organizations can build and manage autonomous agents for tasks across various business functions—not just customer service. This versatility is highlighted by Marc Benioff, Salesforce’s CEO, who described the energy around Agentforce during a recent briefing as “palpable.” Agentforce builds on Salesforce’s data management, security, and customization expertise, uniting these capabilities into an AI framework. Schmaier explains, “It’s about creating trusted, enterprise-ready agents, not just deploying a large language model. We’ve developed over 100 out-of-the-box use cases, from sales account summaries to service reply recommendations, all customizable and easy to deploy.” Agentforce “In Every App” A key announcement is the integration of Agentforce in every app across Salesforce’s product suite, including Sales, Service, Marketing, and Commerce Agents. The Atlas reasoning engine, Agent Builder, and a partner network were also introduced to further enhance its capabilities. The Atlas Reasoning Engine acts as the “brain” behind Agentforce, autonomously generating plans and refining them based on actions it needs to perform, such as running business processes or engaging customers through preferred channels. What Makes an AI Agent? Salesforce AI Agents Explained Building an AI agent with Agentforce requires five key elements: These components leverage existing Salesforce infrastructure, making it easier for businesses to deploy agents through Agent Builder, which is part of the new Agentforce Studio. Agents vs. Chatbots Unlike traditional chatbots, which provide pre-programmed responses, Salesforce’s AI agents use large language models (LLMs) and generative AI to interpret and autonomously execute customer requests based on CRM data. This distinction allows AI agents to perform tasks that go beyond simple queries, driving efficiency in customer service, sales, and other business areas. Practical Applications: Sales, Service, and Marketing Salesforce’s AI agents offer tangible business benefits. For instance, Sales Agent, available as both a Sales Development Representative (SDR) and Sales Coach, automates lead nurturing and inquiry management. It utilizes CRM data to deliver personalized pitches, handle objections, and even suggest meeting times—freeing sales teams to focus on more strategic tasks. In customer service, AI agents manage routine inquiries, allowing human representatives to address more complex customer needs. In marketing, AI agents generate data-driven insights to personalize campaigns, improving customer engagement and conversion rates. The Security and Trust Foundation Security and trust remain core to Salesforce’s approach to AI. The Einstein Trust Layer ensures that data protection, privacy, and ethical guidelines are maintained throughout AI interactions. Schmaier emphasizes, “Our platform defines what data agents can access and how they use it, adhering to strict data integrity standards.” The Trust Layer also prevents AI from training on customer data without consent, ensuring transparency and security. A Partnership Between Humans and AI-Salesforce AI Agents Explained Salesforce’s vision emphasizes the synergy between human employees and AI agents. As Schmaier points out, “AI agents handle routine tasks and deliver insights, allowing employees to focus on more creative and strategic work.” This human-AI partnership boosts productivity and innovation, ultimately improving business outcomes. The Future of AI in Business As AI technology advances, Salesforce is already working on next-generation capabilities for Agentforce, including predictive analytics and more sophisticated autonomous agents. Schmaier forecasts, “These agents will handle a wider range of tasks and provide deeper insights and recommendations.” With Agentforce launching in October 2024, businesses can expect significant returns on investment, thanks to its cost-efficient model starting at $2 per conversation. In summary, Salesforce’s Agentforce is a game-changing innovation, blending AI and human intelligence to transform sales, service, and marketing. As more details unfold, it’s clear that Agentforce will redefine the future of business operations—driving efficiency, personalization, and strategic success. 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 9, 2024in Agentforce Platform, Generative AI, Technology
EU AI Act
07Sep

EU AI Act

The EU AI Act is a complex piece of legislation, packed with various sections, definitions, and guidelines, making it challenging for organizations to navigate. However, understanding the EU AI Act is crucial for companies aiming to innovate with AI while staying compliant with both legal and ethical standards. Arnoud Engelfriet, chief knowledge officer at ICTRecht, an Amsterdam-based legal services firm, specializes in IT, privacy, security, and data law. As the head of ICTRecht Academy, he is responsible for educating others on AI legislation, including the AI Act. In his book AI and Algorithms: Mastering Legal and Ethical Compliance, published by Technics, Engelfriet explores the intersection of AI legislation and ethical AI development, using the AI Act as a key example. He emphasizes that while new AI guidelines can raise concerns about creativity and compliance, it’s quite necessary for organizations to grasp the current and future legal landscape to build trustworthy AI systems. Balancing Compliance and Innovation As of August 2024, the much-anticipated AI Act is in effect, with implementation timelines extending from six months to over a year. Many businesses worry that the regulations might slow down AI innovation, especially given the rapid pace of technological advancements. Engelfriet acknowledges this tension, noting that “compliance and innovation have always been somewhat at odds.” However, he believes the act’s flexible, tiered approach offers space for businesses to adapt. For instance, the inclusion of regulatory sandboxes allows companies to test AI systems safely, without releasing them into the market. Engelfriet suggests that while innovation might slow down, the safety and trustworthiness of AI systems will improve. Ensuring Trustworthy AI The AI Act aims to promote “trustworthy AI,” a term that became central to discussions after its inclusion in the first draft of the act in 2019. Although the concept remains somewhat undefined, the act outlines three key characteristics of trustworthy AI: legality, technical robustness, and ethical soundness. Engelfriet underscores that trust in AI systems is ultimately about trusting the humans behind them. “You cannot really trust a machine,” he explained, “but you can trust its designers and operators.” The AI Act requires transparency around how AI systems function, ensuring they reliably perform their intended tasks, such as making automated decisions or serving as chatbots. Ethics has gained even more prominence with the rise of generative AI. Engelfriet highlights the fragmented nature of AI ethics guidelines, which address everything from data protection to bias prevention. The EU’s Assessment List for Trustworthy AI provides a framework to guide organizations in applying ethical standards, though Engelfriet notes that it may need to be tailored to specific industry needs. The Role of AI Compliance Officers Given the complexity of AI regulations, organizations may find it overwhelming to manage compliance efforts. To meet this growing need, Engelfriet recommends appointing AI compliance officers to help companies integrate AI responsibly into their operations. ICTRecht has also developed a course, based on AI and Algorithms, to teach employees how to navigate AI compliance. Participants from various roles—particularly those in data, privacy, and risk functions—attend the course to expand their knowledge in this increasingly important area. Salesforce is developing Trailblazer content to address these challenges as well. As with GDPR, Engelfriet believes the AI Act will set the tone for future AI regulations. He advises businesses to proactively engage with the AI Act to ensure they are prepared for the evolving regulatory landscape. To get assistance exploring your EU risks, 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 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 7, 2024in Generative AI
Salesforce Data Quality Challenges and AI Integration
05Sep

Salesforce Data Quality Challenges and AI Integration

Salesforce Data Quality Challenges and AI Integration Salesforce is an incredibly powerful CRM tool, but like any system, it’s vulnerable to data quality issues if not properly managed. As organizations race to unlock the power of AI to improve sales and service experiences, they are finding that great AI requires great data. Let’s explore some of the most common Salesforce data quality challenges and how resolving them is key to succeeding in the AI era. 1. Duplicate Records Duplicate data can clutter your Salesforce system, leading to reporting inaccuracies and confusing AI-driven insights. Use Salesforce’s built-in deduplication tools or third-party apps that specialize in identifying and merging duplicate records. Implement validation rules to prevent duplicates from entering the system in the first place, ensuring cleaner data that supports accurate AI outputs. 2. Incomplete Data Incomplete data often results in missed opportunities and poor customer insights. This becomes especially problematic in AI applications, where missing data could skew results or lead to incomplete recommendations. Use Salesforce validation rules to make certain fields mandatory, ensuring critical information is captured during data entry. Regularly audit your system to identify missing data and assign tasks to fill in gaps. This ensures that both structured and unstructured data can be effectively leveraged by AI models. 3. Outdated Information Over time, data in Salesforce can become outdated, particularly customer contact details or preferences. Regularly cleanse and update your data using enrichment services that automatically refresh records with current information. For AI to deliver relevant, real-time insights, your data needs to be fresh and up to date. This is especially important when AI systems analyze both structured data (e.g., CRM entries) and unstructured data (e.g., emails or transcripts). 4. Inconsistent Data Formatting Inconsistent data formatting complicates analysis and weakens AI performance. Standardize data entry using picklists, drop-down menus, and validation rules to enforce proper formatting across all fields. A clean, consistent data set helps AI models more effectively interpret and integrate structured and unstructured data, delivering more relevant insights to both customers and employees. 5. Lack of Data Governance Without clear guidelines, it’s easy for Salesforce data quality to degrade, especially when unstructured data is added to the mix. Establish a data governance framework that includes policies for data entry, updates, and regular cleansing. Good data governance ensures that both structured and unstructured data are properly managed, making them usable by AI technologies like Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). The Role of AI in Enhancing Data Management This year, every organization is racing to understand and unlock the power of AI, especially to improve sales and service experiences. However, great AI requires great data. While traditional CRM systems deal primarily with structured data like rows and columns, every business also holds a treasure trove of unstructured data in documents, emails, transcripts, and other formats. Unstructured data offers invaluable AI-driven insights, leading to more comprehensive, customer-specific interactions. For example, when a customer contacts support, AI-powered chatbots can deliver better service by pulling data from both structured (purchase history) and unstructured sources (warranty contracts or past chats). To ensure AI-generated responses are accurate and contextual, companies must integrate both structured and unstructured data into a unified 360-degree customer view. AI Frameworks for Better Data Utilization An effective way to ensure accuracy in AI is with frameworks like Retrieval Augmented Generation (RAG). RAG enhances AI by augmenting Large Language Models with proprietary, real-time data from both structured and unstructured sources. This method allows companies to deliver contextual, trusted, and relevant AI-driven interactions with customers, boosting overall satisfaction and operational efficiency. Tectonic’s Role in Optimizing Salesforce Data for AI To truly unlock the power of AI, companies must ensure that their data is of high quality and accessible to AI systems. Experts like Tectonic provide tailored Salesforce consulting services to help businesses manage and optimize their data. By ensuring data accuracy, completeness, and governance, Tectonic can support companies in preparing their structured and unstructured data for the AI era. Conclusion: The Intersection of Data Quality and AI In the modern era, data quality isn’t just about ensuring clean CRM records; it’s also about preparing your data for advanced AI applications. Whether it’s eliminating duplicates, filling in missing information, or governing data across touchpoints, maintaining high data quality is essential for leveraging AI effectively. For organizations ready to embrace AI, the first step is understanding where all their data resides and ensuring it’s suitable for their generative AI models. With the right data strategy, businesses can unlock the full potential of AI, transforming sales, service, and customer experiences across the board. 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 5, 2024in Generative AI
Salesforce Einstein SDR and Sales Coach Agents
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

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August 29, 2024in Agentforce Platform, Generative AI, Salesforce Einstein
When The Customers Prefer Self-Service
14Aug

When The Customers Prefer Self-Service

Assistance is crucial for complex issues, but for simpler problems, customers typically prefer the convenience of self-service tools like account portals, FAQs, and chatbots. This preference is especially strong among digital natives, such as millennials and Gen Z. However, deploying self-service tools requires careful planning. For instance, over two-thirds of customers abandon a company’s chatbot after a single negative experience, underscoring the importance of a positive initial interaction. Statistics show that 72% of customers use self-service portals, and 55% engage with self-service chatbots. The willingness of nearly half of all customers, including 60% of millennials, to pay more for superior customer service highlights the importance of customer experience in an era of price sensitivity. Customers expect instant responses, creating a scalability challenge for service teams but also an opportunity to offer premium service. Instant responses can set a company apart, as even well-regarded brands often struggle to maintain quick and seamless connections between customers and agents. Self-service platforms must be easily adjustable, not only to address areas needing improvement but also to adapt to changing market demands. Customers now expect proactive service rather than the traditional reactive approach. Despite this, customer service is often perceived as reactive. The time and effort customers spend resolving service issues are significant, especially when service teams are inconsistently trained and equipped, leading to a perception that quality service is a matter of luck. Consistency across channels, devices, and departments is highly valued but often lacking. Many customers find themselves repeating information to different representatives, indicating a fragmented information environment. Poorly integrated technology and processes leave 55% of customers feeling as if they interact with separate departments rather than a unified company. Disconnected experiences are a major source of frustration. Prompt resolution of issues is a top priority for customers, and many find it quicker to search for answers themselves than to contact the company. Self-service not only facilitates quick problem-solving but also empowers customers to address issues at their own pace and learn as much or as little as they wish. In terms of preferences, over 67% of customers prefer some form of self-service over speaking with a representative. Additionally, 73% prefer using the company’s website for support rather than relying on social media, SMS, or live chat apps. Don’t always assume the “latest and greatest” solutions available are the best solutions for your customers. A self-service strategy involves providing customers with tools to resolve their needs independently, reducing the need for representative assistance. Reduce staffing needs and increase speed to answers for customers. Its a win win. However, implementing self-service can face challenges, such as confusing navigation, lack of ongoing attention, inflexibility, failure to incorporate feedback, constraints on users, extra work, lack of human interaction, difficulty in personalization, and the need for continuous analysis and monitoring. Successful self-service integration requires addressing these factors to meet customer expectations. Contact Tectonic for assistance bringing your self-service solutions to your customers. 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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August 14, 2024in Salesforce Commerce Cloud, Salesforce Health and Life Sciences Solutions, Salesforce Hospitality Travel and Transportation, Salesforce Manufacturing Automotive Energy, Salesforce Optimization, Salesforce Service Cloud, Technology
The Role of Data to Harness AI
13Aug

The Role of Data to Harness AI

Harnessing AI for Enhanced Sales and Service: The Role of Data Organizations are racing to leverage AI to enhance their sales and service experiences. The Role of Data to Harness AI cannot be underestimated. However, great AI solutions rely on quality data. Traditionally, companies have managed structured data—neatly organized into rows and columns, such as customer engagement data from CRM systems. But businesses also hold a wealth of unstructured data in formats like documents, images, audio, and video recordings. This unstructured data can be highly valuable, offering deeper AI insights that are more accurate and comprehensive, grounded in real customer interactions. Yet, many organizations struggle to effectively access, integrate, and utilize their unstructured data to gain a holistic customer view. With advancements in large language models (LLMs) and generative AI, organizations can now bridge this gap. To succeed in the AI era, companies need to develop integrated, federated, intelligent, and actionable solutions across all customer touchpoints while managing complexity. Leveraging Unstructured Data for Superior AI Performance For instance, when a customer seeks help with a recent purchase, they typically start with a company’s chatbot. To ensure a relevant and positive experience, the chatbot must be informed by comprehensive customer data, including recent purchases, warranty details, and past interactions. Additionally, the chatbot should draw on broader company data, such as insights from other customers and internal knowledge base articles. This data can be spread across structured databases and unstructured files, like warranty contracts or knowledge articles. Accessing and utilizing both types of data is crucial for a satisfying interaction. The key to accurate AI responses is augmenting LLMs with both real-time structured and unstructured data from within a company’s systems. An effective approach is Retrieval Augmented Generation (RAG), which combines proprietary data with generative AI to enhance contextuality, timeliness, and relevance. Ensuring Relevance Across Scenarios A unified view of customer data—both structured and unstructured—provides the most relevant information for any situation. For example, financial institutions can leverage this comprehensive data to offer real-time market insights tailored to individual banking needs, providing actionable advice based on current information. Companies are increasingly exploring RAG technology to improve internal processes and deliver precise, up-to-date information to employees. This approach enhances contextual assistance, personalized support, and decision-making efficiency across the organization. The Role of Data to Harness AI Preparing Data for AI: Key Steps By addressing these areas, organizations can harness the full potential of AI, transforming customer interactions and enhancing service efficiency. Talk to Tectonic today if your data is ina disarray. 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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August 13, 2024in Generative AI
Everyone Is Implementing AI
12Aug

Everyone Is Implementing AI

AI is undoubtedly a generational change in software, with its full trajectory still unpredictable. There is a perceived divide between the “Haves” and “Have Nots.” Companies like OpenAI, Microsoft, and Databricks are seen as understanding AI’s potential, with Nvidia providing the necessary hardware support. Many hot start-ups are Gen AI native, continuing to attract unicorn valuations. Meanwhile, several SaaS leaders appear to be lagging behind. We say, Everyone Is Implementing AI. Marc Benioff stated in their latest quarterly call: “Now, we’re working with thousands of customers to power generative AI use cases with our Einstein Copilot, our prompt builder, our Einstein Studio, all of which went live in the first quarter. And we’ve closed hundreds of copilot deals since this incredible technology has gone GA. And in just the last few months, we’re seeing Einstein Copilot develop higher levels of capability. We are absolutely delighted and cannot be more excited about the success that we’re seeing with our customers with this great new capability.” Everyone Is Implementing AI However, it remains unclear whether simply adding AI to classic B2B SaaS products accelerates growth. Despite significant investments in AI, companies like Salesforce, Asana, and ZoomInfo are growing at less than 10% annually. The main point is that while “AI Washing” might impress some investors, AI must significantly accelerate revenue growth to achieve more than market parity. It is essential to see how AI can add real value and integrate it effectively. But AI alone may not be a growth accelerant. Everyone Is Implementing AI Recent data from Emergence Capital shows that 60% of VC-backed SaaS companies have already released GenAI features, with another 24% planning to do so. Achieving “AI Parity” is crucial, but simply adding GenAI features may not be disruptive in the B2B space. Companies must go further to stand out, despite the challenges. 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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August 12, 2024in Generative AI
APIs and Software Development
11Aug

APIs and Software Development

The Role of APIs in Modern Software Development APIs (Application Programming Interfaces) are central to modern software development, enabling teams to integrate external features into their products, including advanced third-party AI systems. For instance, you can use an API to allow users to generate 3D models from prompts on MatchboxXR. The Rise of AI-Powered Applications Many startups focus exclusively on AI, but often they are essentially wrappers around existing technologies like ChatGPT. These applications provide specialized user interfaces for interacting with OpenAI’s GPT models rather than developing new AI from scratch. Some branding might make it seem like they’re creating groundbreaking technology, when in reality, they’re leveraging pre-built AI solutions. Solopreneur-Driven Wrappers Large Language Models (LLMs) enable individuals and small teams to create lightweight apps and websites with AI features quickly. A quick search on Reddit reveals numerous small-scale startups offering: Such features can often be built using ChatGPT or Gemini within minutes for free. Well-Funded Ventures Larger operations invest heavily in polished platforms but may allocate significant budgets to marketing and design. This raises questions about whether these ventures are also just sophisticated wrappers. Examples include: While these products offer interesting functionalities, they often rely on APIs to interact with LLMs, which brings its own set of challenges. The Impact of AI-First, API-Second Approaches Design Considerations Looking Ahead Developer Experience: As AI technologies like LLMs become mainstream, focusing on developer experience (DevEx) will be crucial. Good DevEx involves well-structured schemas, flexible functions, up-to-date documentation, and ample testing data. Future Trends: The future of AI will likely involve more integrations. Imagine: AI is powerful, but the real innovation lies in integrating hardware, data, and interactions effectively. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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August 11, 2024in Salesforce
AI All Grown Up
10Aug

Generative AI Tools

One of the most significant use cases for generative AI in business is customer service and support. Most of us have likely experienced the frustration of dealing with traditional automated systems. However, today’s advanced AI, powered by large language models and natural language chatbots, is rapidly improving these interactions. While many still prefer human agents for complex or sensitive issues, AI is proving highly capable of handling routine inquiries efficiently. Here’s an overview of some of the top AI-powered tools for automating customer service. Although the human element will always be essential in customer experience, these tools free up human agents from repetitive tasks, allowing them to focus on more complex challenges requiring empathy and creativity. Cognigy Cognigy is an AI platform designed to automate customer service voice and chat channels. It goes beyond simply reading FAQ responses by delivering personalized, context-sensitive answers in multiple languages. Cognigy’s AI Copilot feature enhances human contact center workers by offering real-time AI assistance during interactions, making both fully automated and human-augmented support possible. IBM WatsonX Assistant IBM’s WatsonX Assistant helps businesses create AI-powered personal assistants to streamline tasks, including customer support. With its drag-and-drop configuration, companies can set up seamless self-service experiences. The platform uses retrieval-augmented generation (RAG) to ensure that responses are accurate and up-to-date, continuously improving as it learns from customer interactions. Salesforce Einstein Service Cloud Einstein Service Cloud, part of the Salesforce platform, automates routine and complex customer service tasks. Its AI-powered Agentforce bots manage “low-touch” interactions, while “high-touch” cases are overseen by human agents supported by AI. Fully customizable, Einstein ensures that responses align with your brand’s tone and voice, all while leveraging enterprise data securely. Zendesk AI Zendesk, a leader in customer support, integrates generative AI to boost its service offerings. By using machine learning and natural language processing, Zendesk understands customer sentiment and intent, generates personalized responses, and automatically routes inquiries to the most suitable agent—be it human or machine. It also provides human agents with real-time guidance on resolving issues efficiently. Ada Ada is a conversational AI platform built for large-scale customer service automation. Its no-code interface allows businesses to create custom bots, reducing the cost of handling inquiries by up to 78% per ticket. By integrating domain-specific data, Ada helps improve both support efficiency and customer experience across omnichannel support environments. More AI Tools for Customer Service There are numerous other AI tools designed to enhance automated customer support: While AI tools are transforming customer service, the key lies in using them to complement human agents, allowing for a balance of efficiency and personalized care. 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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August 10, 2024in AI Tools, Data, Enterprise, Generative AI, Salesforce, Salesforce Einstein
Small Language Models
10Aug

Small Language Models

Large language models (LLMs) like OpenAI’s GPT-4 have gained acclaim for their versatility across various tasks, but they come with significant resource demands. In response, the AI industry is shifting focus towards smaller, task-specific models designed to be more efficient. Microsoft, alongside other tech giants, is investing in these smaller models. Science often involves breaking complex systems down into their simplest forms to understand their behavior. This reductionist approach is now being applied to AI, with the goal of creating smaller models tailored for specific functions. Sébastien Bubeck, Microsoft’s VP of generative AI, highlights this trend: “You have this miraculous object, but what exactly was needed for this miracle to happen; what are the basic ingredients that are necessary?” In recent years, the proliferation of LLMs like ChatGPT, Gemini, and Claude has been remarkable. However, smaller language models (SLMs) are gaining traction as a more resource-efficient alternative. Despite their smaller size, SLMs promise substantial benefits to businesses. Microsoft introduced Phi-1 in June last year, a smaller model aimed at aiding Python coding. This was followed by Phi-2 and Phi-3, which, though larger than Phi-1, are still much smaller than leading LLMs. For comparison, Phi-3-medium has 14 billion parameters, while GPT-4 is estimated to have 1.76 trillion parameters—about 125 times more. Microsoft touts the Phi-3 models as “the most capable and cost-effective small language models available.” Microsoft’s shift towards SLMs reflects a belief that the dominance of a few large models will give way to a more diverse ecosystem of smaller, specialized models. For instance, an SLM designed specifically for analyzing consumer behavior might be more effective for targeted advertising than a broad, general-purpose model trained on the entire internet. SLMs excel in their focused training on specific domains. “The whole fine-tuning process … is highly specialized for specific use-cases,” explains Silvio Savarese, Chief Scientist at Salesforce, another company advancing SLMs. To illustrate, using a specialized screwdriver for a home repair project is more practical than a multifunction tool that’s more expensive and less focused. This trend towards SLMs reflects a broader shift in the AI industry from hype to practical application. As Brian Yamada of VLM notes, “As we move into the operationalization phase of this AI era, small will be the new big.” Smaller, specialized models or combinations of models will address specific needs, saving time and resources. Some voices express concern over the dominance of a few large models, with figures like Jack Dorsey advocating for a diverse marketplace of algorithms. Philippe Krakowski of IPG also worries that relying on the same models might stifle creativity. SLMs offer the advantage of lower costs, both in development and operation. Microsoft’s Bubeck emphasizes that SLMs are “several orders of magnitude cheaper” than larger models. Typically, SLMs operate with around three to four billion parameters, making them feasible for deployment on devices like smartphones. However, smaller models come with trade-offs. Fewer parameters mean reduced capabilities. “You have to find the right balance between the intelligence that you need versus the cost,” Bubeck acknowledges. Salesforce’s Savarese views SLMs as a step towards a new form of AI, characterized by “agents” capable of performing specific tasks and executing plans autonomously. This vision of AI agents goes beyond today’s chatbots, which can generate travel itineraries but not take action on your behalf. Salesforce recently introduced a 1 billion-parameter SLM that reportedly outperforms some LLMs on targeted tasks. Salesforce CEO Mark Benioff celebrated this advancement, proclaiming, “On-device agentic AI is here!” 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 Guide to Creating a Working Sales Plan Creating a sales plan is a pivotal step in reaching your revenue objectives. To ensure its longevity and adaptability to Read more CRM Cloud Salesforce What is a CRM Cloud Salesforce? Salesforce Service Cloud is a customer relationship management (CRM) platform for Salesforce clients to Read more

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August 10, 2024in Generative AI
Chatbots in Healthcare
10Aug

Chatbots in Healthcare

Not all medical chatbots are created equal, as a recent JAMA Network Open study reveals. The study found that some chatbots are better at tailoring health information to patient health literacy than others. Chatbots in Healthcare may not be ready for prime time. The report compared the free and paid versions of ChatGPT, showing that while the paid version initially provided more readable health information, the difference was minimal once researchers asked the chatbots to explain things at a sixth-grade reading level. The findings suggest that both versions of ChatGPT could potentially widen health disparities in terms of information access and literacy. Chatbots like ChatGPT are becoming increasingly prominent in healthcare, showing potential in improving patient access to health information. However, their quality can vary. The study evaluated the free and paid versions of ChatGPT using the Flesch Reading Ease score for readability and the DISCERN instrument for consumer health information quality. Researchers tested both versions using the five most popular cancer-related queries from 2021 to 2023. They found that while the paid version had slightly higher readability scores (52.6) compared to the free version (62.48) on a 100-point scale, both scores were deemed suboptimal. The study revealed that prompting the free version of ChatGPT to explain concepts at a sixth-grade reading level improved its readability score to 71.55, outperforming the paid version under similar conditions. Even so, when both versions were asked to simplify answers to a sixth-grade reading level, the paid version scored slightly higher at 75.64. Despite these improvements, the overall readability of responses was still problematic. Without the simplification prompt, responses were roughly at a 12th-grade reading level. Even with the prompt, they remained closer to an eighth- or tenth-grade level, possibly due to chatbot confusion about the request. The study raises concerns about health equity. If the paid version of ChatGPT provides more accessible information, individuals with the means to purchase it might have a clear advantage. This disparity could exacerbate existing health inequities, especially for those using the free version. The researchers concluded that until chatbots consistently provide information at a lower reading level, clinicians should guide patients on how to effectively use these tools and encourage them to request information at simpler reading levels. Like Related Posts 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 Guide to Creating a Working Sales Plan Creating a sales plan is a pivotal step in reaching your revenue objectives. To ensure its longevity and adaptability to Read more 50 Advantages of Salesforce Sales Cloud According to the Salesforce 2017 State of Service report, 85% of executives with service oversight identify customer service as a Read more

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August 10, 2024in Generative AI, Salesforce Health and Life Sciences Solutions, Salesforce Optimization

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