- gettectonic.com - Page 6
SaaS Data Protection from Own
08Dec

Educational Salesforce Data Safeguard

Safeguarding Salesforce Data in Educational Institutions Educational institutions thrive on data—whether it’s class registrations, test scores, or admissions records. This data powers their ability to achieve objectives and drive digital transformation. For many, Salesforce and Education Cloud provide the backbone for managing student, faculty, and administrative data, keeping them ahead in the digital transformation race. However, while many institutions excel at leveraging data, the importance of protecting it often remains overlooked. This insight explores why safeguarding Salesforce data should be a top priority and how tools like Own make it easy. The Shared Responsibility Model: Know Your Role Salesforce simplifies data management for educational institutions, but adopting the platform comes with responsibilities. According to the shared responsibility model, Salesforce secures its infrastructure, while customers are accountable for safeguarding and recovering the data stored on the platform. For educational institutions, this means planning protection and recovery efforts proactively to avoid costly disruptions and risks before incidents occur. Why Protecting Salesforce Data is Critical As reliance on Salesforce data grows, having a robust backup and recovery strategy becomes essential. Here’s why: 1. Ensure Operational Continuity Data loss or corruption isn’t a matter of “if” but “when.” Protecting your Salesforce data ensures smooth operations during unexpected incidents. For example, during admissions season, losing prospective student applications due to accidental deletion could result in delays, reputational damage, and resource strain. With a reliable backup and recovery solution, your institution can quickly restore lost data, avoid operational disruptions, and ensure a seamless admissions process. 2. Stay Ahead of Regulatory Compliance Education is governed by strict regulations like FERPA and GDPR, and compliance is non-negotiable. By safeguarding your Salesforce data, you reduce the risks of legal, financial, or reputational penalties. A trusted backup solution ensures your data is securely stored, recoverable, and audit-ready to meet evolving compliance requirements. 3. Accelerate Development with Quality Data Data fuels innovation in Salesforce development. Reliable backups ensure developers have accurate, secure data to work with, reducing errors and speeding up deployment cycles. This enables institutions to adapt quickly to changes and make the most of Salesforce’s powerful capabilities. 4. Unlock AI Potential AI is revolutionizing education, from classrooms to administrative processes. However, successful AI initiatives require high-quality, historical data. By protecting Salesforce data, institutions can access the insights needed to drive AI-driven innovation and transform educational operations. Own: The Solution for Salesforce Data Protection Educational institutions shouldn’t wait for a data loss crisis to prioritize data protection. The Own Data Platform offers a seamless solution to safeguard Salesforce data, enabling institutions to focus on growth and innovation. Own’s Key Features Lessons Learned the Easy Way Data is the foundation of digital transformation in education. By prioritizing Salesforce data protection with tools like Own, educational institutions can safeguard their operations, meet compliance standards, and pave the way for innovation. After all, in both the classroom and the cloud, lessons are best learned proactively—not the hard way. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
December 8, 2024in Data
07Dec

Ushering in the Era of AI-Driven Workforces

Agentforce: Ushering in the Era of AI-Driven Workforces “Agentforce is redefining what’s possible in business and beyond, ushering in a new era of AI abundance and limitless workforces that augment every employee, build deeper customer relationships, and drive unprecedented growth and profitability.”— Marc Benioff, Salesforce CEO Key Features and Takeaways Out-of-the-Box AI Agents Agentforce introduces pre-built, customizable agents that are deployable with low-code or no-code tools, working tirelessly across any channel. Its first generally available solution, the Agentforce Service Agent, surpasses traditional chatbots by handling a diverse range of tasks—from simple inquiries to complex scenarios. Key capabilities include: Seamless Integration with Salesforce Platform Agentforce eliminates the complexity of building AI solutions from scratch. Unlike other platforms that demand intricate data integration and custom automation, Agentforce is fully embedded within the Salesforce ecosystem. With Agentforce, businesses can: Always-On Automation Agentforce operates independently of human intervention. Agents can be triggered by changes in data, predefined business rules, or pre-built automations, ensuring uninterrupted workflows. This blend of autonomous operation and human collaboration creates a symbiotic relationship between people and AI, enhancing productivity and customer satisfaction. A Competitive Edge in the AI Space Salesforce emphasizes that Agentforce goes beyond chatbots and copilots, setting a new benchmark for enterprise AI. In a strategic swipe at competitors, Marc Benioff likened rival offerings—like Microsoft’s Dynamics 365 AI agents—to “Clippy 2.0,” critiquing their inaccuracies and risks of corporate data leaks. By contrast, Agentforce builds on Salesforce’s proven Einstein AI platform, ensuring reliability, scalability, and secure integration. Redefining Customer Success Agentforce isn’t just about automation—it’s about driving outcomes. By enabling businesses to automate complex processes, deepen customer relationships, and scale operations, it paves the way for limitless growth in the age of AI. Ready to transform your workforce? With Agentforce, the future of AI-driven business is already here. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
December 7, 2024in Agentforce Platform, Data, Enterprise, Mulesoft, Salesforce
Spring 25 Update
06Dec

Spring 25 Update

Get Ready for Salesforce Spring ’25: Key Dates and Insights It’s that time of the Salesforce calendar again—the Salesforce ecosystem is buzzing with anticipation as the Spring ’25 release approaches. Packed with innovative features and updates, this release is set to enhance the customer experience and deliver significant value to all Salesforce users. To make the most of this release, there are two key priorities to focus on: preparing your sandbox and understanding when your instance will be upgraded. When Will Your Salesforce Instance Upgrade to Spring ’25? Knowing when your main Salesforce environment will transition to Spring ’25 is critical. Running tests on key system functions before the upgrade ensures everything continues to operate smoothly. While Salesforce releases are generally seamless, proactive testing is always a good practice. The main release weekends for Spring ’25 are: To pinpoint when your specific instance will upgrade: If you’re unsure of your instance name, you can find it by navigating to Setup > Company Information in your Salesforce org. Spring ’25 Sandbox Preview: Your Testing Opportunity Upgrading your sandbox environment is a vital part of preparing for the Spring ’25 release. This step allows you to test the new features and updates with your unique configurations and data. Salesforce provides two upgrade windows for sandboxes, depending on whether your instance is “Preview” or “Non-Preview.” Most sandboxes are expected to upgrade around January 4, 2025. Using a pre-release org is another option to explore the new features before the sandbox upgrade, but keep in mind that pre-release orgs don’t include your specific configurations. Spring ’25 Pre-Release Orgs Pre-release orgs provide a sneak peek at the Spring ’25 updates. These are an excellent way to get hands-on experience with the new features before they roll out to your environment. Stay tuned for the pre-release org link as it becomes available. Spring ’25 Release Notes: A First Look at New Features Perhaps the most eagerly awaited milestone in the Salesforce release cycle is the publication of the official release notes. These notes provide an in-depth overview of the features and upgrades being rolled out across Salesforce products. We’ll link the release notes here as soon as they’re available—so you can dive into what’s coming and prepare to implement the changes that matter most to your organization. With the Spring ’25 release just around the corner, now’s the time to start planning and testing. By staying ahead of the key dates and leveraging your sandbox preview, you’ll ensure a smooth transition to the latest Salesforce innovations. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
December 6, 2024in Data, Salesforce
Shift From AI Agents to AI Agent Tool Use
06Dec

Shift From AI Agents to AI Agent Tool Use

The focus of AI development is evolving—from creating autonomous AI Agents to expanding the tools they use, significantly boosting their capabilities and flexibility. Tool access, described and utilized through natural language, is now a critical factor in the functionality and reach of these agents, enabling them to tackle increasingly complex tasks. The Role of Tools in AI Agent Effectiveness AI Agents thrive in user-specific environments like desktops, where rich context enables them to perform tasks more effectively. Instead of just scaling model power, leading AI companies such as OpenAI and Anthropic are pivoting toward tool-enabled frameworks, allowing agents to interact directly with computer GUI navigation for multi-step workflows. This shift positions tools as essential components of AI ecosystems, bridging the gap between raw computational power and actionable user outcomes. OpenAI’s “Operator” and the Future of Autonomous Agents OpenAI is set to release Operator, an AI Agent designed to autonomously perform tasks such as coding and travel booking on a user’s computer. Available as a research preview in January, Operator is part of a broader industry trend toward Agentic Tools that enable seamless, multi-step task execution with minimal user oversight. This approach reflects a shift toward real-time AI capabilities, moving beyond model-centric enhancements to unlock practical, task-driven use cases for AI Agents. Anthropic’s Desktop AI Agent Anthropic is also advancing the field with a reference implementation for computer use, enabling rapid deployment of AI-powered desktop agents. This implementation allows users to leverage Claude, Anthropic’s AI model, in a virtual machine environment with powerful tools for GUI interaction, command-line operations, and file management. Key Features This system provides a controlled yet versatile environment for AI Agents to operate in a safe, flexible, and efficient manner. Technical Implementation To deploy Anthropic’s computer-use demo: bashCopy codeexport ANTHROPIC_API_KEY=%your_api_key% docker run -e ANTHROPIC_API_KEY=<Your Anthropic API Key Goes Here> -v $HOME/.anthropic:/home/computeruse/.anthropic -p 5900:5900 -p 8501:8501 -p 6080:6080 -p 8080:8080 -it ghcr.io/anthropics/anthropic-quickstarts:computer-use-demo-latest Tools Overview Each session starts fresh but maintains state within the session, enabling smooth task execution. The Bigger Picture AI Agents are no longer defined solely by their autonomous capabilities. Instead, their success now hinges on how effectively they utilize tools to extend their reach and flexibility. Whether it’s through GUI navigation, command-line interactions, or file management, tool access is transforming the way AI Agents deliver value to users. By focusing on tools rather than just AI model power, companies like OpenAI and Anthropic are building the foundation for a new era of AI-driven productivity. Expect to see more advancements in Agentic Tool design, as the emphasis shifts from autonomy to capability. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
December 6, 2024in Salesforce
06Dec

5 Attributes of an Agent

Defining the Role of AI Agents To successfully implement AI agents, organizations must clearly define their function across these five key areas: The Evolution of Agentic Automation Agentic automation represents a major shift in how enterprises leverage AI to drive productivity and efficiency. By seamlessly integrating AI agents, human employees, and automation technologies, businesses can orchestrate complex workflows from start to finish. AI agents are transforming customer service and business operations. These intelligent systems can plan and execute tasks, make informed decisions, and integrate with existing workflows to deliver superior efficiency. With the right AI strategy, businesses can elevate customer experiences by offering proactive, personalized, and highly responsive solutions. The Need for AI Agents Businesses today face mounting challenges: AI agents like Agentforce provide a scalable solution by automating interactions, streamlining processes, and ensuring continuous availability. Let’s explore what AI agents are, how they work, and how to deploy them successfully. What is an AI Agent? An AI agent is an intelligent system that autonomously interacts with customers, processes data, and executes actions without human intervention. Powered by machine learning and natural language processing (NLP), AI agents can: Unlike traditional automation, AI agents learn from interactions, refine their responses, and adapt to evolving business needs. Imagine if every employee—from the CEO to the newest sales rep—had an AI-powered assistant. With today’s AI advancements, that vision is becoming a reality. The Impact of Generative AI Agents More companies are embracing generative AI agents that leverage trusted customer data to deliver real-time insights. Tasks that once required extensive manual effort—like data analysis, trend forecasting, and customer support—can now be automated, freeing employees to focus on higher-value work. Beyond customer service, AI agents help businesses scale, meet key performance indicators, and solve problems before they escalate. The potential of this technology is just beginning to unfold. How AI Agents Work AI agents operate through a four-step process: By integrating these capabilities, AI agents can autonomously manage tasks like product recommendations, troubleshooting, and proactive follow-ups—allowing human employees to focus on strategic initiatives. Types of AI Agents Not all AI agents function the same way. Businesses can leverage different types of agents based on their operational needs: The Future of AI Agents AI-driven automation is redefining the way businesses operate. From enhancing customer experiences to optimizing internal workflows, AI agents are becoming indispensable tools for modern enterprises. As organizations invest in AI strategies, the key question remains: Are you ready to harness the full potential of AI agents to drive business success? Contact Tectonic today! By Tectonic Marketing Operations Director, Shannan Hearne Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
December 6, 2024in Data, Enterprise, Generative AI, Technology

Slack Templates

Sometimes the hardest part of work is just getting started. With templates, your channels, canvases, lists, and workflows come pre-made and ready to roll, so you and your team can dive right in. View all Slack Templates Templates were created for the most common use cases, across every line of business. So whether you’re managing a project or triaging help requests, you can kick work off without starting from scratch. Templates are fully customizable, so you can make them work better for the work you do. Then, save them to your company’s template library, so you and your teammates can use them again and again. Samples and Templates for Slack API Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

Read More
December 6, 2024in Salesforce Slack
Google’s Gemini 1.5 Flash-8B
06Dec

Google’s Gemini 1.5 Flash-8B

Google’s Gemini 1.5 Flash-8B: A Game-Changer in Speed and Affordability Google’s latest AI model, Gemini 1.5 Flash-8B, has taken the spotlight as the company’s fastest and most cost-effective offering to date. Building on the foundation of the original Flash model, 8B introduces key upgrades in pricing, speed, and rate limits, signaling Google’s intent to dominate the affordable AI model market. What Sets Gemini 1.5 Flash-8B Apart? Google has implemented several enhancements to this lightweight model, informed by “developer feedback and testing the limits of what’s possible,” as highlighted in their announcement. These updates focus on three major areas: 1. Unprecedented Price Reduction The cost of using Flash-8B has been slashed in half compared to its predecessor, making it the most budget-friendly model in its class. This dramatic price drop solidifies Flash-8B as a leading choice for developers seeking an affordable yet reliable AI solution. 2. Enhanced Speed The Flash-8B model is 40% faster than its closest competitor, GPT-4o, according to data from Artificial Analysis. This improvement underscores Google’s focus on speed as a critical feature for developers. Whether working in AI Studio or using the Gemini API, users will notice shorter response times and smoother interactions. 3. Increased Rate Limits Flash-8B doubles the rate limits of its predecessor, allowing for 4,000 requests per minute. This improvement ensures developers and users can handle higher volumes of smaller, faster tasks without bottlenecks, enhancing efficiency in real-time applications. Accessing Flash-8B You can start using Flash-8B today through Google AI Studio or via the Gemini API. AI Studio provides a free testing environment, making it a great starting point before transitioning to API integration for larger-scale projects. Comparing Flash-8B to Other Gemini Models Flash-8B positions itself as a faster, cheaper alternative to high-performance models like Gemini 1.5 Pro. While it doesn’t outperform the Pro model across all benchmarks, it excels in cost efficiency and speed, making it ideal for tasks requiring rapid processing at scale. In benchmark evaluations, Flash-8B surpasses the base Flash model in four key areas, with only marginal decreases in other metrics. For developers prioritizing speed and affordability, Flash-8B offers a compelling balance between performance and cost. Why Flash-8B Matters Gemini 1.5 Flash-8B highlights Google’s commitment to providing accessible AI solutions for developers without compromising on quality. With its reduced costs, faster response times, and higher request limits, Flash-8B is poised to redefine expectations for lightweight AI models, catering to a broad spectrum of applications while maintaining an edge in affordability. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

Read More
December 6, 2024in Data, Google
ai arms race
05Dec

AI Arms Race

AI Arms Race: Providers Catching Up to Payers in Claims Review The healthcare sector is in the midst of an escalating AI arms race as providers adopt the same artificial intelligence technologies payers are leveraging for claims review. Insurers currently lead this race, using AI to streamline processes such as prior authorizations, but experts predict providers will soon narrow the gap. Insurers’ AI Advantage Leading payers, including UnitedHealth, Humana, and Cigna, have integrated algorithmic decision tools to assess claims and determine coverage eligibility. These technologies allow insurers to flag services that fall outside plan criteria, ostensibly increasing efficiency. This trend is expanding, as evidenced by Blue Shield of California’s announcement of a partnership with Salesforce to pilot claims automation technology in early 2025. The nonprofit insurer claims this initiative will reduce prior authorization decision times from weeks or days to mere seconds, benefiting providers and patients alike. However, provider experiences paint a more contentious picture. Reports from lawmakers and healthcare executives suggest AI-driven claims processes lead to a surge in denials. For example, Providence CFO Greg Hoffman revealed that AI adoption by payers resulted in a 50% increase in underpayments and initial denials over two years, forcing providers to significantly increase manual interventions to resolve claims. A Battle for Balance The imbalance in AI adoption has prompted providers to take action. Experts like Jeffrey Cribbs, a vice president analyst at Gartner, see this as a forced “arms race” in which both sides are continually refining their tools. While payers focus on flagging potential exceptions, providers are working to develop systems for more efficient claims submissions and dispute resolution. Providence’s strategy includes outsourcing revenue cycle management to R1, a 10-year partnership designed to quickly address rising claims denials. Hoffman explained that building equivalent AI systems internally would take years, making partnerships essential for staying competitive in the short term. Collaboration Among Providers On the provider side, executives like Sara Vaezy, EVP and Chief Strategy Officer at Providence, emphasize the need for collaboration. She advocates for coalitions to share data and establish AI standards, which would allow providers to compete more effectively. Panelists at HLTH echoed this sentiment. Amit Phull, Chief Physician Experience Officer at Doximity, argued that AI could eventually “level the playing field” for providers by reducing the time required for claims documentation. Deloitte principal consultant Bill Fera added that AI would allow providers to quickly analyze policies and determine whether a patient qualifies for coverage under plan terms. The Road Ahead Despite the current disparity, experts believe AI will eventually equalize the claims review process. Providers are beginning to invest in tools that will help them handle vast amounts of data efficiently, offering clarity in disputes and cutting down documentation time. “It’s still early innings,” Phull said, “but the technology is going to go a long way toward leveling that playing field.” For now, however, insurers maintain the upper hand. As providers navigate the complexities of AI adoption, partnerships and collaboration may prove critical in ensuring they remain competitive in this rapidly evolving landscape. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
December 5, 2024in Artificial Intelligence, Data, Salesforce, Technology
computer vision models
05Dec

Computer Vision Models

Introduction to Visual Language Models (VLMs): The Future of Computer Vision Achieving a 28% Boost in Multimodal Image Search Accuracy with VLMs Until recently, artificial intelligence models were narrowly focused—either excelling in understanding text or interpreting images, but rarely both. This siloed approach limited their potential to process and connect different types of data. The development of general-purpose language models, like GPTs, marked a significant leap forward. These models transitioned AI from task-specific systems to powerful, versatile tools capable of handling a wide range of language-driven tasks. Yet, despite their advancements, language models and computer vision systems evolved separately, like having the ability to hear without seeing—or vice versa. Visual Language Models (VLMs) bridge this gap, combining the capabilities of language and vision to create multimodal systems. In this article, we’ll explore VLMs’ architecture, training methods, challenges, and transformative potential in fields like image search. We’ll also examine how implementing VLMs revolutionized an AI-powered search engine. What Are Visual Language Models (VLMs)? VLMs represent the next step in the evolution of AI: multimodal models capable of processing multiple data types, including text, images, audio, and video. Why Multimodal Models? The rise of general-purpose approaches has outpaced narrow, specialized systems in recent years. Here’s why: At their core, VLMs receive input in the form of images and text (called “instructs”) and produce textual responses. Their range of applications includes image classification, description, interpretation, and more. For example: These tasks showcase VLMs’ ability to tackle a variety of challenges, including zero-shot and one-shot scenarios, where minimal prior training is required. How Do VLMs Work? Core Components VLMs typically consist of three main components: Workflow Adapters: The Key to Multimodality Adapters facilitate interaction between the image encoder and LLM. There are two primary types: Training VLMs Pre-Training VLMs are built on pre-trained LLMs and image encoders. Pre-training focuses on linking text and image modalities, as well as embedding world knowledge from visual data. Three types of data are used: Alignment Alignment fine-tunes VLMs for high-quality responses. This involves: Quality Evaluation Evaluating VLM performance involves: Revolutionizing Image Search with VLMs Incorporating VLMs into search engines transforms user experience by integrating text and image inputs. Previous Pipeline VLM-Powered Pipeline With VLMs, image and text inputs are processed together, creating a streamlined, accurate system that outperforms traditional pipelines by 28%. Conclusion Visual Language Models are a game-changer for AI, breaking down barriers between language and vision. From multimodal search engines to advanced problem-solving, VLMs unlock new possibilities in computer vision and beyond. The future of AI is multimodal—and VLMs are leading the way. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

Read More
December 5, 2024in Artificial Intelligence, Data, Technology
rise of digital workers
04Dec

Rise of Digital Workers

The Rise of Digital Workers: Unlocking a New Era of Opportunity Over the past two years, advancements in artificial intelligence have sparked a revolution in how humans work, live, and connect. While impressive generative AI models have garnered significant attention, a new paradigm of autonomous AI agents is emerging, promising transformative changes to industries and societies alike. Unlike traditional “predictive AI,” which analyzes data for recommendations, and “generative AI,” which creates content based on learned patterns, autonomous AI agents go a step further. These agents operate independently, executing tasks, making decisions, and even negotiating with other agents. This evolution introduces an intelligent digital workforce capable of scaling operations, reducing costs, and enhancing productivity. Consider a large retailer during the holiday season. Instead of relying on human workers or pre-programmed software to address customer inquiries or update inventory, autonomous agents can seamlessly manage customer interactions, monitor stock levels, reorder items, and coordinate shipping—all without human intervention. This level of automation represents a groundbreaking shift, enabling businesses to operate on an unprecedented scale. Expanding the Reach of Digital Labor Autonomous AI agents are breaking traditional barriers of human availability and physical constraints, enabling businesses to scale globally and more efficiently. These digital workers are not limited by geography, opening opportunities previously restricted to specific locations. However, this shift comes with challenges. Ensuring trust, accountability, and transparency in AI systems is critical. Equally important is investing in human-centric skills such as creativity, critical thinking, and adaptability, which remain uniquely human. Sustainability is another concern, as AI-driven technologies place increasing demands on energy and resources. By addressing these issues, societies can unlock the full potential of digital labor while safeguarding the planet and human values. Transforming Everyday Lives Beyond businesses, autonomous agents are poised to transform personal lives. Personalized agents can act as tutors for students, guiding them through their learning journeys. For individuals, these agents can manage everyday tasks, from scheduling appointments to coordinating complex logistics. In healthcare, AI agents are already alleviating administrative burdens on providers. For example, intelligent agents can handle patient communications, monitor progress, and schedule follow-ups, freeing doctors and nurses to focus on complex cases. Such innovations hold the potential to revolutionize patient care and improve outcomes across the board. Navigating Disruption and Change Like any transformative technology, the rise of autonomous agents will bring disruptions. Some industries will struggle to adapt, and jobs will inevitably evolve—or, in some cases, disappear. History shows, however, that technological revolutions often create far more opportunities than they displace. For example, the U.S. workforce grew by over 100 million jobs between 1950 and 2020, many in industries that didn’t exist before. The key lies in preparing workers for new roles through education and training. Autonomous agents are essential in addressing global challenges such as labor shortages and stagnant productivity growth. They amplify human capabilities, driving innovation and boosting economic output. For example, in the third quarter of 2024, U.S. productivity rose by 2.2%, fueled in part by AI advancements. Driving Innovation and Collaboration AI agents are also fostering innovation, sparking the creation of new companies and industries. More than 5,000 AI-focused startups have emerged in the past decade in the U.S. alone. This trend mirrors the technological revolutions driven by past innovations like microchips, the internet, and smartphones. However, effectively harnessing agentic AI requires collaboration among governments, businesses, nonprofits, and academia. Initiatives like the G7’s framework for AI accountability and the Bletchley Declaration emphasize transparency, safety, and data privacy, offering critical guardrails as AI adoption accelerates. A Vision for the Future Autonomous agents represent a powerful force for change, offering unprecedented opportunities for businesses and individuals alike. By leveraging these technologies responsibly and investing in human potential, societies can ensure a future of abundance and progress. As Marc Benioff, CEO of Salesforce, emphasizes, “AI has the potential to elevate every company, fuel economic growth, uplift communities, and lead to a future of abundance. If trust is our north star, agents will empower us to make a meaningful impact at an unprecedented scale.” Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
December 4, 2024in Agentforce Platform, Artificial Intelligence, Data, Generative AI, Salesforce, Technology
How to Connect Multiple Data Sources in Power BI Desktop
04Dec

How to Connect Multiple Data Sources in Power BI Desktop

In today’s data-driven world, the ability to analyze diverse data sources can set a business apart. With Power BI Desktop, a Microsoft tool, analysts can seamlessly integrate data from various platforms and transform raw information into actionable insights. For instance, you could combine Excel-based sales figures with financial data from SQL Server and customer information from Salesforce into an interactive report. Mastering these techniques can be easier through structured learning, such as Microsoft Power BI courses, which offer practical insights into leveraging this powerful tool. This guide will help you connect, combine, and visualize multiple data sources in Power BI Desktop to make smarter, data-driven decisions. Why Combine Multiple Data Sources? Organizations often face the challenge of managing data stored across disparate systems. Financial records may reside in SQL Server, sales data in Excel, and customer information in cloud platforms like Salesforce. Insights from these datasets are often hidden unless they are integrated. Using Power BI Desktop, you can load multiple data sources into a unified model, providing a comprehensive view that enables better analysis and decision-making. Getting Started with Power BI Desktop Before integrating datasets, ensure you have Power BI Desktop installed. The tool is available for download from the official Power BI website. Once installed, launch Power BI Desktop to begin connecting your data sources. Step-by-Step Guide 1. Connecting Your First Data Source Follow these steps to connect to your first data source: At this stage, you can use Power Query Editor to clean and transform the data as needed. 2. Adding Additional Data Sources Enhance your report by adding more data sources: For example, you could link an Excel file for sales data, a SQL Server database for product details, and Azure for supplementary information, all within a single report. 3. Combining Data from Multiple Sources To merge data from different sources into a cohesive model: This process creates a unified data model that allows for cross-tabulation and advanced visualizations. 4. Using Power Query Editor for Data Transformation Before combining datasets, you may need to clean and transform the data. Use Power Query Editor to: Access Power Query Editor by selecting Transform Data on the Home tab. 5. Creating Visualizations with Combined Data With your unified data model, you can create compelling visualizations: 6. Refreshing Data Connections Power BI Desktop enables you to refresh data connections effortlessly, ensuring your reports stay updated: Best Practices for Connecting Multiple Data Sources Conclusion Integrating multiple data sources in Power BI Desktop empowers businesses to uncover deep insights and make informed decisions. By following these steps, you can connect, aggregate, and visualize diverse datasets with ease. To further enhance your expertise, explore free resources or consider professional courses to master the versatility of Power BI Desktop—a vital tool for data professionals and business analysts. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
December 4, 2024in Data, Google, Mulesoft, Salesforce
Google Gemini 2.0
04Dec

Google Gemini 2.0

Google Gemini 2.0 Flash: A First Look Google has unveiled an experimental version of Gemini 2.0 Flash, its next-generation large language model (LLM), now accessible to developers via Google AI Studio and the Gemini API. This model builds on the capabilities of its predecessors with improved multimodal features and enhanced support for agentic workflows, positioning it as a major step forward in AI-driven applications. Key Features of Gemini 2.0 Flash Performance and Efficiency According to Google, Gemini 2.0 Flash is twice as fast as Gemini 1.5 while outperforming it on standard benchmarks for AI accuracy. Its efficiency and size make it particularly appealing for real-world applications, as highlighted by David Strauss, CTO of Pantheon: “The emphasis on their Flash model, which is efficient and fast, stands out. Frontier models are great for testing limits but inefficient to run at scale.” Applications and Use Cases Agentic AI and Competitive Edge Gemini 2.0’s standout feature is its agentic AI capabilities, where multiple AI agents collaborate to execute multi-stage workflows. Unlike simpler solutions that link multiple chatbots, Gemini 2.0’s tool-driven, code-based training sets it apart. Chirag Dekate, an analyst at Gartner, notes: “There is a lot of agent-washing in the industry today. Gemini now raises the bar on frontier models that enable native multimodality, extremely large context, and multistage workflow capabilities.” However, challenges remain. As AI systems grow more complex, concerns about security, accuracy, and trust persist. Developers, like Strauss, emphasize the need for human oversight in professional applications: “I would trust an agentic system that formulates prompts into proposed, structured actions, subject to review and approval.” Next Steps and Roadmap Google has not disclosed pricing for Gemini 2.0 Flash, though its free availability is anticipated if it follows the Gemini 1.5 rollout. Looking ahead, Google plans to incorporate the model into its beta-stage AI agents, such as Project Astra, Mariner, and Jules, by 2025. Conclusion With Gemini 2.0 Flash, Google is pushing the boundaries of multimodal and agentic AI. By introducing native tool usage and support for complex workflows, this LLM offers developers a versatile and efficient platform for innovation. As enterprises explore the model’s capabilities, its potential to reshape AI-driven applications in coding, data science, and interactive interfaces is immense—though trust and security considerations remain critical for broader adoption. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more Tectonic’s Successful Salesforce Track Record Salesforce Technology Services Integrator – Tectonic has successfully delivered Salesforce in a variety of industries including Public Sector, Hospitality, Manufacturing, Read more

Read More
December 4, 2024in Agentic AI, Data, Enterprise, Google
Einstein in Salesforce
04Dec

Enable Search Answers or AI-Generated Search Answers

Search Answers retrieves the most relevant snippets from your data sources, while AI-Generated Search Answers generates tailored responses by leveraging knowledge articles and other sources. Note: Requirements Editions User Permissions Enable Search Answers & AI-Generated Search Answers Feature Details Search Answers Extracts the most relevant snippet to answer a question.– Activation time: Up to 24 hours.– Starting Spring ’25 (March 2025): No longer available for enablement. AI-Generated Search Answers Generates answers by combining information from multiple knowledge articles.– Requires Search Answers to be enabled.– Activation time: Up to 24 hours.– Starting Spring ’25 (March 2025): No longer available for enablement. AI-Generated Search Answers from Data Cloud sources Generates answers using up to 5 Data Cloud DMOs as sources.– Requires disabling Search Answers & AI-Generated Search Answers if already enabled. Enable AI-Generated Search Answers for Experience Cloud Sites Note: Next Steps After enabling AI-Generated Search Answers from Data Cloud sources, configure the DMOs and fields to use as sources. Like Related Posts Who is Salesforce? Who is Salesforce? Here is their story in their own words. From our inception, we’ve proudly embraced the identity of Read more Salesforce Marketing Cloud Transactional Emails Salesforce Marketing Cloud Transactional Emails are immediate, automated, non-promotional messages crucial to business operations and customer satisfaction, such as order Read more Salesforce Unites Einstein Analytics with Financial CRM Salesforce has unveiled a comprehensive analytics solution tailored for wealth managers, home office professionals, and retail bankers, merging its Financial Read more AI-Driven Propensity Scores AI plays a crucial role in propensity score estimation as it can discern underlying patterns between treatments and confounding variables Read more

Read More
December 4, 2024in Data, Generative AI, Salesforce
gettectonic.com