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Informed Decision-Making

Informed Decision-Making

Informed Decision-Making Through Data Visualization: Power BI vs. Tableau Today’s businesses need to make informed decisions by leveraging organized and analyzed data. Data visualization is a key method for extracting insights from this data, and Power BI and Tableau are two leading tools that often spark debate among experts. Both are highly regarded for their ability to visualize data, and CTOs frequently compare Power BI vs. Tableau to determine the best fit for their needs. Why Power BI and Tableau Stand OutBoth tools excel at data visualization, making them top choices for business intelligence (BI) solutions. They offer seamless integration with various platforms, can handle large volumes of data, and provide predictive analytics capabilities. To help CTOs and other decision-makers boost efficiency, let’s dive into a comparison of Power BI vs. Tableau and examine how each tool measures up. Power BI Microsoft’s Power BI is a leading BI tool designed to transform data from diverse sources into insightful visual reports. It allows users to create, share, and manage analytical reports, ensuring accessibility at all times. As part of the Microsoft ecosystem, Power BI is ideal for large organizations that already use Microsoft products. Tableau Tableau delivers powerful data visualization with flexible deployment options, allowing users to seamlessly access insights. With its integration into Salesforce Data Cloud, Tableau offers a fast and scalable way to work with customer data in real time. Its strong data-handling capabilities make it popular among larger organizations and data experts. Power BI vs. Tableau: Key Differences Let’s explore the key differences between Power BI and Tableau to guide your informed decision-making. Data Visualization and User Interface Data Integration and Connectivity for Informed Decision-Making Data Handling and Performance Ease of Learning Programming Tools Support Pricing Microsoft Power BI vs. Salesforce Tableau: Pros and Cons Power BI Pros Tableau Pros Which is Better: Power BI or Tableau? When comparing Microsoft Power BI vs. Tableau, the right choice depends on your organization’s size, technical expertise, and specific needs. For smaller businesses and those already using Microsoft tools, Power BI is often the best fit. On the other hand, larger organizations managing substantial datasets might favor Tableau for its advanced capabilities. Ultimately, the decision between Power BI vs. Tableau should be based on your unique business requirements and the level of technical expertise available within your team. 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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Cool and New AI

Cool and New AI Cool and New AI

AI is revolutionizing the way we work, offering a wide range of tools beyond ChatGPT that can enhance efficiency, creativity, and productivity. Whether you’re working with data, code, marketing, videos, images, AI bots, or research, here are the top AI tools that can transform your workflow. Cool and New AI. Don’t get spooked. There will be a cornucopia more in November. 🌟 Code 1️⃣ GlideTurn spreadsheets into powerful mobile apps without writing a single line of code. Glide makes it easy for non-developers to create professional apps with minimal effort. 2️⃣ BubbleA visual programming platform that allows users to build web applications without any coding knowledge. Ideal for entrepreneurs and startups looking to launch digital products quickly. 3️⃣ AskCodiThis AI coding assistant speeds up coding tasks, offers helpful suggestions, and simplifies debugging for developers, making it a must-have tool for coding professionals. 🌟 Data 1️⃣ BasedLabsA robust data analytics platform designed for scientists and engineers. BasedLabs offers complex data processing and model building with exceptional precision. 2️⃣ Coral AIPerfect for data-driven professionals, Coral AI provides efficient edge AI tools for processing large datasets and delivering insights with on-device intelligence, speeding up tasks. 3️⃣ JuliusAn AI-powered tool for market researchers and data analysts, Julius streamlines data processes and offers powerful insights into market trends. 🌟 Marketing 1️⃣ Sprout SocialThis all-in-one social media management platform leverages AI to help marketers optimize their social presence, engage with audiences, and track detailed analytics. 2️⃣ AdCreative AIEnhance your marketing campaigns with AI-generated ads that convert. AdCreative AI allows marketers to design high-impact, creative ads effortlessly. 3️⃣ Jasper AIA top tool for content creators, Jasper AI assists in crafting high-conversion marketing copy, blogs, and ad texts at scale, making it indispensable for digital marketing. 🌟 Video 1️⃣ SynthesiaCreate professional videos without the need for cameras or actors. Synthesia’s AI avatars enable you to produce multilingual videos, making it ideal for corporate and educational content. 2️⃣ HeygenThis AI tool simplifies video production by allowing users to create AI-generated videos, perfect for marketing campaigns or training materials. 3️⃣ Opus ClipOpus Clip transforms long-form video content into short, engaging clips optimized for social media, helping creators repurpose content easily. 🌟 Image 1️⃣ Getimg.AIAutomate image creation with Getimg.AI, which enhances your visual content by generating high-quality images in minutes, speeding up the design process. 2️⃣ PicsartA versatile image editing and design platform with AI tools that make creating stunning visuals effortless, making it ideal for social media, advertising, and creative projects. 3️⃣ Leonardo AIA powerful AI-driven tool for creators, Leonardo AI helps generate high-quality images, illustrations, and graphics, making it an essential tool for designers and artists. 🌟 AI Bot 1️⃣ LiveChatAn AI-powered live chat solution that integrates seamlessly into websites to provide real-time customer support, enhancing business communication. 2️⃣ LandbotThis tool helps create conversational experiences with AI-powered chatbots for customer support, sales, and lead generation, automating client interactions. 3️⃣ CustomGPTA customizable GPT-powered AI chatbot tailored for specific industries and businesses, perfect for providing personalized customer support. 🌟 Research 1️⃣ ChatPDFTurn PDFs into interactive documents with ChatPDF, allowing users to easily navigate and extract information using an AI-based assistant. 2️⃣ VidIQVidIQ provides AI-powered tools to optimize YouTube content for better engagement and visibility, making it invaluable for content creators. 3️⃣ SemrushAn advanced SEO platform powered by AI, Semrush gives marketers and researchers deep insights into online visibility, helping boost content performance. AI extends far beyond ChatGPT. This diverse range of tools is designed to make your work more efficient and productive, whether you’re coding, marketing, creating content, or conducting research. Embrace these AI tools to unlock new levels of creativity and 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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agentblazer

Agentblazers

In every industry, there are leaders who see the potential of cutting-edge technology and act as catalysts for change. In the age of AI, these forward-thinkers are known as Agentblazers. They understand that AI agents can do more than assist—they can transform operations, save costs, and shape the future of business.

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UX Principles for AI in Healthcare

UX Principles for AI in Healthcare

The Role of UX in AI-Driven Healthcare AI is poised to revolutionize the global economy, with predictions it could contribute $15.7 trillion by 2030—more than the combined economic output of China and India. Among the industries likely to see the most transformative impact is healthcare. However, during my time at NHS Digital, I saw how systems that weren’t designed with existing clinical workflows in mind added unnecessary complexity for clinicians, often leading to manual workarounds and errors due to fragmented data entry across systems. The risk is that AI, if not designed with user experience (UX) at the forefront, could exacerbate these issues, creating more disruption rather than solving problems. From diagnostic tools to consumer health apps, the role of UX in AI-driven healthcare is critical to making these innovations effective and user-friendly. This article explores the intersection of UX and AI in healthcare, outlining key UX principles to design better AI-driven experiences and highlighting trends shaping the future of healthcare. The Shift in Human-Computer Interaction with AI AI fundamentally changes how humans interact with computers. Traditionally, users took command by entering inputs—clicking, typing, and adjusting settings until the desired outcome was achieved. The computer followed instructions, while the user remained in control of each step. With AI, this dynamic shifts dramatically. Now, users specify their goal, and the AI determines how to achieve it. For example, rather than manually creating an illustration, users might instruct AI to “design a graphic for AI-driven healthcare with simple shapes and bold colors.” While this saves time, it introduces challenges around ensuring the results meet user expectations, especially when the process behind AI decisions is opaque. The Importance of UX in AI for Healthcare A significant challenge in healthcare AI is the “black box” nature of the systems. For example, consider a radiologist reviewing a lung X-ray that an AI flagged as normal, despite the presence of concerning lesions. Research has shown that commercial AI systems can perform worse than radiologists when multiple health issues are present. When AI decisions are unclear, clinicians may question the system’s reliability, especially if they cannot understand the rationale behind an AI’s recommendation. This opacity hinders feedback, making it difficult to improve the system’s performance. Addressing this issue is essential for UX designers. Bias in AI is another significant issue. Many healthcare AI tools have been documented as biased, such as systems trained on predominantly male cardiovascular data, which can fail to detect heart disease in women. AIs also struggle to identify conditions like melanoma in people with darker skin tones due to insufficient diversity in training datasets. UX can help mitigate these biases by designing interfaces that clearly explain the data used in decisions, highlight missing information, and provide confidence levels for predictions. The movement toward eXplainable AI (XAI) seeks to make AI systems more transparent and interpretable for human users. UX Principles for AI in Healthcare To ensure AI is beneficial in real-world healthcare settings, UX designers must prioritize certain principles. Below are key UX design principles for AI-enabled healthcare applications: Applications of AI in Healthcare AI is already making a significant impact in various healthcare applications, including: Real-world deployments of AI in healthcare have demonstrated that while AI can be useful, its effectiveness depends heavily on usability and UX design. By adhering to the principles of transparency, interpretability, controllability, and human-centered AI, designers can help create AI-enabled healthcare applications that are both powerful and user-friendly. 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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Rise of Agentforce

Rise of Agentforce

The Rise of Agentforce: How AI Agents Are Shaping the Future of Work Salesforce wrapped up its annual Dreamforce conference this September, leaving attendees with more than just memories of John Mulaney’s quips. As the swarms of Waymos ferried participants across a cleaner-than-usual San Francisco, it became clear that AI-powered agents—dubbed Agentforce—are poised to transform the workplace. These agents, controlled within Salesforce’s ecosystem, could significantly change how work is done and how customer experiences are delivered. Dreamforce has always been known for its bold predictions about the future, but this year’s vision of AI-based agents felt particularly compelling. These agents represent the next frontier in workplace automation, but as exciting as this future is, some important questions remain. Reality Check on the Agentforce Vision During his keynote, Salesforce CEO Marc Benioff raised an interesting point: “Why would our agents be so low-hallucinogenic?” While the agents have access to vast amounts of data, workflows, and services, they currently function best within Salesforce’s own environment. Benioff even made the claim that Salesforce pioneered prompt engineering—a statement that, for some, might have evoked a scene from Austin Powers, with Dr. Evil humorously taking credit for inventing the question mark. But can Salesforce fully realize its vision for Agentforce? If they succeed, it could be transformative for how work gets done. However, as with many AI-driven innovations, the real question lies in interoperability. The Open vs. Closed Debate As powerful as Salesforce’s ecosystem is, not all business data and workflows live within it. If the future of work involves a network of AI agents working together, how far can a closed ecosystem like Salesforce’s really go? Apple, Microsoft, Amazon, and other tech giants also have their sights set on AI-driven agents, and the race is on to own this massive opportunity. As we’ve seen in previous waves of technology, this raises familiar debates about open versus closed systems. Without a standard for agents to work together across platforms, businesses could find themselves limited. Closed ecosystems may help solve some problems, but to unlock the full potential of AI agents, they must be able to operate seamlessly across different platforms and boundaries. Looking to the Open Web for Inspiration The solution may lie in the same principles that guide the open web. Just as mobile apps often require a web view to enable an array of outcomes, the same might be necessary in the multi-agent landscape. Tools like Slack’s Block Kit framework allow for simple agent interactions, but they aren’t enough for more complex use cases. Take Clockwise Prism, for example—a sophisticated scheduling agent designed to find meeting times when there’s no obvious availability. When integrated with other agents to secure that critical meeting, businesses will need a flexible interface to explore multiple scheduling options. A web view for agents could be the key. The Need for an Open Multi-Agent Standard Benioff repeatedly stressed that businesses don’t want “DIY agents.” Enterprises seek controlled, repeatable workflows that deliver consistent value—but they also don’t want to be siloed. This is why the future requires an open standard for agents to collaborate across ecosystems and platforms. Imagine initiating a set of work agents from within an Atlassian Jira ticket that’s connected to a Salesforce customer case—or vice versa. For agents to seamlessly interact regardless of the system they originate from, a standard is needed. This would allow businesses to deploy agents in a way that’s consistent, integrated, and scalable. User Experience and Human-in-the-Loop: Crucial Elements for AI Agents A significant insight from the integration of LangChain with Assistant-UI highlighted a crucial factor: user experience (UX). Whether it’s streaming, generative interfaces, or human-in-the-loop functionality, the UX of AI agents is critical. While agents need to respond quickly and efficiently, businesses must have the ability to involve humans in decision-making when necessary. This principle of human-in-the-loop is key to the agent’s scheduling process. While automation is the goal, involving the user at crucial points—such as confirming scheduling options—ensures that the agent remains reliable and adaptable. Any future standard must prioritize this capability, allowing for user involvement where necessary, while also enabling full automation when confidence levels are high. Generative or Native UI? The discussion about user interfaces for agents often leads to a debate between generative UI and native UI. The latter may be the better approach. A native UI, controlled by the responding service or agent, ensures the interface is tailored to the context and specifics of the agent’s task. Whether this UI is rendered using AI or not is an implementation detail that can vary depending on the service. What matters is that the UI feels native to the agent’s task, making the user experience seamless and intuitive. What’s Next? The Push for an Open Multi-Agent Future As we look ahead to the multi-agent future, the need for an open standard is more pressing than ever. At Clockwise, we’ve drafted something we’re calling the Open Multi-Agent Protocol (OMAP), which we hope will foster collaboration and innovation in this space. The future of work is rapidly approaching, where new roles—like Agent Orchestrators—will emerge, enabling people to leverage AI agents in unprecedented ways. While Salesforce’s vision for Agentforce is ambitious, the key to unlocking its full potential lies in creating a standard that allows agents to work together, across platforms, and beyond the boundaries of closed ecosystems. With the right approach, we can create a future where AI agents transform work in ways we’re only beginning to imagine. 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

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Scope of Generative AI

Scope of Generative AI

Generative AI has far more to offer your site than simply mimicking a conversational ChatGPT-like experience or providing features like generating cover letters on resume sites. Let’s explore how you can integrate Generative AI with your product in diverse and innovative ways! There are three key perspectives to consider when integrating Generative AI with your features: system scope, spatial relationship, and functional relationship. Each perspective offers a different lens for exploring integration pathways and can spark valuable conversations about melding AI with your product ecosystem. These categories aren’t mutually exclusive; instead, they overlap and provide flexible ways of envisioning AI’s role. 1. System Scope — The Reach of Generative AI in Your System System scope refers to the breadth of integration within your system. By viewing integration from this angle, you can assess the role AI plays in managing your platform’s overall functionality. While these categories may overlap, they are useful in facilitating strategic conversations. 2. Spatial Relationships — Where AI Interacts with Features Spatial relationships describe where AI features sit in relation to your platform’s functionality: 3. Functional Relationships — How AI Interacts with Features Functional relationships determine how AI and platform features work together. This includes how users engage with AI and how AI content updates based on feature interactions: Scope of Generative AI By considering these different perspectives—system scope, spatial, and functional—you can drive more meaningful conversations about how Generative AI can best enhance your product’s capabilities. Each approach offers unique value, and careful thought can help teams choose the integration path that aligns with their needs and goals. Scope of Generative AI conversations with Tectonic can assist in planning the best ROI approach to AI. Contact us today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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AI in Programming

AI in Programming

Since the launch of ChatGPT in 2022, developers have been split into two camps: those who ban AI in coding and those who embrace it. Many seasoned programmers not only avoid AI-generated code but also prohibit their teams from using it. Their reasoning is simple: “AI-generated code is unreliable.” Even if one doesn’t agree with this anti-AI stance, they’ve likely faced challenges, hurdles, or frustrations when using AI for programming. The key is finding the right strategies to use AI to your advantage. Many are still using outdated AI strategies from two years ago, likened to cutting down a tree with kitchen knives. Two Major Issues with AI for Developers The Wrong Way to Use AI… …can be broken down into two parts: When ChatGPT first launched, the typical way to work with AI was to visit the website and chat with GPT-3.5 in a browser. The process was straightforward: copy code from the IDE, paste it into ChatGPT with a basic prompt like “add comments,” get the revised code, check for errors, and paste it back into the IDE. Many developers, especially beginners and students, are still using this same method. However, the AI landscape has changed significantly over the last two years, and many have not adjusted their approach to fully leverage AI’s potential. Another common pitfall is how developers use AI. They ask the LLM to generate code, test it, and go back and forth to fix any issues. Often, they fall into an endless loop of AI hallucinations when trying to get the LLM to understand what’s wrong. This can be frustrating and unproductive. Four Tools to Boost Programming Productivity with AI 1. Cursor: AI-First IDE Cursor is an AI-first IDE built on VScode but enhanced with AI features. It allows developers to integrate a chatbot API and use AI as an assistant. Some of Cursor’s standout features include: Cursor integrates seamlessly with VScode, making it easy for existing users to transition. It supports various models, including GPT-4, Claude 3.5 Sonnet, and its built-in free cursor-small model. The combination of Cursor and Sonnet 3.5 has been particularly praised for producing reliable coding results. This tool is a significant improvement over copy-pasting code between ChatGPT and an IDE. 2. Micro Agent: Code + Test Case Micro Agent takes a different approach to AI-generated code by focusing on test cases. Instead of generating large chunks of code, it begins by creating test cases based on the prompt, then writes code that passes those tests. This method results in more grounded and reliable output, especially for functions that are tricky but not overly complex. 3. SWE-agent: AI for GitHub Issues Developed by Princeton Language and Intelligence, SWE-agent specializes in resolving real-world GitHub repository issues and submitting pull requests. It’s a powerful tool for managing large repositories, as it reviews codebases, identifies issues, and makes necessary changes. SWE-agent is open-source and has gained considerable popularity on GitHub. 4. AI Commits: git commit -m AI Commits generates meaningful commit messages based on your git diff. This simple tool eliminates the need for vague or repetitive commit messages like “minor changes.” It’s easy to install and uses GPT-3.5 for efficient, AI-generated commit messages. The Path Forward To stay productive and achieve goals in the rapidly evolving AI landscape, developers need the right tools. The limitations of AI, such as hallucinations, can’t be eliminated, but using the appropriate tools can help mitigate them. Simple, manual interactions like generating code or comments through ChatGPT can be frustrating. By adopting the right strategies and tools, developers can avoid these pitfalls and confidently enhance their coding practices. AI is evolving fast, and keeping up with its changes is crucial. The right tools can make all the difference in your programming workflow. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Salesforce and Stripe Integration

Salesforce and Stripe Integration

Processing weekly payments can often become time-consuming, with organizations spending significant time just tracking payments post-invoice. Timely updates on payment processing have been a longstanding priority, particularly for companies needing flexible payment options to meet beneficiary needs. The integration of Stripe with Salesforce addresses these challenges, offering a streamlined, efficient solution. Salesforce and Stripe Integration. With a focus on faster processing, this integration enhances payment management, invoicing, and financial oversight. Here’s an overview of the benefits that Stripe and Salesforce integration brings to businesses. Key Benefits of Integrating Stripe with Salesforce Modern businesses demand efficient, seamless transactions. Here are some of the major advantages of combining Stripe and Salesforce: Why Businesses Should Consider Salesforce and Stripe Integration Having implemented this integration for various clients, we have seen how it supports efficient operations. Here are a few reasons why businesses should consider this approach: Simplify Payments with Salesforce and Stripe Integration The Stripe and Salesforce integration represents a transformative opportunity for businesses to improve operational efficiency and customer experience. Beyond a simple tool pairing, this integration paves the way for scalable, complex business operations. Successful integration, however, requires knowledgeable professionals who understand the nuances of customization to fit each organization’s unique needs. Reach out to experienced experts to optimize your integration and harness its full potential. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI Customer Service Agents Explained

AI Customer Service Agents Explained

AI customer service agents are advanced technologies designed to understand and respond to customer inquiries within defined guidelines. These agents can handle both simple and complex issues, such as answering frequently asked questions or managing product returns, all while offering a personalized, conversational experience. Research shows that 82% of service representatives report that customers ask for more than they used to. As a customer service leader, you’re likely facing increasing pressure to meet these growing expectations while simultaneously reducing costs, speeding up service, and providing personalized, round-the-clock support. This is where AI customer service agents can make a significant impact. Here’s a closer look at how AI agents can enhance your organization’s service operations, improve customer experience, and boost overall productivity and efficiency. What Are AI Customer Service Agents? AI customer service agents are virtual assistants designed to interact with customers and support service operations. Utilizing machine learning and natural language processing (NLP), these agents are capable of handling a broad range of tasks, from answering basic inquiries to resolving complex issues — even managing multiple tasks at once. Importantly, AI agents continuously improve through self-learning. Why Are AI-Powered Customer Service Agents Important? AI-powered customer service technology is becoming essential for several reasons: Benefits of AI Customer Service Agents AI customer service agents help service teams manage growing service demands by taking on routine tasks and providing essential support. Key benefits include: Why Choose Agentforce Service Agent? If you’re considering adding AI customer service agents to your strategy, Agentforce Service Agent offers a comprehensive solution: By embracing AI customer service agents like Agentforce Service Agent, businesses can reduce costs, meet growing customer demands, and stay competitive in an ever-evolving global market. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI Agents and Digital Transformation

AI Agents and Digital Transformation

In the rapidly developingng world of technology, Artificial Intelligence (AI) is revolutionizing industries and reshaping how we interact with digital systems. One of the most promising advancements within AI is the development of AI agents. These intelligent entities, often powered by Large Language Models (LLMs), are driving the next wave of digital transformation by enabling automation, personalization, and enhanced decision-making across various sectors. AI Agents and digital transformation are here to stay. What is an AI Agent? An AI agent, or intelligent agent, is a software entity capable of perceiving its environment, reasoning about its actions, and autonomously working toward specific goals. These agents mimic human-like behavior using advanced algorithms, data processing, and machine-learning models to interact with users and complete tasks. LLMs to AI Agents — An Evolution The evolution of AI agents is closely tied to the rise of Large Language Models (LLMs). Models like GPT (Generative Pre-trained Transformer) have showcased remarkable abilities to understand and generate human-like text. This development has enabled AI agents to interpret complex language inputs, facilitating advanced interactions with users. Key Capabilities of LLM-Based Agents LLM-powered agents possess several key advantages: Two Major Types of LLM Agents LLM agents are classified into two main categories: Multi-Agent Systems (MAS) A Multi-Agent System (MAS) is a group of autonomous agents working together to achieve shared goals or solve complex problems. MAS applications span robotics, economics, and distributed computing, where agents interact to optimize processes. AI Agent Architecture and Key Elements AI agents generally follow a modular architecture comprising: Learning Strategies for LLM-Based Agents AI agents utilize various learning techniques, including supervised, reinforcement, and self-supervised learning, to adapt and improve their performance in dynamic environments. How Autonomous AI Agents Operate Autonomous AI agents act independently of human intervention by perceiving their surroundings, reasoning through possible actions, and making decisions autonomously to achieve set goals. AI Agents’ Transformative Power Across Industries AI agents are transforming numerous industries by automating tasks, enhancing efficiency, and providing data-driven insights. Here’s a look at some key use cases: Platforms Powering AI Agents The Benefits of AI Agents and Digital Transformation AI agents offer several advantages, including: The Future of AI Agents The potential of AI agents is immense, and as AI technology advances, we can expect more sophisticated agents capable of complex reasoning, adaptive learning, and deeper integration into everyday tasks. The future promises a world where AI agents collaborate with humans to drive innovation, enhance efficiency, and unlock new opportunities for growth in the digital age. AI Agents and Digital Transformation By partnering with AI development specialists at Tectonic, organizations can access cutting-edge solutions tailored to their needs, positioning themselves to stay ahead in the rapidly evolving AI-driven market. Agentforce 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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Amazon DynamoDB to Salesforce Data Cloud

Amazon DynamoDB to Salesforce Data Cloud

Ingesting Data from Amazon DynamoDB to Salesforce Data Cloud Salesforce Data Cloud serves as your organization’s digital command center, enabling real-time ingestion, unification, and activation of data from any source. By transforming scattered customer information into actionable insights, it empowers businesses to operate with unparalleled efficiency. Integrating Amazon DynamoDB with Salesforce Data Cloud exemplifies the platform’s capacity to unify and activate enterprise data seamlessly. Follow this step-by-step guide to ingest data from Amazon DynamoDB into Salesforce Data Cloud. Prerequisites Part 1: Amazon DynamoDB Setup 1. AWS Account Setup 2. Create a DynamoDB Table 3. Populate the Table with Data 4. Security Credentials Part 2: Salesforce Data Cloud Configuration 1. Creating the Data Connection 2. Configuring Data Streams Create a New Data Stream Configure the Data Model 3. Data Modeling and Mapping Custom Object Creation Conclusion After completing the setup: This integration underscores Salesforce Data Cloud’s role as a centralized hub, capable of harmonizing diverse data sources, ensuring real-time synchronization, and enabling actionable insights. By connecting Amazon DynamoDB, businesses can unlock the full potential of their data, driving better decision-making and customer experiences. 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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Third Wave of AI at Salesforce

Third Wave of AI at Salesforce

The Third Wave of AI at Salesforce: How Agentforce is Transforming the Landscape At Dreamforce 2024, Salesforce unveiled several exciting innovations, with Agentforce taking center stage. This post explores the key changes and enhancements designed to improve efficiency and elevate customer interactions. Introducing Agentforce Agentforce is a customizable AI agent builder that empowers organizations to create and manage autonomous agents for various business tasks. But what exactly is an agent? An agent is akin to a chatbot but goes beyond traditional capabilities. While typical chatbots are restricted to scripted responses and predefined questions, Agentforce agents leverage large language models (LLMs) and generative AI to comprehend customer inquiries contextually. This enables them to make independent decisions, whether processing requests or resolving issues using real-time data from your company’s customer relationship management (CRM) system. The Role of Atlas At the heart of Agentforce’s functionality lies the Atlas reasoning engine, which acts as the operational brain. Unlike standard assistive tools, Atlas is an agentic system with the autonomy to act on behalf of the user. Atlas formulates a plan based on necessary actions and can adjust that plan based on evaluations or new information. When it’s time to engage, Atlas knows which business processes to activate and connects with customers or employees via their preferred channels. This sophisticated approach allows Agentforce to significantly enhance operational efficiency. By automating routine inquiries, it frees up your team to focus on more complex tasks, delivering a smoother experience for both staff and customers. Speed to Value One of Agentforce’s standout features is its emphasis on rapid implementation. Many AI projects can be resource-intensive and take months or even years to launch. However, Agentforce enables quick deployment by leveraging existing Salesforce infrastructure, allowing organizations to implement solutions rapidly and with greater control. Salesforce also offers pre-built Agentforce agents tailored to specific business needs—such as Service Agent, Sales Development Representative Agent, Sales Coach, Personal Shopper Agent, and Campaign Agent—all customizable with the Agent Builder. Agentforce for Service and Sales will be generally available starting October 25, 2024, with certain elements of the Atlas Reasoning Engine rolling out in February 2025. Pricing begins at $2 per conversation, with volume discounts available. Transforming Customer Insights with Data Cloud and Marketing Cloud Dreamforce also highlighted enhancements to Data Cloud, Salesforce’s backbone for all cloud products. The platform now supports processing unstructured data, which constitutes up to 90% of company data often overlooked by traditional reporting systems. With new capabilities for analyzing various unstructured formats—like video, audio, sales demos, customer service calls, and voicemails—businesses can derive valuable insights and make informed decisions across Customer 360. Furthermore, Data Cloud One enables organizations to connect siloed Salesforce instances effortlessly, promoting seamless data sharing through a no-code, point-and-click setup. The newly announced Marketing Cloud Advanced edition serves as the “big sister” to Marketing Cloud Growth, equipping larger marketing teams with enhanced features like Path Experiment, which tests different content strategies across channels, and Einstein Engagement Scoring for deeper insights into customer behavior. Together, these enhancements empower companies to engage customers more meaningfully and measurably across all touchpoints. Empowering the Workforce Through Education Salesforce is committed to making AI accessible for all. They recently announced free instructor-led courses and AI certifications available through 2025, aimed at equipping the Salesforce community with essential AI and data management skills. To support this initiative, Salesforce is establishing AI centers in major cities, starting with London, to provide hands-on training and resources, fostering AI expertise. They also launched a global Agentforce World Tour to promote understanding and adoption of the new capabilities introduced at Dreamforce, featuring repackaged sessions from the conference and opportunities for specialists to answer questions. The Bottom Line What does this mean for businesses? With the rollout of Agentforce, along with enhancements to Data Cloud and Marketing Cloud, organizations can operate more efficiently and connect with customers in more meaningful ways. Coupled with a focus on education through free courses and global outreach, getting on board has never been easier. If you’d like to discuss how we can help your business maximize its potential with Salesforce through data and AI, connect with us and schedule a meeting with our team. Legacy systems can create significant gaps between operations and employee needs, slowing lead processes and resulting in siloed, out-of-sync data that hampers business efficiency. Responding to inquiries within five minutes offers a 75% chance of converting leads into customers, emphasizing the need for rapid, effective marketing responses. Salesforce aims to help customers strengthen relationships, enhance productivity, and boost margins through its premier AI CRM for sales, service, marketing, and commerce, while also achieving these goals internally. Recognizing the complexity of its decade-old processes, including lead assignment across three systems and 2 million lines of custom code, Salesforce took on the role of “customer zero,” leveraging Data Cloud to create a unified view of customers known as the “Customer 360 Truth Profile.” This consolidation of disparate data laid the groundwork for enterprise-wide AI and automation, improving marketing automation and reducing lead time by 98%. As Michael Andrew, SVP of Marketing Decision Science at Salesforce, noted, this initiative enabled the company to provide high-quality leads to its sales team with enriched data and AI scoring while accelerating time to market and enhancing data quality. Embracing Customer Zero “Almost exactly a year ago, we set out with a beginner’s mind to transform our lead automation process with a solution that would send the best leads to the right sales teams within minutes of capturing their data and support us for the next decade,” said Andrew. The initial success metric was “speed to lead,” aiming to reduce the handoff time from 20 minutes to less than one minute. The focus was also on integrating customer and lead data to develop a more comprehensive 360-degree profile for each prospect, enhancing lead assignment and sales rep productivity. Another objective was to boost business agility by cutting the average time to implement assignment changes from four weeks to mere days. Accelerating Success with

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