Enterprise Archives - gettectonic.com - Page 4

Autonomy, Architecture, and Action

Redefining AI Agents: Autonomy, Architecture, and Action AI agents are reshaping how technology interacts with us and executes tasks. Their mission? To reason, plan, and act independently—following instructions, making autonomous decisions, and completing actions, often without user involvement. These agents adapt to new information, adjust in real time, and pursue their objectives autonomously. This evolution in agentic AI is revolutionizing how goals are accomplished, ushering in a future of semi-autonomous technology. At their foundation, AI agents rely on one or more large language models (LLMs). However, designing agents is far more intricate than building chatbots or generative assistants. While traditional AI applications often depend on user-driven inputs—such as prompt engineering or active supervision—agents operate autonomously. Core Principles of Agentic AI Architectures To enable autonomous functionality, agentic AI systems must incorporate: Essential Infrastructure for AI Agents Building and deploying agentic AI systems requires robust software infrastructure that supports: Agent Development Made Easier with Langflow and Astra DB Langflow simplifies the development of agentic applications with its visual IDE. It integrates with Astra DB, which combines vector and graph capabilities for ultra-low latency data access. This synergy accelerates development by enabling: Transforming Autonomy into Action Agentic AI is fundamentally changing how tasks are executed by empowering systems to act autonomously. By leveraging platforms like Astra DB and Langflow, organizations can simplify agent design and deploy scalable, effective AI applications. Start building the next generation of AI-powered autonomy 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 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Project Management With Asana and Salesforce

Salesforce and Asana Integration Approach

How to Integrate Asana and Salesforce: A Complete Guide Table of Contents Integrating Asana and Salesforce can eliminate workflow silos and accelerate collaboration. Both platforms offer integration capabilities, but their suitability varies based on your needs and resources. This guide will help you navigate the options, evaluate their pros and cons, and choose the one that best suits your organization. Can You Integrate Asana and Salesforce? Yes! Asana and Salesforce integration is possible through three primary methods: Each option comes with unique features, costs, and technical requirements. This guide explores each solution to help you make an informed decision. Why Integrate Asana and Salesforce? Integration can achieve two major goals: Depending on your goals, certain integration methods may be better suited to your needs. Integration Options Overview 1. Asana for Salesforce This official integration is ideal for large organizations with Enterprise-level plans for both Asana and Salesforce. It enables automation of workflows between the two platforms, such as: Pros: Cons: Rating: 2.6/5 on Salesforce AppExchange. 2. Visor Visor offers bi-directional integration with Asana and Salesforce, making it a powerful choice for combining and visualizing data. Key Features: Best For: Setup Steps: Limitations: 3. Zapier Zapier enables custom automation between Asana and Salesforce. It’s perfect for automating simple, repetitive workflows, such as: Pros: Cons: Quick Comparison Table Feature Asana for Salesforce Zapier Visor Automates processes ✔ ✔ ✘ Combines Salesforce & Asana data ✘ ✘ ✔ Gantt charts and project boards ✘ ✘ ✔ Dashboards and timelines ✘ ✘ ✔ Two-way data sync ✘ ✘ ✔ Comparison Table Which Integration Option Is Best for You? The right choice depends on your goals: Get Started with Visor for Free Visor is one integration tool, that helps you bridge the gap between Asana and Salesforce, offering advanced visualization tools and seamless collaboration. Start using Visor for free 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 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Generative AI Energy Consumption Rises

Generative AI Tools

Generative AI Tools: A Comprehensive Overview of Emerging Capabilities The widespread adoption of generative AI services like ChatGPT has sparked immense interest in leveraging these tools for practical enterprise applications. Today, nearly every enterprise app integrates generative AI capabilities to enhance functionality and efficiency. A broad range of AI, data science, and machine learning tools now support generative AI use cases. These tools assist in managing the AI lifecycle, governing data, and addressing security and privacy concerns. While such capabilities also aid in traditional AI development, this discussion focuses on tools specifically designed for generative AI. Not all generative AI relies on large language models (LLMs). Emerging techniques generate images, videos, audio, synthetic data, and translations using methods such as generative adversarial networks (GANs), diffusion models, variational autoencoders, and multimodal approaches. Here is an in-depth look at the top categories of generative AI tools, their capabilities, and notable implementations. It’s worth noting that many leading vendors are expanding their offerings to support multiple categories through acquisitions or integrated platforms. Enterprises may want to explore comprehensive platforms when planning their generative AI strategies. 1. Foundation Models and Services Generative AI tools increasingly simplify the development and responsible use of LLMs, initially pioneered through transformer-based approaches by Google researchers in 2017. 2. Cloud Generative AI Platforms Major cloud providers offer generative AI platforms to streamline development and deployment. These include: 3. Use Case Optimization Tools Foundation models often require optimization for specific tasks. Enterprises use tools such as: 4. Quality Assurance and Hallucination Mitigation Hallucination detection tools address the tendency of generative models to produce inaccurate or misleading information. Leading tools include: 5. Prompt Engineering Tools Prompt engineering tools optimize interactions with LLMs and streamline testing for bias, toxicity, and accuracy. Examples include: 6. Data Aggregation Tools Generative AI tools have evolved to handle larger data contexts efficiently: 7. Agentic and Autonomous AI Tools Developers are creating tools to automate interactions across foundation models and services, paving the way for autonomous AI. Notable examples include: 8. Generative AI Cost Optimization Tools These tools aim to balance performance, accuracy, and cost effectively. Martian’s Model Router is an early example, while traditional cloud cost optimization platforms are expected to expand into this area. Generative AI tools are rapidly transforming enterprise applications, with foundational, cloud-based, and domain-specific solutions leading the way. By addressing challenges like accuracy, hallucination, and cost, these tools unlock new potential across industries and use cases, enabling enterprises to stay ahead in the AI-driven landscape. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Unlocking the Future of AI with Phidata

Data Masking Explained

What is Data Masking? Data masking is a crucial data security technique that replaces sensitive information with realistic yet fictitious values, ensuring the original data remains protected from unauthorized access. This method secures sensitive data—such as personally identifiable information (PII), financial records, or proprietary business data—while still allowing it to be used for testing, development, or analytics. An effective data masking solution should include these core features: Data masking plays a vital role in data governance, helping organizations control access to sensitive information while balancing security and usability. Why Does Data Masking Matter for AI and Agent Testing? As artificial intelligence continues to drive business transformation, it relies heavily on data to train models, generate insights, and automate workflows. However, using real customer and enterprise data in AI development poses significant privacy risks. Data masking mitigates these risks by enabling AI systems to train on realistic yet anonymized datasets, keeping sensitive production data secure. Protecting Sensitive Data Testing AI-powered Salesforce applications often requires realistic datasets, including PII, financial information, and confidential business records. Using unmasked data in non-production environments increases exposure risks, such as insider threats, misconfigurations, or accidental leaks. By replacing sensitive data with masked equivalents, organizations can maintain privacy while enabling effective development and testing. Ensuring Compliance with Data Protection Regulations Regulatory frameworks like GDPR, CCPA, and HIPAA impose strict requirements for handling sensitive data—even in testing environments. GDPR, for example, mandates pseudonymization or anonymization to protect privacy. Failure to implement proper data masking strategies can result in severe fines and reputational damage. Masking ensures compliance while maintaining a secure foundation for Salesforce testing. Enhancing Test Accuracy AI-driven Salesforce applications require realistic testing scenarios to ensure functionality and accuracy. Masked data preserves the structure and variability of original CRM datasets, allowing developers to simulate real-world interactions without exposing sensitive information. This approach improves test precision and accelerates the deployment of high-quality applications. Reducing Bias and Promoting Fairness Data masking also supports fairness in AI models by removing personally identifiable details that could unintentionally introduce bias. Anonymizing sensitive attributes helps organizations build ethical, unbiased AI systems that support diverse and equitable outcomes. Key Considerations for Implementing Data Masking To effectively implement data masking in Salesforce environments, organizations should consider the following: Define Scope and Objectives Before masking data, determine what needs protection—whether it’s customer records, financial transactions, or proprietary insights. Align masking strategies with business goals, such as development, testing, or compliance, to ensure maximum effectiveness. Select the Right Masking Techniques Different masking methods serve distinct purposes: By integrating data masking into privacy-first strategies, organizations not only ensure compliance but also foster secure innovation and long-term digital trust. A Privacy-First Approach to AI Development As privacy becomes a defining factor in AI and trust-driven application development, data masking is an essential safeguard for security, compliance, and ethical innovation. For organizations leveraging Salesforce AI solutions like Agentforce, masking enables the safe use of realistic but anonymized datasets, ensuring privacy while accelerating AI-driven transformation. Start with Salesforce’s built-in data masking tools to secure sensitive information and empower secure, compliant, and forward-thinking AI development. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Scope of Generative AI

Exploring Generative AI

Like most employees at most companies, I wear a few different hats around Tectonic. Whether I’m building a data model, creating and scheduing an email campaign, standing up a platform generative AI is always at my fingertips. At my very core, I’m a marketer. Have been for so long I do it without eveven thinking. Or at least, everyuthing I do has a hat tip to its future marketing needs. Today I want to share some of the AI content generators I’ve been using, am looking to use, or just heard about. But before we rip into the insight, here’s a primer. Types of AI Content Generators ChatGPT, a powerful AI chatbot, drew significant attention upon its November 2022 release. While the GPT-3 language model behind it had existed for some time, ChatGPT made this technology accessible to nontechnical users, showcasing how AI can generate content. Over two years later, numerous AI content generators have emerged to cater to diverse use cases. This rapid development raises questions about the technology’s impact on work. Schools are grappling with fears of plagiarism, while others are embracing AI. Legal debates about copyright and digital media authenticity continue. President Joe Biden’s October 2023 executive order addressed AI’s risks and opportunities in areas like education, workforce, and consumer privacy, underscoring generative AI’s transformative potential. What is AI-Generated Content? AI-generated content, also known as generative AI, refers to algorithms that automatically create new content across digital media. These algorithms are trained on extensive datasets and require minimal user input to produce novel outputs. For instance, ChatGPT sets a standard for AI-generated content. Based on GPT-4o, it processes text, images, and audio, offering natural language and multimodal capabilities. Many other generative AI tools operate similarly, leveraging large language models (LLMs) and multimodal frameworks to create diverse outputs. What are the Different Types of AI-Generated Content? AI-generated content spans multiple media types: Despite their varied outputs, most generative AI systems are built on advanced LLMs like GPT-4 and Google Gemini. These multimodal models process and generate content across multiple formats, with enhanced capabilities evolving over time. How Generative AI is Used Generative AI applications span industries: These tools often combine outputs from various media for complex, multifaceted projects. AI Content Generators AI content generators exist across various media. Below are good examples organized by gen ai type: Written Content Generators Image Content Generators Music Content Generators Code Content Generators Other AI Content Generators These tools showcase how AI-powered content generation is revolutionizing industries, making content creation faster and more accessible. I do hope you will comment below on your favorites, other AI tools not showcased above, or anything else AI-related that is on your mind. Written by Tectonic’s Marketing Operations Director, 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 contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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From Chatbots to Agentic AI

From Chatbots to Agentic AI

The transition from LLM-powered chatbots to agentic systems, or agentic AI, can be summed up by the old saying: “Less talk, more action.” Keeping up with advancements in AI can be overwhelming, especially when managing an existing business. The speed and complexity of innovation can make it feel like the first day of school all over again. This insight offers a comprehensive look at AI agents, their components, and key characteristics. The introductory section breaks down the elements that form the term “AI agent,” providing a clear definition. After establishing this foundation, we explore the evolution of LLM applications, particularly the shift from traditional chatbots to agentic systems. The goal is to understand why AI agents are becoming increasingly vital in AI development and how they differ from LLM-powered chatbots. By the end of this guide, you will have a deeper understanding of AI agents, their potential applications, and their impact on organizational workflows. For those of you with a technical background who prefer to get hands-on, click here for the best repository for AI developers and builders. What is an AI Agent? Components of AI Agents To understand the term “AI agent,” we need to examine its two main components. First, let’s consider artificial intelligence, or AI. Artificial Intelligence (AI) refers to non-biological intelligence that mimics human cognition to perform tasks traditionally requiring human intellect. Through machine learning and deep learning techniques, algorithms—especially neural networks—learn patterns from data. AI systems are used for tasks such as detection, classification, and prediction, with content generation becoming a prominent domain due to transformer-based models. These systems can match or exceed human performance in specific scenarios. The second component is “agent,” a term commonly used in both technology and human contexts. In computer science, an agent refers to a software entity with environmental awareness, able to perceive and act within its surroundings. A computational agent typically has the ability to: In human contexts, an agent is someone who acts on behalf of another person or organization, making decisions, gathering information, and facilitating interactions. They often play intermediary roles in transactions and decision-making. To define an AI agent, we combine these two perspectives: it is a computational entity with environmental awareness, capable of perceiving inputs, acting with tools, and processing information using foundation models backed by both long-term and short-term memory. Key Components and Characteristics of AI Agents From LLMs to AI Agents Now, let’s take a step back and understand how we arrived at the concept of AI agents, particularly by looking at how LLM applications have evolved. The shift from traditional chatbots to LLM-powered applications has been rapid and transformative. Form Factor Evolution of LLM Applications Traditional Chatbots to LLM-Powered Chatbots Traditional chatbots, which existed before generative AI, were simpler and relied on heuristic responses: “If this, then that.” They followed predefined rules and decision trees to generate responses. These systems had limited interactivity, with the fallback option of “Speak to a human” for complex scenarios. LLM-Powered Chatbots The release of OpenAI’s ChatGPT on November 30, 2022, marked the introduction of LLM-powered chatbots, fundamentally changing the game. These chatbots, like ChatGPT, were built on GPT-3.5, a large language model trained on massive datasets. Unlike traditional chatbots, LLM-powered systems can generate human-like responses, offering a much more flexible and intelligent interaction. However, challenges remained. LLM-powered chatbots struggled with personalization and consistency, often generating plausible but incorrect information—a phenomenon known as “hallucination.” This led to efforts in grounding LLM responses through techniques like retrieval-augmented generation (RAG). RAG Chatbots RAG is a method that combines data retrieval with LLM generation, allowing systems to access real-time or proprietary data, improving accuracy and relevance. This hybrid approach addresses the hallucination problem, ensuring more reliable outputs. LLM-Powered Chatbots to AI Agents As LLMs expanded, their abilities grew more sophisticated, incorporating advanced reasoning, multi-step planning, and the use of external tools (function calling). Tool use refers to an LLM’s ability to invoke specific functions, enabling it to perform more complex tasks. Tool-Augmented LLMs and AI Agents As LLMs became tool-augmented, the emergence of AI agents followed. These agents integrate reasoning, planning, and tool use into an autonomous, goal-driven system that can operate iteratively within a dynamic environment. Unlike traditional chatbot interfaces, AI agents leverage a broader set of tools to interact with various systems and accomplish tasks. Agentic Systems Agentic systems—computational architectures that include AI agents—embody these advanced capabilities. They can autonomously interact with systems, make decisions, and adapt to feedback, forming the foundation for more complex AI applications. Components of an AI Agent AI agents consist of several key components: Characteristics of AI Agents AI agents are defined by the following traits: Conclusion AI agents represent a significant leap from traditional chatbots, offering greater autonomy, complexity, and interactivity. However, the term “AI agent” remains fluid, with no universal industry standard. Instead, it exists on a continuum, with varying degrees of autonomy, adaptability, and proactive behavior defining agentic systems. Value and Impact of AI Agents The key benefits of AI agents lie in their ability to automate manual processes, reduce decision-making burdens, and enhance workflows in enterprise environments. By “agentifying” repetitive tasks, AI agents offer substantial productivity gains and the potential to transform how businesses operate. As AI agents evolve, their applications will only expand, driving new efficiencies and enabling organizations to leverage AI in increasingly sophisticated ways. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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What is Heroku

What is Heroku

What is Heroku? Heroku is a modern, container-based Platform as a Service (PaaS) that enables developers to deploy, manage, and scale applications with ease. Designed for simplicity, flexibility, and elegance, it provides the fastest path for developers to take their apps to market. Key Features of Heroku: The Evolution of Heroku Heroku has recently undergone a transformation, becoming fully cloud-native with advanced integrations like Kubernetes, OpenTelemetry, and Agentforce, an AI-powered enhancement to its platform. These upgrades retain the platform’s hallmark simplicity while delivering more performance and tools, such as Graviton processors, EKS, ECR, and AWS Global Accelerator. AI-Powered Innovation: Agentforce Agentforce, Heroku’s latest feature, brings AI-powered automation to app development. It empowers both technical and non-technical users by offering natural language workflows for building applications, making it accessible to a wider range of business users. According to Betty Junod, Heroku’s Chief Marketing Officer at Salesforce, the platform now seamlessly combines user-friendly experiences with cutting-edge AI capabilities: “We’ve replatformed while keeping the experience as simple as ever, but now with added horsepower, Graviton performance, and managed AI tools like Bedrock.” Agentforce is particularly impactful for non-developers, guiding them through building apps and automating processes with no-code or low-code tools. This innovation aligns with Heroku’s mission to make app creation easier and more interactive: “It’s not just apps serving information anymore; users are engaging with them in entirely new ways.” Deliver Apps, Your Way Heroku is designed to serve a variety of needs, from quick prototypes to mission-critical enterprise applications. Its fully managed ecosystem allows you to build and scale apps efficiently, leveraging the tools and languages you already know and love. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Digital Marketing for Casinos

Unveiling the Casino Experience: Harnessing the Power of Digital Marketing In the exciting world of casinos—where entertainment meets sophistication—the influence of digital marketing is undeniable. A strategic approach is packed with actionable insights designed to boost online presence, engage audiences, and drive sustained success. Discover how to craft compelling content, wield social media’s dynamic power, utilize a customer relationship platform, and optimize visibility to ensure your casino stands out in an increasingly competitive digital arena. Tectonic has a successful, winning track record in Salesforce implementation for casinos. Whether you’re promoting exclusive guest events, captivating diverse demographics, or showcasing the unique experience of your casino, this insight equips you to master the digital space with Salesforce. With these strategies, casinos can create a ever-growing online presence that not only strengthens bonds with existing patrons but also entices new guests. Generating New and Repeat Guest Traffic with Salesforce Key Takeaways Why Digital Marketing is a Critical Component for Casinos Digital marketing serves as a critical driver of customer engagement, loyalty, and revenue growth in the casino industry. Strategies such as SEO, email marketing, and social media engagement empower casinos to connect with target audiences and continuously refine their efforts to remain competitive. In today’s crowded and competitive gaming world, leveraging data-driven marketing offers the competitive edge needed to captivate and retain customers. Winning Strategies for Casino Marketing 1. Search Engine Optimization (SEO):Ensure your casino is easy to find with these tactics: 2. Pay-Per-Click Advertising (PPC):Drive traffic with targeted PPC campaigns by: 3. Social Media Marketing:Create buzz with engaging social media campaigns: 4. Email Marketing:Maintain direct communication with: 5. Salesforce 360 Degree Guest View:Maintain personalized communication with: Reaching the Right Audience with Precision Audience Segmentation:Segmenting your audience by behavior, demographics, and preferences ensures more effective marketing. Navigating Legal and Ethical Challenges in Casino Marketing Compliance is essential in maintaining trust and navigating complex regulations. Measuring Success: Metrics and Optimization Key Metrics to Monitor: Campaign Optimization: Addressing Industry Challenges with Marketing 1. Rising Competition:Stand out by delivering unmatched gaming experiences and innovative promotions. 2. High Player Churn:Combat churn with data-driven marketing and personalized offerings to boost player lifetime value. 3. ROI Challenges:Optimize your mix of games and services to balance player satisfaction and profitability. The Road Ahead: Commitment to Digital Transformation With the global online gaming market projected to grow at a significant pace, casinos must embrace a future grounded in digital and data-driven marketing. Investments in technology, analytics, and talent will be pivotal in securing long-term profitability and differentiation. In an industry where chance often rules, success lies in a deliberate, strategic approach to digital marketing. This insight equips you with the tools to not only compete but thrive in this dynamic landscape. Contact Tectonic today to explore Salesforce tools to better reach, engage, and serve your guests. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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Speed to Launch of Agentforce

Speed to Launch of Agentforce

Agentforce isn’t just another AI platform that requires months of customization. At most customers, they quickly saw its power, launching transformative generative AI experiences in just days—no AI engineers needed. For companies with larger admin teams, the benefits can be even greater. Unlike other platforms, Agentforce places a strong emphasis on data privacy, building on the trust that Salesforce is known for, making these virtual assistants invaluable. We began with employee-facing use cases, saving our team several hours per week. Now, with Agentforce, we’re seeing even more opportunities to drive efficiencies and better serve our customers. “We’re excited to leverage Agentforce to completely overhaul recruitment and enrollment at Unity Environmental University. Instead of traditional forms or chatbots, our students will soon engage with an autonomous recruitment agent directly on our website, offering personalized support throughout the college application process.”– Dr. Melik Khoury, President & CEO, Unity Environmental University “For first-generation college students, the 1:385 coach-to-student ratio makes personalized guidance challenging. By integrating Agentforce into our platform, we’re deploying cutting-edge solutions to better support students. These agents enable our coaches to focus on high-touch, personalized experiences while handling vital tasks like sharing deadlines and answering common questions—24/7.”– Siva Kumari, CEO, College Possible “Agentforce offers organizations a unique opportunity to move beyond incremental improvements and achieve exponential ROI. By automating customer interactions, improving outcomes, and reducing costs, it integrates data, flows, and user interfaces to mitigate risks and accelerate value creation. This agent-based platform approach allows businesses to harness AI’s full potential, revolutionizing customer engagement and paving the way for exponential growth.”– Rebecca Wettemann, CEO and Principal Analyst, Valoir “Autonomous agents powered by Salesforce’s Agentforce are revolutionizing customer experiences by providing fast, accurate, and personalized support around the clock. With advanced AI making decisions and taking actions autonomously, businesses can resolve customer issues more efficiently, fostering deeper interactions and enhancing satisfaction. This innovation enables companies to reallocate human resources to more complex tasks, boosting individual productivity and scaling business growth. Agentforce is setting new standards for seamless sales, service, marketing, and commerce interactions, reinforcing its leadership in customer experience.”– Michael Fauscette, CEO and Chief Analyst, Arion Research LLC “The best way to predict the future is to invent it.” — Alan Kay, Computer Science Pioneer Technology progresses in what biologists call punctuated equilibrium, with new capabilities slowly emerging from labs and tinkerers until a breakthrough shifts the axis of possibility. These pioneering feats create new paradigms, unleashing waves of innovation—much like the Apple Macintosh, the iPhone, and the Salesforce Platform, which revolutionized the enterprise software-as-a-service (SaaS) model and sparked an entire industry. The Age of Agentforce Begins At Dreamforce 2024, Salesforce Futures reflected on the launch of Agentforce, inspired by visions like the Apple Knowledge Navigator. In 2023, we used this inspiration to craft our Salesforce 2030 film, which showcased the collaboration between humans and autonomous AI agents. Now, with Agentforce, we’re witnessing that vision come to life. Agentforce is a suite of customizable AI agents and tools built on the Salesforce Platform, offering an elegant solution to the complexity of AI deployment. It addresses the challenges of integrating data, models, infrastructure, and applications into a unified system. With powerful tools like Agent Builder and Model Builder, organizations can easily create, customize, and deploy AI agents. Salesforce’s Atlas Reasoning Engine empowers these agents to handle both routine and complex tasks autonomously. A New Era of AI Innovation At Dreamforce 2024, over 10,000 attendees raced to build their own agents using the “Agent Builder” experience, turning verbal instructions into fully functioning agents in under 15 minutes. This wasn’t just another chatbot—it’s a new breed of AI that could transform how businesses operate and deliver superior customer experiences. Companies like Saks, OpenTable, and Wiley have quickly embraced this technology. As Mick Costigan and David Berthy of Salesforce Futures explain, “When we see signals like this, it pushes us toward the future. Soon, we’ll see complex, multi-agent systems solving higher-order challenges, both in the enterprise and in consumer devices.” Shaping the Future Agentforce isn’t just a product—it’s a platform for experimentation. With hundreds of thousands of Salesforce customers soon gaining access, the full potential of these tools will unfold in ways we can’t yet imagine. As with every major technological shift, the real magic will lie in how people use it. Every interaction, every agent deployed, and every problem solved will shape the future in unexpected ways. Platform Evolution Adam Evans, Salesforce SVP of Product, notes that Agentforce builds on the company’s transformation over the past four years, following the pattern of Salesforce’s original disruption of enterprise software. Unlike traditional solutions, Agentforce eliminates the need for customers to build their own AI infrastructure, providing a ready-to-use solution. At the core of Agentforce is the Atlas Reasoning Engine, delivering results that are twice as relevant and 33% more accurate than competing solutions. This engine integrates Salesforce Data Cloud, Flow for automation, and the Einstein Trust Layer for governance. Early Customer Results Early Agentforce deployments highlight how organizations are using autonomous agents to enhance, rather than replace, human workers: George Pokorny, Senior VP of Global Customer Success at OpenTable, shared, “Just saving two minutes on a ten-minute call lets our service reps focus on strengthening customer relationships, thanks to seamless integration with Service Cloud, giving us a unified view of diner preferences and history.” 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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The Subscription Economy

The Subscription Economy: A $1.5 Trillion Opportunity by 2025The subscription economy has expanded by 435% over the last decade and is projected to reach a staggering $1.5 trillion market size by 2025, cementing its status as a dominant business model. Companies leveraging subscription and servitization strategies are unlocking consistent revenue streams and delivering tailored services that foster customer loyalty and satisfaction. The Challenge of Managing RenewalsWhile the subscription economy thrives, managing renewals effectively remains a critical challenge. Without a robust system, businesses risk: This is where Salesforce CPQ (Configure, Price, Quote) becomes essential. It not only automates the renewal process but also empowers businesses to deliver accurate, timely, and personalized renewal experiences. The Role of Salesforce CPQ in Renewal ManagementSalesforce CPQ streamlines the renewal process by automating workflows, ensuring pricing accuracy, and creating personalized customer experiences. Key benefits include: With Salesforce CPQ, renewals become a strategic opportunity to retain customers, drive revenue growth, and deliver exceptional service. The Importance of Renewal Metrics Tracking the right renewal metrics is crucial for understanding performance and identifying areas for improvement. These metrics provide actionable insights that drive better customer retention and revenue growth. 1. Renewal Rate 2. Churn Rate 3. Renewal Revenue 4. Net Revenue Retention (NRR) 5. Customer Lifetime Value (CLV) 6. Time to Renewal (TTR) 7. Renewal Upsell Rate 8. Renewal Discount Utilization Enhancing Renewal Metrics with Salesforce CPQ Salesforce CPQ transforms how you track and manage renewal metrics through automation, analytics, and workflow optimization. Here’s how: 1. Automate Renewal Tracking 2. Enhance Reporting and Analytics 3. Optimize Workflows 4. Analyze and Improve 5. Train and Support Teams Conclusion Salesforce CPQ revolutionizes renewal management by automating processes, reducing errors, and delivering actionable insights through renewal metrics. By implementing these tools and strategies, you can improve customer retention, increase revenue, and build stronger customer relationships. Whether managing hundreds or thousands of renewals, Salesforce CPQ ensures your business stays ahead with precision and care. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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AI Agents in Business 2025

AI Agents in Business 2025

AI Agents Are No Longer Just Hype—They’re Transforming Business in 2025 For years, AI agents have been more of a promise than a reality. But that’s changing in 2025. According to Salesforce, AI agents are finally becoming a tangible force in enterprise operations. A new survey from MuleSoft reveals that 93% of enterprise IT leaders have already implemented or plan to implement AI agents within the next two years. Despite this momentum, enterprises still face hurdles in deploying AI effectively. For example, 29% missed their delivery goals in 2024, and 80% cite data integration as a major challenge. “Integration challenges hinder companies from fully realizing the technology’s potential to create a limitless digital workforce,” said Andrew Comstock, SVP and GM of MuleSoft. “AI agents depend on connected data to understand context and execute tasks effectively.” AI Agents Are Taking Shape—But Integration Remains a Challenge Salesforce’s 10th annual MuleSoft Connectivity Benchmark Report, which surveyed 1,050 enterprise IT leaders, underscores the complexity of modern IT environments: Poor integration directly impacts AI agent accuracy and usefulness. AI agents must pull both structured and unstructured data from diverse sources—ERP, CRM, HCM platforms, emails, PDFs, Slack, and more—to make informed decisions. The Power of APIs in AI Agent Deployment APIs are proving critical in enabling AI agents to function effectively. IT leaders see them as essential for: ✅ Improving IT infrastructure✅ Sharing data across teams✅ Integrating disconnected systems By leveraging APIs, AI agents interact with existing automations and systems instead of requiring enterprises to rebuild workflows from scratch. “With correct integration and APIs, enterprises won’t have to refit everything for the AI world,” Comstock explained. IT Teams Are Overwhelmed—AI Can Help Close the Gap The report also reveals a growing demand for IT resources: “That’s an incredibly high percentage of time spent on cumbersome work,” said Comstock. “AI agents can close the IT delivery gap.” AI’s Impact on Developer Productivity and the Future of “Super Agents” IT leaders are optimistic about AI’s potential, with 93% believing AI will boost developer productivity over the next three years. Beyond simple AI agents, “super agents” are on the horizon. Unlike traditional AI agents that execute single commands, super agents pursue goals and handle complex, multi-step tasks. “A digital labor workforce can act autonomously, carrying out both simple and complex tasks to drive productivity and efficiency,” Comstock noted. How Enterprises Are Using AI Agents Today AI agents are already proving their value in industries ranging from financial services to talent acquisition. PenFed Credit Union: AI-Powered Customer Support PenFed Credit Union, the third-largest federal credit union in the U.S., implemented live chat and AI-powered chatbots built on Agentforce in just eight weeks—with only one engineer. By integrating data through MuleSoft, PenFed’s service agents now have a 360-degree view of member interactions, resulting in: “Members get the support they need faster, without repeating themselves,” Comstock explained. Adecco: AI-Driven Hiring Automation Adecco, a global staffing company processing 300 million job applications annually, faced a challenge: recruiters could only respond to a fraction of candidates. With Agentforce, MuleSoft, and Salesforce Data Cloud, Adecco is now automating hiring workflows: ✅ AI autonomously sorts resumes and generates candidate shortlists.✅ AI notifies candidates who weren’t selected and suggests alternative roles.✅ AI identifies the best job boards for listings, eliminating manual posting. “The goal is to respond to 100% of applicants, improving both recruiter efficiency and candidate experience,” said Comstock. The AI Adoption Feedback Loop As enterprises deploy AI agents, their effectiveness improves over time. AI models continuously refine themselves, making each new iteration more intelligent and efficient. “The best companies will embrace this cycle, gaining a major competitive edge,” said Comstock. AI Agents Aren’t Replacing Humans—They’re Augmenting Them Despite fears of job displacement, Comstock sees AI agents as enhancements, not replacements. They free employees from repetitive tasks, allowing them to focus on higher-value work. “This is where AI becomes real,” said Comstock. “Enterprises are moving beyond theory—AI agents are already delivering measurable business impact.” 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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2024 The Year of Generative AI

Was 2024 the Year Generative AI Delivered? Here’s What Happened Industry experts hailed 2024 as the year generative AI would take center stage. Operational use cases were emerging, technology was simplifying access, and general artificial intelligence felt imminent. So, how much of that actually came true? Well… sort of. As the year wraps up, some predictions have hit their mark, while others — like general AI — remain firmly in development. Let’s break down the trends, insights from investor Tomasz Tunguz, and what’s ahead for 2025. 1. A World Without Reason Three years into our AI evolution, businesses are finding value, but not universally. Tomasz Tunguz categorizes AI’s current capabilities into: While prediction and search have gained traction, reasoning models still struggle. Why? Model accuracy. Tunguz notes that unless a model has repeatedly seen a specific pattern, it falters. For example, an AI generating an FP&A chart might succeed — but introduce a twist, like usage-based billing, and it’s lost. For now, copilots and modestly accurate search reign supreme. 2. Process Over Tooling A tool’s value lies in how well it fits into established processes. As data teams adopt AI, they’re realizing that production-ready AI demands robust processes, not just shiny tools. Take data quality — a critical pillar for AI success. Sampling a few dbt tests or point solutions won’t cut it anymore. Teams need comprehensive solutions that deliver immediate value. In 2025, expect a shift toward end-to-end platforms that simplify incident management, enhance data quality ownership, and enable domain-level solutions. The tools that integrate seamlessly and address these priorities will shape AI’s future. 3. AI: Cost Cutter, Not Revenue Generator For now, AI’s primary business value lies in cost reduction, not revenue generation. Tools like AI-driven SDRs can increase sales pipelines, but often at the cost of quality. Instead, companies are leveraging AI to cut costs in areas like labor. Examples include Klarna reducing two-thirds of its workforce and Microsoft boosting engineering productivity by 50-75%. Cost reduction works best in scenarios with repetitive tasks, hiring challenges, or labor shortages. Meanwhile, specialized services like EvenUp, which automates legal demand letters, show potential for revenue-focused AI use cases. 4. A Slower but Smarter Adoption Curve While 2023 saw a wave of experimentation with AI, 2024 marked a period of reflection. Early adopters have faced challenges with implementation, ROI, and rapidly changing tech. According to Tunguz, this “dress rehearsal” phase has informed organizations about what works and what doesn’t. Heading into 2025, expect a more calculated wave of AI adoption, with leaders focusing on tools that deliver measurable value — and faster. 5. Small Models for Big Gains In enterprise AI, small, fine-tuned models are gaining favor over massive, general-purpose ones. Why? Small models are cheaper to run and often outperform their larger counterparts when fine-tuned for specific tasks. For example, training an 8-billion-parameter model on 10,000 support tickets can yield better results than a general model trained on a broad corpus. Legal and cost challenges surrounding large proprietary models further push enterprises toward smaller, open-source solutions, especially in highly regulated industries. 6. Blurring Lines Between Analysts and Engineers The demand for data and AI solutions is driving a shift in responsibilities. AI-enabled pipelines are lowering barriers to entry, making self-serve data workflows more accessible. This trend could consolidate analytical and engineering roles, streamlining collaboration and boosting productivity in 2025. 7. Synthetic Data: A Necessary Stopgap With finite real-world training data, synthetic datasets are emerging as a stopgap solution. Tools like Tonic and Gretel create synthetic data for AI training, particularly in regulated industries. However, synthetic data has limits. Over time, relying too heavily on it could degrade model performance, akin to a diet lacking fresh nutrients. The challenge will be finding a balance between real and synthetic data as AI advances. 8. The Rise of the Unstructured Data Stack Unstructured data — long underutilized — is poised to become a cornerstone of enterprise AI. Only about half of unstructured data is analyzed today, but as AI adoption grows, this figure will rise. Organizations are exploring tools and strategies to harness unstructured data for training and analytics, unlocking its untapped potential. 2025 will likely see the emergence of a robust “unstructured data stack” designed to drive business value from this vast, underutilized resource. 9. Agentic AI: Not Ready for Prime Time While AI copilots have proven useful, multi-step AI agents still face significant challenges. Due to compounding accuracy issues (e.g., 90% accuracy over three steps drops to ~50%), these agents are not yet ready for production use. For now, agentic AI remains more of a conversation piece than a practical tool. 10. Data Pipelines Are Growing, But Quality Isn’t As enterprises scale their AI efforts, the number of data pipelines is exploding. Smaller, fine-tuned models are being deployed at scale, often requiring hundreds of millions of pipelines. However, this rapid growth introduces data quality risks. Without robust quality management practices, teams risk inconsistent outputs, bottlenecks, and missed opportunities. Looking Ahead to 2025 As AI evolves, enterprises will face growing pains, but the opportunities are undeniable. From streamlining processes to leveraging unstructured data, 2025 promises advancements that will redefine how organizations approach AI and data strategy. The real challenge? Turning potential into measurable, lasting impact. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, 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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ThoughtSpot AI agent Spotter enables conversational BI

ThoughtSpot AI agent Spotter enables conversational BI

ThoughtSpot Unveils Spotter: A Generative AI-Powered Data Agent ThoughtSpot, a leading analytics vendor, has launched Spotter, an advanced generative AI-powered agent designed to revolutionize how users interact with data. Spotter enables conversational data exploration, contextual understanding, and autonomous analysis, making it a significant leap forward in the analytics landscape. Spotter’s Role in ThoughtSpot’s Evolution Spotter replaces Sage, ThoughtSpot’s earlier generative AI-powered interface, which debuted in March 2023. Despite moving from private to public preview and gaining new capabilities, Sage never reached general availability. Spotter is now generally available for ThoughtSpot Analytics, while its embedded version is in beta testing. Unlike earlier AI tools that focused on question-and-answer interactions, such as Sage and Microsoft’s copilots, Spotter takes the concept further by integrating contextual awareness and autonomous decision-making. Spotter doesn’t just respond to queries; it suggests follow-up questions, identifies anomalies, and provides proactive insights, functioning more like a virtual analyst than a reactive chatbot. Key Features of Spotter Spotter is built to enhance productivity and insight generation through the following capabilities: Generative AI’s Growing Impact on BI ThoughtSpot has long aimed to make analytics accessible to non-technical users through natural language search. However, previous NLP tools often required users to learn specific vocabularies, limiting widespread adoption. Generative AI bridges this gap. By leveraging extensive vocabularies and LLM technology, tools like Spotter enable users of all skill levels to access and analyze data effortlessly. Spotter stands out with its ability to deliver proactive insights, identify trends, and adapt to user behavior, enhancing the decision-making process. Expert Perspectives on Spotter Donald Farmer, founder of TreeHive Strategy, highlighted Spotter’s autonomy as a game-changer: “Spotter is a big move forward for ThoughtSpot and AI. The natural language interface is more conversational, but the key advantage is its autonomous analysis, which identifies trends and insights without users needing to ask.” Mike Leone, an analyst at TechTarget’s Enterprise Strategy Group, emphasized Spotter’s ability to adapt to users: “Spotter’s ability to deliver personalized and contextually relevant responses is critical for organizations pursuing generative AI initiatives. This goes a long way in delivering unique value across a business.” Farmer also pointed to Spotter’s embedded capabilities, noting its growing appeal as an embedded analytics solution integrated with productivity tools like Salesforce and ServiceNow. Competitive Positioning Spotter aligns ThoughtSpot with other vendors embracing agentic AI in analytics. Google recently introduced Conversational Analytics in Looker, and Salesforce’s Tableau platform now includes Tableau Agent. ThoughtSpot’s approach builds on its core strength in search-based analytics while expanding into generative AI-driven capabilities. Leone observed: “ThoughtSpot is right in line with the market in delivering an agentic experience and is laying the groundwork for broader AI functionality over time.” A Step Toward the Future of Analytics With Spotter, ThoughtSpot is redefining the role of AI in business intelligence. The tool combines conversational ease, proactive insights, and seamless integration, empowering users to make data-driven decisions more efficiently. As generative AI continues to evolve, tools like Spotter demonstrate how businesses can unlock the full potential of their data. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more

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