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Salesforce Data Cloud
27Feb

Data Cloud March 2025 Release

Cross Cloud Updates for Data Cloud March 2025 Release Enhanced Marketing Cloud Integration Multi-Instance Event Routing Key Marketing Features: Expanded Data Sharing & Collaboration Real-Time Data Processing Event-Triggered Flows (Beta) Server-to-Server Connections Enterprise Tools Sandbox Migration External Data Connectivity Analytics & Security Key Details Feature Availability Editions Notes Multi-Instance Event Routing March 2025 Enterprise+ Action rules required 250-Object Data Shares March 2025 Enterprise+ AI/ML use cases Event-Triggered Flows March 2025 Enterprise+ Beta, add-on required This release delivers:✅ Precision marketing with cross-instance event routing✅ Scalable collaboration via expanded data sharing✅ Instant automation through real-time triggers Note: Beta features governed by Salesforce Beta Terms. 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

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February 27, 2025in Data, Enterprise, Google, Salesforce, Salesforce Data Cloud, Snowflake

Five9 Deepens Salesforce Partnership

Five9 Deepens Salesforce Partnership to Advance AI-Powered Contact Centers Five9 is strengthening its collaboration with Salesforce to help mutual customers streamline service environments and implement AI-powered agents that enhance customer interactions. A Strategic Partnership in AI & Customer Service The announcement comes as Five9 celebrates a 17% year-over-year (YoY) revenue growth, with Chairman & CEO Mike Burkland crediting the company’s expanding partner network for driving success. While acknowledging partnerships with Microsoft, Google, ServiceNow, and Verint, Burkland highlighted Five9’s deepening relationship with Salesforce, emphasizing: “Salesforce and Five9 share a vision where AI agents and human agents work together to elevate customer experiences.” A key focus of this partnership is enhancing integration between Five9 and Agentforce, Salesforce’s platform for autonomous AI agents. This marks the first CCaaS vendor integration with Agentforce, opening up new opportunities for intelligent, industry-specific AI applications. Industry-Specific AI Agents: A Game Changer By embedding Agentforce capabilities within the Five9-Salesforce CCaaS-CRM ecosystem, businesses can automate critical customer service workflows. Some examples include: These are just a few use cases demonstrating how AI-driven automation can transform customer engagement across industries. CCaaS vs. CRM: Who Will Lead AI in Contact Centers? As AI reshapes customer service, industry analysts have questioned whether businesses will favor their CRM provider over their CCaaS vendor for AI-driven automation. Burkland dismissed this as a false choice, explaining: “It’s going to be a mix. Even if an organization chooses Salesforce for their AI, they still need access to all the contextual data in our platform. Salesforce knows they need us, and we welcome that relationship. Our goal is to do what’s best for the customer.” The Future of AI-Powered Customer Engagement By deepening its Salesforce integration and leading the way in AI-driven service automation, Five9 is positioning itself as a key player in the evolution of intelligent contact centers. As businesses increasingly seek to blend human expertise with AI efficiency, this partnership paves the way for seamless, personalized, and automated customer experiences at 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

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February 26, 2025in Data, Google, Salesforce
salesforce end to end
24Feb

Salesforce and Google Announcement

Salesforce (NYSE:CRM) has entered into a deal with Google (NASDAQ:GOOGL) to offer its customer relations management software, Agentforce artificial intelligence assistants, and Data Cloud offerings through Google Cloud, the companies announced today. Google and Salesforce already have many of the same clients, and this new deal will allow for more product integration between Google Workspace and Salesforce’s customer relationship management and AI offerings. Salesforce already uses Amazon (AMZN) Web Services for much of its cloud computing. “Our mutual customers have asked us to be able to work more seamlessly across Salesforce and Google Cloud, and this expanded partnership will help them accelerate their AI transformations with agentic AI, state-of-the-art AI models, data analytics, and more,” said Thomas Kurian, CEO of Google Cloud. The deal is expected to total $2.5B over the next seven years, according to a report by Bloomberg. Salesforce and Google today announced a major expansion of their strategic partnership, delivering choice in the models and capabilities businesses use to build and deploy AI-powered agents. In today’s constantly evolving AI landscape, innovations like autonomous agents are emerging so quickly that businesses struggle to keep pace. This expanded partnership provides crucial flexibility, empowering customers to develop tailored AI solutions that meet their specific needs, rather than being locked into a single model provider. Google Cloud is at the forefront of enterprise AI innovation with millions of developers building with Google’s cutting-edge Gemini models and on Google Cloud’s AI-optimized infrastructure. This expanded partnership will empower Salesforce customers to build Agentforce agents using Gemini and to deploy Salesforce on Google Cloud. This is an expansion of the existing partnership that allows customers to use data from Data Cloud and Google BigQuery bi-directionally via zero-copy technology—further equipping customers with the data, AI, trust, and actions they need to bring autonomous agents into their businesses. Additionally, this integration empowers Agentforce agents with the ability to reference up-to-the-minute data, news, current events, and credible citations, substantially enhancing their contextual awareness and ability to deliver accurate, evidence-backed responses. For example, in supply chain management and logistics, an agent built with Agentforce could track shipments and monitor inventory levels in Salesforce Commerce Cloud and proactively identify potential disruptions using real-time data from Google Search, including weather conditions, port congestion, and geopolitical events. Availability is expected in the coming months. AI: Unlocking the Power of Choice and Flexibility with Gemini and Agentforce Businesses need the freedom to choose the best models for their needs rather than be locked into one vendor. In 2025, Google’s Gemini models will also be available for prompt building and reasoning directly within Agentforce. With Gemini and Agentforce, businesses will benefit from: For example, an insurance customer can submit a claim with photos of the damage and an audio voicemail from a witness. Agentforce, using Gemini, can then help the insurance provider deliver better customer experiences by processing all these inputs, assessing the claim’s validity, and even using text-to-speech to contact the customer with a resolution, streamlining the traditionally lengthy claims process. Availability is expected this year. Trust: Salesforce Platform deployed on Google Cloud Customers will be able to use Salesforce’s unified platform (Agentforce, Data Cloud, Customer 360) on Google Cloud’s highly secure, AI-optimized infrastructure, benefiting from features like dynamic grounding, zero data retention, and toxicity detection provided by the Einstein Trust Layer. Once Salesforce products are available on Google Cloud, customers will also have the ability to procure Salesforce offerings through the Google Cloud Marketplace, opening up new possibilities for global businesses to optimize their investments across Salesforce and Google Cloud and benefiting thousands of existing joint customers. Action: Enhanced Employee Productivity and Customer Service with AI-Powered Integrations Millions use Salesforce and Google Cloud daily. This partnership prioritizes choice and flexibility, enabling seamless cross-platform work. New and deeper connections between platforms like Salesforce Service Cloud and Google Cloud’s Customer Engagement Suite, as well as Slack and Google Workspace, will empower AI agents and service representatives with unified data access, streamlined workflows, and advanced AI capabilities, regardless of platform. Salesforce and Google Cloud are deeply integrating their customer service platforms—Salesforce Service Cloud and Google Cloud’s Customer Engagement Suite—to create a seamless and intelligent support experience. Expected later this year, this unified approach empowers AI agents in Service Cloud with: Salesforce and Google Cloud are also exploring deeper integrations between Slack and Google Workspace, boosting productivity and creating a more cohesive digital workspace for teams and organizations. The companies are currently exploring use cases such as: Expanding Partnership Capabilities and Integrations This partnership goes beyond core product integrations to deliver a more connected and intelligent data foundation for businesses. Expected availability throughout 2025: This landmark partnership between Salesforce and Google represents a strategic paradigm shift in enterprise AI deployment, emphasizing infrastructure innovation, AI capability enhancement, and enterprise value. The integration of Google Search grounding provides a unique competitive advantage, offering real-time, factual responses backed by the world’s most comprehensive search engine. The companies are committed to ongoing innovation and deeper collaboration to empower businesses with even more powerful solutions. Like1 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

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February 24, 2025in Agentic AI, Artificial Intelligence, Data, Enterprise, Google, Salesforce, Salesforce Commerce Cloud, Salesforce Service Cloud, Technology
Neuro-symbolic AI
24Feb

Neuro-symbolic AI

Neuro-Symbolic AI: Bridging Neural Networks and Symbolic Processing for Smarter AI Systems Neuro-symbolic AI integrates neural networks with rules-based symbolic processing to enhance artificial intelligence systems’ accuracy, explainability, and precision. Neural networks leverage statistical deep learning to identify patterns in large datasets, while symbolic AI applies logic and rules-based reasoning common in mathematics, programming languages, and expert systems. The Balance Between Neural and Symbolic AIThe fusion of neural and symbolic methods has revived debates in the AI community regarding their relative strengths. Neural AI excels in deep learning, including generative AI, by distilling patterns from data through distributed statistical processing across interconnected neurons. However, this approach often requires significant computational resources and may struggle with explainability. Conversely, symbolic AI, which relies on predefined rules and logic, has historically powered applications like fraud detection, expert systems, and argument mining. While symbolic systems are faster and more interpretable, their reliance on manual rule creation has been a limitation. Innovations in training generative AI models now allow more efficient automation of these processes, though challenges like hallucinations and poor mathematical reasoning persist. Complementary Thinking ModelsPsychologist Daniel Kahneman’s analogy of System 1 and System 2 thinking aptly describes the interplay between neural and symbolic AI. Neural AI, akin to System 1, is intuitive and fast—ideal for tasks like image recognition. Symbolic AI mirrors System 2, engaging in slower, deliberate reasoning, such as understanding the context and relationships in a scene. Core Concepts of Neural NetworksArtificial neural networks (ANNs) mimic the statistical connections between biological neurons. By modeling patterns in data, ANNs enable learning and feature extraction at different abstraction levels, such as edges, shapes, and objects in images. Key ANN architectures include: Despite their strengths, neural networks are prone to hallucinations, particularly when overconfident in their predictions, making human oversight crucial. The Role of Symbolic ReasoningSymbolic reasoning underpins modern programming languages, where logical constructs (e.g., “if-then” statements) drive decision-making. Symbolic AI excels in structured applications like solving math problems, representing knowledge, and decision-making. Algorithms like expert systems, Bayesian networks, and fuzzy logic offer precision and efficiency in well-defined workflows but struggle with ambiguity and edge cases. Although symbolic systems like IBM Watson demonstrated success in trivia and reasoning, scaling them to broader, dynamic applications has proven challenging due to their dependency on manual configuration. Neuro-Symbolic IntegrationThe integration of neural and symbolic AI spans a spectrum of techniques, from loosely coupled processes to tightly integrated systems. Examples of integration include: History of Neuro-Symbolic AIBoth neural and symbolic AI trace their roots to the 1950s, with symbolic methods dominating early AI due to their logical approach. Neural networks fell out of favor until the 1980s when innovations like backpropagation revived interest. The 2010s saw a breakthrough with GPUs enabling scalable neural network training, ushering in today’s deep learning era. Applications and Future DirectionsApplications of neuro-symbolic AI include: The next wave of innovation aims to merge these approaches more deeply. For instance, combining granular structural information from neural networks with symbolic abstraction can improve explainability and efficiency in AI systems like intelligent document processing or IoT data interpretation. Neuro-symbolic AI offers the potential to create smarter, more explainable systems by blending the pattern-recognition capabilities of neural networks with the precision of symbolic reasoning. As research advances, this synergy may unlock new horizons in AI capabilities. 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

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February 24, 2025in Artificial Intelligence, Data, Generative AI, Google
$15 Million to AI Training for U.S. Government Workforce
19Feb

AI Adoption in the Federal Government

AI Adoption in the Federal Government: A New Era Under the Trump Administration With a new administration in Washington and a $500 billion AI infrastructure initiative underway, the U.S. federal government may be entering a phase of accelerated AI adoption. Federal AI Expansion AI adoption grew under the Biden administration, with agencies leveraging it for fraud detection, workflow automation, and data analysis. However, experts predict that the Trump administration will further expand federal AI use. “Trump and his advisers have spoken about ‘unleashing AI,’ signaling a push for broader adoption within government agencies,” said Darrell West, a senior fellow at the Brookings Institution’s Center for Technology Innovation. As the administration scales back AI safety regulations and deepens ties with major tech firms, federal AI usage is expected to rise. However, ensuring transparency and educating the public remain crucial for building trust in government AI applications. AI Governance Framework The foundation for federal AI governance was established under Trump’s first term, with executive orders EO 13859 (2019) and EO 13960 (2020). EO 13960 mandated an annual AI use case inventory, significantly expanding under Biden—from 710 cases in 2023 to 2,133 in 2024. Reggie Townsend, VP of Data Ethics at SAS and a National AI Advisory Committee (NAIAC) member, emphasized the importance of this transparency: “The inventory was a crucial first step in building public trust.” Biden’s EO 14110 (2023) introduced stronger AI guardrails, requiring agencies to designate chief AI officers, disclose safety-related AI use cases, and implement risk management guidelines. However, on his first day in office, Trump rescinded EO 14110, signaling a shift toward deregulation. AI Applications in Government The 2024 federal AI inventory reported 2,133 AI use cases across 41 agencies. The Department of Health and Human Services (HHS) led with 271 cases, reflecting a 66% increase from the previous year. Key applications include: Harvard Kennedy School adjunct lecturer Bruce Schneier anticipates even broader AI integration in government, from automating reports to drafting legislation and conducting audits. Despite growing interest, the federal government lags behind the private sector in AI adoption, especially for generative AI, due to concerns over bias, reliability, and transparency. AI Under a Second Trump Term Trump’s return to office in 2025 signals an AI policy shift favoring reduced oversight and enhanced global AI leadership. “Federal AI adoption will accelerate under Trump,” West said, citing efforts to integrate major tech figures into federal initiatives. Notably, Trump appointed xAI owner Elon Musk to lead the newly rebranded Department of Government Efficiency, formerly the U.S. Digital Service. This agency is tasked with modernizing federal technology, reducing costs, and driving deregulation. With EO 14110 rescinded, the scope of AI governance under Trump remains uncertain. “Will he eliminate all guardrails, or keep some protections? That’s something to watch,” West noted. Big Tech’s Role in Federal AI Trump’s inauguration underscored tech industry influence, with Elon Musk, Mark Zuckerberg, Jeff Bezos, and Sundar Pichai in attendance. Major tech firms, including Amazon, Google, and Microsoft, each contributed $1 million to the event, while OpenAI CEO Sam Altman made a personal $1 million donation. Some companies are aligning with the administration’s stance on AI and content moderation. Meta, for instance, has replaced its fact-checking services with a community-driven model similar to X’s Community Notes and relaxed its moderation policies. A deregulated AI landscape could benefit big tech, particularly in areas like AI safety standards and data copyright issues, while advancing the administration’s vision for U.S. AI dominance. AI’s Future in Government On his second day in office, Trump announced a $500 billion AI infrastructure investment, forming Stargate—a coalition of OpenAI, SoftBank, MGX, and Oracle—to expand AI infrastructure nationwide. “This will be the largest AI infrastructure project in history,” Trump declared, emphasizing the need for AI leadership against global competitors like China. However, West warned that accelerated adoption must be managed carefully: “It’s critical that AI is implemented fairly, with privacy and security safeguards in place.” Building AI Literacy Effective AI deployment requires education within federal agencies. “Many government workers lack AI expertise, making it difficult to procure and implement AI solutions effectively,” West said. NAIAC’s Townsend advocates for structured AI training, tailored to different federal roles. Public AI literacy is also crucial, with initiatives like the National AI Research Resource (NAIRR) promoting equitable access to AI education and development. “The public must be informed enough to hold the government accountable on AI issues,” Townsend concluded. As AI adoption accelerates, striking a balance between innovation, oversight, and public trust will define the next phase of federal AI policy. 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

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February 19, 2025in Data, Generative AI, Google, Public Sector Solutions, Technology
ai trust layer
18Feb

Gen AI Trust Layers

Addressing the Generative AI Production Gap with Trust Layers Despite the growing excitement around generative AI, only a small percentage of projects have successfully moved into production. A key barrier is the persistent concern over large language models (LLMs) generating hallucinations—responses that are inconsistent or completely disconnected from reality. To address these issues, organizations are increasingly adopting AI trust layers to enhance reliability and mitigate risk. Understanding the Challenge Generative AI models, like LLMs, are powerful tools trained on vast amounts of unstructured data, enabling them to answer questions and complete tasks based on text, documents, recordings, images, and videos. This capability has revolutionized the creation of chatbots, co-pilots, and even semi-autonomous agents. However, these models are inherently non-deterministic, meaning they don’t always produce consistent outputs. This lack of predictability leads to the infamous phenomenon of hallucination—what the National Institute of Standards and Technology (NIST) terms “confabulation.” While hallucination is a byproduct of how generative models function, its risks in mission-critical applications cannot be ignored. Implementing AI Trust Layers To address these challenges, organizations are turning to AI trust layers—frameworks designed to monitor and control generative AI behavior. These trust layers vary in implementation: Galileo: Building AI Trust from the Ground Up Galileo, founded in 2021 by Yash Sheth, Atindriyo Sanyal, and Vikram Chatterji, has emerged as a leader in developing AI trust solutions. Drawing on his decade of experience at Google building LLMs for speech recognition, Sheth recognized early on that non-deterministic AI systems needed robust trust frameworks to achieve widespread adoption in enterprise settings. The Need for Trust in Mission-Critical AI “Sheth explained: ‘Generative AI doesn’t give you the same answer every time. To mitigate risk in mission-critical tasks, you need a trust framework to ensure these models behave as expected in production.’ Enterprises, which prioritize privacy, security, and reputation, require this level of assurance before deploying LLMs at scale. Galileo’s Approach to Trust Layers Galileo’s AI trust layer is built on its proprietary foundation model, which evaluates the behavior of target LLMs. This approach is bolstered by metrics and real-time guardrails to block undesirable outcomes, such as hallucinations, data leaks, or harmful outputs. Key Products in Galileo’s Suite Sheth described the underlying technology: “Our evaluation foundation models are dependable, reliable, and scalable. They run continuously in production, ensuring bad outcomes are blocked in real time.” By combining these components, Galileo provides enterprises with a trust layer that gives them confidence in their generative AI applications, mirroring the reliability of traditional software systems. From Research to Real-World Impact Unlike vendors who quickly adapted traditional machine learning frameworks for generative AI, Galileo spent two years conducting research and developing its Generative AI Studio, launched in August 2023. This thorough approach has started to pay off: A Crucial Moment for AI Trust Layers As enterprises prepare to move generative AI experiments into production, trust layers are becoming essential. These frameworks address lingering concerns about the unpredictable nature of LLMs, allowing organizations to scale AI while minimizing risk. Sheth emphasized the stakes: “When mission-critical software starts becoming infused with AI, trust layers will define whether we progress or regress to the stone ages of software. That’s what’s holding back proof-of-concepts from reaching production.” With Galileo’s innovative approach, enterprises now have a path to unlock the full potential of generative AI—responsibly, securely, and at 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

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February 18, 2025in Data, Enterprise, Generative AI, Google, Salesforce, Technology
ChatGPT 5.0 is Coming
17Feb

ChatGPT Search

OpenAI’s ChatGPT Search: Everything You Need to Know ChatGPT Search is OpenAI’s generative AI-powered search engine, designed to provide real-time information while eliminating the limitations of traditional language models’ knowledge cutoffs. It combines conversational AI with real-time web search, offering up-to-date insights, summaries, and more. Here’s a deep dive into what makes ChatGPT Search unique and how it compares to existing solutions like Google. Overcoming Knowledge Cutoffs Earlier iterations of OpenAI’s models, like GPT-4 (October 2023 cutoff) and GPT-3 (September 2021 cutoff), lacked the ability to access real-time data, a significant drawback for users seeking the latest information. By integrating live search capabilities, ChatGPT Search resolves this issue. Unlike traditional search engines like Google, which continuously crawl and update web indexes, ChatGPT combines the strengths of its GPT-4o model with live web access, bridging the gap between generative AI and real-time search. What Is ChatGPT Search? Launched on October 31, 2024, after being prototyped as “SearchGPT,” ChatGPT Search pairs OpenAI’s advanced language models with live web search. Initially available to ChatGPT Plus and Team users, it will expand to Enterprise, Education, and free-tier users by early 2025. Key Features of ChatGPT Search How Does It Work? ChatGPT Search leverages the following technologies: Accessing ChatGPT Search ChatGPT Search is accessible through multiple platforms: Why ChatGPT Search Challenges Google While Google dominates the search market, OpenAI’s ChatGPT Search introduces key differentiators: AI-Powered Search Engine Comparison Search Engine Platform Integration Publisher Collaboration Ads Cost ChatGPT Search OpenAI infrastructure Strong media partnerships Ad-free Free (Premium tiers planned) Google AI Overviews Google infrastructure SEO-focused partnerships Ads included Free Bing AI Microsoft infrastructure SEO-focused partnerships Ads included Free Perplexity AI Independent, standalone Basic attribution Ad-free Free; $20/month premium You.com Multi-mode AI assistant Basic attribution Ad-free Free; premium available Brave Search Independent index Basic attribution Ad-free Free The Roadmap for ChatGPT Search OpenAI has ambitious plans to refine and expand ChatGPT Search, including: Conclusion ChatGPT Search marks a pivotal shift in how users interact with AI and access information. By combining the generative power of GPT-4o with real-time search, OpenAI has created a tool that rivals traditional search engines with conversational AI, summarized insights, and ad-free functionality. As OpenAI continues to refine the platform, ChatGPT Search is poised to redefine the way we find and interact with information—offering a glimpse into the future of search. 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

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February 17, 2025in Data, Enterprise, Generative AI, Google
The Hidden Risks of Over-Reliance on AI
14Feb

The Hidden Risks of Over-Reliance on AI

Are Marketers Trusting AI Too Much? How to Avoid Losing Your Strategic Edge AI tools have revolutionized how marketers approach research, content creation, and decision-making. However, an overreliance on these tools could undermine critical thinking and strategic planning, leaving marketers vulnerable in a fast-evolving landscape. Here’s how to balance the power of automation with human insight. The Rise of AI in Search and Marketing In late December, SEO consultancy Previsible shared a striking report: Google’s search dominance has plateaued and is now being challenged by AI-assisted search tools. These tools, such as ChatGPT, Claude, and Google’s own AI-enhanced search, are growing in popularity due to their ability to deliver contextually relevant and personalized results. Unlike traditional search, which relies on keyword matching, AI-driven search processes intent and context. This shift is reshaping how users find information and make decisions. How AI Is Changing User Behavior The increasing sophistication of AI tools brings both opportunities and risks. Users often trust AI-generated outputs without question, assuming they’re accurate and complete. Traditional search, by contrast, forces users to critically analyze and filter multiple sources. This blind trust in AI mirrors the concept of “System 1 thinking,” as described by Nobel Prize-winning psychologist Daniel Kahneman in Thinking, Fast and Slow. As AI models like ChatGPT operate primarily as “System 1 thinkers,” users risk adopting a similar approach, bypassing critical analysis in favor of convenience. The Hidden Risks of Over-Reliance on AI Younger marketers may be especially at risk of falling into this trap. Many are using AI tools like ChatGPT to summarize information or generate ideas, often without questioning the accuracy of the outputs. For B2B marketers, the allure of AI lies in its speed and perceived accuracy. However, this reliance on automation could lead to a generation of marketers who lack the ability—or inclination—to think strategically. The danger is clear: unchecked dependence on AI tools could foster a “groupthink” mentality, where creativity and critical thinking are sidelined. Without intervention, marketing departments risk becoming overly reliant on tools that were designed to enhance human efforts, not replace them. How Marketing Leaders Can Address This Threat To counter this trend, marketing leaders must actively promote the development of strategic skills. Here’s how: In a world increasingly driven by AI, marketers who can blend automation with strategic thinking will be best positioned for success. Using AI to Enhance, Not Replace, Strategic Thinking AI should empower marketers to make better decisions—not serve as the sole decision-maker. As one professor aptly put it, “Use AI to become a better student, not to be the student.” The key is balance. By combining the intuitive capabilities of AI with the deliberate, analytical approach of System 2 thinking, marketers can leverage technology without sacrificing creativity or strategy. In short, AI is a tool—not a replacement for human ingenuity. Those who recognize this distinction will thrive in an increasingly automated world. 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

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February 14, 2025in AI Tools, Google, Technology
agentforce digital workforce
13Feb

How Agentforce Works

Salesforce Agentforce: Everything You Need to Know Salesforce Agentforce represents a paradigm shift from generative AI to agentic AI—a new class of AI capable of autonomous action. Since its launch at Dreamforce in September 2024, Agentforce has redefined the conversation around AI, customer service, and experience management. To meet skyrocketing demand, Salesforce announced plans to hire more than 1,000 employees shortly after the launch. What is Salesforce Agentforce? Agentforce is a next-generation platform layer within the Salesforce ecosystem. While its bots leverage generative AI capabilities, they differ significantly from platforms like ChatGPT or Google Gemini. Agentforce bots are designed not just to generate responses but to act autonomously within predefined organizational guardrails. Unlike traditional chatbots, which follow scripted patterns, Agentforce AI agents are trained on proprietary data, enabling flexible and contextually accurate responses. They also integrate with Salesforce’s Data Cloud, enhancing their capacity to access and utilize customer data effectively. Agentforce combines three core tools—Agent Builder, Model Builder, and Prompt Builder—allowing businesses to create customized bots using low-code tools. Key Features of Agentforce The platform offers ready-to-deploy AI agents tailored for various industries, including: Agentforce officially became available on October 25, 2024, with pricing starting at $2 per conversation, and volume discounts offered for enterprise customers. Salesforce also launched the Agentforce Partner Network, enabling third-party integrations and custom agent designs for expanded functionality. How Agentforce Works Salesforce designed Agentforce for users without deep technical expertise in AI. As CEO Marc Benioff said, “This is AI for the rest of us.” The platform is powered by the upgraded Atlas Reasoning Engine, a component of Salesforce Einstein AI, which mimics human reasoning and planning. Like self-driving cars, Agentforce interprets real-time data to adapt its actions and operates autonomously within its established parameters. Enhanced Atlas Reasoning Engine In December 2024, Salesforce enhanced the Atlas Reasoning Engine with retrieval-augmented generation (RAG) and advanced reasoning capabilities. These upgrades allow agents to: Seamless Integrations with Salesforce Tools Agentforce is deeply integrated with Salesforce’s ecosystem: Key Developments Agentforce Testing Center Launched in December 2024, the Testing Center allows businesses to test agents before deployment, ensuring they are accurate, fast, and aligned with organizational goals. Skill and Integration Library Salesforce introduced a pre-built library for CRM, Slack, Tableau, and MuleSoft integrations, simplifying agent customization. Examples include: Industry-Specific Expansion Agentforce for Retail Announced at the NRF conference in January 2025, this solution offers pre-built skills tailored to retail, such as: Additionally, Salesforce unveiled Retail Cloud with Modern POS, unifying online and offline inventory data. Notable Agentforce Customers Looking Ahead Marc Benioff calls Agentforce “the third wave of AI”, advancing beyond copilots into a new era of autonomous, low-hallucination intelligent agents. With its robust capabilities, Agentforce is positioned to transform how businesses interact with customers, automate workflows, and drive success. 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

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February 13, 2025in Agentforce Platform, Agentic AI, Data, Enterprise, Generative AI, Google, Mulesoft, Salesforce, Salesforce Einstein
ai
07Feb

Generative AI Integration

What it is: How it works: Benefits: Example Use Cases: 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

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February 7, 2025in AI Tools, Data, Generative AI, Generative AI Tools, Google
unpatched ai
05Feb

Unpatched.ai

The Mystery of Unpatched.ai: AI-Powered Vulnerability Discovery Raises Questions During January’s Patch Tuesday, Microsoft credited Unpatched.ai for reporting multiple high-severity vulnerabilities. Yet, despite its contributions, the AI-driven bug-finding tool remains an enigma to the cybersecurity community. Last month, Microsoft addressed 159 new vulnerabilities across its widely used products. Among them, Unpatched.ai was acknowledged for identifying three remote code execution flaws—CVE-2025-21186, CVE-2025-21366, and CVE-2025-21395—all of which affect Microsoft Access and received a CVSS score of 7.8. While Microsoft’s recognition highlights Unpatched.ai’s role in vulnerability discovery, little is known about the tool itself. Informa TechTarget reached out to multiple security vendors and experts for insights, but responses only deepened the mystery. A Cryptic Online Presence Unpatched.ai describes itself as “vulnerability discovery by an AI-guided cybersecurity platform” on its website. It provides a list of reported vulnerabilities, which consists solely of Microsoft-related flaws—primarily within Microsoft Access. The platform states that it collaborates with “select enterprise, government, and security vendors based in the U.S. and ally countries.” The company’s “About” page sheds some light on its mission, attributing its research to the need for greater transparency around unpatched software flaws: “We find unpatched issues in software to help customers better identify and manage cyber risk. Many issues are unknown or silently fixed by software vendors, hiding the true risk profile of their products. With the help of AI, we are developing an automated platform to help find and analyze these issues for our customers.” Beyond the website, Unpatched.ai maintains an X account, though much of its activity has been erased. A now-deleted post from January 29 warned that Microsoft’s patch for CVE-2025-21396 was insufficient. When contacted about the post, a Microsoft spokesperson responded, “We are aware of these reports and will take action as needed to help protect customers.” However, Microsoft did not provide additional background on Unpatched.ai. Attempts to reach Unpatched.ai directly have gone unanswered. Piecing Together the Puzzle Efforts to uncover more about Unpatched.ai yielded few concrete details. The domain was registered through Namecheap in September, with ownership masked by a privacy service based in Reykjavik, Iceland. Adam Barnett, lead software engineer at Rapid7, noted that beyond Unpatched.ai’s website, information is scarce. However, he identified a Reddit user, “Fit_Tie_9430,” who has claimed affiliation with the platform. This user shared details about Unpatched.ai’s vulnerability discoveries and linked to now-private YouTube videos demonstrating exploits against Microsoft Access vulnerabilities. Barnett pointed out that Unpatched.ai was also credited for a December Patch Tuesday flaw, CVE-2024-49142. Initially published without attribution, Microsoft later updated the advisory to acknowledge Unpatched.ai’s discovery. Interestingly, the Unpatched.ai website’s favicon—a simple “:)” emoticon—appears to reference the Windows Blue Screen of Death’s “:(” symbol. “It’s a nice touch,” Barnett said, “but I still don’t know who’s behind it. It could be just about anyone with the time, resources, and skills.” Other industry experts share the same uncertainty. Satnam Narang, senior staff research engineer at Tenable, observed that Unpatched.ai’s X account follows only a handful of infosec professionals. “It’s unclear if the service is still in a closed-door phase and will eventually provide more insights about its leadership and team, or who may be backing it,” he said. Alon Yamin, co-founder and CEO of Copyleaks, noted that an AI-driven vulnerability discovery platform was inevitable given the surge in software flaws. While AI can be a game-changer for proactive threat detection, he cautioned against potential misuse. “It’s crucial that Unpatched.ai is deployed carefully, responsibly, and ethically, with safeguards to prevent attackers from exploiting the vulnerabilities it identifies,” Yamin said. The Future of AI-Powered Bug Hunting AI-driven vulnerability discovery is an emerging focus in cybersecurity, though few major breakthroughs have been publicly confirmed. In November, Google announced it had discovered a zero-day vulnerability using AI. Google Project Zero and DeepMind’s AI-powered agent, Big Sleep, identified a buffer stack underflow flaw in the SQLite open-source database engine. With Unpatched.ai making waves yet remaining elusive, the cybersecurity community is left with more questions than answers. Is this the beginning of a new era in AI-powered vulnerability research, or is Unpatched.ai an outlier? Until more information surfaces, the mystery remains. 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

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February 5, 2025in Data, Enterprise, Google, Laws
pydanticai
03Feb

Pydantic AI

The evaluation of agentic applications is most effective when integrated into the development process, rather than being an afterthought. For this to succeed, developers must be able to mock both internal and external dependencies of the agent being built. PydanticAI introduces a groundbreaking framework that supports dependency injection from the start, enabling developers to build agentic applications with an evaluation-driven approach. An architectural parallel can be drawn to the historic Krakow Cloth Hall, a structure refined over centuries through evaluation-driven enhancements. Similarly, PydanticAI allows developers to iteratively address challenges during development, ensuring optimal outcomes. Challenges in Developing GenAI Applications Developers of LLM-based applications face recurring challenges, which become significant during production deployment: To address non-determinism, developers must adopt evaluation-driven development, a method akin to test-driven development. This approach focuses on designing software with guardrails, real-time monitoring, and human oversight, accommodating systems that are only x% correct. The Promise of PydanticAI PydanticAI stands out as an agent framework that supports dependency injection, model-agnostic workflows, and evaluation-driven development. Its design is Pythonic and simplifies testing by allowing the injection of mock dependencies. For instance, in contrast to frameworks like Langchain, where dependency injection is cumbersome, PydanticAI streamlines this process, making the workflows more readable and efficient. Building an Evaluation-Driven Application with PydanticAI Example Use Case: Evaluating Mountain Data By employing tools like Wikipedia as a data source, the agent can fetch accurate mountain heights during production. For testing, developers can inject mocked responses, ensuring predictable outputs and faster development cycles. Advancing Agentic Applications with PydanticAI PydanticAI provides the building blocks for creating scalable, evaluation-driven GenAI applications. Its support for dependency injection, structured outputs, and model-agnostic workflows addresses core challenges, empowering developers to create robust and adaptive LLM-powered systems. This paradigm shift ensures that evaluation is seamlessly embedded into the development lifecycle, paving the way for more reliable and efficient agentic applications. 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

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February 3, 2025in Salesforce
copilots and agentic ai
03Feb

Transforming Industries and Redefining Workflows

The Rise of Agentic AI: Transforming Industries and Redefining Workflows Artificial Intelligence (AI) is evolving faster than we anticipated. No longer limited to predicting outcomes or generating content, AI systems are now capable of handling complex tasks and making autonomous decisions. This new era—driven by Agentic AI—is set to redefine the workplace and transform industries. From Prediction to Autonomy: The Three Waves of AI To understand where we’re headed, it’s important to see how far AI has come. Arun Parameswaran, SVP & MD of Salesforce India, describes it as a fundamental shift: “What has changed with agents is their ability to handle complex reasoning… and, most importantly, to take action.” Unlike previous AI models that recommend or predict, Agentic AI executes tasks, reshaping customer experiences and operational workflows. Agentic AI in Action: Industry Applications At a recent Mint x Salesforce India deep-dive event on AI, industry leaders explored how Agentic AI is driving transformation across sectors. The panel featured: Here’s how Agentic AI is already making an impact: 1. Revolutionizing Customer Support Traditional chatbots have limited capabilities. Agentic AI, however, understands urgency and context. 2. Accelerating Business Decisions In finance and supply chain management, AI agents analyze vast amounts of data and execute decisions autonomously. 3. Transforming Travel & Aviation Airlines are leveraging AI to optimize booking systems, reduce costs, and enhance efficiency. 4. Automating Wealth Management AI agents in financial services monitor markets, adjust strategies, and offer personalized investment recommendations in real time. The Risks & Responsibilities of Agentic AI With great autonomy comes great responsibility. The potential of Agentic AI is vast—but so are the challenges: The Future of Work: AI as a Partner, Not a Replacement Despite concerns about job displacement, AI is more likely to reshape rather than replace roles. What Are AI Agents? AI agents go beyond traditional models like ChatGPT or Gemini. They are proactive, self-learning systems that: They fall into two categories: “AI agents don’t just wait for commands; they anticipate needs and act,” says Dr. Tomer Simon, Chief Scientist at Microsoft Research Israel. AI Agents in the Workplace: A Shift in Roles AI agents streamline processes, but they don’t eliminate the need for human oversight. Salesforce’s Agentforce is a prime example: “Companies need to integrate AI, not fear it. Those who fail to adopt AI tools risk drowning in tasks AI can handle,” warns Dr. Omri Allouche, Chief Scientist at Gong. The Road Ahead: AI-Driven Business Growth Agentic AI is not about replacing people—it’s about empowering them. As organizations re-evaluate workflows and embrace AI collaboration, the companies that act early will gain a competitive edge in efficiency and innovation. Final Thought The AI revolution is here, and Agentic AI is at its forefront. The key question isn’t whether AI will transform industries—it’s how organizations will adapt and thrive in this new era. 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

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February 3, 2025in Agentic AI, AI Tools, Artificial Intelligence, Data, Generative AI, Google, Laws, Salesforce, Technology

Einstein Service Agent

It’s been a little over a year since the global surge in GenAI chatbots, sparked by the excitement around ChatGPT. Since then, numerous vendors, both large and mid-sized, have invested heavily in the technology, and many users have already adopted AI-powered chatbots. The competition is intensifying, with CRM giant Salesforce releasing its own GenAI chatbot software, Einstein Service Agent. Einstein Service Agent, built on the Einstein 1 Platform, is Salesforce’s first fully autonomous AI agent. It interacts with large language models (LLMs) by analyzing the context of customer messages to determine the next actions. Utilizing GenAI, the agent generates conversational responses grounded in a company’s trusted business data, including Salesforce CRM data. Salesforce claims that service organizations can now significantly reduce the number of tedious inquiries that hinder productivity, allowing human agents to focus on more complex tasks. For customers, this means getting answers faster without waiting for human agents. Additionally, the service promises 24/7 availability for customer communication in natural language, with an easy handoff to human agents for more complicated issues. Businesses are increasingly turning to AI-based chatbots because, unlike traditional chatbots, they don’t rely on specific programmed queries and can understand context and nuance. Alongside Salesforce, other tech leaders like AWS and Google Cloud have released their own chatbots, such as Amazon Lex and Vertex AI, continuously enhancing their software. Recently, AWS updated its chatbot with the QnAIntent capability in Amazon Lex, allowing integration with a knowledge base in Amazon Bedrock. Similarly, Google released Vertex AI Agent Builder earlier this year, enabling organizations to build AI agents with no code, which can function together with one main agent and subagents. The AI arms race is just beginning, with more vendors developing software to meet market demands. For users, this means that while AI takes over many manual and tedious tasks, the primary challenge will be choosing the right vendor that best suits the needs and resources of their business. 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

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January 31, 2025in Data, Einstein 1 Platform, Google, Salesforce, Technology
gettectonic.com