This announcement precedes the release of Salesforce’s competing Agentforce platform, set to debut for general use on Oct. 25. Salesforce CEO Marc Benioff has publicly criticized Microsoft’s AI technology, calling out Copilot’s data security risks and expressing doubts about its value for business customers.
Salesforce Launches Agentforce: The Future of AI for Business Salesforce has officially announced the general availability of its Agentforce platform, introducing what the company calls the next generation of AI-powered business solutions. First unveiled at Dreamforce 2024, Agentforce represents a transformative approach to building and deploying AI agents tailored to enhance workplace efficiency and customer engagement. Revolutionizing AI in BusinessAs part of the Salesforce ecosystem, Agentforce aims to move beyond traditional chatbots and copilot tools, offering businesses AI agents that deliver smart, personalized, and proactive experiences. “Agentforce is redefining what’s possible in business and beyond, ushering in a new era of AI abundance and limitless workforces that augment every employee, build deeper customer relationships, and drive unprecedented growth and profitability,” said Marc Benioff, Chair and CEO of Salesforce. Built on Salesforce’s trusted and customizable platform, Agentforce seamlessly integrates enterprise data, metadata, AI models, workflows, security protocols, and applications. According to Benioff, “This is what AI was meant to be.” Tailored AI AgentsAgentforce’s initial suite includes pre-built agents designed for roles like sales representatives, service agents, personal shoppers, and sales coaches. Businesses can also tailor these AI agents to their specific requirements using a low-code builder, ensuring flexibility and rapid deployment. The platform excels in automating early-stage interactions, such as gathering information or resolving routine issues. It then generates summaries for human agents to review and finalize, allowing employees to focus on higher-value, more rewarding tasks. Affordable and ScalableSalesforce announced that pricing for Agentforce’s Service Agent starts at $2 per conversation, making it accessible to a wide range of businesses. Addressing Consumer Pain PointsIn conjunction with the launch, Salesforce shared new research revealing that consumers often spend up to nine hours navigating customer service for a single issue. This frustration frequently results in abandoned purchases and broken relationships. Agentforce seeks to resolve this by streamlining interactions, reducing resolution times, and delivering consistent, high-quality support experiences. With its comprehensive capabilities and focus on customization, Agentforce positions itself as a game-changer in the AI-driven transformation of customer and workforce experiences. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more
Three Key Generative AI Data Privacy and Security Concerns The rise of generative AI is reshaping the digital landscape, introducing powerful tools like ChatGPT and Microsoft Copilot into the hands of professionals, students, and casual users alike. From creating AI-generated art to summarizing complex texts, generative AI (GenAI) is transforming workflows and sparking innovation. However, for information security and privacy professionals, this rapid proliferation also brings significant challenges in data governance and protection. Below are three critical data privacy and security concerns tied to generative AI: 1. Who Owns the Data? Data ownership is a contentious issue in the age of generative AI. In the European Union, the General Data Protection Regulation (GDPR) asserts that individuals own their personal data. In contrast, data ownership laws in the United States are less clear-cut, with recent state-level regulations echoing GDPR’s principles but failing to resolve ambiguity. Generative AI often ingests vast amounts of data, much of which may not belong to the person uploading it. This creates legal risks for both users and AI model providers, especially when third-party data is involved. Cases surrounding intellectual property, such as controversies involving Slack, Reddit, and LinkedIn, highlight public resistance to having personal data used for AI training. As lawsuits in this arena emerge, prior intellectual property rulings could shape the legal landscape for generative AI. 2. What Data Can Be Derived from LLM Output? Generative AI models are designed to be helpful, but they can inadvertently expose sensitive or proprietary information submitted during training. This risk has made many wary of uploading critical data into AI models. Techniques like tokenization, anonymization, and pseudonymization can reduce these risks by obscuring sensitive data before it is fed into AI systems. However, these practices may compromise the model’s performance by limiting the quality and specificity of the training data. Advocates for GenAI stress that high-quality, accurate data is essential to achieving the best results, which adds to the complexity of balancing privacy with performance. 3. Can the Output Be Trusted? The phenomenon of “hallucinations” — when generative AI produces incorrect or fabricated information — poses another significant concern. Whether these errors stem from poor training, flawed data, or malicious intent, they raise questions about the reliability of GenAI outputs. The impact of hallucinations varies depending on the context. While some errors may cause minor inconveniences, others could have serious or even dangerous consequences, particularly in sensitive domains like healthcare or legal advisory. As generative AI continues to evolve, ensuring the accuracy and integrity of its outputs will remain a top priority. The Generative AI Data Governance Imperative Generative AI’s transformative power lies in its ability to leverage vast amounts of information. For information security, data privacy, and governance professionals, this means grappling with key questions, such as: With high stakes and no way to reverse intellectual property violations, the need for robust data governance frameworks is urgent. As society navigates this transformative era, balancing innovation with responsibility will determine whether generative AI becomes a tool for progress or a source of new challenges. While generative AI heralds a bold future, history reminds us that groundbreaking advancements often come with growing pains. It is the responsibility of stakeholders to anticipate and address these challenges to ensure a safer and more equitable AI-powered world. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Intelligent Adoption Framework Marks a New Era for AI IntegrationAfter a surge of initial excitement, AI has now entered a phase of more thoughtful and strategic adoption, focusing on sustainable progress and measurable results. Following years of hype in which artificial intelligence was hailed as a revolutionary force poised to instantly transform industries, AI is now facing a more tempered reality. As it settles into Gartner’s “Trough of Disillusionment,” organizations are grappling with the reality of high costs and challenges scaling experimental projects. However, this phase of learning is typical for any emerging technology, and the journey to unlock AI’s full potential is far from over. Steve Daly, Senior Vice President of Solutions at New Era Technology, explains: “AI has been around for 70 years, but the recent hype inflated expectations. At $30 per user per month for tools like Microsoft 365 Copilot, they’re appealing for proof-of-concept projects. But once those initial tests are over, many companies struggle to find a clear ROI when scaling.” Cost is not the only barrier to broader AI adoption. Concerns over data security and sharing sensitive information are top priorities for many organizations. Daly adds, “New Era’s robust data and security practice has shifted to offer Copilot Studio, allowing companies to build GenAI solutions with tighter security controls. With Copilot Studio, you can limit access to specific files or libraries, ensuring greater control over sensitive data.” Moving Beyond OverpromisesBuilding confidence in AI requires addressing several factors. First, organizations must tackle security and data control issues, alongside developing a clear business model to justify AI investments. Equally important is maintaining momentum—patience and persistence are key to seeing projects through to success, or determining when to pivot. Daly observes, “We’re seeing many projects lose steam. Around half of AI initiatives stall due to poor security practices and suboptimal data management. Projects must demonstrate progress, and that’s difficult in the innovation phase when you don’t always know what you don’t know.” Introducing Intelligent AdoptionThis is where Copilot Studio and New Era’s Intelligent Adoption Framework come into play. The framework is designed to help organizations chart their AI development journey and ensure investments yield tangible results. Copilot Studio supports IT teams by focusing on the tasks that truly drive value, helping them stay on track toward their goals. The Intelligent Adoption Framework is built around three core pillars: technical redesign, organizational readiness, and user readiness. New Era’s framework leverages its expertise to guide businesses through the steps necessary to define their AI strategy, align their corporate vision, and identify the most valuable use cases for AI adoption. Daly concludes, “It’s not just about purchasing licenses—it’s about creating a roadmap for successful adoption. We’re developing packaged solutions, such as ‘train the trainer’ programs from day one, followed by proof-of-concept demonstrations using Copilot Studio. Our goal is to help customers answer key questions, like when to build a GenAI chatbot, while navigating the complexities of AI adoption and managing the pressures CIOs face from stakeholders.” In this new era of AI, success will be determined not by rushed deployment, but by strategic, intelligent adoption that ensures sustained value over time. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
In the rapidly evolving world of large language models and generative AI, a new concept is gaining momentum: AI agents. AI Agents Interview explores. AI agents are advanced tools designed to handle complex tasks that traditionally required human intervention. While they may be confused with robotic process automation (RPA) bots, AI agents are much more sophisticated, leveraging generative AI technology to execute tasks autonomously. Companies like Google are positioning AI agents as virtual assistants that can drive productivity across industries. In this Q&A, Jason Gelman, Director of Product Management for Vertex AI at Google Cloud, shares insights into Google’s vision for AI agents and some of the challenges that come with this emerging technology. AI Agents Interview How does Google define AI agents? Jason Gelman: An AI agent is something that acts on your behalf. There are two key components. First, you empower the agent to act on your behalf by providing instructions and granting necessary permissions—like authentication to access systems. Second, the agent must be capable of completing tasks. This is where large language models (LLMs) come in, as they can plan out the steps to accomplish a task. What used to require human planning is now handled by the AI, including gathering information and executing various steps. What are current use cases where AI agents can thrive? Gelman: AI agents can be useful across a wide range of industries. Call centers are a common example where customers already expect AI support, and we’re seeing demand there. In healthcare, organizations like Mayo Clinic are using AI agents to sift through vast amounts of information, helping professionals navigate data more efficiently. Different industries are exploring this technology in unique ways, and it’s gaining traction across many sectors. What are some misconceptions about AI agents? Gelman: One major misconception is that the technology is more advanced than it actually is. We’re still in the early stages, building critical infrastructure like authentication and function-calling capabilities. Right now, AI agents are more like interns—they can assist, but they’re not yet fully autonomous decision-makers. While LLMs appear powerful, we’re still some time away from having AI agents that can handle everything independently. Developing the technology and building trust with users are key challenges. I often compare this to driverless cars. While they might be safer than human drivers, we still roll them out cautiously. With AI agents, the risks aren’t physical, but we still need transparency, monitoring, and debugging capabilities to ensure they operate effectively. How can enterprises balance trust in AI agents while acknowledging the technology is still evolving? Gelman: Start simple and set clear guardrails. Build an AI agent that does one task reliably, then expand from there. Once you’ve proven the technology’s capability, you can layer in additional tasks, eventually creating a network of agents that handle multiple responsibilities. Right now, most organizations are still in the proof-of-concept phase. Some companies are using AI agents for more complex tasks, but for critical areas like financial services or healthcare, humans remain in the loop to oversee decision-making. It will take time before we can fully hand over tasks to AI agents. AI Agents Interview What is the difference between Google’s AI agent and Microsoft Copilot? Gelman: Microsoft Copilot is a product designed for business users to assist with personal tasks. Google’s approach with AI agents, particularly through Vertex AI, is more focused on API-driven, developer-based solutions that can be integrated into applications. In essence, while Copilot serves as a visible assistant for users, Vertex AI operates behind the scenes, embedded within applications, offering greater flexibility and control for enterprise customers. The real potential of AI agents lies in their ability to execute a wide range of tasks at the API level, without the limitations of a low-code/no-code interface. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more
Salesforce cites data from its research, revealing that U.S. consumers spend up to nine hours resolving a single customer service issue, with 67% ending the process frustrated. Agentforce aims to eliminate these inefficiencies by providing always-on support and actionable solutions.
The rise of agentic AI has dominated recent discussions in enterprise technology, sparking debates over its transformative potential and practical applications. Just weeks ago, few had heard of the term. Now, every tech vendor is racing to stake their claim in this emerging space, positioning agentic AI as the successor to AI co-pilots. While co-pilots assist users with tasks, agentic AI represents the next step: delegating tasks to intelligent agents capable of independent execution, akin to assigning work to a junior colleague. But beyond the buzz, the pressing questions remain: Cutting Through the Hype Recent launches provide a snapshot of how enterprises are beginning to deploy agentic AI. Salesforce’s Agentforce, Asana’s AI Studio, and Atlassian’s Rovo AI Assistant all emphasize the ability of these agents to streamline workflows by interpreting unstructured data and automating complex tasks. These tools promise flexibility over previous rigid, rule-based systems. For example, instead of painstakingly scripting every step, users can instruct an agent to “follow documented policies, analyze data, and propose actions,” reserving human approval for final execution. However, the performance of these agents hinges on data quality and system robustness. Salesforce’s Marc Benioff, for instance, critiques Microsoft’s Copilot for lacking a robust data model, emphasizing Salesforce’s own structured approach as a competitive edge. Similarly, Asana and Atlassian highlight the structured work graphs underpinning their platforms as critical for accurate and reliable outputs. Key Challenges Despite the promise, there are significant challenges to deploying agentic AI effectively: Early Wins and Future Potential Early adopters are seeing value in high-volume, repetitive scenarios such as customer service. For example: However, these successes represent low-hanging fruit. The true promise lies in rethinking how enterprises work. As one panelist at Atlassian’s event noted: “We shouldn’t just use this AI to enhance existing processes. We should ask whether these are the processes we want for the future.” The Path Forward The transformative potential of agentic AI will depend on broader process standardization. Just as standardized shipping containers revolutionized logistics, and virtual containers transformed IT operations, similar breakthroughs in process design could unlock exponential gains for AI-driven workflows. For now, enterprises should: Conclusion Agentic AI holds immense potential, but its real power lies in enabling enterprises to question and redesign how work gets done. While it may still be in its early days, businesses that align their AI investments with strategic goals—and not just immediate fixes—will be best positioned to thrive in this new era of intelligent automation. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more
Generative AI has far more to offer your site than simply mimicking a conversational ChatGPT-like experience or providing features like generating cover letters on resume sites. Let’s explore how you can integrate Generative AI with your product in diverse and innovative ways! There are three key perspectives to consider when integrating Generative AI with your features: system scope, spatial relationship, and functional relationship. Each perspective offers a different lens for exploring integration pathways and can spark valuable conversations about melding AI with your product ecosystem. These categories aren’t mutually exclusive; instead, they overlap and provide flexible ways of envisioning AI’s role. 1. System Scope — The Reach of Generative AI in Your System System scope refers to the breadth of integration within your system. By viewing integration from this angle, you can assess the role AI plays in managing your platform’s overall functionality. While these categories may overlap, they are useful in facilitating strategic conversations. 2. Spatial Relationships — Where AI Interacts with Features Spatial relationships describe where AI features sit in relation to your platform’s functionality: 3. Functional Relationships — How AI Interacts with Features Functional relationships determine how AI and platform features work together. This includes how users engage with AI and how AI content updates based on feature interactions: Scope of Generative AI By considering these different perspectives—system scope, spatial, and functional—you can drive more meaningful conversations about how Generative AI can best enhance your product’s capabilities. Each approach offers unique value, and careful thought can help teams choose the integration path that aligns with their needs and goals. Scope of Generative AI conversations with Tectonic can assist in planning the best ROI approach to AI. Contact us today. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more Health Cloud Brings Healthcare Transformation Following swiftly after last week’s successful launch of Financial Services Cloud, Salesforce has announced the second installment in its series Read more
Artificial Intelligence and Sales Cloud AI enhances the sales process at every stage, making it more efficient and effective. Salesforce’s AI technology—Einstein—streamlines data entry and offers predictive analysis, empowering sales teams to maximize every opportunity. Artificial Intelligence and Sales Cloud explained. Artificial Intelligence and Sales Cloud Sales Cloud integrates several AI-driven features powered by Einstein and machine learning. To get the most out of these tools, review which features align with your needs and check the licensing requirements for each one. Einstein and Data Usage in Sales Cloud Einstein thrives on data. To fully leverage its capabilities within Sales Cloud, consult the data usage table to understand which types of data Einstein features rely on. Setting Up Einstein Opportunity Scoring in Sales Cloud Einstein Opportunity Scoring, part of the Sales Cloud Einstein suite, is available to eligible customers at no additional cost. Simply activate Einstein, and the system will handle the rest, offering predictive insights to improve your sales pipeline. Managing Access to Einstein Features in Sales Cloud Sales Cloud users can access Einstein Opportunity Scoring through the Sales Cloud Einstein For Everyone permission set. Ensure the right team members have access by reviewing the permissions, features included, and how to manage assignments. Einstein Copilot Setup for Sales Einstein Copilot helps sales teams stay organized by guiding them through deal management, closing strategies, customer communications, and sales forecasting. Each Copilot action corresponds to specific topics designed to optimize the sales process. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more
Many organizations follow a hybrid approach to AI infrastructure, combining public clouds, colocation facilities, and on-prem solutions. Specialized GPU-as-a-service vendors, for instance, are becoming popular for handling high-demand AI computations, helping businesses manage costs without compromising performance. Business process outsourcing company TaskUs, for example, focuses on optimizing compute and data flows as it scales its gen AI deployments, while Cognizant advises that companies distinguish between training and inference needs, each with different latency requirements.
Best Practices for Safely Deploying Salesforce Einstein Copilot When deploying Salesforce Einstein Copilot, following best practices ensures a secure, efficient, and effective integration of AI into your workflows. Here are the key steps to safely deploy Einstein Copilot: By adhering to these best practices, you can ensure a smooth, secure, and successful deployment of Salesforce Einstein Copilot, enhancing your team’s productivity while maintaining data integrity and security. 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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more
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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more
Salesforce CEO Marc Benioff recently critiqued Microsoft for overhyping the capabilities of its Copilot AI tool, arguing that the tech giant has done a “tremendous disservice” to the industry. In a Rapid Response interview with Bob Safian, Benioff emphasized that Salesforce’s Agentforce is “what AI was meant to be.” More excited than ever, he sees Agentforce as a technology poised to transform industries in ways comparable to past cloud, mobile, and social revolutions. Benioff Excited About AI. Reflecting on Dreamforce 2024Benioff called this year’s Dreamforce the most significant yet. With 45,000 attendees and millions joining online, Agentforce took center stage, allowing users to build their own AI agents firsthand. This hands-on experience was vital, he said, to clear up misconceptions caused by overpromised AI products. Salesforce already handles trillions of AI transactions through its Einstein platform, but Benioff believes Agentforce represents a groundbreaking shift in enterprise AI. Agentforce vs. Copilot: A Clear DifferenceBenioff drew a sharp contrast between Agentforce and Microsoft’s Copilot, comparing the latter to the infamous Microsoft Clippy. According to Benioff, Copilot often fails to deliver meaningful results, creating confusion and dissatisfaction among customers. In contrast, Agentforce is set to deliver powerful outcomes by connecting customers, raising revenues, and augmenting employees. He anticipates that within a year, Salesforce will operate over a billion AI agents worldwide. Benioff Excited About AI. Benioff Excited About AI Agentforce’s Real-World ImpactSharing a story from the healthcare sector, Benioff illustrated how Agentforce has resolved over 90% of patient inquiries and scheduling needs for one large provider, enabling rapid and meaningful interactions. He foresees similar applications across media, finance, and travel, as Agentforce helps industries implement AI-driven agents with high success rates. Scheduled to go live on October 25, Agentforce is expected to be adopted by hundreds of thousands of companies. MIT IDE Annual Conference Insights: AI’s Potential and ChallengesWhile businesses explore AI’s possibilities, researchers at MIT’s Initiative on the Digital Economy (IDE) are investigating the complexities and ethical considerations of AI. At the 2024 MIT IDE Annual Conference, findings on AI’s influence on various domains were presented, with highlights including: These MIT findings highlight both the immense promise and the challenges AI presents, as companies like Salesforce aim to harness AI’s true potential while navigating ethical and practical concerns. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more
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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more
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 Service Cloud with AI-Driven Intelligence Salesforce Enhances Service Cloud with AI-Driven Intelligence Engine Data science and analytics are rapidly becoming standard features in enterprise applications, Read more
