AI Tools Archives - gettectonic.com - Page 7
Build Launch and Track Campaigns
10Oct

Build Launch and Track Campaigns

Revolutionizing Campaigns: How Marketing Agents Empower Your Marketing Team Marketing agents are transforming how businesses create, launch, and track campaigns—delivering better results while boosting internal team productivity and cohesion. With the power of AI and data, these agents act as collaborative partners, enhancing marketing efficiency and creativity in unprecedented ways. A Smarter Approach to Campaign Challenges Marketers have long faced the challenge of creating quality content at scale. According to the Content Marketing Institute, 54% of B2B marketers struggle to meet this demand, while B2C marketers often lack the resources to make their efforts scalable and consistent. On top of this, they must ensure campaigns are efficient, customer-centric, and stand out in a competitive landscape. Enter marketing agents—AI-powered tools that help teams manage and optimize campaigns, from strategy to execution. At Dreamforce 2024, Salesforce unveiled Agentforce, a suite of intelligent agents integrated across the Customer 360 platform, including Agentforce Campaigns. With 71% of marketers planning to adopt generative and predictive AI within the next 18 months, as per Salesforce’s State of Marketing report, tools like Agentforce are poised to redefine how campaigns are built and delivered. How Humans and AI Agents Work Together Marketing agents are AI-powered virtual assistants that collaborate with humans to analyze data, generate insights, and execute marketing plans. Unlike traditional tools, they understand the context behind your needs and suggest actionable solutions—whether that’s creating content, optimizing campaigns, or analyzing results. By automating time-consuming tasks, marketing agents free teams to focus on high-value activities like strategy and personalization. But the key to maximizing their potential lies in shifting your mindset: instead of simply seeking efficiency, aim to transform how you deliver exceptional customer experiences. 8 Ways Agentforce Campaigns Elevates Your Marketing 1. Intelligent Recommendations Agentforce Campaigns turns insights into actions. For example, Marketing Cloud’s Einstein not only tracks your goals but also suggests adjustments or new campaigns tailored to your objectives, helping you stay ahead. 2. Instant Campaign Briefs Building a campaign starts with a solid brief. With Agentforce, you can create one in seconds using natural language prompts. The AI-generated brief incorporates your goals and guidelines, making collaboration and approvals seamless. 3. Contextual Content Creation Agentforce generates emails, landing pages, and calls to action directly aligned with your brand’s tone and campaign goals. Marketers can refine outputs with natural language prompts, ensuring a perfect fit for their strategy. 4. Effortless Audience Segmentation No SQL skills? No problem. Describe your ideal audience in natural language, and Agentforce will translate that into actionable segments—helping you target precisely the right customers. 5. Automated Journey Activation Agentforce simplifies multi-channel journey creation by drafting personalized campaign flows. You can refine, approve, and activate these journeys with ease, saving time while enhancing impact. 6. Unlimited Content Variations AI eliminates content constraints, allowing you to generate multiple variations for personalized campaigns. Target high-value customers, newcomers, or loyal fans with tailored messages—all at scale. 7. Explore Nuanced Segments Agentforce enables marketers to create segments without relying on overburdened data science teams. Dive into deeper audience insights, such as churn rates based on location, age, or past behavior, with just a prompt. 8. Embed Continuous Testing Testing is often deprioritized due to time limitations. Agentforce automates testing workflows, making it easier to incorporate A/B testing and iterative learning into every campaign. Getting Started with Agentforce Campaigns Agentforce Campaigns is available in Marketing Cloud Growth and Advanced Editions, designed to empower businesses of all sizes. By integrating AI-driven tools into your workflow, you can elevate your marketing to new heights—enhancing creativity, efficiency, and customer engagement. Ready to revolutionize your campaigns? Explore how Agentforce can help you win customers and foster a more productive, cohesive marketing team. Salesforce Disclaimer: Unreleased features mentioned here are subject to change and may not become available as described. Make purchasing decisions based on currently available features. 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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October 10, 2024in Agentforce Platform, Artificial Intelligence, Data, Google, Salesforce, Salesforce Personalization
AI Causes Job Flux
10Oct

AI Causes Job Flux

AI Barometer Signals Job Disruption Amid Global Productivity Gains A recent PwC report highlights significant productivity improvements worldwide, but also points to potential job disruption due to artificial intelligence (AI). Described as the “Industrial Revolution of knowledge work,” AI is transforming how workers utilize information, generate content, and deliver results at unprecedented speed and scale. The 2024 AI Jobs Barometer, released by PwC, aims to provide empirical data on the impact of AI on global employment. AI Causes Job Flux but not necessarly job loss. AI Causes Job Flux The analysis involved examining over half a billion job ads across 15 advanced economies, including the U.S., Canada, Singapore, Australia, New Zealand, and several European nations. PwC sought to uncover the effects of AI on jobs, skills, wages, and productivity by monitoring the rise of positions requiring specialist AI skills across various industries and regions. The findings show that AI adoption is accelerating, with workers proficient in AI commanding substantial wage premiums. Broader Workforce Impact Interestingly, the impact of AI extends beyond workers with specialized AI skills. According to PwC, the majority of workers leveraging AI tools do not require such expertise. In many cases, a small number of AI specialists design tools that are then used by thousands of customer service agents, analysts, or legal professionals—none of whom possess advanced AI knowledge. This trend is driven largely by generative AI applications, which can typically be operated using simple, everyday language without technical skills. AI’s Economic Promise AI is leading a productivity revolution. Labor productivity growth has stagnated in many OECD countries over the past two decades, but AI may offer a solution. To better understand its effect on productivity, PwC analyzed jobs based on their “AI exposure,” indicating the extent to which AI can assist with tasks within specific roles. The report found that industries with higher AI exposure are experiencing much greater labor productivity growth. Knowledge-based jobs, in particular, show the highest AI exposure and the greatest demand for workers with advanced AI skills. Sectors such as financial services, professional services, and information and communications are leading the way, with AI-related job shares 2.8x, 3x, and 5x higher, respectively, than other industries. Overall, these sectors are witnessing nearly fivefold productivity growth due to AI integration. AI is also playing a role in alleviating labor shortages. Jobs in customer service, administration, and IT, among others, are still growing but at a slower rate. AI-driven productivity may help fill gaps caused by shrinking working-age populations in advanced economies. Wage Premiums for AI Skills Workers in AI-specialist roles are seeing significant wage premiums—up to 25% on average. Since 2016, demand for these roles has outpaced the growth of the overall job market. The highest wage premiums are found in the U.S. (25%) and the U.K. (14%), with data specialists commanding premiums of over 50% in both countries. Financial analysts, lawyers, and marketing managers also enjoy substantial wage boosts. The Disruption of Job Markets The skills required for AI-exposed jobs are evolving rapidly. PwC’s report reveals that new skills are emerging 25% faster in AI-exposed occupations compared to those less affected by AI. Jobs requiring AI proficiency have grown 3.5 times faster than other roles since 2016, and this trend predates the rise of popular tools like ChatGPT. However, while AI is driving demand for new skills, it is also reducing the need for certain old ones. Jobs in fields like IT, design, sales, and data analysis are seeing slower growth, as tasks in these areas are increasingly automated by AI technologies. The Future of Work The PwC report stresses that AI will not necessarily result in fewer jobs overall, but will change the nature of work. Instead of asking whether AI can replicate existing tasks, the focus should be on how AI enables new opportunities and industries. Tectonic recommends you work on this trail of thought by implementing AI Acceptable Use Policies in your company. Encourage your teams to explore AI tools that increase productivity but clearly outline what is and is not acceptable AI usage. PwC outlines several steps for policymakers, business leaders, and workers to take to ensure a positive transition into the AI era. Policymakers are encouraged to promote AI adoption through supportive policies, digital infrastructure, and workforce development. Business leaders should embrace AI as a complement to human workers, focusing on generating new ways to create value. Meanwhile, workers must build AI-complementary skills and experiment with AI tools to remain competitive in the evolving job market. Ultimately, while AI is disrupting the job landscape, it also presents vast opportunities for those who are willing to adapt. Like past technological revolutions, those who embrace change stand to benefit the most from AI’s transformative power. 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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October 10, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Salesforce
Marketing Cloud Website Activity Collection
09Oct

Marketing Cloud Website Activity Collection

Leveraging Website Activity Data in Salesforce Marketing Cloud Understanding how users interact with your website is essential for delivering personalized customer experiences. Salesforce Marketing Cloud (SFMC) offers robust tools to capture website activity and transform this data into actionable insights, enhancing your marketing strategies. This guide walks you through the process of collecting website activity data in SFMC. Marketing Cloud Website Activity Collection Before diving into the setup process, it’s important to understand the benefits of collecting website activity data: Now, let’s explore how to set up website activity tracking in Salesforce Marketing Cloud. Set Up Marketing Cloud Website Activity Collection Step 1: Install Salesforce Marketing Cloud Tracking Code To begin collecting website activity, install the Salesforce Marketing Cloud tracking code on your website. Known as the “Web Collect” code, this script captures visitor behavior data and sends it to SFMC. Step 2: Configure Data Extensions After installing the tracking code, set up data extensions in SFMC to store the website activity data you collect. Step 3: Set Up Behavioral Triggers To maximize the value of your data, set up behavioral triggers in SFMC. These triggers can automatically send personalized communications based on specific website actions. Step 4: Leverage Advertising Studio for Retargeting To further enhance your marketing efforts, use Advertising Studio to create retargeting campaigns based on website activity data. Step 5: Monitor and Optimize After setting up website activity tracking, regularly monitor the performance of your campaigns and the quality of your collected data. Final Thoughts Collecting website activity data in Salesforce Marketing Cloud enables you to understand customer behavior better and deliver more personalized experiences. By following these steps—installing the tracking code, configuring data extensions, setting up behavioral triggers, and leveraging retargeting—you can effectively harness website activity data to elevate your marketing efforts. Start implementing these strategies today to unlock the full potential of Salesforce Marketing Cloud and drive deeper engagement and conversions. 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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October 9, 2024in Analytics Builder, Data, Google, Salesforce, Salesforce Marketing Cloud
09Oct

Enterprise AI

Enterprise AI: Revolutionizing Business Operations for a Competitive Edge Enterprise AI refers to the suite of advanced artificial intelligence technologies—such as machine learning, natural language processing (NLP), robotics, and computer vision—that organizations use to transform operations, enhance efficiency, and gain a competitive advantage. These technologies demand high-quality data, skilled expertise, and adaptability to rapid advancements. Businesses increasingly adopt enterprise AI because of its ability to automate critical processes, reduce costs, optimize operations, and enable data-driven decision-making. According to McKinsey’s 2024 report, 72% of organizations now integrate AI into their operations, a significant increase from 50% just six years ago. However, implementing AI presents challenges, such as employee mistrust, data biases, lack of explainability, and managing AI’s fast evolution. Successful adoption requires aligning AI initiatives with organizational goals, fostering data trust, and building internal expertise. This guide provides a strategic roadmap for embracing enterprise AI, covering foundational concepts, advanced use cases, and ways to navigate common pitfalls. Why AI Matters in the Enterprise Enterprise AI is a transformative force, similar to how the internet revolutionized global businesses. By integrating AI into their operations, organizations can achieve: AI-driven applications are reshaping industries by enabling hyper-personalized customer experiences, optimizing supply chains, and automating repetitive tasks to free employees for higher-value contributions. The rapid pace of AI innovation requires leaders to consistently re-evaluate its alignment with their strategies while maintaining effective data management and staying informed on evolving tools and regulations. AI’s Transformational Impact on Business AI’s potential is as groundbreaking as electrification in the 20th century. Its immediate influence lies in automating tasks and augmenting human workflows. For example: Generative AI tools like ChatGPT and Copilot further accelerate adoption by automating creative and intellectual tasks. Key Benefits of Enterprise AI Challenges of Enterprise AI Despite its benefits, AI adoption comes with hurdles: Ethical concerns, such as workforce displacement and societal impacts, also demand proactive strategies. AI and Big Data: A Symbiotic Relationship AI thrives on large, high-quality datasets, while big data analytics leverage AI to extract deeper insights. The rise of cloud computing amplifies this synergy, enabling scalable, cost-effective AI deployments. Evolving AI Use Cases AI continues to redefine industries, turning complex tasks into routine operations: Future AI Trends to Watch Building the Future with Responsible AI As AI advances, organizations must prioritize responsible AI practices, balancing innovation with ethical considerations. Developing robust frameworks for transparency and governance is essential to maintaining trust and fostering sustainable growth. AI’s future offers vast opportunities for businesses willing to adapt and innovate. By aligning AI initiatives with strategic goals and investing in robust ecosystems, enterprises can unlock new efficiencies, drive innovation, and lead in 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 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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October 9, 2024in AI Tools, Artificial Intelligence, Data, Enterprise, Generative AI
Smartsheet and AWS Collaborate
09Oct

Smartsheet and AWS Collaborate

Smartsheet and AWS Collaborate to Enhance AI-Driven Decision-Making with New Amazon Q Business Connector October 8, 2024 — During its annual ENGAGE customer conference, Smartsheet (NYSE: SMAR), the enterprise work management platform, announced a partnership with AWS to introduce a new connector that integrates Smartsheet data with Amazon Q Business. This generative AI-powered assistant can answer questions, provide summaries, generate content, and securely complete tasks using data from customers’ enterprise systems. This integration will allow Amazon Q Business users to access insights about their projects and processes managed in Smartsheet, facilitating a cohesive search experience that empowers employees to make informed, data-driven decisions. Smartsheet and AWS Collaborate. As organizations increasingly recognize the importance of data-driven decisions, data silos remain a major hurdle. Research from Salesforce in 2024 indicates that only about 28% of business applications are interconnected. The new connector aims to address this issue by securely merging Smartsheet data with other sources integrated into Amazon Q Business, such as Salesforce, Slack, Microsoft Teams, and AWS. This will benefit over 13 million Smartsheet users globally, including around 85% of the 2024 Fortune 500 companies, allowing them to access their work management data, including sheets, conversations, and files, through AWS’s generative AI-powered assistant. This integration enhances decision-making, productivity, and efficiency. Smartsheet and AWS Collaborate “The Smartsheet connector furthers our strategy to securely integrate Smartsheet with leading enterprise AI tools, allowing customers to work seamlessly across their business applications,” said Ben Canning, SVP of Product Experiences at Smartsheet. “By combining our flexible data model with Amazon Q Business, we’re unlocking access to work management data for our mutual customers, enabling them to focus on achieving business outcomes without worrying about data storage.” For instance, service operations managers can utilize the new connector to manage complex projects more effectively. By posing specific questions to the Amazon Q Business assistant, teams can gain insights from various data sources, including sheets, conversations, and attachments in Smartsheet. The AI assistant conducts thorough searches while respecting access permissions, saving time and enhancing project oversight. This streamlined approach improves client retention, accuracy, and overall service quality. “Generative AI presents a unique opportunity for organizations to transform their internal workflows. The key is securely accessing their own data, regardless of its location or format,” stated Dilip Kumar, Vice President of Amazon Q Business at AWS. “Many enterprises use Smartsheet as their primary collaboration hub, storing billions of rows of data. Allowing Amazon Q Business users to interact with their Smartsheet data in a simple, secure manner boosts productivity, analysis, and decision-making.” “Generative AI is driving a significant shift in how enterprise knowledge is stored, accessed, and utilized,” noted Dion Hinchcliffe, VP of the CIO Practice at The Futurum Group. “This transition offers a chance to redefine what’s possible in data management. A strategic, informed approach to adopting this technology is crucial. By integrating work management data into Amazon Q Business, Smartsheet and AWS are creating a unified AI search experience across their knowledge base, unlocking the true potential of their data.” Empowering Teams to Achieve More with Generative AI Smartsheet is collaborating with industry leaders like AWS to develop AI capabilities that help enterprises manage their critical tasks more strategically and efficiently. Earlier this year, Smartsheet implemented Amazon Q Business internally to enhance knowledge management and boost employee productivity in the cloud. The Smartsheet connector exemplifies how both organizations are delivering powerful AI tools that revolutionize team workflows. Smartsheet continues to integrate generative AI throughout its platform, designed with practicality, transparency, and customer needs in mind. Smartsheet’s AI tools enable organizations to swiftly extract insights from data, create automated processes, generate text and summaries, and accomplish more with the AI assistant. Through the end of December, Smartsheet is offering its entire suite of AI tools to all customers, allowing everyone to leverage AI’s capabilities within the platform. The Smartsheet connector is currently available to Amazon Q Business customers in public preview. About Smartsheet Smartsheet is a modern enterprise work management platform trusted by millions globally, including approximately 85% of the 2024 Fortune 500 companies. As a pioneering leader in its category, Smartsheet delivers powerful solutions that drive performance and foster innovation. Visit www.smartsheet.com for more information. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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October 9, 2024in AI Tools, Data, Enterprise, Generative AI, Salesforce, Technology
Salesforce Agentforce Integration
07Oct

Salesforce Agentforce Integration

The rise of AI-powered solutions is transforming customer service, support, and automation. For organizations such as nonprofits, national associations, and large enterprises, delivering seamless customer experiences has become crucial. Salesforce’s Agentforce, a next-generation conversational AI tool, plays a vital role in this transformation. Designed to elevate customer support and interaction, Agentforce provides an intelligent and scalable solution for integrating AI chatbots into content management systems (CMS) like WordPress, Drupal, and HubSpot. Salesforce Agentforce Integration. In this detailed feature review, we will dive into the extensive capabilities of Salesforce Agentforce, analyzing its role as a conversational tool, its technical requirements, and the benefits it provides for nonprofits, national associations, and businesses. We’ll also compare its applications across various CMS platforms like Drupal, WordPress, and HubSpot, exploring its potential as a powerful AI assistant for website automation and customer interaction. Salesforce Agentforce: A Technical Perspective Salesforce Agentforce is an advanced AI-driven conversational assistant that seamlessly integrates into the Salesforce environment. By tapping into Salesforce CRM’s vast data resources, Agentforce serves as an intelligent interface, automating everything from initial customer inquiries to more personalized interactions. Utilizing natural language processing (NLP) and machine learning, it continually refines responses and scales interactions, making it an indispensable tool for organizations aiming to enhance customer service workflows. Agentforce integrates smoothly with Salesforce Service Cloud, automating both live chat support and chatbot responses. Additionally, it can connect with third-party platforms, including popular CMS solutions like WordPress, Drupal, and HubSpot, allowing organizations to centralize customer service operations in one interface. Core Features and Technical Architecture of Agentforce Natural Language Understanding (NLU) and Processing (NLP) Agentforce’s NLP capabilities are its backbone, allowing it to understand complex human language and respond contextually. This empowers it to manage initial inquiries as well as more sophisticated support requests. Agentforce’s NLU also enables it to work across different languages, dialects, and industry-specific terminology, making it particularly valuable for global organizations and national associations serving diverse audiences. Machine Learning for Continuous Improvement Agentforce’s machine learning feature enhances its ability to improve accuracy and understanding over time. Each interaction helps the chatbot evolve, making it more effective at delivering relevant, real-time responses. This model integrates directly with Salesforce’s data infrastructure, giving Agentforce access to historical data and interactions to offer highly personalized, context-aware answers. Deep Integration with Salesforce CRM As a Salesforce-native tool, Agentforce can harness CRM data in ways other AI tools cannot. By accessing customer histories, purchase data, and previous interactions, it creates personalized experiences that build customer trust. Nonprofits and associations can use this data to improve donor or member interactions, offering targeted support and outreach. Agentforce can also be tailored to retrieve specific datasets, such as an individual’s support history or ongoing service case updates. Cross-Platform Flexibility and API Integration Agentforce offers flexible APIs that enable integration with third-party systems, including CMS platforms like WordPress, Drupal, and HubSpot. This flexibility ensures that AI-powered chatbots can be deployed on organizational websites, providing a seamless experience for customers, donors, and members alike. Whether it’s a nonprofit using Drupal or a business on WordPress, Agentforce acts as the central hub for support and engagement, offering fluid interactions on top of your CMS. HubSpot users can further leverage Agentforce’s marketing features to align lead generation with personalized, chat-based interactions. Use Cases for Agentforce in Nonprofits, National Associations, and Businesses Nonprofit Organizations For nonprofits managing donor, volunteer, and beneficiary relationships, Agentforce offers scalable, automated support: National Associations National associations can use Agentforce to handle high volumes of inquiries from members and professionals: Businesses For service-based enterprises, Agentforce is essential for customer service: Salesforce Agentforce and CMS Integration: WordPress, Drupal, and HubSpot WordPress and Salesforce Agentforce Integration For WordPress users, Agentforce offers customizable chatbot widgets that enhance customer engagement, handle ecommerce inquiries, and integrate with WooCommerce for product support. Drupal and Agentforce Integration Drupal’s modular architecture allows Agentforce to automate membership management, provide multilingual support, and distribute content for nonprofits and associations. HubSpot and Agentforce Integration HubSpot users benefit from Agentforce’s ability to automate lead nurturing, sales and marketing workflows, and customer support, all while keeping HubSpot and Salesforce CRM data synchronized. Tectonic and Salesforce Agentforce Integration At Tectonic, we understand that adopting AI-powered solutions like Salesforce Agentforce is only the first step toward delivering exceptional customer experiences. We specialize in crafting, training, and implementing tailored AI chatbot solutions that enhance engagement, streamline processes, and drive growth, all while seamlessly integrating with your current website or mobile app. As a full-service digital strategy firm, Tectonic excels in integrating advanced tools like Salesforce Agentforce into platforms like WordPress, Drupal, and HubSpot, ensuring your automation strategies are executed with precision. From custom chatbot implementations to comprehensive digital strategy services, our team is dedicated to optimizing your website for engagement and lead generation. 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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October 7, 2024in Agentforce Platform, AI Tools, Data, Enterprise, Salesforce, Salesforce Service Cloud
AI All Grown Up
07Oct

AI All Grown Up

If you thought Salesforce had fully embraced AI, think again. The company has much more in store. AI All Grown Up and Salesforce is the educator! Alongside the announcement of the new Agentforce platform, Salesforce has teased plans to offer free premium instructor-led courses and AI certifications throughout 2025, reflecting a bold commitment to fostering AI skills and expertise. We’ve talked quite a bit over the last year about the need for AI education, and lo and behold here comes Salesforce to the rescue! AI All Grown Up Ah, they grow up so fast. Once just a baby cradeled in our arms with endless possibilities and potential. It was just like a year or so ago we heard of ChatGPT. Prior to that most people’s main exposure to artificial intelligence was their smart phones, which today we realize weren’t reall that smart. Generative, predictive and agentic AI have barreled down the pipeline increasing our vocabulary, and understanding, of what artificial intelligence can do. From generative content to sounds and images, AI continued to amaze us. Then predictive AI did our calculations faster than we could have imagined. Then agentic AI did nearly everything imaginable. AI All Grown Up. Like a very proud mentor of the process, I want to talk about Salesforce’s major contribution. Addressing the AI Skills Gap: Salesforce’s $50 Million Investment As the veritable plethora of AI tools rapidly expands, Salesforce is taking proactive steps to address the growing AI skills gap by investing $50 million into workforce upskilling initiatives. The company aims to ensure that businesses and individuals are prepared to utilize their new wave of AI tools effectively. While the full details have yet to be released, Salesforce has revealed that its premium AI courses and certifications will be made available for free via Trailhead by the end of 2025. This could mean certifications such as AI Associate and AI Specialist, which currently cost $75 and $200 respectively, may soon be offered at no cost. Gratis. Free, Salesforce has also mentioned “premium instructor-led training,” sparking speculation that AI-focused, instructor-led Trailhead Academy courses could become accessible to everyone in the Salesforce ecosystem. Expanding AI Education with Global AI Centers Salesforce’s AI upskilling push is part of a broader initiative to establish “AI Centers” across the globe. Following the opening of its first center in London in June, Salesforce is planning to launch additional AI hubs in cities like Chicago, Tokyo, Sydney, and even a pop-up center in San Francisco. These centers will host in-person premium courses and serve as gathering spaces for industry experts, partners, and customers. This initiative benefits not only the Salesforce ecosystem by increasing AI knowledge where expertise is scarce, but also aligns with Salesforce’s strategy of bringing AI-driven solutions to market through new products like Copilot Studio, Data Cloud, and the newly launched Agentforce platform. Agentforce: Salesforce’s Third Wave of AI On August 28, 2024, Salesforce introduced Agentforce, a suite of autonomous AI agents that marks a significant leap in how businesses engage with customers. Described as the “Third Wave of AI,” Agentforce goes beyond traditional chatbots, providing intelligent agents capable of driving customer success with minimal human intervention. What is Agentforce? Agentforce is a comprehensive platform designed for organizations to build, customize, and deploy autonomous AI agents across various business functions, such as customer service, sales, marketing, and commerce. These agents operate independently, accessing data, crafting action plans, and executing tasks without needing constant human oversight. It is like Artificial Intelligence just graduated highschool and is off to a world of new adventures and growth opportunities at college or university! Key Features of Agentforce: The Technology Behind Agentforce At the core of Agentforce is the Atlas Reasoning Engine, a system designed to mimic human reasoning. Here’s how it works: Customization Tools: Agent Builder Agentforce provides tools like Agent Builder, a low-code platform for customizing out-of-the-box agents or creating new ones for specific business needs. With this tool, users can: The Agentforce Partner Network Salesforce’s partner ecosystem plays a key role in Agentforce’s versatility, with contributions from companies like AWS, Google, IBM, and Workday. Together, they’ve developed over 20 agent actions available through the Salesforce AppExchange. As proud parents we watch our Artificial Intelligence child venture into the world making friends along the way. Learning social skills. Benefits and Impact of Agentforce Early Adopters and Success Stories Several companies are already benefiting from Agentforce: Availability and Pricing of Salesforce’s AI All Grown Up Agentforce for Service and Sales will be generally available on October 25, 2024, with some components of the Atlas Reasoning Engine launching in February 2025. Pricing starts at $2 per conversation, with volume discounts available. The Future of AI and Work Salesforce’s ambitious vision is to empower one billion AI agents with Agentforce by the end of 2025. This reflects their belief that the future of work will involve a hybrid workforce, where humans and AI agents collaborate to drive customer success. AI All Grown Up and We Couldn’t Be Prouder Our amazing AI child has graduated college and ventured out into the workforce. Agentforce vs. Einstein Bots: What’s the Difference? Conclusion Agentforce represents a major leap forward in AI-powered customer engagement. By providing autonomous, intelligent agents capable of managing complex tasks, Salesforce is positioning itself at the forefront of AI innovation. As businesses continue to explore ways to improve efficiency and customer satisfaction, Agentforce could redefine how organizations interact with customers and streamline their operations. If this is the Third Wave of AI, what will the fourth wave bring? Written by Tectonic’s Solutions Architect, Shannan Hearne 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

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October 7, 2024in Agentforce Platform, AI Tools, Data, Google, Mulesoft, Salesforce, Technology
Generative AI and Patient Engagement
07Oct

Generative AI and Patient Engagement

The healthcare industry is undergoing a significant digital transformation, with generative AI and chatbots playing a prominent role in various patient engagement applications. Technologies such as online symptom checkers, appointment scheduling, patient navigation tools, medical search engines, and patient portal messaging are prime examples of how AI is enhancing patient-facing interactions. These advancements aim to alleviate staff workload while improving the overall patient experience, according to industry experts. However, even these patient-centric applications face challenges, such as the risk of generating medical misinformation or biased outcomes. As healthcare professionals explore the potential of generative AI and chatbots, they must also implement safeguards to prevent the spread of false information and mitigate disparities in care. Online Symptom Checkers Online symptom checkers allow patients to input their symptoms and receive a list of potential diagnoses, helping them decide the appropriate level of care, whether it’s urgent care or self-care at home. These tools hold promise for improving patient experiences and operational efficiency, reducing unnecessary healthcare visits. For healthcare providers, they help triage patients, ensuring those who need critical care receive it. However, the effectiveness of online symptom checkers is mixed. A 2022 literature review revealed that diagnostic accuracy ranged between 19% and 37.9%, while triage accuracy was higher, between 48.9% and 90%. Patient reception to these tools has been lukewarm as well, with some expressing dissatisfaction with the COVID-19 symptom checkers during the pandemic, mainly when the tools did not emulate human interaction. Moreover, studies have indicated that these tools might exacerbate health inequities, as users tend to be younger, female, and more digitally literate. To mitigate this, developers must ensure that chatbots can communicate in multiple languages, replicate human interactions, and escalate to human providers when needed. Self-Scheduling and Patient Navigation Generative AI and conversational AI have shown promise in addressing lower-level patient inquiries, such as appointment scheduling and navigation, reducing the strain on healthcare staff. AI-driven scheduling systems help fill gaps in navigation by assisting patients with appointment bookings and answering logistical questions, like parking or directions. A December 2023 review noted that AI-optimized patient scheduling reduces provider time burdens and improves patient satisfaction. However, barriers such as health equity, access to broadband, and patient trust must be addressed to ensure effective implementation. While organizations need to ensure these systems are accessible to all, AI is a valuable tool for managing routine patient requests, freeing staff to focus on more complex issues. Online Medical Research AI tools like ChatGPT are expanding on the “Dr. Google” phenomenon, offering patients a way to search for medical information. Despite initial concerns from clinicians about online medical searches, recent studies show that generative AI tools can provide accurate and understandable information. For instance, ChatGPT accurately answered breast cancer screening questions 88% of the time in one 2023 study and offered adequate colonoscopy preparation information in another. However, patients remain cautious about AI-generated medical advice. A 2023 survey revealed that nearly half of respondents were concerned about potential misinformation, and many were unsure about the sources AI tools use. Addressing these concerns by validating source material and providing supplementary educational resources will be crucial for building patient trust. Patient Portal Messaging and Provider Communication Generative AI is also finding its place in patient portal messaging, where it can generate responses to patient inquiries, helping to alleviate clinician burnout. In a 2024 study, AI-generated responses within a patient portal were often indistinguishable from those written by clinicians, requiring human editing in only 58% of cases. While chatbot-generated messages have been found to be more empathetic than those written by overworked providers, it’s important to ensure AI-generated responses are always reviewed by healthcare professionals to catch any potential errors. In addition to patient engagement, generative AI is being used in clinical decision support and ambient documentation, showcasing its potential to improve healthcare efficiency. However, developers and healthcare organizations must remain vigilant about preventing algorithmic bias and other AI-related 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 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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October 7, 2024in AI Tools, Generative AI, Google, Google Analytics, Salesforce Development Services, Salesforce Health and Life Sciences Solutions, Salesforce Implementation Services, Salesforce Optimization, Technology
Natural Language Processing Explained
06Oct

Natural Language Processing Explained

What is Natural Language Processing (NLP)? Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that enables computers to interpret, analyze, and generate human language. By leveraging machine learning, computational linguistics, and deep learning, NLP helps machines understand written and spoken words, making communication between humans and computers more seamless. I apologize folks. I am feeling like the unicorn who missed the Ark. Tectonic has been providing you with tons of great material on artificial intelligence, but we left out a basic building block. Without further ado, Natural Language Processing Explained. Like a lot of components of AI, we often are using it without knowing we are using it. NLP is widely used in everyday applications such as: How Does NLP Work? Natural Language Processing combines several techniques, including computational linguistics, machine learning, and deep learning. It works by breaking down language into smaller components, analyzing these components, and then drawing conclusions based on patterns. If you have ever read a first grader’s reading primer it is the same thing. Learn a little three letter word. Recognize the meaning of the word. Understand it in the greater context of the sentence. Key NLP preprocessing steps include: Why Is NLP Important? NLP plays a vital role in automating and improving human-computer interactions by enabling systems to interpret, process, and respond to vast amounts of textual and spoken data. By automating tasks like sentiment analysis, content classification, and question answering, NLP boosts efficiency and accuracy across industries. For example: Key Use Cases of NLP in Business NLP Tasks NLP enables machines to handle various language tasks, including: Approaches to NLP Future of NLP NLP is becoming more integral in daily life as technology improves. From customer service chatbots to medical record summarization, NLP continues to evolve, but challenges remain, including improving coherence and reducing biases in machine-generated text. Essentially, NLP transforms the way machines and humans interact, making technology more intuitive and accessible across a range of industries. By Tectonic Solutions Architect – Shannan Hearne 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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October 6, 2024in Artificial Intelligence, Data, Google, Technology
Promising Patient Engagement Use Cases for GenAI and Chatbots
06Oct

Promising Patient Engagement Use Cases for GenAI and Chatbots

Promising Patient Engagement Use Cases for GenAI and Chatbots Generative AI (GenAI) is showing great potential in enhancing patient engagement by easing the burden on healthcare staff and clinicians while streamlining the overall patient experience. As healthcare undergoes its digital transformation, various patient engagement applications for GenAI and chatbots are emerging as promising tools. Let’s look at Promising Patient Engagement Use Cases for GenAI and Chatbots. Key applications of GenAI and patient-facing chatbots include online symptom checkers, appointment scheduling, patient navigation, medical search engines, and even patient portal messaging. These technologies aim to alleviate staff workloads while improving the patient journey, according to some experts. However, patient-facing AI applications are not without challenges, such as the risk of generating medical misinformation or exacerbating healthcare disparities through biased algorithms. As healthcare professionals explore the potential of GenAI and chatbots for patient engagement, they must also ensure safeguards are in place to prevent the spread of inaccuracies and avoid creating health inequities. Online Symptom Checkers Online symptom checkers allow healthcare organizations to assess patients’ medical concerns without requiring an in-person visit. Patients can input their symptoms, and the AI-powered chatbot will generate a list of possible diagnoses, helping them decide whether to seek urgent care, visit the emergency department, or manage symptoms at home. These tools promise to improve both patient experience and operational efficiency by directing patients to the right care setting, thus reducing unnecessary visits. For healthcare providers, symptom checkers can help triage patients and ensure high-acuity areas are available for those needing critical care. Despite their potential, studies show mixed results regarding the diagnostic accuracy of online symptom checkers. A 2022 literature review found that diagnostic accuracy for these tools ranged from 19% to 37.9%. However, triage accuracy—referring patients to the correct care setting—was better, ranging between 48.9% and 90%. Patient reception to symptom checkers has also been varied. For example, during the COVID-19 pandemic, symptom checkers were designed to help patients assess whether their symptoms were virus-related. While patients appreciated the tools, they preferred chatbots that displayed human-like qualities and competence. Tools perceived as similar in quality to human interactions were favored. Furthermore, some studies indicate that online symptom checkers could deepen health inequities, as users tend to be younger, female, and more digitally literate. To mitigate this, AI developers must create chatbots that can communicate in multiple languages, mimic human interaction, and easily escalate issues to human professionals when needed. Self-Scheduling and Patient Navigation GenAI and conversational AI are proving valuable in addressing routine patient queries, like appointment scheduling and patient navigation, tasks that typically fall on healthcare staff. With a strained medical workforce, using AI for lower-level inquiries allows clinicians to focus on more complex tasks. AI-enhanced appointment scheduling systems, for example, not only help patients book visits but also answer logistical questions like parking directions or department locations within a clinic. A December 2023 literature review highlighted that AI-optimized scheduling could reduce provider workload, increase patient satisfaction, and make healthcare more patient-centered. However, key considerations for AI integration include ensuring health equity, broadband access, and patient trust. While AI can manage routine requests, healthcare organizations need to ensure their tools are accessible and functional for diverse populations. Online Medical Research GenAI tools like ChatGPT are contributing to the “Dr. Google” phenomenon, where patients search online for medical information before seeing a healthcare provider. While some clinicians have been cautious about these tools, research suggests they can effectively provide accurate medical information. For instance, an April 2023 study showed that ChatGPT answered 88% of breast cancer screening questions correctly. Another study in May 2023 demonstrated that the tool could adequately educate patients on colonoscopy preparation. In both cases, the information was presented in an easy-to-understand format, essential for improving health literacy. However, GenAI is not without flaws. Patients express concern about the reliability of AI-generated information, with a 2023 Wolters Kluwer survey showing that 49% of respondents worry about false information from GenAI. Additionally, many are uneasy about the unknown sources and validation processes behind the information. To build patient trust, AI developers must ensure the accuracy of their source material and provide supplementary authoritative resources like patient education materials. Patient Portal Messaging and Provider Communication Generative AI has also found use in patient portal messaging, where it can draft responses on behalf of healthcare providers. This feature has the potential to reduce clinician burnout by handling routine inquiries. A study conducted at Mass General Brigham in April 2024 revealed that a large language model embedded in a secure messaging tool could generate acceptable responses to patient questions. In 58% of cases, chatbot-generated messages required human editing. Promising Patient Engagement Use Cases for GenAI and Chatbots Interestingly, other research has found that AI-generated responses in patient portals are often more empathetic than those written by overworked healthcare providers. Nevertheless, AI responses should always be reviewed by a clinician to ensure accuracy before being sent to patients. Generative AI is also making strides in clinical decision support and ambient documentation, further boosting healthcare efficiency. However, as healthcare organizations adopt these technologies, they must address concerns around algorithmic bias and ensure patient safety remains a top priority. 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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October 6, 2024in Agentforce Platform, AI Tools, Generative AI, Google, Laws, Life Sciences Cloud, Salesforce Development Services, Salesforce Health and Life Sciences Solutions, Salesforce Health Cloud
Ethical AI Implementation
06Oct

Ethical AI Implementation

AI technologies are rapidly evolving, becoming a practical solution to support essential business operations. However, creating true business value from AI requires a well-balanced approach that considers people, processes, and technology. Ethical AI Implementation. AI encompasses various forms, including machine learning, deep learning, predictive analytics, natural language processing, computer vision, and automation. To leverage AI’s competitive advantages, companies need a strong foundation and a realistic strategy aligned with their business goals. “Artificial intelligence is multifaceted,” said John Carey, managing director at AArete, a business management consultancy. “There’s often hype and, at times, exaggeration about how ‘intelligent’ AI truly is.” Business Advantages of AI Adoption Recent advancements in generative AI, such as ChatGPT and Dall-E, have showcased AI’s significant impact on businesses. According to a McKinsey Global Survey, global AI adoption surged from around 50% over the past six years to 72% in 2024. Some key benefits of adopting AI include: Prerequisites for AI Implementation Successfully implementing AI can be complex. A detailed understanding of the following prerequisites is crucial for achieving positive results: 13 Steps for Successful AI Implementation Common AI Implementation Mistakes Organizations often stumble by: Key Challenges in Ethical AI Implementation Human-related challenges often present the biggest hurdles. To overcome them, organizations must foster data literacy and build trust among stakeholders. Additionally, challenges around data management, model governance, system integration, and intellectual property need to be addressed. Ensuring Ethical AI Implementation To ensure responsible AI use, companies should: Ethical AI implementation requires a continuous commitment to transparency, fairness, and inclusivity across all levels of the organization. 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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October 6, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Salesforce, Technology
05Oct

Agentforce Autonomous Agents

Agentforce: Transforming Business Operations with Autonomous Agents Agentforce empowers organizations to create and manage autonomous agents that streamline tasks across various business departments. These include Sales Agents, Service Agents, Marketing Agents, Commerce Agents, and Platform Agents—truly delivering on the vision of “an Agentforce in every app.” But how does Agentforce work, and what are the building blocks for configuring these agents? Salesforce emphasizes that Agentforce is built with clicks, not code, making it highly accessible to users. This claim was validated by many attendees at the ‘Agentforce Launchpad’ during Dreamforce, who noted that the tool is as declarative and user-friendly as Salesforce promised. The Building Blocks of Agentforce 1. Agent Builder The journey begins with the Agent Builder within Agentforce Studio. This configuration tool allows users to define their agent’s attributes, such as the avatar, name, and description, using natural language inputs—essentially describing the agent in conversational terms. Salesforce describes it as: “If you can dream it, Agentforce can do it.” The Agent Builder interface comprises: Salesforce also provides out-of-the-box agents, such as Sales Agents, which can be enabled via guided setup. 2. Agent Topics Topics are the foundational building blocks that determine an agent’s scope of work. For example, a topic like “Order Management” grants the agent access to data such as order histories and product specifications. In the Dreamforce keynote, Saks’ service agent demonstrated the importance of topics by resolving customer queries tied to its assigned topics. However, queries outside the defined topics were flagged as “guardrails,” ensuring the agent stayed within its designated scope. 3. Topic Actions Actions, tied to topics, define what an agent can do. These actions are often flows, such as querying a CRM database or triggering automated processes. Users can assign existing actions or create new ones by referencing Apex, Flow, prompts, or MuleSoft APIs. For example, integrating external data sources requires defining a new Agentforce action tied to a MuleSoft API. This allows the agent to query data just as human users would. Testing Agents with the Atlas Reasoning Engine Agentforce’s Atlas Reasoning Engine powers agents with advanced capabilities. Users can test agents within the Agent Builder interface, following the reasoning process step-by-step: Once configured, agents are ready to operate across their assigned communication channels (e.g., email, WhatsApp, voice). Omni Supervisor: Real-Time Agent Monitoring Omni Supervisor, originally a Service Cloud feature, now extends to monitoring agents. It provides insights into overall trends, allows real-time oversight of interactions, and even enables listening to recent conversations. The Role of Data Cloud in Agentforce Data powers Agentforce, enabling agents to provide highly contextual responses. The Data Cloud processes both structured data (e.g., Salesforce records) and unstructured data (e.g., emails, voice memos) using its Vector Database for advanced processing. 1. Retrieval Augmented Generation (RAG) Salesforce employs RAG to enhance the accuracy of agent responses. RAG integrates the Atlas Reasoning Engine with Data Cloud, creating a feedback loop. Data Cloud enriches user prompts by retrieving relevant data, making agent responses more contextual and informed. 2. New Data Streams To enhance Agentforce capabilities, data can be ingested into the platform in three ways: For instance, connecting an order management system like Snowflake is streamlined via Salesforce’s prebuilt connectors. 3. Data Graphs Data Graphs visualize relationships between Data Model Objects (DMOs), enabling users to ensure all necessary data is available for optimal agent performance. Real-time Data Graphs enhance identity resolution, segmentation, and action execution for seamless data flow. Inside Prompt Builder Prompt Builder allows users to create or refine prompts that power Agentforce actions. Low-code tools guide users through the process, offering features such as previewing results and assessing feedback toxicity ratings. Search Index in RAG The Search Index is a critical component of RAG. It retrieves relevant data from Data Cloud to enhance agent reasoning. Search parameters can be configured in three ways: Tectonic’s Thoughts Agentforce, powered by Data Cloud and advanced AI tools like the Atlas Reasoning Engine, represents a new era of automation and efficiency for businesses. Whether through Sales, Service, or Marketing Agents, organizations can leverage this technology to streamline operations, personalize customer experiences, and achieve better outcomes. With over 5,200 customers implementing Agentforce in their sandboxes within the first two days of Dreamforce, the platform is already proving its transformative potential. By 2025 over a billion agents had been created! Agentforce isn’t just about improving efficiency; it’s about redefining what’s possible for business operations. Content updated January 2025. 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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October 5, 2024in AI Tools, Data, Mulesoft, Salesforce, Snowflake, Technology
AI Era Demands an Intelligent Data Infrastructure
05Oct

AI Era Demands an Intelligent Data Infrastructure

Why the AI Era Demands an Intelligent Data Infrastructure NetApp aims to bridge the gap between cloud-based AI models and on-premises data management systems, addressing a major challenge many organizations face as they explore emerging AI capabilities, such as generative AI. AI Era Demands an Intelligent Data Infrastructure. While the most advanced AI tools often reside in hyperscale clouds, the key data of most organizations remains on-premises. As companies move from AI experimentation to scaling their initiatives, they face the complex task of closing the divide between their data and AI. At first glance, the solution seems simple: Organizations can integrate their proprietary data with cloud-based AI services to enhance large language models (LLMs) and other AI tools, leveraging their industry-specific expertise to generate unique insights and drive value. In fact, a recent Enterprise Strategy Group (ESG) survey found that 84% of respondents believed incorporating enterprise data was critical to supporting their generative AI efforts. However, achieving this at scale is far more difficult than it appears. Many organizations are reluctant to send their valuable data to public clouds due to concerns about security, such as exposing sensitive intellectual property or personal information. Additionally, managing the process of transferring large datasets, creating data copies, updating models with fresh data, and keeping track of everything over time adds cost and complexity. Beyond these logistical issues, many organizations struggle with a fundamental challenge: poor data management. Rapid data growth, fragmentation, and a lack of understanding of their own data forces business users and data scientists to spend excessive time “data wrangling”—the process of identifying, gathering, and preparing data for AI models. This inefficiency delays training and deploying models, pushing back the point of inference, which is the key to unlocking value from AI. Increased latency also raises the risk that inferencing is based on outdated data, leading to repeated cycles and further inefficiency. This challenge has made data management a critical obstacle in AI adoption. ESG research identified poor data quality as the top challenge organizations face when implementing AI. Inadequate data is rapidly becoming a roadblock that hampers AI initiatives across industries. To address these challenges, NetApp has shifted its focus toward building an “intelligent data infrastructure” for the AI era. At the company’s Insight 2024 conference, NetApp outlined a strategy to support AI success by bridging the gap between cloud-based AI models and on-premises data environments. The company’s vision includes simplifying, automating, and mitigating risks in the data management workflow needed to scale AI in the enterprise. NetApp’s approach includes innovations like a global metadata namespace and enhancements to its OnTap software, which enable exploration, classification, and management of data across the NetApp ecosystem. These capabilities integrate directly into AI data pipelines, enabling scalable searches and retrieval-augmented generation inferencing. Additionally, NetApp’s new “disaggregated” storage architecture will support more cost-effective scaling for compute-heavy AI tasks such as model training. NetApp’s strategy also extends to the public cloud, where it already has strong partnerships with all three hyperscalers, offering NetApp services as first-party options in their clouds. The company is expanding this by developing additional cloud-native capabilities for AI, such as integrating Azure NetApp Files with Microsoft Azure AI services, FSx for NetApp OnTap with Amazon Bedrock, and Google Cloud NetApp Volumes with Google Vertex AI and BigQuery. These integrations will allow organizations to securely enrich public cloud-based AI models with their on-premises data, creating a scalable and secure environment for AI development. While some aspects of NetApp’s strategy will be delivered over the next year, many cloud integrations will be available sooner. NetApp is also building a partner ecosystem, including hardware partners like Lenovo and Nvidia, software ISVs, and service providers like Domino Data Labs, in addition to its cloud alliances. With this vision for intelligent data infrastructure, NetApp is positioning itself as a key enabler of AI-driven innovation, and it will be intriguing to watch how this strategy unfolds in the coming months. 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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October 5, 2024in AI Tools, Data, Enterprise, Generative AI, Google
Generative AI for Match Commentary
05Oct

Generative AI for Match Commentary

SAN FRANCISCO (KGO) — Companies are exploring the use of artificial intelligence for sports commentary, showcasing one of the many innovative applications of this technology in the sports arena. ABC7 reporter J.R. Stone recently got a firsthand look at IBM’s integration of Generative AI to analyze and enhance playing abilities during a demonstration at Dreamforce 2024 in San Francisco. This same technology has also been implemented at prestigious events like Wimbledon and the US Open. “This year marks the introduction of Generative AI for match commentary, which utilizes data collected during the games to create real-time analysis and match summaries,” explained Nick Otto from IBM. In a related segment, Salesforce CEO Marc Benioff revealed a new AI system called “Agent Force,” while Senator Scott Wiener introduced a bill focused on AI safety. The AI tracks various metrics, including average ball and swing speeds, as well as performance on forehand and backhand shots. To put the technology to the test, Stone faced off against Otto in a ping-pong match, where Otto emerged victorious with a score of 11-7. After the match, the AI generated an entertaining summary: “Nick’s arm must have felt like a whirlwind, spinning the ball at an average speed of 8.45 mph. J.R. tried to keep up, but his 30 forehand shots and 5.56 mph swing speed were no match.” While the advancements in AI are exciting, UCLA Professor Ramesh Srinivasan emphasizes the need for caution. “This technology is both incredible and concerning because it raises questions about the future of human journalists and commentators,” he noted. 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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October 5, 2024in Artificial Intelligence, Data, Generative AI, Salesforce, Technology

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