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Acceptable AI Use Policies

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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Large and Small Language Models

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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AI-Driven Chatbots in Education

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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Einstein Features Cheat Sheet

Einstein Features Cheat Sheet

Salesforce has published a great resource for Einstein users. The Einstein Cheat Sheet puts all the Einstein features and resources at your fingertips. Download here. Einstein Discover the power of the #1 AI for CRM with Einstein. Built into the Salesforce Platform, Einstein uses powerful machine learning and large language models to personalize customer interactions and make employees more productive. With Einstein powering the Customer 360, teams can accelerate time to value, predict outcomes, and automatically generate contentwithin the flow of work. Einstein is for everyone, empowering business users, Salesforce Admins and Developers to embed AI into every experience with low code. 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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Salesforce and Tenyx

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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AI-Powered Field Service

AI-Powered Field Service

Salesforce has introduced new AI-powered field service capabilities designed to streamline operations for dispatchers, technicians, and field service leaders. Leveraging the Salesforce platform and Data Cloud, these innovations aim to expedite time-consuming processes and enhance customer satisfaction by making field service operations more proactive and efficient. Why it matters: Field service teams currently spend only 32% of their time interacting with customers, with the remaining 68% consumed by administrative tasks like manually entering case notes. With 78% of field service workers in AI-enabled organizations reporting that AI helps save time, Salesforce’s new tools address these inefficiencies head-on. Key AI-driven innovations for Field Service: Availability: Paul Whitelam, GM & SVP of Salesforce Field Service, notes, “The future of field service lies in the seamless integration of AI, data, and human expertise. Our new capabilities set new standards for efficiency and service delivery.” Rudi Khoury, Chief Digital Officer at Fisher & Paykel, adds, “With Salesforce Field Service, we’re not just embracing AI and data-driven insights — we’re advancing into the future of field service, achieving unprecedented efficiency and exceptional service.” Like Related Posts 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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Salesforce AI Agents Explained

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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Exploring Large Action Models

Exploring Large Action Models

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

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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2024 AI Glossary

2024 AI Glossary

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

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AI and Big Data

AI and Big Data

Over the past decade, enterprises have accumulated vast amounts of data, capturing everything from business processes to inventory statistics. This surge in data marked the onset of the big data revolution. However, merely storing and managing big data is no longer sufficient to extract its full value. As organizations become adept at handling big data, forward-thinking companies are now leveraging advanced analytics and the latest AI and machine learning techniques to unlock even greater insights. These technologies can identify patterns and provide cognitive capabilities across vast datasets, enabling organizations to elevate their data analytics to new levels. Additionally, the adoption of generative AI systems is on the rise, offering more conversational approaches to data analysis and enhancement. This allows organizations to extract significant insights from information that would otherwise remain untapped in data stores. How Are AI and Big Data Related? Applying machine learning algorithms to big data is a logical progression for companies aiming to maximize the potential of their data. Unlike traditional rules-based approaches that follow explicit instructions, machine learning systems use data-driven algorithms and statistical models to analyze and detect patterns in data. Big data serves as the raw material for these systems, which derive valuable insights from it. Organizations are increasingly recognizing the benefits of integrating big data with machine learning. However, to fully harness the power of both, it’s crucial to understand their individual capabilities. Understanding Big Data Big data involves extracting and analyzing information from large quantities of data, but volume is just one aspect. Other critical “Vs” of big data that enterprises must manage include velocity, variety, veracity, validity, visualization, and value. Understanding Machine Learning Machine learning, the backbone of modern AI, adds significant value to big data applications by deriving deeper insights. These systems learn and adapt over time without the need for explicit programming, using statistical models to analyze and infer patterns from data. Historically, companies relied on complex, rules-based systems for reporting, which often proved inflexible and unable to cope with constant changes. Today, machine learning and deep learning enable systems to learn from big data, enhancing decision-making, business intelligence, and predictive analysis. The strength of machine learning lies in its ability to discover patterns in data. The more data available, the more these algorithms can identify patterns and apply them to future data. Applications range from recommendation systems and anomaly detection to image recognition and natural language processing (NLP). Categories of Machine Learning Algorithms Machine learning algorithms generally fall into three categories: The most powerful large language models (LLMs), which underpin today’s widely used generative AI systems, utilize a combination of these methods, learning from massive datasets. Understanding Generative AI Generative AI models are among the most powerful and popular AI applications, creating new data based on patterns learned from extensive training datasets. These models, which interact with users through conversational interfaces, are trained on vast amounts of internet data, including conversations, interviews, and social media posts. With pre-trained LLMs, users can generate new text, images, audio, and other outputs using natural language prompts, without the need for coding or specialized models. How Does AI Benefit Big Data? AI, combined with big data, is transforming businesses across various sectors. Key benefits include: Big Data and Machine Learning: A Synergistic Relationship Big data and machine learning are not competing concepts; when combined, they deliver remarkable results. Emerging big data techniques offer powerful ways to manage and analyze data, while machine learning models extract valuable insights from it. Successfully handling the various “Vs” of big data enhances the accuracy and power of machine learning models, leading to better business outcomes. The volume of data is expected to grow exponentially, with predictions of over 660 zettabytes of data worldwide by 2030. As data continues to amass, machine learning will become increasingly reliant on big data, and companies that fail to leverage this combination will struggle to keep up. Examples of AI and Big Data in Action Many organizations are already harnessing the power of machine learning-enhanced big data analytics: Conclusion The integration of AI and big data is crucial for organizations seeking to drive digital transformation and gain a competitive edge. As companies continue to combine these technologies, they will unlock new opportunities for personalization, efficiency, and innovation, ensuring they remain at the forefront of their industries. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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Key Insights on Navigating AI Compliance

Key Insights on Navigating AI Compliance

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

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AI Assisting Nursing

AI Assisting Nursing

Leveraging AI to Alleviate the Documentation Burden in Nursing As the nursing profession grapples with increasing burnout, researchers are investigating the potential of large language models to streamline clinical documentation and care planning. Nurses play an essential role in delivering high-quality care and improving patient outcomes, but the profession is under significant strain due to shortages and burnout. AI Assisting Nursing could lessoning burnout while improving communication. What role could Salesforce play? The American Nurses Association (ANA) emphasizes that to maximize nurses’ potential, healthcare organizations must prioritize maintaining an adequate workforce, fostering healthy work environments, and supporting policies that back nurses. The COVID-19 pandemic has exacerbated existing challenges, including increased healthcare demand, insufficient workforce support, and a wave of retirements outpacing the influx of new nurses. Tectonic has nearly two decades of experience providing IT solutions for the health care industry. Salesforce, as a leader in the field of artificial intelligence, is a top tool for health care IT. AI Assisting Nursing In response to these growing demands, some experts argue that AI technologies could help alleviate some of the burden, particularly in areaTes like clinical documentation and administrative tasks. In a recent study published in the Journal of the American Medical Informatics Association, Dr. Fabiana Dos Santos, a post-doctoral research scientist at Columbia University School of Nursing, led a team to explore how a ChatGPT-based framework could assist in generating care plan suggestions for a lung cancer patient. In an interview with Healthtech Analytics, Dr. Santos discussed the potential and challenges of using AI chatbots in nursing. Challenges in Nursing Care Plan Documentation Creating care plans is vital for ensuring patients receive timely, adequate care tailored to their needs. Nurses are central to this process, yet they face significant obstacles when documenting care plans. AI Assisting Nursing and Salesforce as a customer relationship solution addresses those challenges. “Nurses are on the front line of care and spend a considerable amount of time interacting closely with patients, contributing valuable clinical assessments to electronic health records (EHRs),” Dr. Santos explained. “However, many documentation systems are cumbersome, leading to a documentation burden where nurses spend much of their workday interacting with EHRs. This can result in cognitive burden, stress, frustration, and disruptions to direct patient care.” The American Association of Critical-Care Nurses (AACN) highlights that electronic documentation is a significant burden, consuming an average of 40% of a nurse’s shift. Time spent on documentation inversely correlates with time spent on patient care, leading to increased burnout, cognitive load, and decreased job satisfaction. These factors, in turn, contribute to patient-related issues such as a higher risk of medical errors and hospital-acquired infections, which lower patient satisfaction. When combined with the heavy workloads nurses already manage, inefficient documentation tools can make care planning even more challenging. AI Assisting Nursing and Care Plans “The demands of direct patient care and managing multiple administrative tasks simultaneously limit nurses’ time to develop individualized care plans,” Dr. Santos continued. “The non-user-friendly interfaces of many EHR systems exacerbate this challenge, making it difficult to capture all aspects of a patient’s condition, including physical, psychological, social, cultural, and spiritual dimensions.” To address these challenges, Dr. Santos and her team explored the potential of ChatGPT to improve clinical documentation. “These negative impacts on a nurse’s workday underscore the urgency of improving EHR documentation systems to reduce these issues,” she noted. “AI tools, if well designed, can improve the process of developing individualized care plans and reduce the burden of EHR-related documentation.” The Promises and Pitfalls of AI Developing care plans requires nurses to draw from their expertise to address issues like symptom management and comfort care, especially for patients with complex needs. Dr. Santos emphasized that advanced technologies, such as generative AI (GenAI), could streamline this process by enhancing documentation workflows and assisting with administrative tasks. AI tools can rapidly process large amounts of data and generate care plans more quickly than traditional methods, potentially allowing nurses to spend more time on direct and holistic patient care. However, Dr. Santos stressed the importance of carefully validating AI models, ensuring that nurses’ clinical judgment and expertise play a central role in evaluating AI-generated care plans. “New technologies can help nurses improve documentation, leading to better descriptions of patient conditions, more accurate capture of care processes, and ultimately, improved patient outcomes,” she said. “This presents an important opportunity to use novel generative AI solutions to reduce nurses’ workload and act as a supportive documentation tool.” Despite the promise of AI as a support tool, Dr. Santos cautioned that chatbots require further development to be effectively implemented in nursing care plans. AI-generated outputs can contain inaccuracies or irrelevant information, necessitating careful review and validation by nurses. Additionally, AI tools may lack the nuanced understanding of a patient’s unique needs, which only a nurse can provide through personal, empathetic interactions, such as interpreting specific cultural or spiritual needs. Despite these challenges, large language models (LLMs) and other GenAI tools are generating significant interest in the healthcare industry. They are expected to be deployed in various applications, including EHR workflows and nursing efficiency. Dr. Santos’ research contributes to this growing field. To conduct the study, the researchers developed and validated a method for structuring ChatGPT prompts—guidelines that the LLM uses to generate responses—that could produce high-quality nursing care plans. The approach involved providing detailed patient information and specific questions to consider when creating an appropriate care plan. The research team refined the Patient’s Needs Framework over ten rounds using 22 diverse hypothetical patient cases, ensuring that the ChatGPT-generated plans were consistent and aligned with typical nursing care plans. “Our findings revealed that ChatGPT could prioritize critical aspects of care, such as oxygenation, infection prevention, fall risk, and emotional support, while also providing thorough explanations for each suggested intervention, making it a valuable tool for nurses,” Dr. Santos indicated. The Future of AI in Nursing While the study focused on care plans for lung cancer, Dr. Santos emphasized that this research is just the beginning of

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Copilots in the Workplace

Copilots in the Workplace

The Rise of AI-Powered Copilots in the Workplace: The New Age of Office Helpers As more businesses embrace AI tools, the tech world is buzzing with a new kind of office assistant: the AI-powered copilot. These digital sidekicks are here to revolutionize how we interact with information—think of them as the high-tech, caffeine-free version of your office buddy who always knows where the stapler is. Copilots in the Workplace are here. AI-powered copilots use large language models (LLMs) to help users wade through vast amounts of data, often with the grace of a caffeinated librarian. By facilitating conversations instead of requiring precise queries, these tools let you ask for help without needing to channel your inner tech wizard. Hugo Sarrazin, Chief Product and Technology Officer at UKG, points out that many of these AI copilots are essentially “search functions dressed up in a snazzy new outfit.” UKG’s own digital assistant, UKG Bryte, made its debut last November—just in time to help you find out why your vacation request hasn’t been approved yet. These AI assistants offer an enhanced chatbot experience by understanding a wide range of queries through generative AI. Imagine asking your chatbot, “Hey, what’s the deadline for open enrollment?” and getting a response that doesn’t involve translating your question into a techie dialect. “Generative AI isn’t stuck on keywords and rigid queries. It’s like a magic eight ball with a PhD,” Sarrazin explains. Traditional systems often force users through pre-set menus and workflows—kind of like a bureaucratic maze—but copilots let you skip the detours and get straight to the point. With AI copilots, you can ask in plain language and receive useful answers without needing to consult a human. Picture this: an HR chatbot that knows exactly what the per diem is for your conference, or which days you’re free for the next company holiday—like having a personal assistant who never needs a coffee break. Salesforce employees, for instance, are getting a taste of this futuristic help with their Einstein copilot. Since the introduction of Einstein, Salesforce has seen an uptick in productivity and a drop in mundane tasks. Nathalie Scardino, Salesforce’s Chief People Officer, says the company has been working to seamlessly integrate AI tools into daily workflows—because nothing says “we care” like a virtual assistant who understands your workload better than you do. After Salesforce acquired Slack in 2020, the Einstein-powered Slack app launched in February. This tool helps with scheduling, document summarization, and general inquiries, effectively turning your to-do list into a “done” list. Research showed that desk workers spend 41% of their time on tasks that aren’t exactly rocket science, and Einstein is here to tackle those chores. Scardino and Salesforce’s CIO, Juan Perez, have been busy ensuring that AI tools fit perfectly into the company’s workflow. Einstein is also making waves in HR by integrating with Basecamp, Salesforce’s hub for employee info. This integration has answered over 88,000 queries and cut resolution times from two days to just 30 minutes—making it the office hero you didn’t know you needed. “The big win here is bringing all those disparate systems together and making information accessible without needing a PhD,” Scardino quips. “No more hopping between six systems just to find out about your healthcare benefits.” In this brave new world of AI-assisted work, copilots like Einstein are proving that getting the right information quickly is no longer a sci-fi dream. They’re here to make our office lives smoother, smarter, and a little less dependent on those old-fashioned human helpers. 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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The Role of Data to Harness AI

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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