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chatGPT open ai 01
21Sep

ChatGPT Open AI o1

OpenAI has firmly established itself as a leader in the generative AI space, with its ChatGPT being one of the most well-known applications of AI today. Powered by the GPT family of large language models (LLMs), ChatGPT’s primary models, as of September 2024, are GPT-4o and GPT-3.5. In August and September 2024, rumors surfaced about a new model from OpenAI, codenamed “Strawberry.” Speculation grew as to whether this was a successor to GPT-4o or something else entirely. The mystery was resolved on September 12, 2024, when OpenAI launched its new o1 models, including o1-preview and o1-mini. What Is OpenAI o1? The OpenAI o1 family is a series of large language models optimized for enhanced reasoning capabilities. Unlike GPT-4o, the o1 models are designed to offer a different type of user experience, focusing more on multistep reasoning and complex problem-solving. As with all OpenAI models, o1 is a transformer-based architecture that excels in tasks such as content summarization, content generation, coding, and answering questions. What sets o1 apart is its improved reasoning ability. Instead of prioritizing speed, the o1 models spend more time “thinking” about the best approach to solve a problem, making them better suited for complex queries. The o1 models use chain-of-thought prompting, reasoning step by step through a problem, and employ reinforcement learning techniques to enhance performance. Initial Launch On September 12, 2024, OpenAI introduced two versions of the o1 models: Key Capabilities of OpenAI o1 OpenAI o1 can handle a variety of tasks, but it is particularly well-suited for certain use cases due to its advanced reasoning functionality: How to Use OpenAI o1 There are several ways to access the o1 models: Limitations of OpenAI o1 As an early iteration, the o1 models have several limitations: How OpenAI o1 Enhances Safety OpenAI released a System Card alongside the o1 models, detailing the safety and risk assessments conducted during their development. This includes evaluations in areas like cybersecurity, persuasion, and model autonomy. The o1 models incorporate several key safety features: GPT-4o vs. OpenAI o1: A Comparison Here’s a side-by-side comparison of GPT-4o and OpenAI o1: Feature GPT-4o o1 Models Release Date May 13, 2024 Sept. 12, 2024 Model Variants Single Model Two: o1-preview and o1-mini Reasoning Capabilities Good Enhanced, especially in STEM fields Performance Benchmarks 13% on Math Olympiad 83% on Math Olympiad, PhD-level accuracy in STEM Multimodal Capabilities Text, images, audio, video Primarily text, with developing image capabilities Context Window 128K tokens 128K tokens Speed Fast Slower due to more reasoning processes Cost (per million tokens) Input: $5; Output: $15 o1-preview: $15 input, $60 output; o1-mini: $3 input, $12 output Availability Widely available Limited to specific users Features Includes web browsing, file uploads Lacks some features from GPT-4o, like web browsing Safety and Alignment Focus on safety Improved safety, better resistance to jailbreaking ChatGPT Open AI o1 OpenAI o1 marks a significant advancement in reasoning capabilities, setting a new standard for complex problem-solving with LLMs. With enhanced safety features and the ability to tackle intricate tasks, o1 models offer a distinct upgrade over their predecessors. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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September 21, 2024in Data, Enterprise, Generative AI
Training and Testing Data
21Sep

Training and Testing Data

Data plays a pivotal role in machine learning (ML) and artificial intelligence (AI). Tasks such as recognition, decision-making, and prediction rely on knowledge acquired through training. Much like a parent teaches their child to distinguish between a cat and a bird, or an executive learns to identify business risks hidden within detailed quarterly reports, ML models require structured training using high-quality, relevant data. As AI continues to reshape the modern business landscape, the significance of training data becomes increasingly crucial. What is Training Data? The two primary strengths of ML and AI lie in their ability to identify patterns in data and make informed decisions based on that data. To execute these tasks effectively, models need a reference framework. Training data provides this framework by establishing a baseline against which models can assess new data. For instance, consider the example of image recognition for distinguishing cats from birds. ML models cannot inherently differentiate between objects; they must be taught to do so. In this scenario, training data would consist of thousands of labeled images of cats and birds, highlighting relevant features—such as a cat’s fur, pointed ears, and four legs versus a bird’s feathers, absence of ears, and two feet. Training data is generally extensive and diverse. For the image recognition case, the dataset might include numerous examples of various cats and birds in different poses, lighting conditions, and settings. The data must be consistent enough to capture common traits while being varied enough to represent natural differences, such as cats of different fur colors in various postures like crouching, sitting, standing, and jumping. In business analytics, an ML model first needs to learn the operational patterns of a business by analyzing historical financial and operational data before it can identify problems or recognize opportunities. Once trained, the model can detect unusual patterns, like abnormally low sales for a specific item, or suggest new opportunities, such as a more cost-effective shipping option. After ML models are trained, tested, and validated, they can be applied to real-world data. For the cat versus bird example, a trained model could be integrated into an AI platform that uses real-time camera feeds to identify animals as they appear. How is Training Data Selected? The adage “garbage in, garbage out” resonates particularly well in the context of ML training data; the performance of ML models is directly tied to the quality of their training data. This underscores the importance of data sources, relevance, diversity, and quality for ML and AI developers. Data SourcesTraining data is seldom available off-the-shelf, although this is evolving. Sourcing raw data can be a complex task—imagine locating and obtaining thousands of images of cats and birds for the relatively straightforward model described earlier. Moreover, raw data alone is insufficient for supervised learning; it must be meticulously labeled to emphasize key features that the ML model should focus on. Proper labeling is crucial, as messy or inaccurately labeled data can provide little to no training value. In-house teams can collect and annotate data, but this process can be costly and time-consuming. Alternatively, businesses might acquire data from government databases, open datasets, or crowdsourced efforts, though these sources also necessitate careful attention to data quality criteria. In essence, training data must deliver a complete, diverse, and accurate representation for the intended use case. Data RelevanceTraining data should be timely, meaningful, and pertinent to the subject at hand. For example, a dataset containing thousands of animal images without any cat pictures would be useless for training an ML model to recognize cats. Furthermore, training data must relate directly to the model‘s intended application. For instance, business financial and operational data might be historically accurate and complete, but if it reflects outdated workflows and policies, any ML decisions based on it today would be irrelevant. Data Diversity and BiasA sufficiently diverse training dataset is essential for constructing an effective ML model. If a model’s goal is to identify cats in various poses, its training data should encompass images of cats in multiple positions. Conversely, if the dataset solely contains images of black cats, the model’s ability to identify white, calico, or gray cats may be severely limited. This issue, known as bias, can lead to incomplete or inaccurate predictions and diminish model performance. Data QualityTraining data must be of high quality. Problems such as inaccuracies, missing data, or poor resolution can significantly undermine a model’s effectiveness. For instance, a business’s training data may contain customer names, addresses, and other information. However, if any of these details are incorrect or missing, the ML model is unlikely to produce the expected results. Similarly, low-quality images of cats and birds that are distant, blurry, or poorly lit detract from their usefulness as training data. How is Training Data Utilized in AI and Machine Learning? Training data is input into an ML model, where algorithms analyze it to detect patterns. This process enables the ML model to make more accurate predictions or classifications on future, similar data. There are three primary training techniques: Where Does Reinforcement Learning Fit In? Unlike supervised and unsupervised learning, which rely on predefined training datasets, reinforcement learning adopts a trial-and-error approach, where an agent interacts with its environment. Feedback in the form of rewards or penalties guides the agent’s strategy improvement over time. Whereas supervised learning depends on labeled data and unsupervised learning identifies patterns in raw data, reinforcement learning emphasizes dynamic decision-making, prioritizing ongoing experience over static training data. This approach is particularly effective in fields like robotics, gaming, and other real-time applications. The Role of Humans in Supervised Training The supervised training process typically begins with raw data since comprehensive and appropriately pre-labeled datasets are rare. This data can be sourced from various locations or even generated in-house. Training Data vs. Testing Data Post-training, ML models undergo validation through testing, akin to how teachers assess students after lessons. Test data ensures that the model has been adequately trained and can deliver results within acceptable accuracy and performance ranges. In supervised learning,

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September 21, 2024in Artificial Intelligence, Data
Agentforce and Thinking AI
21Sep

Agentforce and Thinking AI

Agentforce is how humans with AI drive customer success together, equips organizations with autonomous agents that boost scale, efficiency, and satisfaction across service, sales, marketing, commerce, and more New Agentforce Atlas Reasoning Engine autonomously analyzes data, makes decisions, and completes tasks, providing reliable and accurate results With Agentforce, any organization can build, customize, and deploy their own agents quickly and easily, with low-code tools New Agentforce Partner Network allows customers to deploy pre-built agents and use agent actions from partners like Amazon Web Services, Google, IBM, Workday, and more Customers like OpenTable, Saks, and Wiley are turning to Agentforce because it is integrated with their apps, works across customer channels, augments their employees, and scales capacity for business needs SAN FRANCISCO — September 12, 2024 – Salesforce (NYSE: CRM), the world’s #1 AI CRM, today unveiled Agentforce, a groundbreaking suite of autonomous AI agents that augment employees and handle tasks in service, sales, marketing, and commerce, driving unprecedented efficiency and customer satisfaction. Agentforce enables companies to scale their workforces on demand with a few clicks. Agentforce’s limitless digital workforce of AI agents can analyze data, make decisions, and take action on tasks like answering customer service inquiries, qualifying sales leads, and optimizing marketing campaigns. With Agentforce, any organization can easily build, customize, and deploy their own agents for any use case across any industry. The future of AI is agents, and it’s here. Our vision is bold: to empower one billion agents with Agentforce by the end of 2025. This is what AI is meant to be.” MARC BENIOFF, CHAIR, CEO & CO-FOUNDER, SALESFORCE “Agentforce represents the Third Wave of AI—advancing beyond copilots to a new era of highly accurate, low-hallucination intelligent agents that actively drive customer success. Unlike other platforms, Agentforce is a revolutionary and trusted solution that seamlessly integrates AI across every workflow, embedding itself deeply into the heart of the customer journey. This means anticipating needs, strengthening relationships, driving growth, and taking proactive action at every touchpoint,” said Marc Benioff, Chair and CEO, Salesforce. “While others require you to DIY your AI, Agentforce offers a fully tailored, enterprise-ready platform designed for immediate impact and scalability. With advanced security features, compliance with industry standards, and unmatched flexibility. Our vision is bold: to empower one billion agents with Agentforce by the end of 2025. This is what AI is meant to be.” In contrast to now-outdated copilots and chatbots that rely on human requests and struggle with complex or multi-step tasks, Agentforce offers a new level of sophistication by operating autonomously, retrieving the right data on demand, building action plans for any task, and executing these plans without requiring human intervention. Like a self-driving car, Agentforce uses real-time data to adapt to changing conditions and operates independently within an organizations’ customized guardrails, ensuring every customer interaction is informed, relevant, and valuable. And when desired, Agentforce seamlessly hands off to human employees with a summary of the interaction, an overview of the customer’s details, and recommendations for what to do next. Industry leaders like OpenTable, Saks, and Wiley are already experiencing the transformative power of Agentforce. For example, Agentforce is helping organizations like Wiley provide customers with dynamic, conversational self-service. Agentforce is configured to answer questions using Wiley’s knowledge base already built into Salesforce so it can automatically resolve account access. It also triages registration and payment issues, directing customers to the appropriate resources. With Agentforce handling routine inquiries, Wiley has seen an over 40% increase in case resolution, outperforming their old chatbot and giving their human agents more time to focus on complex cases. Why it Matters An estimated 41% of employee time is spent on repetitive, low-impact work, and 65% of desk workers believe generative AI will allow them to be more strategic, according to the Salesforce Trends in AI Report. Every company has more jobs to be done than the resources available to do them. As a result, many jobs go unaddressed or uncompleted. Agentforce provides relief to overstretched teams with its ability to scale capacity on demand so humans can focus on higher-touch, higher-value, and more strategic outcomes. The future of work is a hybrid workforce composed of humans with agents, enabling companies to compete in an ever-changing world. Supporting Customer Quotes “Piloting Agentforce has made a noticeable difference during one of our busiest periods — back-to-school season. It’s been exciting to go live with our first agent thanks to the no-code builder, and we’ve seen a more than 40% increase in case resolution, outperforming our old bot. Agentforce helps to manage routine responsibilities and free up our service teams for more complex cases.” – Kevin Quigley, Senior Manager, Continuous Improvement, Wiley “Every interaction that restaurants and diners have with our support team must be accurate, fast, and reflective of the hospitality that restaurants show their guests. Agentforce has incredible potential to help us deliver that high touch attentiveness and support while significantly freeing up our team to address more complex needs.” – George Pokorny, SVP Customer Success, OpenTable “As we advance our personalization strategy, we believe Agentforce and its AI-powered capabilities have the potential to make a real impact on our approach to customer engagement, raising the bar in luxury retail. Agentforce will improve our effectiveness across customer touchpoints, empowering our employees and augmenting their ability to deliver the elevated and more individualized shopping experiences for which Saks is known.” – Mike Hite, Chief Technology Officer, Saks Global Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced

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September 21, 2024in Data, Enterprise, Generative AI, Google, Salesforce, Technology
Large Action Models and AI Agents
21Sep

Large Action Models and AI Agents

The introduction of LAMs marks a significant advancement in AI, focusing on actionable intelligence. By enabling robust, dynamic interactions through function calling and structured output generation, LAMs are set to redefine the capabilities of AI agents across industries.

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September 21, 2024in Data
Data Governance Frameworks
21Sep

Data Governance Frameworks

Examples of Data Governance Frameworks Data governance is not a one-size-fits-all approach. Organizations must carefully choose a framework that aligns with their unique goals, structure, and culture. Data is one of an organization’s most valuable assets, and proper governance is key to unlocking its potential. Without a well-designed framework, companies risk poor data quality, privacy breaches, regulatory noncompliance, and missed insights. A data governance framework provides a structured way to manage data throughout its lifecycle, including policies, processes, and standards to ensure data is accurate, accessible, and secure. By putting clear guidelines in place, organizations can increase trust in their data and improve decision-making. Key Pillars of a Data Governance Frameworks A robust data governance framework typically rests on four key pillars: 1. Center-Out Model The center-out model places a centralized team, such as a data governance council, at the core of the governance process. This group establishes policies and oversees data management across the organization, balancing consistency with flexibility for different departments. The Data Governance Institute’s framework is an example of this model. It focuses on creating a Data Governance Office responsible for managing key governance functions such as setting data policies, assigning data stewards, and monitoring compliance. The framework provides a clear structure while allowing business units some leeway in adapting governance practices to their needs. PwC’s model also adopts a center-out approach, with an emphasis on using data governance to monetize data assets. It highlights the importance of maintaining consistency while minimizing the risk of data silos. 2. Top-Down Model In the top-down model, data governance is driven by executive leadership, ensuring alignment with strategic goals. This model provides authority for enforcing governance standards but may face challenges if business units feel disconnected from the central governance team. McKinsey’s framework exemplifies this approach, focusing on integrating data governance with broader business transformation efforts. Executive leadership plays a key role in ensuring that governance initiatives receive the necessary attention and resources. 3. Hybrid Model The hybrid model combines centralized governance with flexibility for individual business units. It establishes an enterprise-wide framework while allowing departments to adapt governance practices to their specific needs. The Eckerson Group’s Modern Data Governance Framework represents a hybrid approach. It emphasizes the importance of people and culture, alongside technology and processes, and encourages organizations to create a roadmap for governance that evolves as needs change. This model provides a balance between centralized control and decentralized flexibility. 4. Bottom-Up Model In the bottom-up model, data governance is driven by subject matter experts and data stakeholders across the organization. This approach promotes collaboration and buy-in from the people closest to the data, ensuring that governance policies are practical and effective. The DAMA-DMBOK framework, developed by the Data Management Association, is a prime example. Although flexible, it often starts as a bottom-up initiative, driven by IT departments and data experts who later gain executive support. 5. Silo-In Model The silo-in model allows individual business units or departments to create their own governance practices. While this approach addresses localized data issues, it often leads to inconsistencies and challenges when the organization needs to integrate data across the enterprise. Though not widely recommended, the silo-in approach may emerge when specific business units take the initiative to establish governance due to regulatory requirements or data management needs within their domains. However, as organizations mature, they often transition to more holistic frameworks to support cross-functional collaboration and data integration. Choosing the Right Framework Selecting the right data governance framework involves evaluating the organization’s needs, structure, and culture. Whether an organization adopts a center-out, top-down, hybrid, bottom-up, or silo-in approach, success depends on involving key stakeholders, securing executive buy-in, and committing to continuous improvement. By treating data as a critical asset and implementing a governance framework that aligns with its business strategy, an organization can ensure that its data management practices support growth, innovation, and regulatory compliance. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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September 21, 2024in Data, Enterprise, Technology
Thoughts on Workday With Illuminate
20Sep

Thoughts on Workday With Illuminate

Workday Expands AI Across HR and Finance Platforms with ‘Illuminate’ Workday is significantly enhancing its AI capabilities within its HR and finance platforms through a new set of updates called Illuminate. These updates aim to improve automation and increase productivity by embedding AI more broadly across various HR processes. From routine tasks like content generation to complex problem-solving, Workday’s AI now identifies inefficiencies in HR workflows and offers recommendations for improvement. Thoughts on Workday With Illuminate follow. A key feature of Illuminate is a series of AI agents designed to assist in areas such as succession planning. These agents can suggest internal candidates that HR teams might overlook, helping organizations identify potential leaders within their workforce. During a press briefing ahead of the Workday Rising conference, TechTarget asked if the AI agent used in succession planning could fully capture the intricacies of the employee experience and assess leadership potential. David Somers, Chief Product Officer at Workday, acknowledged the sensitivity of succession planning but emphasized that AI is used to augment—not replace—human decision-making. The agents provide recommendations, while the final hiring decisions still involve talent acquisition professionals and interview panels. Workday’s updates include tools for a wide range of tasks, from content summarization to more advanced capabilities such as detecting bottlenecks in onboarding processes and suggesting optimizations. “These AI agents will streamline common business workflows, boosting productivity and freeing up users to focus on strategic, meaningful work,” Somers explained. While AI has long been part of Workday’s offerings, generative AI is now driving rapid transformation in HR practices. Workday’s Illuminate platform combines data with contextual insights, offering features like compensation data tailored to a company’s specific information. Users can access these AI capabilities through Workday Assistant, a generative AI chatbot that integrates with Microsoft Teams and Slack. This tool will be generally available early next year, making it easier for teams to interact with Workday’s AI-powered systems. HR industry expert Josh Bersin sees Workday’s Illuminate as part of a broader trend of AI agents in the HR space, similar to SAP’s Joule. He believes Workday’s new AI agents will be a major focus for the company, though building out all the necessary Workday transactions into these tools will take time. Bersin does not foresee trust issues among Workday users regarding Illuminate, noting that the platform isn’t open to non-Workday data, which limits concerns around data security. Bersin’s own AI assistant, Galileo, is also expected to integrate with Workday’s platform in the future, further enhancing its capabilities. rativAccording to recent Gartner surveys from March and June, the majority of HR leaders are adopting AI in their organizations. Only 15% of respondents indicated they had no plans to incorporate generative AI into their HR processes, signaling widespread acceptance of AI tools like those Workday is rolling out with Illuminate. 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 20, 2024in AI Tools, Data, Generative AI
Collaborative Business Intelligence
20Sep

Collaborative Business Intelligence

Collaborative Business Intelligence: Connecting Data and Teams In today’s data-driven world, the ability to interact with business intelligence (BI) tools is essential for making informed decisions. Collaborative business intelligence (BI), also known as social BI, allows users to engage with their organization’s data and communicate with data experts through the same platforms where they already collaborate. While self-service BI empowers users to generate insights, understanding the data’s context is critical to avoid misunderstandings that can derail decision-making. Collaborative BI integrates BI tools with collaboration platforms to bridge the gap between data analysis and communication, reducing the risks of misinterpretation. Traditional Business Intelligence Traditional BI involves the use of technology to analyze data and present insights clearly. Before BI platforms became widespread, data scientists and statisticians handled data analysis, making it challenging for non-technical professionals to digest the insights. BI evolved to automate visualizations, such as charts and dashboards, making data more accessible to business users. Previously, BI reports were typically available only to high-level executives. However, modern self-service BI tools democratize access, enabling more users—regardless of technical expertise—to create reports and visualize data, fostering better decision-making across the organization. The Emergence of Collaborative BI Collaborative BI is a growing trend, combining BI applications with collaboration tools. This approach allows users to work together synchronously or asynchronously within a shared platform, making it easier to discuss data reports in real time or leave comments for others to review. Whether it’s through Slack, Microsoft Teams, or social media apps, users can receive and discuss BI insights within their usual communication channels. This seamless integration of BI and collaboration tools offers a competitive edge, simplifying the process of sharing knowledge and clarifying data without switching between applications. Key Benefits of Collaborative Business Intelligence Leading Collaborative BI Platforms Here’s a look at some of the top collaborative BI platforms driving innovation in the market: Conclusion Collaborative BI empowers organizations by improving decision-making, democratizing data access, optimizing data quality, and ensuring data security. By integrating BI tools with collaboration platforms, businesses can streamline their operations, foster a culture of data-driven decision-making, and enhance overall efficiency. Choosing the right platform is key to maximizing the benefits of collaborative BI. 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 20, 2024in Data, Google, Salesforce, Technology
Salesforce AI Agents and New Data Cloud Features
19Sep

Salesforce AI Agents and New Data Cloud Features

Salesforce has announced several major updates to its Data Cloud at the annual Dreamforce conference, aimed at enhancing data analysis and boosting the capabilities of its new AI agents, Agentforce. These updates include support for unstructured data in audio and video formats, 50 new data connectors, and a low-latency pipeline to ensure faster AI responses. Salesforce describes Data Cloud as a customer data platform designed to unify data from various sources, providing actionable insights for sales, service, and marketing teams. The latest features will further improve this functionality, according to Rahul Auradkar, Salesforce’s General Manager of Unified Data Services and Einstein. Key Updates 1. Unstructured Data Support: Audio and Video Formats Salesforce has introduced the ability to process unstructured audio and video data. This allows enterprises to analyze information from sources like customer calls, training sessions, product demos, and webinars. The unstructured data can enrich customer profiles, provide deeper insights into behavior, and enhance the performance of AI agents within Agentforce by improving response accuracy. IDC’s Hayley Sutherland highlighted that vectorization of this content enables retrieval-augmented generation (RAG), enhancing the use of large language models (LLMs) to generate more effective and context-aware responses. 2. 50 New Data Connectors Salesforce has expanded its connector library with 50 new options, raising the total to 200. This helps businesses integrate diverse data sources, facilitating improved personalization and decision-making. These connectors are also critical for feeding data to AI agents, enabling them to generate more accurate insights. 3. Sub-second Data Layer for Real-time Responses A new sub-second data layer has been rolled out, allowing enterprises to ingest, unify, analyze, and act on data in real time. This layer powers Einstein Personalization, enabling instant AI-driven recommendations and analytics. It’s especially important for the low-latency needs of Agentforce’s autonomous AI agents, ensuring faster, more accurate customer interactions. 4. Governance and Security Enhancements Salesforce has introduced AI tagging and classification features for unstructured data, automating labeling and organization to ensure secure access. Policy-based governance will offer granular control over data access based on tags, metadata, and user attributes. Additionally, customer-managed encryption keys ensure data security, while the newly available private connect feature allows secure data sharing between Data Cloud and public cloud environments. 5. Data Cloud One and Hybrid Search Data Cloud One, a new feature, will allow enterprises to connect multiple Salesforce accounts across different departments or regions into a single instance, simplifying data management. The hybrid search feature, which combines vector and keyword search, is designed to improve the speed and accuracy of finding industry-specific information. Other updates include Tableau Semantics, which helps organize data based on its meaning, and a new data community platform to foster collaboration among data professionals. These updates reflect Salesforce’s commitment to enhancing data-driven decision-making and delivering powerful AI capabilities for enterprises. 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 19, 2024in Data, Enterprise
Apple New AI
19Sep

Apple New AI

Apple Unveils New AI Features at “Glowtime” Event In typical fashion, Apple revealed its latest product updates on Monday with a pre-recorded keynote titled “Glowtime,” referencing the glowing ring around the screen when Apple Intelligence is activated. Though primarily a hardware event, the real highlight was the suite of AI-powered features coming to the new iPhone models this fall. The 98-minute presentation covered updates to iPhones, AirPods, and the Apple Watch, with Apple Intelligence being the thread tying together user experiences across all devices. MacRumors has published a detailed list of all announcements, including the sleep apnea detection feature for the Apple Watch and new hearing health tools for AirPods Pro 2. Key AI Developments for Brand Marketers Apple Intelligence was first introduced at its WWDC event in June, focusing on using Apple’s large language model (LLM) to perform tasks on-device with personalized results. It draws from user data in native apps like Calendar and Mail, enabling AI to handle tasks like image generation, photo searches, and AI-generated notifications. The keynote also introduced a new “Visual Intelligence” feature for iPhone 16 models, acting as a native visual search tool. By pressing the new “camera control” button, users can access this feature to perform searches directly from their camera, such as getting restaurant info or recognizing a dog breed. Apple’s AI-powered visual search offers a strategic opportunity for brands. The information for local businesses is pulled from Apple Maps, which relies on sources like Yelp and Foursquare. Brands should ensure their listings are well-maintained on these platforms and consider optimizing their digital presence for visual search tools like Google Lens, which integrates with Apple’s search. The Camera as an Input Device and the Rise of Spatial Content The camera’s role as an input device has been expanding, with Apple emphasizing photography as a key feature of its new iPhones. This year, the iPhone 16 introduces a new camera control button, offering enhanced haptic feedback for smoother control. Third-party apps like Snapchat will also benefit from this addition, giving users more refined camera capabilities. More importantly, iPhone 16 models can now capture spatial content, including both photos and audio, optimized for the Vision Pro mixed-reality headset. Apple’s move to integrate spatial content aligns with its goal to position the iPhone as a professional creator tool. Brands can capitalize on this by exploring augmented reality (AR) features or creating immersive user-generated content experiences. Apple’s Measured Approach to AI While Apple is clearly pushing AI, it is taking a cautious, phased approach. Though the new iPhones will hit the market soon, the full range of Apple Intelligence features will roll out gradually, starting in October with tools like the AI writing assistant and photo cleanup. More advanced features will debut next spring. This measured approach allows Apple to fine-tune its AI, avoiding rushed releases that could compromise user experience. For brands, this offers a lesson in pacing AI adoption: prioritize quality and customer experience over speed. Rather than rushing to integrate AI, companies should take time to understand how it can meaningfully enhance user interactions, focusing on trust and consistency to maintain customer loyalty. By following Apple’s lead and gradually introducing AI capabilities, brands can build trust, sustain anticipation, and ensure they offer technology that genuinely improves the customer experience. 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 19, 2024in Data, Google, Technology
Chatbot-less AI-ifying
19Sep

Chatbot-less AI-ifying

AI-ify Your Product Without Adding a Chatbot: Inspiration from Top AI Use Cases Artificial intelligence doesn’t always need to look like a chatbot. Some of the most innovative implementations of AI have created intuitive user experiences (UX) without relying on traditional conversational interfaces. Here are seven standout patterns from leading companies and startups that demonstrate how AI can elevate your product in ways that feel natural and empowering for users. These are just a preview of the 24 trending AI-UX patterns featured in the “Trending AI-UX Patterns” ebook by AIverse—perfect for borrowing (or expensing to your company). Pattern 1: Linear Back-and-Forth (Classic Chat) While chat interfaces revolutionized access to AI, this pattern is just the beginning. Think of ChatGPT—its conversational simplicity opened the door to powerful LLMs for non-tech audiences. But beyond basic chat, consider integrating generative UI commands or API-based functionality into your product to transform linear data access into something seamless and engaging. Pattern 2: Non-Linear Conversations Inspired by Subform, this pattern mirrors how humans think—connecting ideas in a web, not a straight line. Non-linear exploration allows users to navigate through information like dots on a map, offering a flexible, intuitive flow. For example, imagine an AI that surfaces related ideas or actions based on user input—ideal for creative tools or brainstorming apps. Pattern 3: Context Bundling Why stop at simple text input when you can bundle context visually? Figma’s dual-tone matrix simplifies tone adjustments for text by letting users drag across a 2D grid. It eliminates the need for complex prompts while maintaining control over customization. Think of ways to integrate pre-bundled prompts directly into your UI to create an intuitive, visually driven experience. Pattern 4: Living Documents Tools like Elicit bring AI into familiar interfaces like spreadsheets by enhancing workflows without disrupting them. Elicit’s bulk data extraction uses subtle animations and transparency—highlighting “low confidence” answers for clarity. This hybrid approach integrates AI in a way that feels natural and predictable, making it a great choice for data-heavy tools or reporting systems. Pattern 5: Work With Me One of the most human-centered AI patterns comes from Granola, which uses meeting summaries based on your rough notes. Instead of overwhelming users with full transcriptions, it creates concise, actionable insights, perfectly blending human oversight with AI-powered efficiency. This pattern exemplifies the “human-in-the-loop” trend, ensuring collaboration between the user and AI. Pattern 6: Highlight and Curate Take inspiration from Lex’s “@lex” comment feature, which allows users to highlight and comment directly in the flow of their work—no app switching or disruption required. By building on familiar text-interaction patterns, this approach integrates AI subtly, offering suggestions or enhancements without breaking the user’s autonomy. Pattern 7: Invisible AI (Agentive UX) AI can work quietly in the background until needed, as demonstrated by Ford’s lane assist. This feature seamlessly takes control during critical moments (e.g., steering) and hands it back to the user effortlessly. Visual, auditory, and haptic feedback make the transition intuitive and reassuring. This “agentive” pattern is perfect for products where AI acts as a silent partner, ready to assist only when necessary. Tectonic Conclusions These patterns prove that AI can elevate your product without resorting to a chatbot. Whether through non-linear exploration, visual bundling, or seamless agentive experiences, the key is to integrate AI in a way that feels intuitive, empowering, and aligned with user needs. 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 19, 2024in Artificial Intelligence, Data
Vonage Premier for Salesforce Service Cloud Voice
19Sep

Vonage Premier for Salesforce Service Cloud Voice

HOLMDEL, N.J., Sept. 18, 2024 /PRNewswire/ — Vonage, a global leader in cloud communications helping businesses accelerate their digital transformation and a part of Ericsson (NASDAQ: ERIC), is one of the first contact center providers to join Salesforce’s Bring Your Own Channel for Contact Center as a Service (BYOC for CCaaS) pilot program. With BYOC for CCaaS, Vonage Premier for Salesforce Service Cloud Voice customers will have the ability to integrate Vonage omnichannel and AI-powered capabilities into their existing contact center solutions, including voice, SMS, chat, social messaging apps like WhatsApp, and more – delivering faster resolution times and creating a more native, personalized and meaningful experience for customers by connecting with them on their channel of choice. “We are very excited to have Vonage, a leading Salesforce Service Cloud Voice partner, take this very important step to expand its deep Salesforce integration through BYOC for CCaaS, delivering the omnichannel capabilities – and the APIs to enable them – that create the kind of customer experiences that drive meaningful engagement,” said Tony Flores, Senior Director of Product Management for Salesforce. With BYOC for CCaaS, Vonage Premier for Service Cloud Voice customers will now be able to connect with customers across various communications channels, as well as access data insights and AI-based agent productivity tools, to create a better overall customer journey and a more productive and efficient agent experience. The solution’s single routing and agent capacity model also increases contact center capacity, leading to more customer interactions being resolved better and faster. Workforce Engagement Management (WEM) is also provided through Vonage’s seamless integrations with leading WEM solutions Verint, Calabrio Teleopti, Playvox and injixo, ensuring optimum planning, scheduling, tracking, and management of the contact center workforce. “Today’s contact center agents play a vital role in support of the businesses they represent and in meeting the increasing demands of tech-savvy customers who want to connect from anywhere, on their preferred communications channels,” said Reggie Scales, Acting Head of Applications for Vonage. “These agents are also frequently working from anywhere and need the tools to access critical information to troubleshoot common customer issues and provide real-time customer support. Having all of these capabilities in a single user interface – omnichannel modes of communication coupled with a 360 view of customer information and key knowledge bases – this is the contact center of the future.” A key differentiator for Vonage as a pilot partner in this program is its ability to source a single AI-based Virtual Agent solution for self-service automations across voice and digital channels using Vonage AI studio – while also leveraging Salesforce for all Live Agent Assist and Analytics needs. Vonage can also integrate its own Vonage Communications APIs to power pre-built programmable capabilities for voice, SMS, social and chat, directly into the contact center – all on one combined Salesforce and Vonage platform. This singular view also enhances efficiency by keeping agents and supervisors in a single Salesforce desktop to eliminate application switching and the need to toggle between screens. “Modern contact centers are experiencing increasing pressure and demand to deliver better, more personalized, omnichannel interactions, as well as quicker and more accurate responses to customer issues,” said Jim Lundy, CEO, Founder & Lead Analyst, Aragon Research. “With BYOC for CCaaS, Vonage aims to address the increasing demand for a unified and customizable customer experience across all communication channels, leveraging existing Salesforce platforms and AI-powered insights and automation.” Vonage Premier for Service Cloud Voice is currently available on the Salesforce AppExchange with Salesforce BYOC for CCaaS integrated capabilities now available for customers to pilot. To find out more about Vonage Premier for Service Cloud Voice, visit www.vonage.com. Salesforce, AppExchange, Service Cloud Voice, Einstein and others are among the trademarks of Salesforce, inc. About Vonage Vonage, a global cloud communications leader, helps businesses accelerate their digital transformation. Vonage’s Communications Platform is fully programmable and allows for the integration of Video, Voice, Chat, Messaging, AI and Verification into existing products, workflows and systems. The Vonage conversational commerce application enables businesses to create AI-powered omnichannel experiences that boost sales and increase customer satisfaction. Vonage’s fully programmable unified communications, contact center and conversational commerce applications are built from the Vonage platform and enable companies to transform how they communicate and operate from the office or remotely – providing the flexibility required to create meaningful engagements. Vonage is headquartered in New Jersey, with offices throughout the United States, Europe, Israel and Asia and is a wholly-owned subsidiary of Ericsson (NASDAQ: ERIC), and a business area within the Ericsson Group called Business Area Global Communications Platform. To follow Vonage on X (formerly known as Twitter), please visit twitter.com/vonage. To follow on LinkedIn, visit linkedin.com/company/Vonage/. To become a fan on Facebook, go to facebook.com/vonage. To subscribe on YouTube, visit youtube.com/vonage. SOURCE Vonage 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 19, 2024in Data, Salesforce Service Cloud
Salesforce Free AI Training
19Sep

Salesforce Free AI Training

Salesforce Expands Access to Free AI Training to Address Global Skills Gap SAN FRANCISCO — September 18, 2024 – Salesforce (NYSE: CRM), the #1 AI-powered CRM, has announced a significant expansion of its AI skilling initiatives. Starting today, Salesforce will offer its premium AI courses and certifications free of charge to anyone via its online learning platform, Trailhead, through the end of 2025. This initiative aims to help bridge the growing AI skills gap by providing accessible education for individuals across industries. To further support these efforts, Salesforce will open new physical training spaces at its San Francisco headquarters, including a pop-up AI Center offering in-person community courses and a dedicated AI skilling floor for employees. This investment, valued at over $50 million, is designed to empower the workforce with essential AI skills as the demand for AI talent surges globally. Research from Slack highlights a growing urgency among executives to integrate AI into business operations, with interest increasing sevenfold in the last six months. However, more than two-thirds of workers have yet to engage with AI tools, and only 15% feel they possess the necessary training to use them effectively. “The advent of AI and agents represents the biggest technological shift of our generation and will radically change how people work,” said Brian Millham, President and Chief Operating Officer at Salesforce. “We need to ensure everyone has the skills to succeed in this new AI-driven world.” Expanding AI Training Opportunities Salesforce has already helped thousands of professionals acquire technical skills through premium, instructor-led training and certifications. With these new offerings, the company aims to reach an additional 100,000 learners, empowering every Trailblazer to become an “Agentblazer” in this evolving AI landscape. You don’t need to spend thousands of dollars in AI education for yourself or your workforce. Salesforce has it at your fingertips for free. Trailhead now offers a wide range of AI-specific courses, covering topics like AI fundamentals, ethical AI use, and prompt engineering. Since June 2023, learners have earned over 2.6 million AI and data badges, helping unlock critical skills for the future of work. Creating Spaces for Hands-on AI Learning In addition to expanding its online offerings, Salesforce is building AI training spaces around the globe. After launching its first AI Center in London, the company will open a pop-up AI Center at its San Francisco headquarters in 2025, with plans for additional locations in Chicago, Tokyo, and Sydney. These centers will host in-person Trailhead courses and bring together experts, partners, and customers to advance AI innovation. Introducing Agentforce — A Groundbreaking AI Suite As part of its ongoing AI revolution, Salesforce is also upskilling its 72,000-strong workforce through quarterly AI learning days and immersive experiences at the newly created AI Knowledge Center in San Francisco. The centerpiece of this initiative is Agentforce, an innovative suite of AI agents designed to enhance productivity in service, sales, marketing, and commerce. By automating repetitive tasks, these agents allow employees to focus on higher-value work. Since the launch of Slack AI in February, Salesforce employees have saved nearly 3 million work hours through AI-driven tools that summarize information, find answers, and generate new ideas. Nearly 40% of the AI and data badges earned on Trailhead belong to Salesforce employees, demonstrating the company’s commitment to internal skilling and innovation. “AI will transform the workforce, creating new roles and opportunities. It’s our responsibility as employers to provide training that prepares workers for the future,” said Nathalie Scardino, President and Chief People Officer at Salesforce. “Grounded in our values, we’re leveraging Salesforce’s full power to help everyone succeed in this AI-driven era.” Related Resources For more information about Salesforce, visit www.salesforce.com or call 1-800-NO-SOFTWARE. About SalesforceSalesforce is the world’s leading AI-powered CRM, helping organizations of all sizes reimagine their business for the AI age. Powered by its trusted platform, Agentforce, Salesforce brings humans and AI agents together to drive customer success through data-driven insights and actions. Salesforce is headquartered in San Francisco, with offices globally, and trades on the NYSE under the ticker symbol “CRM.” 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 19, 2024in AI Tools, Data
Power BI
18Sep

Connect Salesforce and Power BI

Hello, Im trying to connect a filtered case list (https://company.lightning.force.com/lightning/o/Case/list?filterName=blahblah) containing customer reviews in the case description into a Power BI table and connect it to my AI Hub custom prompt bot that categorises text. Ideally, when new cases get added to that filtered list –  the Power BI table automatically refreshes with the case id, subject, description and an additional column where the categorised text gets added in. eg) Case ID Case Subject Case description Category 332432 AAAA blah blah customer complaint 4243242 BBBB something product quality 424234 CCCC bleh customer praise Thanks! You might find it helpful to follow these steps: 1. Connect Salesforce filtered case list to Power BI. 2. Use Power Apps AI Builder to categorise case descriptions: 3. Configure Power BI to automatically refresh for the latest classification results. 4. Displaying Classified Data in Power BI 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 18, 2024in Data, Salesforce, Salesforce User Questions
Salesforce and IBM Partnership
17Sep

Salesforce and IBM Partnership

Salesforce and IBM are advancing their longstanding partnership by focusing on transforming sales and service processes with AI, particularly for organizations in regulated industries that seek to leverage enterprise data for automation. The collaboration aims to deliver pre-built AI agents and tools that integrate seamlessly within customers’ IT environments, enabling them to use their proprietary data while maintaining full control over their systems. By merging Salesforce’s Agentforce, a suite of autonomous agents, with IBM’s watsonx capabilities, the partnership will empower businesses to utilize AI agents within their daily applications. IBM’s watsonx Orchestrate will enhance Agentforce with autonomous agents that improve productivity, security, and regulatory compliance. Additionally, IBM customers will have the ability to interact with these agents via Slack, facilitating dynamic conversational experiences. Planned integrations between Salesforce Data Cloud and IBM Data Gate for watsonx will enable access to business data from IBM Z mainframes and Db2 databases, supporting AI workflows across the Agentforce platform. This integration will enhance data analysis and fuel AI-driven processes. Customers will also benefit from a broader range of AI model and deployment options through integration with IBM watsonx.ai. This will include access to IBM’s Granite foundation models, designed for enterprise applications. Enhancing Business Automation with Tailored Autonomous Agents Through the Agentforce Partner Network, businesses can develop and customize AI agents to interact with various enterprise tools and platforms. These agents are designed to perform multi-step tasks, make decisions based on triggers or interactions, and seek user approval for actions beyond their scope. They will help automate routine tasks, increase efficiency, streamline operations, and enhance customer service. IBM’s watsonx Orchestrate will integrate with Salesforce Agentforce to develop new pre-built agents for specific business challenges. These agents will leverage data and AI from both Salesforce and IBM to address various needs: Expanding Data Integration for AI Salesforce and IBM are also advancing data integration strategies through the Zero Copy integration between Salesforce Data Cloud and watsonx.data. This allows data to remain in place while being utilized for AI use cases, without duplication. Joint customers, particularly in financial services, insurance, manufacturing, and telecommunications, will leverage this integration to access and use mainframe datasets from IBM Z and Db2 databases on Salesforce’s platform. IBM will be the first Zero Copy partner to facilitate data flow between IBM Z and Salesforce Cloud, offering secure access to critical enterprise data and enhancing AI agent functionality. With IBM Z handling over 70% of global transaction value, this partnership ensures high standards of security, privacy, and compliance. Improving Efficiency with Slack and IBM watsonx Orchestrate IBM customers will now engage with watsonx Orchestrate agents directly within Slack, supporting AI app experiences with a new interface. This integration allows for seamless interaction with AI agents, automating tasks and enhancing collaboration across systems without leaving Slack. Expanding AI Model and Deployment Options with watsonx.ai A new integration with watsonx.ai will enable customers to deploy customized large language models (LLMs) within Salesforce Model Builder. This includes access to a range of third-party models and IBM’s Granite foundation models, which offer transparency and compliance with regulatory requirements. IBM Granite models are expected to be available within the Salesforce ecosystem by October. Partnering with IBM Consulting for Tailored AI Solutions IBM Consulting will leverage its expertise in Salesforce and AI to help joint customers accelerate the implementation of Agentforce. Through IBM Consulting Advantage, the AI-powered delivery platform, businesses will receive support in selecting, customizing, deploying, and scaling AI agents to meet specific industry needs. Customer Perspective Tectonic is transforming its service stations into preferred journey stops with the help of Salesforce and IBM. The collaboration offers unprecedented flexibility in AI utilization, enabling Tectonic to deliver hyper-personalized services through Agentforce and IBM’s watsonx AI, enhancing customer engagement and satisfaction. 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 17, 2024in Agentforce Platform, Data, Enterprise, Salesforce, Salesforce Data Cloud
More Cool AI Tools
15Sep

More Cool AI Tools

In today’s fast-paced digital world, AI is no longer a luxury but a necessity for maximizing work efficiency. With the right AI tools, businesses and individuals can automate tasks, enhance creativity, improve customer engagement, and streamline operations. Here’s a breakdown of the Top 21 AI tools you should explore to elevate your productivity and stay ahead of the curve! 1️⃣ Video CreationSynthesiaWebsite: SynthesiaAn AI video creation tool that lets you generate high-quality videos from text. Ideal for creating marketing, training, and explainer videos quickly and professionally. VeedWebsite: VeedVeed helps you create, edit, and share professional videos with ease, incorporating AI to streamline the process of adding captions, effects, and edits. SubmagicWebsite: SubmagicSubmagic uses AI to automatically generate subtitles for videos, improving accessibility and viewer engagement. 2️⃣ Customer Relationship Management (CRM)HubSpotWebsite: HubSpotHubSpot’s AI-powered CRM system streamlines customer interactions, helping businesses improve customer satisfaction, sales, and retention. FreshworksWebsite: FreshworksThis tool offers AI-driven solutions for customer service, sales, and marketing, helping companies improve relationships and resolve issues faster. HighLevelWebsite: HighLevelHighLevel integrates AI to improve customer management processes, including lead nurturing and campaign tracking. 3️⃣ Website Design and BrandingWizard AIWebsite: Wizard AIA design tool that helps you create stunning visuals and branding for your website using AI. Whether you’re looking to revamp your website or create a logo, Wizard AI makes it simple. LookaWebsite: LookaLooka offers AI-powered logo creation, making it easy for businesses and startups to design professional logos in just minutes. TurbologoWebsite: TurbologoTurbologo is another intuitive logo maker that uses AI to generate custom logo designs based on your business type and preferences. 4️⃣ Project Management and CollaborationMondayWebsite: MondayAn all-in-one project management platform that uses AI to automate workflows, track progress, and enhance team collaboration. ClickUpWebsite: ClickUpClickUp leverages AI to provide real-time project insights, task automation, and comprehensive team collaboration tools for businesses of all sizes. Golf AIWebsite: Golf AIGolf AI helps golfers refine their game with AI insights, but its technology can also be applied in the professional world, improving focus, strategy, and decision-making in various projects. 5️⃣ Marketing and Lead GenerationPipedriveWebsite: PipedriveA popular tool that helps businesses track leads and automate marketing workflows, making lead generation more efficient and scalable. Apollo AIWebsite: Apollo AIApollo enables businesses to automate sales and lead generation by using AI to find and reach potential customers, helping you connect with decision-makers faster. EnvizWebsite: EnvizThis platform uses AI to provide intelligent data analysis and insights, allowing businesses to fine-tune their marketing strategies. 6️⃣ AI for Audio and VoiceMurf AIWebsite: Murf AIAn AI voice generator that converts text into lifelike voiceovers. Ideal for creators, marketers, and educators who want to generate high-quality audio content. SpeechifyWebsite: SpeechifySpeechify turns written text into audio, helping users consume content faster. It’s perfect for multitaskers and individuals with reading disabilities. ElevenLabsWebsite: ElevenLabsElevenLabs offers state-of-the-art AI technology to generate and clone natural-sounding voices, ideal for podcasts, audiobooks, and interactive audio experiences. 🌐 Explore More AI-Powered ToolsUnlock your productivity potential with these top AI tools. Whether you’re managing projects, creating content, or building customer relationships, AI is your key to efficiency. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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September 15, 2024in AI Tools, Data, Technology

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