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Agentic AI is Here
14Jan

On Premise Gen AI

In 2025, enterprises transitioning generative AI (GenAI) into production after years of experimentation are increasingly considering on-premises deployment as a cost-effective alternative to the cloud. Since OpenAI ignited the AI revolution in late 2022, organizations have tested large language models powering GenAI services on platforms like AWS, Microsoft Azure, and Google Cloud. These experiments demonstrated GenAI’s potential to enhance business operations while exposing the substantial costs of cloud usage. To avoid difficult conversations with CFOs about escalating cloud expenses, CIOs are exploring on-premises AI as a financially viable solution. Advances in software from startups and packaged infrastructure from vendors such as HPE and Dell are making private data centers an attractive option for managing costs. A survey conducted by Menlo Ventures in late 2024 found that 47% of U.S. enterprises with at least 50 employees were developing GenAI solutions in-house. Similarly, Informa TechTarget’s Enterprise Strategy Group reported a rise in enterprises considering on-premises and public cloud equally for new applications—from 37% in 2024 to 45% in 2025. This shift is reflected in hardware sales. HPE reported a 16% revenue increase in AI systems, reaching $1.5 billion in Q4 2024. During the same period, Dell recorded a record $3.6 billion in AI server orders, with its sales pipeline expanding by over 50% across various customer segments. “Customers are seeking diverse AI-capable server solutions,” noted David Schmidt, senior director of Dell’s PowerEdge server line. While heavily regulated industries have traditionally relied on on-premises systems to ensure data privacy and security, broader adoption is now driven by the need for cost control. Fortune 2000 companies are leading this trend, opting for private infrastructure over the cloud due to more predictable expenses. “It’s not unusual to see cloud bills exceeding 0,000 or even million per month,” said John Annand, an analyst at Info-Tech Research Group. Global manufacturing giant Jabil primarily uses AWS for GenAI development but emphasizes ongoing cost management. “Does moving to the cloud provide a cost advantage? Sometimes it doesn’t,” said CIO May Yap. Jabil employs a continuous cloud financial optimization process to maximize efficiency. On-Premises AI: Technology and Trends Enterprises now have alternatives to cloud infrastructure, including as-a-service solutions like Dell APEX and HPE GreenLake, which offer flexible pay-per-use pricing for AI servers, storage, and networking tailored for private data centers or colocation facilities. “The high cost of cloud drives organizations to seek more predictable expenses,” said Tiffany Osias, vice president of global colocation services at Equinix. Walmart exemplifies in-house AI development, creating tools like a document summarization app for its benefits help desk and an AI assistant for corporate employees. Startups are also enabling enterprises to build AI applications with turnkey solutions. “About 80% of GenAI requirements can now be addressed with push-button solutions from startups,” said Tim Tully, partner at Menlo Ventures. Companies like Ragie (RAG-as-a-service) and Lamatic.ai (GenAI platform-as-a-service) are driving this innovation. Others, like Squid AI, integrate custom AI agents with existing enterprise infrastructure. Open-source frameworks like LangChain further empower on-premises development, offering tools for creating chatbots, virtual assistants, and intelligent search systems. Its extension, LangGraph, adds functionality for building multi-agent workflows. As enterprises develop AI applications internally, consulting services will play a pivotal role. “Companies offering guidance on effective AI tool usage and aligning them with business outcomes will thrive,” Annand said. This evolution in AI deployment highlights the growing importance of balancing technological innovation with financial sustainability. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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January 14, 2025in Data, Enterprise, Generative AI, Google, Technology0 Comments
AI evolves with tools like Agentforce and Atlas
13Jan

Agentforce Powered Marketing

Maximize Team Productivity and Customer Engagement with Agentforce and AI-Powered Marketing Tools Transform your marketing operations with Agentforce, an advanced AI-powered suite seamlessly integrated into your platform. From building end-to-end campaigns to personalizing touchpoints in real time, Agentforce empowers your team to optimize performance with actionable AI insights. Here’s how: Revolutionize Campaign Management with Agentforce Agent-Driven Campaign Briefs Streamline campaign creation with Agentforce, which uses structured and unstructured data from Data Cloud to create tailored campaign briefs. Define your target segments and key messages effortlessly with the support of AI. AI-Powered Content Creation Leverage Agentforce to generate on-brand content at scale, including email subject lines, body copy, and SMS messages. Every piece of content aligns with your brand guidelines and campaign goals, ensuring consistency and relevance across audiences. Unified SMS Conversations Turn static promotions into dynamic, two-way conversations with Agentforce Unified SMS. Automatically connect customers to AI agents for tasks like appointment scheduling and offer redemption, delivering seamless customer experiences. Supercharge Insights and Actions with Data Cloud Agent-Driven AI Segmentation Create target audience segments in minutes using natural language prompts. With Agentforce and Data Cloud working in harmony, agents translate prompts into precise segment attributes—no technical expertise or SQL required. Integrate or Build Custom AI Models Develop predictive AI models with clicks, not code, or bring in existing models via direct integrations with tools like Amazon SageMaker, Google Vertex AI, or Databricks. Use these models to generate actionable predictions, such as purchase propensity or churn likelihood. Secure, Harmonized Data Foundation Keep your data safe on the Einstein Trust Layer while enabling agents to analyze harmonized, structured, and unstructured data in Data Cloud. This ensures informed decision-making without compromising security. Automate Intelligent Journeys with Marketing Cloud Engagement Journey Optimization Automate personalized campaign variations with predictive AI. Optimize engagement by tailoring content, timing, channels, and frequency dynamically across customer journeys. Generative AI for Content Creation Solve the content bottleneck with generative AI tools that instantly create on-brand copy and visuals grounded in first-party data, campaign insights, and brand guidelines—all while safeguarding trust. Real-Time Messaging Insights Stay proactive with Einstein Messaging Insights, which flags engagement anomalies like sudden drops in click-through rates. These real-time insights enable quick resolutions, preventing performance surprises. Unified WhatsApp Conversations Transform WhatsApp into a dynamic two-way engagement channel. Use a single WhatsApp number to connect marketing and service teams while enabling AI-driven self-service actions like appointment booking and offer redemptions. Scale Lead Generation and Account-Based Marketing Agent-Driven Campaign Creation Accelerate campaign planning with Agentforce, which handles everything from briefs to audience segmentation, content, and journey creation. Ground campaigns in real-time customer data for accurate targeting, all with marketer oversight for approvals. AI Lead and Account Scoring Boost alignment between marketing and sales with Einstein AI Scoring, which identifies top leads and prospects automatically. Improve ABM strategies with automated account rankings based on historical and behavioral data, driving higher conversions. Full-Funnel Attribution Gain end-to-end visibility with AI-powered multi-touch attribution. Use models like Einstein Attribution to measure the impact of each channel, event, or team activity on your pipeline, boosting ROI and campaign efficiency. Personalization on Auto-Pilot with AI Objective-Based AI Recommendations Set business objectives and let AI optimize product and content recommendations to achieve those goals. AI-Automated Offers Combine real-time customer behavior data with AI-driven insights to personalize offers across touchpoints. This results in higher satisfaction and conversion rates tailored to each individual customer. Real-Time Affinity Profiling Use AI to uncover customer affinities, preferences, and intent in real time. Deliver hyper-personalized messaging and offers across your website, app, and other channels for maximum engagement. Optimize Spend, Planning, and Performance with Marketing Cloud Intelligence AI-Powered Data Integration Say goodbye to spreadsheets and manual data maintenance. Automate data unification, KPI standardization, and cross-channel analytics with AI-powered connectors, saving time and boosting campaign effectiveness. AI Campaign Performance Insights Get interactive visualizations and AI-generated insights to adjust campaign spend and offers mid-flight. Use these insights to optimize ROI and maximize in-the-moment opportunities. Predictive Budgeting and Planning Allocate budgets more effectively with predictive AI. Real-time alerts help prevent overages or underspending, ensuring your marketing dollars are used efficiently for maximum return. With Agentforce and AI marketing tools, your team can focus on what matters most—building stronger customer relationships and driving measurable results. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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January 13, 2025in AI Tools, Data, Generative AI, Google0 Comments
Amazon Q Business
12Jan

Amazon Q Business

Amazon Q Business: Revolutionizing Enterprise Productivity with Generative AI and Plugins Amazon Q Business is a generative AI-powered assistant that empowers employees by solving problems, generating content, and offering actionable insights from across enterprise data sources. In addition to its robust search capabilities across indexed third-party services, Amazon Q Business enables real-time access to dynamic data like stock prices, vacation balances, and location tracking through its plugins. These plugins also allow employees to perform direct actions—such as prioritizing service tickets—within enterprise applications, all through a single interface. This eliminates the need to toggle between systems, saving valuable time and increasing productivity. This insight delves into how Amazon Q Business plugins seamlessly integrate with enterprise applications through built-in and custom configurations. We’ll explore: Simplifying Enterprise Tasks with Plugins Amazon Q Business enables users to access non-indexed data—such as calendar availability, stock prices, or PTO balances—and execute actions like booking a meeting or submitting PTO using services like Jira, ServiceNow, Salesforce, Fidelity, Vanguard, ADP, Workday, and Google Calendar. This unified approach streamlines workflows and minimizes reliance on multiple apps for task completion. Solution Overview Amazon Q Business connects to over 50 enterprise applications using connectors and plugins: Plugins are categorized into two types: Built-in Plugins Amazon Q Business supports more than 50 actions across applications: Category Application Sample Actions Ticketing ServiceNow Create, update, delete tickets Zendesk Suite Search, create, update tickets Project Management Jira Cloud Read, create, update, delete issues Smartsheet Search and manage sheets and reports CRM Salesforce Manage accounts, opportunities, and cases Communication Microsoft Teams Send private or channel messages Productivity Google Calendar Find events, list calendars Salesforce Plugin Example The Salesforce plugin allows users to: Configuration Steps: Custom Plugins For scenarios not covered by built-in plugins, custom plugins enable seamless integration with proprietary systems. For example: HR Time Off Plugin Example This plugin allows employees to: Setup Steps: End-to-End Use Cases 1. Salesforce Integration Sam, a Customer Success Manager, retrieves high-value opportunities using the Salesforce plugin. She creates a new case directly from the Amazon Q interface, enhancing efficiency by reducing application switching. 2. ServiceNow Ticket Management Sam uses Amazon Q Business to resolve a laptop email sync issue. After referencing indexed IT documentation, she creates a ServiceNow ticket and escalates it directly through the plugin interface. 3. HR System Integration Sam checks her PTO balance and submits a vacation request using the HR Time Off custom plugin, ensuring seamless task completion without switching to another app. Impact on Workflow Efficiency Amazon Q Business plugins simplify workflows by: Conclusion Amazon Q Business plugins represent a transformative step in automating enterprise workflows and enhancing employee productivity. From preconfigured integrations to custom-built solutions, these plugins provide unparalleled flexibility to adapt to diverse business needs. How can Amazon Q Business transform workflows in your organization? Whether through built-in integrations or custom solutions, explore the power of Amazon Q Business plugins to unlock new levels of efficiency. Share your feedback and use cases to inspire innovation across enterprises! Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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January 12, 2025in Data, Enterprise, Generative AI, Google, Salesforce0 Comments
Scope of Generative AI
07Jan

Exploring Generative AI

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

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January 7, 2025in AI Tools, Data, Enterprise, Generative AI, Google, Technology0 Comments
ThoughtSpot AI agent Spotter enables conversational BI
29Dec

ThoughtSpot AI agent Spotter enables conversational BI

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

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December 29, 2024in AI Tools, Data, Enterprise, Generative AI, Google, Salesforce, Technology0 Comments
Salesforce Omni-Channel
27Dec

Salesforce Channels

Channels Email Messaging Voice Open CTI Social Media Chat Channel Tools Email Updates Messaging Enhancements Voice Improvements Social Media Chat Updates Other Channel Tools These updates enhance the messaging, email, voice, and chat experiences, streamlining agent workflows, improving customer interactions, and providing greater customization. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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December 27, 2024in Google, Salesforce0 Comments

1 Billion Enterprise AI Agents

Inside Salesforce’s Ambition to Deploy 1 Billion Enterprise AI Agents Salesforce is making a bold play in the enterprise AI space with its recently launched Agentforce platform. Introduced at the annual Dreamforce conference, Agentforce is positioned to revolutionize sales, marketing, commerce, and operations with autonomous AI agents, marking a significant evolution from Salesforce’s previous Einstein AI platform. What Makes Agentforce Different? Agentforce operates as more than just a chatbot platform. It uses real-time data and user-defined business rules to proactively manage tasks, aiming to boost efficiency and enhance customer satisfaction. Built on Salesforce’s Data Cloud, the platform simplifies deployment while maintaining powerful customization capabilities: “Salesforce takes care of 80% of the foundational work, leaving customers to focus on the 20% that truly differentiates their business,” explains Adam Forrest, SVP of Marketing at Salesforce. Forrest highlights how Agentforce enables businesses to build custom agents tailored to specific needs by incorporating their own rules and data sources. This user-centric approach empowers admins, developers, and technology teams to deploy AI without extensive technical resources. Early Adoption Across Industries Major brands have already adopted Agentforce for diverse use cases: These real-world applications illustrate Agentforce’s potential to transform workflows in industries ranging from retail to hospitality and education. AI Agents in Marketing: The New Frontier Salesforce emphasizes that Agentforce isn’t just for operations; it’s poised to redefine marketing. AI agents can automate lead qualification, optimize outreach strategies, and enhance personalization. For example, in account-based marketing, agents can analyze customer data to identify high-value opportunities, craft tailored strategies, and recommend optimal engagement times based on user behavior. “AI agents streamline lead qualification by evaluating intent signals and scoring leads, allowing sales teams to focus on high-priority prospects,” says Jonathan Franchell, CEO of B2B marketing agency Ironpaper. Once campaigns are launched, Agentforce monitors performance in real time, offering suggestions to improve ROI and resource allocation. By integrating seamlessly with CRM platforms, the tool also facilitates better collaboration between marketing and sales teams. Beyond B2C applications, AI agents in B2B contexts can evaluate customer-specific needs and provide tailored product or service recommendations, further enhancing client relationships. Enabling Creativity Through Automation By automating repetitive tasks, Agentforce aims to free marketers to focus on strategy and creativity. Dan Gardner, co-founder of Code and Theory, describes this vision: “Agentic AI eliminates friction and dissolves silos in data, organizational structures, and customer touchpoints. The result? Smarter insights, efficient distribution, and more time for creatives to do what they do best: creating.” Competitive Landscape and Challenges Despite its promise, Salesforce faces stiff competition. Microsoft—backed by its integration with OpenAI’s ChatGPT—has unveiled AI tools like Copilot, and other players such as Google, ServiceNow, and HubSpot are advancing their own AI platforms. Salesforce CEO Marc Benioff has not shied away from the rivalry. On the Masters of Scale podcast, he criticized Microsoft for overpromising on products like Copilot, asserting that Salesforce delivers tangible value: “Our tools show users exactly what is possible, what is real, and how easy it is to derive huge value from AI.” Salesforce must also demonstrate Agentforce’s scalability across diverse industries to capture a significant share of the enterprise AI market. A Transformative Vision for the Future Agentforce represents Salesforce’s commitment to bringing AI-powered automation to the forefront of enterprise operations. With its focus on seamless deployment, powerful customization, and real-time capabilities, the platform aims to reshape how businesses interact with customers and optimize internal processes. By targeting diverse use cases and emphasizing accessibility for both technical and non-technical users, Salesforce is betting on Agentforce to drive adoption at scale—and position itself as a leader in the increasingly competitive AI market. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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December 27, 2024in Agentforce Platform, AI Tools, Data, Enterprise, Google, Salesforce, Technology0 Comments
AI Agents Set to Break Through in 2025
20Dec

AI Agents Set to Break Through in 2025

2025: The Year AI Agents Transform Work and Life Despite years of hype around artificial intelligence, its true disruptive impact has so far been limited. However, industry experts believe that’s about to change in 2025 as autonomous AI agents prepare to enter and reshape nearly every facet of our lives. Since OpenAI’s ChatGPT took the world by storm in late 2022, billions of dollars have been funneled into the AI sector. Big tech and startups alike are racing to harness the transformative potential of the technology. Yet, while millions now interact with AI chatbots daily, turning them into tools that deliver tangible business value has proven challenging. A recent study by Boston Consulting Group revealed that only 26% of companies experimenting with AI have progressed beyond proof of concept to derive measurable value. This lag reflects the limitations of current AI tools, which serve primarily as copilots—capable of assisting but requiring constant oversight and remaining prone to errors. AI Agents Set to Break Through in 2025 The status quo, however, is poised for a radical shift. Autonomous AI agents—capable of independently analyzing information, making decisions, and taking action—are expected to emerge as the industry’s next big breakthrough. “For the first time, technology isn’t just offering tools for humans to do work,” Salesforce CEO Marc Benioff wrote in Time. “It’s providing intelligent, scalable digital labor that performs tasks autonomously. Instead of waiting for human input, agents can analyze information, make decisions, and adapt as they go.” At their core, AI agents leverage the same large language models (LLMs) that power tools like ChatGPT. But these agents take it further, acting as reasoning engines that develop step-by-step strategies to execute tasks. Armed with access to external data sources like customer records or financial databases and equipped with software tools, agents can achieve goals independently. While current LLMs still face reasoning limitations, advancements are on the horizon. New models like OpenAI’s “o1” and DeepSeek’s “R1” are specialized for reasoning, sparking hope that 2025 will see agents grow far more capable. Big Tech and Startups Betting Big Major players are already gearing up for this new era. Startups are also eager to carve out their share of the market. According to Pitchbook, funding deals for agent-focused ventures surged by over 80% in 2024, with the median deal value increasing nearly 50%. Challenges to Overcome Despite the enthusiasm, significant hurdles remain. 2025: A Turning Point Despite these challenges, many experts believe 2025 will mark the mainstream adoption of AI agents. A New World of Work No matter the pace, it’s clear that AI agents will dominate the industry’s focus in 2025. If the technology delivers on its promise, the workplace could undergo a profound transformation, enabling entirely new ways of working and automating tasks that once required human intervention. The question isn’t if agents will redefine the way we work—it’s how fast. By the end of 2025, the shift could be undeniable. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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December 20, 2024in AI Tools, Artificial Intelligence, Data, Generative AI, Google, Salesforce, Technology0 Comments
Meta Joins the Race to Reinvent Search with AI
18Dec

Meta Joins the Race to Reinvent Search with AI

Meta Joins the Race to Reinvent Search with AI Meta, the parent company of Facebook, Instagram, and WhatsApp, is stepping into the evolving AI-driven search landscape. As vendors increasingly embrace generative AI to transform search experiences, Meta aims to challenge Google’s dominance in this space. The company is reportedly developing an AI-powered search engine designed to provide conversational, AI-generated summaries of recent events and news. These summaries would be delivered via Meta’s AI chatbot, supported by a multiyear partnership with Reuters for real-time news insights, according to The Information. AI Search: A Growing Opportunity The push comes as generative AI reshapes search technology across the industry. Google, the long-standing leader, has integrated AI features such as AI Overviews into its search platform, offering users summarized search results, product comparisons, and more. This feature, now available in over 100 countries as of October 2024, signals a shift in traditional search strategies. Similarly, OpenAI, the creator of ChatGPT, has been exploring its own AI search model, SearchGPT, and forging partnerships with media organizations like the Associated Press and Hearst. However, OpenAI faces legal challenges, such as a lawsuit from The New York Times over alleged copyright infringement. Meta’s entry into AI-powered search aligns with a broader trend among tech giants. “It makes sense for Meta to explore this,” said Mark Beccue, an analyst with TechTarget’s Enterprise Strategy Group. He noted that Meta’s approach seems more targeted at consumer engagement than enterprise solutions, particularly appealing to younger audiences who are shifting away from traditional search behaviors. Shifting User Preferences Generational changes in search habits are creating opportunities for new players in the market. Younger users, particularly Gen Z and Gen Alpha, are increasingly turning to platforms like TikTok for lifestyle advice and Amazon for product recommendations, bypassing traditional search engines like Google. “Recent studies show younger generations are no longer using ‘Google’ as a verb,” said Lisa Martin, an analyst with the Futurum Group. “This opens the playing field for competitors like Meta and OpenAI.” Forrester Research corroborates this trend, noting a diversification in search behaviors. “ChatGPT’s popularity has accelerated this shift,” said Nikhil Lai, a Forrester analyst. He added that these changes could challenge Google’s search ad market, with its dominance potentially waning in the years ahead. Meta’s AI Search Potential Meta’s foray into AI search offers an opportunity to enhance user experiences and deepen engagement. Rather than pushing news content into users’ feeds—an approach that has drawn criticism—AI-driven search could empower users to decide what content they see and when they see it. “If implemented thoughtfully, it could transform the user experience and give users more control,” said Martin. This approach could also boost engagement by keeping users within Meta’s ecosystem. The Race for Revenue and Trust While AI-powered search is expected to increase engagement, monetization strategies remain uncertain. Google has yet to monetize its AI Overviews, and OpenAI’s plans for SearchGPT remain unclear. Other vendors, like Perplexity AI, are experimenting with models such as sponsored questions instead of traditional results. Trust remains a critical factor in the evolving search landscape. “Google is still seen as more trustworthy,” Lai noted, with users often returning to Google to verify AI-generated information. Despite the competition, the conversational AI search market lacks a definitive leader. “Google dominated traditional search, but the race for conversational search is far more open-ended,” Lai concluded. Meta’s entry into this competitive space underscores the ongoing evolution of search technology, setting the stage for a reshaped digital landscape driven by AI innovation. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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December 18, 2024in Enterprise, Generative AI, Google, Laws, Salesforce, Technology0 Comments
Gen AI Unleased With Vector Database
18Dec

Knowledge Graphs and Vector Databases

The Role of Knowledge Graphs and Vector Databases in Retrieval-Augmented Generation (RAG) In the dynamic AI landscape, Retrieval-Augmented Generation (RAG) systems are revolutionizing data retrieval by combining artificial intelligence with external data sources to deliver contextual, relevant outputs. Two core technologies driving this innovation are Knowledge Graphs and Vector Databases. While fundamentally different in their design and functionality, these tools complement one another, unlocking new potential for solving complex data problems across industries. Understanding Knowledge Graphs: Connecting the Dots Knowledge Graphs organize data into a network of relationships, creating a structured representation of entities and how they interact. These graphs emphasize understanding and reasoning through data, offering explainable and highly contextual results. How They Work Strengths Limitations Applications Vector Databases: The Power of Similarity In contrast, Vector Databases thrive in handling unstructured data such as text, images, and audio. By representing data as high-dimensional vectors, they excel at identifying similarities, enabling semantic understanding. How They Work Strengths Limitations Applications Combining Knowledge Graphs and Vector Databases: A Hybrid Approach While both technologies excel independently, their combination can amplify RAG systems. Knowledge Graphs bring reasoning and structure, while Vector Databases offer rapid, similarity-based retrieval, creating hybrid systems that are more intelligent and versatile. Example Use Cases Knowledge Graphs vs. Vector Databases: Key Differences Feature Knowledge Graphs Vector Databases Data Type Structured Unstructured Core Strength Relational reasoning Similarity-based retrieval Explainability High Low Scalability Limited for large datasets Efficient for massive datasets Flexibility Schema-dependent Schema-free Challenges in Implementation Future Trends: The Path to Convergence As AI evolves, the distinction between Knowledge Graphs and Vector Databases is beginning to blur. Emerging trends include: This convergence is paving the way for smarter, more adaptive systems that can handle both structured and unstructured data seamlessly. Conclusion Knowledge Graphs and Vector Databases represent two foundational technologies in the realm of Retrieval-Augmented Generation. Knowledge Graphs excel at reasoning through structured relationships, while Vector Databases shine in unstructured data retrieval. By combining their strengths, organizations can create hybrid systems that offer unparalleled insights, efficiency, and scalability. In a world where data continues to grow in complexity, leveraging these complementary tools is essential. Whether building intelligent healthcare systems, enhancing recommendation engines, or powering semantic search, the synergy between Knowledge Graphs and Vector Databases is unlocking the next frontier of AI innovation, transforming how industries harness the power of their data. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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December 18, 2024in Artificial Intelligence, Data, Enterprise, Google0 Comments
Google Prepares AI-Powered Jarvis Agent
18Dec

Google Prepares AI-Powered Jarvis Agent

Google Prepares AI-Powered Jarvis Agent for Automated Browser Tasks in Chrome Google is reportedly gearing up to launch “Project Jarvis,” an AI-powered browser agent designed to automate tasks directly within the Chrome ecosystem. According to The Information, the tool is expected to roll out in December to select users and will leverage Google’s advanced Gemini 2.0 AI model. Jarvis aims to simplify repetitive online tasks, such as organizing information or booking reservations, offering a seamless and efficient digital assistant embedded within Chrome. This initiative reflects Google’s broader vision to enhance user experiences by automating web-based routines, making its browser a central hub for task automation. Anthropic Expands Desktop Automation with Claude 3.5 Sonnet Anthropic, a key player in the AI landscape, has advanced its Claude 3.5 model with a new “Computer Use” feature, enabling direct interaction with a user’s desktop. This update allows Claude to perform tasks such as typing, clicking, and managing multiple applications, making it a powerful tool for automating workflows like data entry, document management, and customer service. Available through APIs and platforms like Amazon Bedrock and Google Cloud’s Vertex AI, Claude’s new capabilities position it as a versatile solution for businesses seeking desktop-level automation, contrasting Google Jarvis’s browser-specific approach. By interpreting screen elements, Claude’s “Computer Use” mode supports broader applications beyond web tasks, offering businesses an edge in efficiency and scalability. How Google Jarvis Stands Out Unlike Anthropic’s desktop-oriented Claude Sonnet, Google Jarvis focuses on automating tasks within Chrome. Jarvis analyzes screenshots of web pages, interprets user commands, and executes actions like clicks or data entry. While still in development, Jarvis’s design suggests a future where mundane web-based tasks are seamlessly handled by AI. Powered by Google’s Gemini 2.0 language model, Jarvis is tailored for users who prioritize web-specific functions, creating a user-friendly assistant that requires no external software. This aligns with Google’s strategy to deepen integration within its ecosystem, making Chrome a more intuitive and productive environment. Microsoft’s Copilot Agents Lead Business Automation Microsoft, meanwhile, continues to enhance its Copilot AI agents, particularly within Dynamics 365. These specialized agents are designed to automate industry-specific workflows, from lead qualification in sales to financial data reconciliation. Unlike Google Jarvis or Anthropic Claude, Microsoft’s Copilot agents target enterprise users, embedding automation within business applications like Teams, Outlook, and SharePoint. With tools like Copilot Studio, organizations can customize workflows to meet specific needs, offering a level of flexibility that resonates with enterprise clients. Early adopters, including Vodafone and Cognizant, have reported significant productivity gains through these integrations. Microsoft’s efforts position Copilot as a robust partner for day-to-day operations, transforming tasks like analysis, project coordination, and document management into automated, efficient processes. Competing Visions for AI Agents As Google, Anthropic, and Microsoft refine their AI strategies, they’re carving out distinct niches in the AI agent landscape: These approaches highlight the diverse applications of AI agents, from enhancing individual user experiences to transforming business operations. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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December 18, 2024in Data, Enterprise, Google, Salesforce0 Comments
AI and UX Design
16Dec

AI and UX Design

This insight comprehensively covers how AI is transforming UX design, presenting both opportunities and challenges while emphasizing the importance of maintaining a human-centric approach. Here’s a polished and slightly condensed version, retaining the core points for better clarity and engagement: AI in UX Design: Transforming Experiences in 2024 and Beyond In 2024, artificial intelligence (AI) is redefining user experience (UX) design and research. From streamlining processes to elevating personalization, UX professionals are integrating AI into their workflows to create experiences that are more intuitive and efficient. This insight explores how AI is reshaping UX and how designers can leverage it while preserving the human touch. How AI is Revolutionizing UX Design 1. Advanced AI Technologies in UXAI technologies like machine learning (ML), natural language processing (NLP), and computer vision are empowering designers with tools to understand user behavior better, build conversational interfaces, and create accessible, adaptable designs. These innovations provide deeper insights into user preferences and help refine interfaces to align with evolving needs. 2. Automating Routine Design TasksAI is taking over repetitive tasks such as rapid prototyping, A/B testing, and user data analysis, allowing designers to focus on creative, strategic challenges. For example: 3. Enhanced PersonalizationAI-driven systems offer dynamic content delivery, adaptive interfaces, and predictive behavior modeling to craft uniquely tailored experiences. These enhancements not only engage users but also foster loyalty by addressing individual preferences in real time. Balancing AI and Human-Centric Design While AI accelerates UX processes, maintaining a human-centered approach is essential. Successful integration requires: Best Practices for AI-Driven UX Design Ethical Considerations in AI-Enhanced UX Ethics remain at the forefront of AI in UX. Key concerns include: Learning from Case Studies These examples highlight how thoughtful AI integration can transform UX into a seamless, user-friendly journey. Preparing for Future Trends Looking ahead to 2025 and beyond, AI will continue to introduce innovations like emotional recognition and generative design, enabling even more intuitive user experiences. However, challenges such as data privacy concerns and high implementation costs will persist. UX professionals must adapt by blending AI-driven insights with human creativity, ensuring that designs remain empathetic and accessible. Conclusion AI is revolutionizing UX design, offering tools to enhance efficiency, personalization, and user engagement. The key to success lies in using AI as a complement to creativity rather than a replacement. By balancing automation with human-centered principles and committing to ethical practices, businesses can harness AI to create transformative, user-focused designs that truly resonate. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more Top Ten Reasons Why Tectonic Loves the Cloud The Cloud is Good for Everyone – Why Tectonic loves the cloud You don’t need to worry about tracking licenses. Read more

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December 16, 2024in Artificial Intelligence, Data, Google0 Comments
AI Agents and Consumer Trust
13Dec

AI Agents Next AI Evolution

AI agents are being hailed as the next big leap in artificial intelligence, but there’s no universally accepted definition of what they are—or what they should do. Even within the tech community, there’s debate about what constitutes an AI agent. At its core, an AI agent can be described as software powered by artificial intelligence that performs tasks once handled by human roles, such as customer service agents, HR representatives, or IT help desk staff. However, their potential spans much further. These agents don’t just answer questions—they take action, often working across multiple systems. For example, Perplexity recently launched an AI agent to assist with holiday shopping, while Google introduced Project Mariner, an agent that helps users book flights, find recipes, and shop for household items. While the idea seems straightforward, it’s muddied by inconsistent definitions. For Google, AI agents are task-based assistants tailored to specific roles, like coding help for developers or troubleshooting issues for IT professionals. In contrast, Asana views agents as digital co-workers that take on assigned tasks, and Sierra—a startup led by former Salesforce co-CEO Bret Taylor—envisions agents as sophisticated customer experience tools that surpass traditional chatbots by tackling complex problems. This lack of consensus adds to the uncertainty around what AI agents can truly achieve. Rudina Seseri, founder and managing partner at Glasswing Ventures, explains this ambiguity stems from the technology’s infancy. She describes AI agents as intelligent systems capable of perceiving their environment, reasoning, making decisions, and taking actions to achieve specific goals autonomously. These agents rely on a mix of AI technologies, including natural language processing, machine learning, and computer vision, to operate in dynamic environments. Optimists, like Box CEO Aaron Levie, believe AI agents will improve rapidly as advancements in GPU performance, model efficiency, and AI frameworks create a self-reinforcing cycle of innovation. However, skeptics like MIT robotics pioneer Rodney Brooks caution against overestimating progress, noting that solving real-world problems—especially those involving legacy systems with limited API access—can be far more challenging than anticipated. David Cushman of HFS Research likens current AI agents to assistants rather than fully autonomous entities, with their capabilities limited to helping users complete specific tasks within pre-defined boundaries. True autonomy, where AI agents handle contingencies and perform at scale without human oversight, remains a distant goal. Jon Turow, a partner at Madrona Ventures, emphasizes the need for dedicated infrastructure to support the development of AI agents. He envisions a tech stack that allows developers to focus on product differentiation while leaving scalability and reliability to the platform. This infrastructure would likely involve multiple specialized models working together under a routing layer, rather than relying on a single large language model (LLM). Fred Havemeyer of Macquarie US Equity Research agrees, noting that the most effective AI agents will combine various models to handle complex tasks. He imagines a future where agents act like autonomous supervisors, delegating tasks and reasoning through multi-step processes to achieve abstract goals. While this vision is compelling, the current state of AI agents suggests we’re still in a transitional phase. The progress so far is promising, but several breakthroughs are needed before agents can operate as envisioned—truly autonomous, multi-functional, and capable of seamless collaboration across diverse systems. This story, originally published on July 13, 2024, has been updated to reflect new developments from Perplexity and Google. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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December 13, 2024in Artificial Intelligence, Google, Salesforce, Technology0 Comments
google agentspace
13Dec

Google Agentspace

Google Agentspace: Boosting Productivity with AI-Powered Agents Google has unveiled Agentspace, a cutting-edge tool designed to revolutionize workplace productivity by combining the power of AI agents, Google Gemini 2.0, and its advanced search capabilities. This tool aims to streamline workflows, enhance information discovery, and empower enterprises to unlock the full potential of their data. What is Google Agentspace? Google Agentspace is an enterprise-focused productivity platform that simplifies complex tasks involving planning, research, and content generation. By integrating AI-powered tools like NotebookLM Plus, it enables employees to uncover insights, interact with unstructured and structured data, and make informed decisions—all in one centralized platform. Key features include: Core Benefits of Google Agentspace 1. Streamlined Information Discovery Employees often waste hours sifting through fragmented data in emails, documents, and spreadsheets. Agentspace serves as a centralized knowledge hub, offering conversational assistance, proactive suggestions, and actionable insights from both unstructured and structured data sources. With pre-built connectors for tools like Google Drive, Jira, Microsoft SharePoint, and ServiceNow, Agentspace ensures seamless integration with existing systems, providing employees with relevant information faster. 2. Enhanced Multimodal Capabilities Agentspace leverages Google’s search expertise and Gemini 2.0 to provide advanced reasoning capabilities. Employees can query in multiple formats (text, audio, video), translate information into different languages, and generate audio summaries, enhancing productivity and accessibility. 3. Task Automation Across Departments Agentspace empowers teams across various functions to automate repetitive tasks, such as: 4. Scalable AI for Enterprises Agentspace offers a low-code visual tool for creating custom AI agents tailored to specific business needs. These agents can automate multi-step workflows, conduct in-depth research, and assist with data-driven content generation, enabling enterprises to scale AI adoption effortlessly. Security and Responsible AI Google Agentspace is built on Google Cloud’s secure-by-design infrastructure, ensuring that enterprises can deploy AI tools with confidence. Key Security Features Google is also addressing responsible AI concerns with tools for evaluation, content moderation, and bias mitigation, ensuring ethical and explainable AI use in the workplace. Use Cases Google Agentspace provides solutions tailored to various enterprise needs: Challenges and Future Directions Despite its potential, Agentspace faces hurdles such as employee training and adoption. Organizations must ensure that employees understand how to incorporate the tool into their daily workflows effectively. Moreover, Google’s approach to responsible AI will be closely scrutinized. Addressing issues like explainability, bias prevention, and robust data infrastructure will be crucial for building trust and driving adoption. Early Access and the Road Ahead Google is offering early access to Agentspace, allowing enterprises to explore its potential and provide feedback. As AI continues to reshape the workplace, tools like Agentspace position Google as a leader in productivity-enhancing solutions for businesses. For enterprises looking to harness AI to unlock creativity, improve decision-making, and automate workflows, Agentspace is the next step in digital transformation. Sign up for early access today to bring the future of work to your organization. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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December 13, 2024in AI Tools, Data, Enterprise, Generative AI, Google0 Comments

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