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AI Customer Service Agents Explained

AI Customer Service Agents Explained

AI customer service agents are advanced technologies designed to understand and respond to customer inquiries within defined guidelines. These agents can handle both simple and complex issues, such as answering frequently asked questions or managing product returns, all while offering a personalized, conversational experience. Research shows that 82% of service representatives report that customers ask for more than they used to. As a customer service leader, you’re likely facing increasing pressure to meet these growing expectations while simultaneously reducing costs, speeding up service, and providing personalized, round-the-clock support. This is where AI customer service agents can make a significant impact. Here’s a closer look at how AI agents can enhance your organization’s service operations, improve customer experience, and boost overall productivity and efficiency. What Are AI Customer Service Agents? AI customer service agents are virtual assistants designed to interact with customers and support service operations. Utilizing machine learning and natural language processing (NLP), these agents are capable of handling a broad range of tasks, from answering basic inquiries to resolving complex issues — even managing multiple tasks at once. Importantly, AI agents continuously improve through self-learning. Why Are AI-Powered Customer Service Agents Important? AI-powered customer service technology is becoming essential for several reasons: Benefits of AI Customer Service Agents AI customer service agents help service teams manage growing service demands by taking on routine tasks and providing essential support. Key benefits include: Why Choose Agentforce Service Agent? If you’re considering adding AI customer service agents to your strategy, Agentforce Service Agent offers a comprehensive solution: By embracing AI customer service agents like Agentforce Service Agent, businesses can reduce costs, meet growing customer demands, and stay competitive in an ever-evolving global market. Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Customer Engagement with AI

Customer Engagement with AI

Funlab Explores AI to Boost Customer Engagement in Leisure Venues In a push to enhance customer experiences across its “leisure-tainment” venues, Funlab has begun experimenting with artificial intelligence. Speaking at a Salesforce Agentforce event in Sydney, Funlab’s Head of Customer Relationships and Retention, Tracy Tanti, shared that the company is “excited to be able to start experimenting” with AI. Agentforce, a Salesforce platform designed to create autonomous agents for supporting employees and customers, serves as a key part of Funlab’s AI exploration efforts. According to Tanti, Funlab has a range of AI-focused projects on its roadmap, with the goal of blending digital experiences into real-life interactions and supporting both venue and corporate teams with AI-driven tools. Reflecting the company’s dedication to careful planning, Tanti described how Salesforce connected Funlab with another customer, Norths Collective, to discuss its own AI implementation journey. Robert Lopez, Chief Marketing and Innovation Officer at Norths Collective, has seen success with enhanced personalization and analytics, which have contributed to increased membership and engagement. Tanti noted that Norths Collective’s transformation work would provide valuable insights for Funlab as it optimizes its data in preparation for AI adoption. Currently, Funlab is in a post-digital transformation phase, refining its processes to deliver more connected and personalized guest experiences throughout the customer lifecycle. With ongoing expansion into the U.S. market—including recent openings of Holey Moley venues—Funlab is also focusing on building robust support infrastructure and engaging local audiences through Salesforce. Tanti highlighted the company’s vision for the U.S. to become a significant portion of total revenues and emphasized how Salesforce will help Funlab nurture a strong customer database in this new market. Additionally, Funlab is leveraging Salesforce to grow its event and function sales, which are projected to reach 39% of total online revenue by year’s end, up from 23% earlier this year. This expansion underscores Funlab’s commitment to using AI and data-driven insights to fuel growth and deepen customer engagement across all its markets and venues. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI Agents and Digital Transformation

AI Agents and Digital Transformation

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

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GPUs and AI Development

GPUs and AI Development

Graphics processing units (GPUs) have become widely recognized due to their growing role in AI development. However, a lesser-known but critical technology is also gaining attention: high-bandwidth memory (HBM). HBM is a high-density memory designed to overcome bottlenecks and maximize data transfer speeds between storage and processors. AI chipmakers like Nvidia rely on HBM for its superior bandwidth and energy efficiency. Its placement next to the GPU’s processor chip gives it a performance edge over traditional server RAM, which resides between storage and the processing unit. HBM’s ability to consume less power makes it ideal for AI model training, which demands significant energy resources. However, as the AI landscape transitions from model training to AI inferencing, HBM’s widespread adoption may slow. According to Gartner’s 2023 forecast, the use of accelerator chips incorporating HBM for AI model training is expected to decline from 65% in 2022 to 30% by 2027, as inferencing becomes more cost-effective with traditional technologies. How HBM Differs from Other Memory HBM shares similarities with other memory technologies, such as graphics double data rate (GDDR), in delivering high bandwidth for graphics-intensive tasks. But HBM stands out due to its unique positioning. Unlike GDDR, which sits on the printed circuit board of the GPU, HBM is placed directly beside the processor, enhancing speed by reducing signal delays caused by longer interconnections. This proximity, combined with its stacked DRAM architecture, boosts performance compared to GDDR’s side-by-side chip design. However, this stacked approach adds complexity. HBM relies on through-silicon via (TSV), a process that connects DRAM chips using electrical wires drilled through them, requiring larger die sizes and increasing production costs. According to analysts, this makes HBM more expensive and less efficient to manufacture than server DRAM, leading to higher yield losses during production. AI’s Demand for HBM Despite its manufacturing challenges, demand for HBM is surging due to its importance in AI model training. Major suppliers like SK Hynix, Samsung, and Micron have expanded production to meet this demand, with Micron reporting that its HBM is sold out through 2025. In fact, TrendForce predicts that HBM will contribute to record revenues for the memory industry in 2025. The high demand for GPUs, especially from Nvidia, drives the need for HBM as AI companies focus on accelerating model training. Hyperscalers, looking to monetize AI, are investing heavily in HBM to speed up the process. HBM’s Future in AI While HBM has proven essential for AI training, its future may be uncertain as the focus shifts to AI inferencing, which requires less intensive memory resources. As inferencing becomes more prevalent, companies may opt for more affordable and widely available memory solutions. Experts also see HBM following the same trajectory as other memory technologies, with continuous efforts to increase bandwidth and density. The next generation, HBM3E, is already in production, with HBM4 planned for release in 2026, promising even higher speeds. Ultimately, the adoption of HBM will depend on market demand, especially from hyperscalers. If AI continues to push the limits of GPU performance, HBM could remain a critical component. However, if businesses prioritize cost efficiency over peak performance, HBM’s growth may level off. 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 Alphabet Soup of Cloud Terminology As with any technology, the cloud brings its own alphabet soup of terms. This insight will hopefully help you navigate Read more

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Third Wave of AI at Salesforce

Third Wave of AI at Salesforce

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

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Zendesk Launches AI Agent Builder

The State of AI

The State of AI: How We Got Here (and What’s Next) Artificial intelligence (AI) has evolved from the realm of science fiction into a transformative force reshaping industries and lives around the world. But how did AI develop into the technology we know today, and where is it headed next? At Dreamforce, two of Salesforce’s leading minds in AI—Chief Scientist Silvio Savarese and Chief Futurist Peter Schwartz—offered insights into AI’s past, present, and future. How We Got Here: The Evolution of AI AI’s roots trace back decades, and its journey has been defined by cycles of innovation and setbacks. Peter Schwartz, Salesforce’s Chief Futurist, shared a firsthand perspective on these developments. Having been involved in AI since the 1970s, Schwartz witnessed the first “AI winter,” a period of reduced funding and interest due to the immense challenges of understanding and replicating the human brain. In the 1990s and early 2000s, AI shifted from attempting to mimic human cognition to adopting data-driven models. This new direction opened up possibilities beyond the constraints of brain-inspired approaches. By the 2010s, neural networks re-emerged, revolutionizing AI by enabling systems to process raw data without extensive pre-processing. Savarese, who began his AI research during one of these challenging periods, emphasized the breakthroughs in neural networks and their successor, transformers. These advancements culminated in large language models (LLMs), which can now process massive datasets, generate natural language, and perform tasks ranging from creating content to developing action plans. Today, AI has progressed to a new frontier: large action models. These systems go beyond generating text, enabling AI to take actions, adapt through feedback, and refine performance autonomously. Where We Are Now: The Present State of AI The pace of AI innovation is staggering. Just a year ago, discussions centered on copilots—AI systems designed to assist humans. Now, the conversation has shifted to autonomous AI agents capable of performing complex tasks with minimal human oversight. Peter Schwartz highlighted the current uncertainties surrounding AI, particularly in regulated industries like banking and healthcare. Leaders are grappling with questions about deployment speed, regulatory hurdles, and the broader societal implications of AI. While many startups in the AI space will fail, some will emerge as the giants of the next generation. Salesforce’s own advancements, such as the Atlas Reasoning Engine, underscore the rapid progress. These technologies are shaping products like Agentforce, an AI-powered suite designed to revolutionize customer interactions and operational efficiency. What’s Next: The Future of AI According to Savarese, the future lies in autonomous AI systems, which include two categories: The Road Ahead As AI continues to evolve, it’s clear that its potential is boundless. However, the path forward will require careful navigation of ethical, regulatory, and practical challenges. The key to success lies in innovation, collaboration, and a commitment to creating systems that enhance human capabilities. For Salesforce, the journey has only just begun. With groundbreaking technologies and visionary leadership, the company is not just predicting the future of AI—it’s creating it. The State of AI. 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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Generative AI Replaces Legacy Systems

Generative AI Replaces Legacy Systems

Generative AI Will Overtake Legacy Stack Vendors With the rise of generative AI, legacy software vendors like Appian, IBM, Salesforce, SAP, Pegasystems, IFS, Oracle, Software AG, TIBCO, and UIPath are becoming increasingly obsolete. These vendors represent the old guard, clinging to outdated business process automation systems, while the future clearly belongs to AI-driven innovation. Back in the early 2010s, discussions around dynamic processes—self-assembling workflows created by artificial intelligence—were already gaining traction. The vision was to bypass the need for traditional process mapping or manually designing new interfaces. Instead, AI would dynamically generate processes in response to specific tasks, allowing for far greater flexibility and adaptability. However, business rules within BPMS (Business Process Management Systems) often imposed constraints that limited decision-making flexibility. Today, this vision is finally within reach. Many traditional stack vendors are scrambling to integrate generative AI into their offerings in a desperate bid to remain relevant. But the truth is, generative AI renders these vendors largely unnecessary. For instance, Pegasystems, like many others, now incorporates generative AI into its software, but users are still bound to old workflows and low-code development systems. The reliance on building processes, regardless of AI assistance, keeps them stuck in the past. Across the board—whether it’s ERP, CRM, or RPA—vendors such as Salesforce, SAP, and IFS remain tethered to their outdated systems, even though they possess all the necessary data, both structured and unstructured, to benefit from a simpler, AI-powered approach. All that’s needed is a generative AI layer on top to handle tasks like customer complaints. Consider a customer complaint scenario: traditionally, a complaint is processed through a defined workflow, often requiring the creation of expensive, custom SaaS solutions. But what if an LLM (Large Language Model) could handle this instead? The LLM could analyze the complaint, extract key information, assess urgency through sentiment analysis, and generate a custom workflow on the fly. It could even generate backend code in real-time to process refunds or update databases, all without relying on legacy front-end systems. The LLM’s ability to create and execute dynamic workflows eliminates the need for static business processes. The AI generates temporary code and UI elements to handle a specific interaction, then discards them once the task is complete. This shifts the focus away from traditional, bloated enterprise systems and towards dynamic, JIT (Just-In-Time) interactions that are tailored to each individual customer. The efficiency gains are not in cutting jobs but in eliminating the need for costly, antiquated software and lengthy digital transformation programs. Generative AI doesn’t require massive ERP or CRM implementations, and businesses can converse directly with customer data through AI, bypassing the need for complex system integrations. Master Data Management, which once consumed millions of dollars and years of effort, is now positioned to become a simple, AI-powered solution. Enterprises already have well-structured and clean data, and adding a generative AI layer could remove the need for integrating or syncing legacy systems. The era of major vendors selling AI-enhanced solutions built on top of decaying software stacks is coming to an end. The idea of using generative AI as the foundation for a new business operating system, without the need for bloated, legacy software, is increasingly appealing. With the global workflow automation market projected to grow to .4 billion by 2030, the future clearly belongs to AI-driven solutions. 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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Battle of Copilots

Battle of Copilots

Salesforce is directly challenging Microsoft in the growing battle of AI copilots, which are designed to enhance customer experience (CX) across key business functions like sales and support. In this competitive landscape, Salesforce is taking on not only Microsoft but also major AI rivals such as Google Gemini, OpenAI GPT, and IBM watsonx. At the heart of this strategy is Salesforce Agentforce, a platform that leverages autonomous decision-making to meet enterprise demands for data and AI abstraction. Salesforce Dreamforce Highlights One of the most significant takeaways from last month’s Dreamforce conference in San Francisco was the unveiling of autonomous agents, bringing advanced GenAI capabilities to the app development process. CEO Marc Benioff and other Salesforce executives made it clear that Salesforce is positioning itself to compete with Microsoft’s Copilot, rebranding and advancing its own AI assistant, previously known as Einstein AI. Microsoft’s stronghold, however, lies in Copilot’s seamless integration with widely used products like Teams, Outlook, PowerPoint, and Word. Furthermore, Microsoft has established itself as a developer’s favorite, especially with GitHub Copilot and the Azure portfolio, which are integral to app modernization in many enterprises. “Salesforce faces an uphill battle in capturing market share from these established players,” says Charlotte Dunlap, Research Director at GlobalData. “Salesforce’s best chance lies in highlighting the autonomous capabilities of Agentforce—enabling businesses to automate more processes, moving beyond basic chatbot functions, and delivering a personalized customer experience.” This emphasis on autonomy is vital, given that many enterprises are still grappling with the complexities of emerging GenAI technologies. Dunlap points out that DevOps teams are struggling to find third-party expertise that understands how GenAI fits within existing IT systems, particularly around security and governance concerns. Salesforce’s focus on automation, combined with the integration prowess of MuleSoft, positions it as a key player in making GenAI tools more accessible and intuitive for businesses. Elevating AI Abstraction and Automation Salesforce has increasingly focused on the idea of abstracting data and AI, exemplified by its Data Cloud and low-level UI capabilities. Now, with models like the Atlas Reasoning Engine, Salesforce is looking to push beyond traditional AI assistants. These tools are designed to automate complex, previously human-dependent tasks, spanning functions like sales, service, and marketing. Simplifying the Developer Experience The true measure of Salesforce’s success in its GenAI strategy will emerge in the coming months. The company is well aware that its ability to simplify the developer experience is critical. Enterprises are looking for more than just AI innovation—they want thought leadership that can help secure budget and executive support for AI initiatives. Many companies report ongoing struggles in gaining that internal buy-in, further underscoring the importance of strong, strategic partnerships with technology providers like Salesforce. In its pursuit to rival Microsoft Copilot, Salesforce’s future hinges on how effectively it can build on its track record of simplifying the developer experience while promoting the unique autonomous qualities of Agentforce. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI and Legacy

AI and Legacy

In most new application builds, AI is rarely considered an active consumer. The prevailing assumption seems to be that AI is just a variation of reporting, which essentially translates to “not my problem” for application developers. In this mindset, the data platform gets treated like an afterthought, receiving the “exhaust fumes” of the application without much concern for data quality. Even when data or AI is acknowledged as important, it’s often sidelined, with data becoming one of the first things sacrificed during the development process. In the past, this was merely a “minor” problem that led to the rise of the data quality industry. AI and Legacy. But as we move forward, this will become a significant issue due to one undeniable fact: AI will be the primary consumer of applications and data. Old Thinking Creates Instant Legacy What this means is that if you’re building a new application—whether it’s a website, ERP, CRM, or anything else—and you’re not considering AI as a user, you’re actively choosing to implement a legacy system. Even if your system has an AI solution baked in, if the core application isn’t designed for a data-driven world, the best you’ll achieve is an AI sidecar—just a nice wrapper, but limited in scope. Tools like Microsoft Copilot or Salesforce Agentforce, for instance, can easily be implemented in a way that minimizes or even eliminates opportunities for AI to thrive. If you’re building applications that treat data as merely a reporting tool and assume AI is a downstream consumer, you’re engaging in legacy thinking in a world increasingly powered by AI. Don’t Build Legacy Systems Avoiding legacy systems isn’t difficult. If you believe AI and data are important, treat them as such from the outset. This boils down to one simple principle: Design for the destination. If you think AI will be a primary consumer of applications in the next one, two, or five years, you should design your applications with that challenge in mind. This means considering AI personas, figuring out how AI assistants will integrate into human workflows, and planning how AI automation bots will function within the system. It also requires embracing a crucial decision: Your design should prioritize data, and assume AI is a primary consumer. This doesn’t mean just designing a robust database schema. It means ensuring your application’s operational reality can accurately reflect the business situation for both human and AI users. It’s not about technical database design—it’s about understanding the business’s accountability for digital accuracy and establishing the mechanisms to maintain that accuracy and represent it effectively. Building Legacy Is a Choice Everyone Is Making To be clear, this isn’t about adopting some “holistic” view or designing for every possible scenario. It’s about designing from a data and digital perspective first. Instead of treating use cases or business processes as the main design focus, the primary design thread should be the ability to reflect the reality of the business. Use cases and business processes still matter at the execution level, but they should not drive application design in a data-driven, AI-enabled world. You must assume that AI will be the primary consumer of your application and design accordingly, rather than focusing solely on human users and screens. Right now, nearly every application is still built as though data is a byproduct of transactions, with the assumption that AI is merely a sidecar, not an active participant. AI and Legacy. In the words of Sir Humphrey, that is a “courageous” decision. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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AI Agents, Tech's Next Big Bet

AI Agents, Tech’s Next Big Bet

What Marketers Need to Know About AI Agents, Tech’s Next Big Bet Companies like Salesforce and OpenAI are making significant investments in AI agents, which are digital assistants poised to represent the next evolution of artificial intelligence. These agents promise to autonomously handle a variety of tasks, from making reservations to negotiating business deals. During OpenAI’s DevDay event in San Francisco last week, the company showcased a voice bot that successfully ordered 400 chocolate-covered strawberries from a local delivery service, specifying delivery and payment terms with minimal issues. OpenAI CEO Sam Altman stated, “2025 is when agents will work,” highlighting the potential for these technologies to revolutionize workflows. While this may seem futuristic, companies like Salesforce, HubSpot, and Pactum AI are already implementing their own AI agents, though examples from brands like Qantas Airways remain relatively scarce—a point of discussion at Advertising Week New York. What Are AI Agents? AI agents extend beyond mere chatbots. According to Parasvil Patel, a partner at Radical Ventures, they lack a single unifying definition and encompass a wide range of functionalities, from automating workflows to scheduling meetings. The overarching goal, however, is clear: “The ultimate aim is to execute work autonomously,” Patel explained. Currently, AI agents are in the “co-pilot” phase, handling specific tasks such as summarizing meetings. The true breakthrough will occur when they transition to “autopilot,” managing more complex tasks without human intervention. According to Patel, this shift could take up to 24 months. When Did They Emerge? AI agents first gained attention on social media in early 2023, with various startups, including AutoGPT—an open-source application built on OpenAI’s models—promising autonomous capabilities. However, Patel notes that many of these early experiments were not robust enough to be deployed effectively in production environments. How Are Companies Using AI Agents? The appeal of AI agents lies in their ability to save time, enhance efficiency, and free employees from repetitive tasks. For instance, a large distribution company struggling to manage 100,000 suppliers utilized Pactum’s AI, which deploys autonomous agents for negotiations. Instead of seeing negotiations as a dead end, these AI agents continuously customized payment deals based on the speed of suppliers’ goods. This approach led to price discounts, rebates, and allowances. Salesforce has also seen positive results with its AI agents. Its pilot program, AgentForce, launched with five clients—including OpenTable and global publisher Wiley—and achieved a 40% increase in case resolution compared to its previous chatbot for Wiley. At the firm’s Dreamforce event, Salesforce demonstrated AgentForce with Ask Astro, assisting attendees in planning their schedules by suggesting sessions and making reservations. Salesforce’s chief marketing officer, Ariel Kelman, stated that the company has heavily invested in developing its AI agent platform in response to client demand. “What companies are figuring out with generative AI is how to deliver productivity improvements for employees and provide meaningful interactions with customers,” he noted. What About Roadblocks? The journey to fully functional AI agents is not without challenges. Managing different data formats—text, images, and videos—can be complex, as highlighted by William Chen, director of product management for AI & emerging tech at Agora. “Your system is only as good as your data source,” he said. For Salesforce, the challenge lies in the nascent customer adoption of AI agents, with companies just beginning to explore how to leverage them for productivity, according to Kelman. The key challenge is determining what solutions work best for employees and customers across various use cases. Are Jobs at Risk? Not necessarily. AI agents are unlikely to replace jobs in the immediate future. Instead, they allow employees to focus on more strategic and meaningful tasks. Rand explained, “The role of people will shift to configuring the autopilot, rather than flying the plane, which is a positive change.” For example, a major logistics client of Pactum, which previously relied on human negotiators for managing deals with freight providers, can now use AI agents to negotiate more efficiently. This adaptability allows companies to dynamically shift their business strategies based on market conditions. What’s Next? While early adopters of AI agents are seeing initial successes, there’s much more to discover. Salesforce plans to launch its next AI agent later this month: a Sales Development Representative (SDR) designed to manage early-stage sales interactions. Typically, human SDRs follow up on marketing leads through emails and calls, but this AI agent will qualify leads, providing human salespeople with a targeted list of 50 to 100 prospects eager to engage. “Instead of receiving a list of 500 leads, they’ll get a refined list of those who actually want to talk,” Kelman concluded. 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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SingleStore Acquires BryteFlow

SingleStore Acquires BryteFlow

SingleStore Acquires BryteFlow, Paving the Way for Real-Time Analytics and Next-Gen AI Use Cases SingleStore, the world’s only database designed to transact, analyze, and search petabytes of data in milliseconds, has announced its acquisition of BryteFlow, a leading data integration platform. This move enhances SingleStore’s capabilities to ingest data from diverse sources—including SAP, Oracle, and Salesforce—while empowering users to operationalize data from their CRM and ERP systems. With the acquisition, SingleStore will integrate BryteFlow’s data integration technology into its core offering, launching a new experience called SingleConnect. This addition will complement SingleStore’s existing functionalities, enabling users to gain deeper insights from their data, accelerate real-time analytics, and support emerging generative AI (GenAI) use cases. “This acquisition marks a pivotal step in our mission to deliver unparalleled speed, scale, and simplicity,” said Raj Verma, CEO of SingleStore. “Customer demands are evolving rapidly due to shifts in big data storage formats and advancements in generative AI. We believe that data is the foundation of all intelligence, and SingleConnect comes at a perfect time to address this need.” BryteFlow’s platform provides scalable change data capture (CDC) capabilities across multiple data sources, ensuring data integrity between source and target. It integrates seamlessly with major cloud platforms like AWS, Microsoft Azure, and Google Cloud, making it a powerful tool for cloud-based data warehouses and data lakes. Its no-code interface allows for easy and accessible data integration, ensuring that existing BryteFlow customers will experience uninterrupted service and ongoing support. “By combining BryteFlow’s real-time data integration expertise with SingleStore’s capabilities, we aim to help global organizations extract maximum value from their data and scale modern applications,” said Pradnya Bhandary, CEO of BryteFlow. “With SingleConnect, developers will find it easier and faster to access enterprise data sources, tackle complex workloads, and deliver exceptional experiences to their customers.” 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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Autonomous Agents on the Agentforce Platform

Autonomous Agents on the Agentforce Platform

In early September, Salesforce introduced its latest innovation: Salesforce Agentforce. This AI-powered suite is part of Salesforce’s expanding portfolio aimed at enhancing efficiency and streamlining business operations. Autonomous Agents on the Agentforce Platform are here. What is Salesforce Agentforce? Salesforce Agentforce is a platform designed to build autonomous AI agents, allowing businesses to manage critical tasks without requiring human involvement. What are Autonomous Agents on the Agentforce Platform ? Autonomous AI Service AgentsAn AI agent is an intelligent assistant that autonomously handles customer service and sales functions. These agents operate continuously, addressing basic queries without needing complex dialog systems, Natural Language Processing (NLP), or pre-configured workflows. Autonomous Agents on the Agentforce Platform Agentforce Service Agent The Agentforce Service Agent is an AI-powered customer support assistant that delivers autonomous, natural service. Unlike traditional chatbots, these generative AI agents provide brand-aligned responses while handling tasks, making decisions, and operating around the clock across self-service portals and messaging channels. Key Benefits of Agentforce Service Agent: Agentforce SDR Agent The Agentforce SDR Agent is designed to help businesses engage and qualify inbound leads. It manages prospect inquiries, addresses objections, and leverages customer insights to schedule meetings with the appropriate sales representatives. Key Benefits of Agentforce SDR Agent: Agentforce is Already Delivering Results! As a premier pilot partner for Salesforce we has been working with customers to implement Agentforce, generating rapid success. Stay tuned for more exciting updates and opportunities with Agentforce! Like1 Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Salesforce AI Introduces SFR-Judge

Salesforce AI Introduces SFR-Judge

Salesforce AI Introduces SFR-Judge: A Family of Three Evaluation Models with 8B, 12B, and 70B Parameters, Powered by Meta Llama 3 and Mistral NeMO The rapid development of large language models (LLMs) has transformed natural language processing, making the need for accurate evaluation of these models more critical than ever. Traditional human evaluations, while effective, are time-consuming and impractical for the fast-paced evolution of AI models. Salesforce AI Introduces SFR-Judge. To address this, Salesforce AI Research has introduced SFR-Judge, a family of LLM-based judge models designed to revolutionize how AI outputs are evaluated. Built using Meta Llama 3 and Mistral NeMO, the SFR-Judge family includes models with 8 billion (8B), 12 billion (12B), and 70 billion (70B) parameters. These models are designed to handle evaluation tasks such as pairwise comparisons, single ratings, and binary classifications, streamlining the evaluation process for AI researchers. Overcoming Limitations in Traditional Judge Models Traditional LLMs used for evaluation often suffer from biases such as position bias (favoring responses based on their order) and length bias (preferring longer responses regardless of their accuracy). SFR-Judge addresses these issues by leveraging Direct Preference Optimization (DPO), a training method that enables the model to learn from both positive and negative examples, reducing bias and ensuring more consistent and accurate evaluations. Performance and Benchmarking SFR-Judge has been rigorously tested across 13 benchmarks covering three key evaluation tasks. It outperformed existing judge models, including proprietary models like GPT-4o, achieving top performance on 10 of the 13 benchmarks. Notably, on the RewardBench leaderboard, SFR-Judge achieved a 92.7% accuracy, marking a new high in LLM-based evaluation and demonstrating its potential not only as an evaluation tool but also as a reward model for reinforcement learning from human feedback (RLHF) scenarios. Innovative Training Approach The SFR-Judge models were trained using three distinct data formats: These diverse data formats allow SFR-Judge to generate well-rounded, accurate evaluations, making it a more reliable and robust tool for model assessment. Bias Mitigation and Robustness SFR-Judge was tested on EvalBiasBench, a benchmark designed to measure six types of bias. The results demonstrated significantly lower bias levels compared to competing models, along with high consistency in pairwise order comparisons. This robustness ensures that SFR-Judge’s evaluations remain stable, even when the order of responses is altered, making it a scalable and reliable alternative to human annotation. Key Takeaways: Conclusion Salesforce AI Research’s introduction of SFR-Judge represents a breakthrough in the automated evaluation of large language models. By incorporating Direct Preference Optimization and a diverse training approach, SFR-Judge sets a new standard for accuracy, bias reduction, and consistency. Its ability to provide detailed feedback and adapt to various evaluation tasks makes it a powerful tool for the AI community, streamlining the process of LLM assessment and setting the stage for future advancements in AI evaluation. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more

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Salesforce and Sprout Social

Salesforce and Sprout Social

Salesforce Spring ’25 Update: Important Changes to CSP Directives & Sprout iframe Starting with Salesforce’s Spring ’25 release, stricter Content Security Policy (CSP) directives will be enforced on Lightning Pages. These new rules are designed to keep your Salesforce environment secure by preventing cross-site scripting and other code injection attacks that can occur from loading externally hosted resources like scripts, fonts, images, audio, video and stylesheets in Salesforce Lightning Web Pages. What This Means for Sprout Social Users This update will block the Sprout Social iframe from loading in the Lightning Web Component used in your Case page layout—unless you make a few easy changes to avoid any disruptions. Here’s what to do: You can check your current settings by going to Setup > Security > Session Settings > Content Security Policy (CSP) Directive Rendering. Look for the option to adopt the updated CSP directives, which will be automatically applied when Spring 25 rolls out. 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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Nvidia and Salesforce

Nvidia and Salesforce

Salesforce and Nvidia have announced a groundbreaking collaboration to push the boundaries of AI, transforming both customer and employee experiences. Redefining AI in Enterprise Software As businesses worldwide face the complexities and costs of integrating AI into their operations, Salesforce and Nvidia are stepping in with a strategic partnership designed to redefine AI capabilities. This collaboration merges Salesforce’s extensive CRM and enterprise software expertise with Nvidia’s advanced AI and high-performance computing technologies. The goal is to create a new generation of AI agents and avatars that can operate autonomously, grasp complex business contexts, and engage with humans in a more natural, intuitive manner. Marc Benioff, Chair and CEO of Salesforce, states: “Together with Nvidia, we’re spearheading the third wave of the AI revolution—moving beyond copilots to a seamless integration of humans and intelligent agents driving customer success.” Enhancing Salesforce’s Platform The partnership focuses on integrating Nvidia’s accelerated computing and AI software to enhance Salesforce’s platform performance. Key to this effort is the optimization of Salesforce Data Cloud, which harmonizes structured and unstructured customer data in real time. Nvidia’s full-stack accelerated computing platform will significantly increase compute resources, leading to faster insights and improved AI performance across Salesforce’s offerings. AI-Powered Avatars and Beyond A major innovation from this collaboration is the development of AI-powered avatars. By combining Nvidia ACE, a suite of digital human technologies, with Salesforce’s new Agentforce platform, the companies aim to create more engaging, human-like experiences for interactions with customers and employees. These avatars will leverage multi-modal AI models for speech recognition, text-to-speech, and contextual visual responses, potentially revolutionizing business communication. Nvidia founder and CEO Jensen Huang envisions a future where “every company, every job will be enhanced by a wide range of AI agents—assistants that will transform how we work.” He adds, “Nvidia and Salesforce are uniting our technologies to accelerate the development of AI agents, supercharging productivity for companies.” Transforming Business Operations The Salesforce-Nvidia partnership is more than a technological alliance; it’s a strategic move to meet the increasing demand for AI-driven enterprise solutions. The collaboration positions both companies at the forefront of the AI revolution in enterprise software, aiming to reshape how businesses interact with customers and manage their operations. Key facts include: Real-World Applications The potential applications of this technology are extensive. For example: Looking Ahead As Salesforce and Nvidia’s partnership unfolds, it promises not only technological advancements but a fundamental shift in how businesses leverage AI for growth, efficiency, and customer satisfaction. Marc Benioff highlights the potential: “By combining Nvidia’s AI platform with Agentforce, we’re amplifying AI performance and creating dynamic digital avatars, delivering more engaging, intelligent, and immersive customer experiences than ever before.” This collaboration is set to lead the third wave of the AI revolution, integrating humans and intelligent agents to drive unprecedented customer success. 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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