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Generative ai energy consumption

Growing Energy Consumption in Generative AI

Growing Energy Consumption in Generative AI, but ROI Impact Remains Unclear The rising energy costs associated with generative AI aren’t always central in enterprise financial considerations, yet experts suggest IT leaders should take note. Building a business case for generative AI involves both obvious and hidden expenses. Licensing fees for large language models (LLMs) and SaaS subscriptions are visible expenses, but less apparent costs include data preparation, cloud infrastructure upgrades, and managing organizational change. Growing Energy Consumption in Generative AI. One under-the-radar cost is the energy required by generative AI. Training LLMs demands vast computing power, and even routine AI tasks like answering user queries or generating images consume energy. These intensive processes require robust cooling systems in data centers, adding to energy use. While energy costs haven’t been a focus for GenAI adopters, growing awareness has prompted the International Energy Agency (IEA) to predict a doubling of data center electricity consumption by 2026, attributing much of the increase to AI. Goldman Sachs echoed these concerns, projecting data center power consumption to more than double by 2030. For now, generative AI’s anticipated benefits outweigh energy cost concerns for most enterprises, with hyperscalers like Google bearing the brunt of these costs. Google recently reported a 13% increase in greenhouse gas emissions, citing AI as a major contributor and suggesting that reducing emissions might become more challenging with AI’s continued growth. Growing Energy Consumption in Generative AI While not a barrier to adoption, energy costs play into generative AI’s long-term viability, noted Scott Likens, global AI engineering leader at PwC, emphasizing that “there’s energy being used — you don’t take it for granted.” Energy Costs and Enterprise Adoption Generative AI users might not see a line item for energy costs, yet these are embedded in fees. Ryan Gross of Caylent points out that the costs are mainly tied to model training and inferencing, with each model query, though individually minor, adding up over time. These expenses are often spread across the customer base, as companies pay for generative AI access through a licensing model. A PwC sustainability study showed that GenAI power costs, particularly from model training, are distributed among licensees. Token-based pricing for LLM usage also reflects inferencing costs, though these charges have decreased. Likens noted that the largest expenses still come from infrastructure and data management rather than energy. Potential Efficiency Gains Though energy isn’t a primary consideration, enterprises could reduce consumption indirectly through technological advancements. Newer, more cost-efficient models like OpenAI’s GPT-4o mini are 60% less expensive per token than prior versions, enabling organizations to deploy GenAI on a larger scale while keeping costs lower. Small, fine-tuned models can be used to address latency and lower energy consumption, part of a “multimodel” approach that can provide different accuracy and latency levels with varying energy demands. Agentic AI also offers opportunities for cost and energy savings. By breaking down tasks and routing them through specialized models, companies can minimize latency and reduce power usage. According to Likens, using agentic architecture could cut costs and consumption, particularly when tasks are routed to more efficient models. Rising Data Center Energy Needs While enterprises may feel shielded from direct energy costs, data centers bear the growing power demand. Cooling solutions are evolving, with liquid cooling systems becoming more prevalent for AI workloads. As data centers face the “AI growth cycle,” the demand for energy-efficient cooling solutions has fueled a resurgence in thermal management investment. Liquid cooling, being more efficient than air cooling, is gaining traction due to the power demands of AI and high-performance computing. IDTechEx projects that data center liquid cooling revenue could exceed $50 billion by 2035. Meanwhile, data centers are exploring nuclear power, with AWS, Google, and Microsoft among those considering nuclear energy as a sustainable solution to meet AI’s power demands. Future ROI Considerations While enterprises remain shielded from the full energy costs of generative AI, careful model selection and architectural choices could help curb consumption. PwC, for instance, factors in the “carbon impact” as part of its GenAI deployment strategy, recognizing that energy considerations are now a part of the generative AI value proposition. As organizations increasingly factor sustainability into their tech decisions, energy efficiency might soon play a larger role in generative AI ROI calculations. 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

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Salesforce AI Evolves with the Generative AI Landscape

Salesforce AI Evolves with the Generative AI Landscape

Salesforce AI: Powering Customer Relationship Management Salesforce is a leading CRM solution that has long delivered cutting-edge cloud technologies to manage customer relationships effectively. In recent months, the platform has further advanced with the integration of generative AI and AI-powered features, primarily through its AI engine, Einstein. Salesforce AI Evolves with the Generative AI Landscape. To explore how AI operates within the Salesforce ecosystem and how various business teams can leverage these innovations, this guide delves into Salesforce’s AI capabilities, products, and features. Salesforce AI: Transforming CRM Capabilities Salesforce remains a top choice in the CRM software market, offering one of the most comprehensive solutions for managing relationships across departments, industries, and initiatives. Through dedicated cloud platforms, Salesforce enables teams to oversee marketing, sales, customer service, e-commerce, and more, with tools focused on delivering enhanced customer experiences supported by powerful data analytics. With the introduction of generative AI, Salesforce has significantly elevated its native automation, workflow management, data analytics, and assistive capabilities for customer lifecycle management. Einstein Copilot exemplifies this innovation, aiding internal users with tasks such as outreach, analysis, and improving external user experiences. What is Salesforce Einstein? Salesforce Einstein is an AI-driven suite of tools integrated natively into various Salesforce Cloud applications, including Sales Cloud, Marketing Cloud, Service Cloud, and Commerce Cloud. It also operates through assistive technologies like Einstein Copilot. Einstein is built on a multitenant platform and incorporates numerous automated machine learning features to unify organizational data with CRM capabilities. Designed to make intelligent, data-driven decisions, Einstein requires no additional installation, offering a seamless user experience when paired with a compatible subscription plan. 7 Key Features of Salesforce Einstein 7 Applications of Salesforce Einstein Future Trends in Salesforce AI Bottom Line: Salesforce AI Evolves with the Generative AI Landscape Salesforce continues to enhance its AI-powered features, keeping pace with advancements in generative and predictive AI. Whether new to the platform or a seasoned user, Salesforce offers innovative, AI-centric solutions to streamline customer relationship management and business operations. 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

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Agentic AI is Here

AI Agent Myths

Myths About AI Agents Agents will transform how we work, but separating fact from fiction is essential. AI agents are revolutionizing business operations, yet misconceptions persist about their capabilities and value. Understanding these myths—and the truth behind them—can help your organization unlock their potential. Myth #1: AI Agents Are Just Glorified Chatbots While chatbots and AI agents both use artificial intelligence, their functionality and complexity differ significantly. For instance, a chatbot might provide an overview of your sales metrics, but an AI agent can analyze those metrics, forecast demand, adjust inventory levels, update marketing strategies, and even notify suppliers—all proactively and autonomously. This leap in capability allows agents to optimize workflows, make strategic recommendations, and dynamically respond to changing conditions. They’re not just answering questions—they’re driving outcomes. Myth #2: They’re unpredictable and uncontrollablePopular culture often paints AI as rogue systems—think 2001: A Space Odyssey or The Terminator—but in reality, modern AI agents are designed with safety, trust, and precision at their core. The most effective agents today use advanced techniques to prevent errors and ensure their actions stay within strict boundaries. At the heart of this is a reasoning engine. This engine doesn’t just execute tasks—it creates action plans based on the user’s goals, evaluates those plans, and refines them by pulling data from customer relationship management (CRM) systems and other platforms. It then determines the correct processes to execute and iterates until the task is completed successfully, improving with each interaction. When tasks fall outside an organization’s predefined guardrails—like user permissions or compliance rules—the reasoning engine automatically flags the task and escalates it for human oversight. “Helping an agent perform accurately while understanding what it is not allowed to do is a complex task,” says Krishna Gandikota, Manager of Solution Engineering at Salesforce. “The reasoning engine plans and evaluates the AI’s approach before it takes any action. It also assesses whether it has the necessary skills and information to proceed.” This process is further enhanced by continuous learning, enabling agents to refine their decision-making and actions over time. Grounded in DataThe best agents are contextually aware, leveraging relevant, up-to-date information to perform tasks accurately. Techniques like retrieval-augmented generation (RAG) help by sourcing the most relevant data, while semantic search ensures that agents retrieve the latest and most accurate information. Salesforce’s Agentforce employs these methods using Data Cloud, which enables agents to access real-time data without physically copying or modifying it—thanks to zero-copy architecture. This ensures speed, accuracy, and compliance across all agent-driven actions. Myth #3: They’re complicated, time-consuming, and expensive to set upIt’s easy to assume that deploying AI agents would require months of integration work and millions of dollars, but that’s no longer the case. Advances in generative AI and large language models (LLMs) have drastically simplified the process. Agents can now be deployed in minutes with prebuilt topics—specific areas of focus—and actions for common tasks in customer service, sales, and commerce. For more tailored needs, low-code tools make it easy to create custom agents. Using natural language processing (NLP), you simply describe what the agent needs to do, and the system builds it for you. For instance, Agent Builder automatically suggests guardrails and resources based on the task description. By scanning an app’s metadata, it identifies semantically similar processes, creating a smarter, context-aware agent that aligns with your business operations. “All the sophistication is already built into the platform,” Gandikota explains. “The Einstein Trust Layer, reasoning engine, and vector database for RAG and semantic search work seamlessly. With this foundation, you can build a team of agents quickly and confidently.” Myth #4: They’re always fully autonomousAI agents don’t need to operate completely autonomously to deliver value. Their autonomy depends on the complexity of their tasks and the industry they serve. “Agents don’t always need to take actions autonomously,” Gandikota explains. “They’re designed to understand requests, assess whether they can proceed independently, and involve humans when necessary.” Myth #5: They won’t deliver real business valueSome businesses using generic AI tools haven’t seen the ROI they expected. That’s because generic AI isn’t tailored to specific business needs. AI agents, on the other hand, are purpose-built to perform specialized tasks with precision. Whether it’s nurturing sales leads, brainstorming marketing campaigns, or resolving service tickets, targeted AI agents excel at solving specific problems. Unlike generic AI, they don’t just provide insights—they take action, driving measurable outcomes. For example, educational publisher Wiley improved support case resolution by over 40% after adopting AI agents. By handling routine tasks, the agents freed up Wiley’s service teams to focus on more complex cases. Similarly, early adopters like OpenTable and ADP have reported significant improvements in customer satisfaction and efficiency. According to MarketsandMarkets, AI agents are driving demand for automation by enhancing decision-making, scalability, and efficiency. The global market for AI agents is expected to grow from .1 billion in 2024 to billion by 2030. The Bottom LineUnderstanding the myths—and realities—of AI agents is critical for business leaders. Misconceptions can lead to missed opportunities, while clarity around their capabilities can help organizations work smarter, faster, and more efficiently. With trusted, adaptable, and purpose-built agents, the future of business automation is already here. 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

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Mapping Data Salesforce to Canva

Mapping Data Salesforce to Canva

Mapping Data Fields in Salesforce for Canva Integration Salesforce administrators can map data fields from a brand template to Salesforce objects, enabling data from Salesforce to automatically populate placeholders in Canva designs. This feature is available exclusively for Canva Enterprise users and integrates with Salesforce Professional, Enterprise, or Unlimited editions. Mapping Data Salesforce to Canva. Steps for Mapping Data Fields in Salesforce: Pre-requisites: The following are the steps to set up field mapping using the Canva for Salesforce app. Step 1: Sync Brand Templates Before mapping fields, you need to sync brand templates from Canva to Salesforce. Here’s how: Step 2: Create a Template Mapping Template mapping connects data fields from a Salesforce object to placeholders in a Canva brand template, allowing Salesforce data to autofill the design. You need to create a separate template mapping for each Salesforce object. Unmapped Fields: You don’t have to map every field. If a field is unmapped, the placeholder in the Canva template will remain unchanged in the final design. Additional Information: Connecting Data Source Apps to Canva for Autofill You can connect data sources like Salesforce to Canva to autofill elements in your designs. Here’s a brief overview of how to connect and use Salesforce data: Creating Brand Templates for Salesforce To use Canva for Salesforce to generate sales collateral, brand designers must first create and publish a brand template. These templates include data fields that act as placeholders for Salesforce data. Mapping Data Salesforce to Canva With this setup, Salesforce admins can easily map data fields and auto-generate designs based on Salesforce data. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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

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A Company in Transition

A Company in Transition

OpenAI Restructures: Increased Flexibility, But Raises Concerns OpenAI’s decision to restructure into a for-profit entity offers more freedom for the company and its investors but raises questions about its commitment to ethical AI development. Founded in 2015 as a nonprofit, OpenAI transitioned to a hybrid model in 2019 with the creation of a for-profit subsidiary. Now, its restructuring, widely reported this week, signals a shift where the nonprofit arm will no longer influence the day-to-day operations of the for-profit side. CEO Sam Altman is set to receive equity in the newly restructured company, which will operate as a benefit corporation (B Corp), similar to competitors like Anthropic and Sama. A Company in Transition This move comes on the heels of a turbulent year. OpenAI’s board initially voted to remove Altman over concerns about transparency, but later rehired him after significant backlash and the resignation of several board members. The company has seen a number of high-profile departures since, including co-founder Ilya Sutskever, who left in May to start Safe Superintelligence (SSI), an AI safety-focused venture that recently secured $1 billion in funding. This week, CTO Mira Murati, along with key research leaders Bob McGrew and Barret Zoph, also announced their departures. OpenAI’s restructuring also coincides with an anticipated multi-billion-dollar investment round involving major players such as Nvidia, Apple, and Microsoft, potentially pushing the company’s valuation to as high as $150 billion. Complex But Expected Move According to Michael Bennett, AI policy advisor at Northeastern University, the restructuring isn’t surprising given OpenAI’s rapid growth and increasingly complex structure. “Considering OpenAI’s valuation, it’s understandable that the company would simplify its governance to better align with investor priorities,” said Bennett. The transition to a benefit corporation signals a shift towards prioritizing shareholder interests, but it also raises concerns about whether OpenAI will maintain its ethical obligations. “By moving away from its nonprofit roots, OpenAI may scale back its commitment to ethical AI,” Bennett noted. Ethical and Safety Concerns OpenAI has faced scrutiny over its rapid deployment of generative AI models, including its release of ChatGPT in November 2022. Critics, including Elon Musk, have accused the company of failing to be transparent about the data and methods it uses to train its models. Musk, a co-founder of OpenAI, even filed a lawsuit alleging breach of contract. Concerns persist that the restructuring could lead to less ethical oversight, particularly in preventing issues like biased outputs, hallucinations, and broader societal harm from AI. Despite the potential risks, Bennett acknowledged that the company would have greater operational freedom. “They will likely move faster and with greater focus on what benefits their shareholders,” he said. This could come at the expense of the ethical commitments OpenAI previously emphasized when it was a nonprofit. Governance and Regulation Some industry voices, however, argue that OpenAI’s structure shouldn’t dictate its commitment to ethical AI. Veera Siivonen, co-founder and chief commercial officer of AI governance vendor Saidot, emphasized the role of regulation in ensuring responsible AI development. “Major players like Anthropic, Cohere, and tech giants such as Google and Meta are all for-profit entities,” Siivonen said. “It’s unfair to expect OpenAI to operate under a nonprofit model when others in the industry aren’t bound by the same restrictions.” Siivonen also pointed to OpenAI’s participation in global AI governance initiatives. The company recently signed the European Union AI Pact, a voluntary agreement to adhere to the principles of the EU’s AI Act, signaling its commitment to safety and ethics. Challenges for Enterprises The restructuring raises potential concerns for enterprises relying on OpenAI’s technology, said Dion Hinchcliffe, an analyst with Futurum Group. OpenAI may be able to innovate faster under its new structure, but the reduced influence of nonprofit oversight could make some companies question the vendor’s long-term commitment to safety. Hinchcliffe noted that the departure of key staff could signal a shift away from prioritizing AI safety, potentially prompting enterprises to reconsider their trust in OpenAI. New Developments Amid Restructuring Despite the ongoing changes, OpenAI continues to roll out new technologies. The company recently introduced a new moderation model, “omni-moderation-latest,” built on GPT-4o. This model, available through the Moderation API, enables developers to flag harmful content in both text and image outputs. A Company in Transition As OpenAI navigates its restructuring, balancing rapid innovation with maintaining ethical standards will be crucial to sustaining enterprise trust and market leadership. 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

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AI Agents Connect Tool Calling and Reasoning

AI Agents Connect Tool Calling and Reasoning

AI Agents: Bridging Tool Calling and Reasoning in Generative AI Exploring Problem Solving and Tool-Driven Decision Making in AI Introduction: The Emergence of Agentic AI Recent advancements in libraries and low-code platforms have simplified the creation of AI agents, often referred to as digital workers. Tool calling stands out as a key capability that enhances the “agentic” nature of Generative AI models, enabling them to move beyond mere conversational tasks. By executing tools (functions), these agents can act on your behalf and tackle intricate, multi-step problems requiring sound decision-making and interaction with diverse external data sources. This insight explores the role of reasoning in tool calling, examines the challenges associated with tool usage, discusses common evaluation methods for tool-calling proficiency, and provides examples of how various models and agents engage with tools. Reasoning as a Means of Problem-Solving Successful agents rely on two fundamental expressions of reasoning: reasoning through evaluation and planning, and reasoning through tool use. While both reasoning expressions are vital, they don’t always need to be combined to yield powerful solutions. For instance, OpenAI’s new o1 model excels in reasoning through evaluation and planning, having been trained to utilize chain of thought effectively. This has notably enhanced its ability to address complex challenges, achieving human PhD-level accuracy on benchmarks like GPQA across physics, biology, and chemistry, and ranking in the 86th-93rd percentile on Codeforces contests. However, the o1 model currently lacks explicit tool calling capabilities. Conversely, many models are specifically fine-tuned for reasoning through tool use, allowing them to generate function calls and interact with APIs effectively. These models focus on executing the right tool at the right moment but may not evaluate their results as thoroughly as the o1 model. The Berkeley Function Calling Leaderboard (BFCL) serves as an excellent resource for comparing the performance of various models on tool-calling tasks and provides an evaluation suite for assessing fine-tuned models against challenging scenarios. The recently released BFCL v3 now includes multi-step, multi-turn function calling, raising the standards for tool-based reasoning tasks. Both reasoning types are powerful in their own right, and their combination holds the potential to develop agents that can effectively deconstruct complex tasks and autonomously interact with their environments. For more insights into AI agent architectures for reasoning, planning, and tool calling, check out my team’s survey paper on ArXiv. Challenges in Tool Calling: Navigating Complex Agent Behaviors Creating robust and reliable agents necessitates overcoming various challenges. In tackling complex problems, an agent often must juggle multiple tasks simultaneously, including planning, timely tool interactions, accurate formatting of tool calls, retaining outputs from prior steps, avoiding repetitive loops, and adhering to guidelines to safeguard the system against jailbreaks and prompt injections. Such demands can easily overwhelm a single agent, leading to a trend where what appears to an end user as a single agent is actually a coordinated effort of multiple agents and prompts working in unison to divide and conquer the task. This division enables tasks to be segmented and addressed concurrently by distinct models and agents, each tailored to tackle specific components of the problem. This is where models with exceptional tool-calling capabilities come into play. While tool calling is a potent method for empowering productive agents, it introduces its own set of challenges. Agents must grasp the available tools, choose the appropriate one from a potentially similar set, accurately format the inputs, execute calls in the correct sequence, and potentially integrate feedback or instructions from other agents or humans. Many models are fine-tuned specifically for tool calling, allowing them to specialize in selecting functions accurately at the right time. Key considerations when fine-tuning a model for tool calling include: Common Benchmarks for Evaluating Tool Calling As tool usage in language models becomes increasingly significant, numerous datasets have emerged to facilitate the evaluation and enhancement of model tool-calling capabilities. Two prominent benchmarks include the Berkeley Function Calling Leaderboard and the Nexus Function Calling Benchmark, both utilized by Meta to assess the performance of their Llama 3.1 model series. The recent ToolACE paper illustrates how agents can generate a diverse dataset for fine-tuning and evaluating model tool use. Here’s a closer look at each benchmark: Each of these benchmarks enhances our ability to evaluate model reasoning through tool calling. They reflect a growing trend toward developing specialized models for specific tasks and extending the capabilities of LLMs to interact with the real world. Practical Applications of Tool Calling If you’re interested in observing tool calling in action, here are some examples to consider, categorized by ease of use, from simple built-in tools to utilizing fine-tuned models and agents with tool-calling capabilities. While the built-in web search feature is convenient, most applications require defining custom tools that can be integrated into your model workflows. This leads us to the next complexity level. To observe how models articulate tool calls, you can use the Databricks Playground. For example, select the Llama 3.1 405B model and grant access to sample tools like get_distance_between_locations and get_current_weather. When prompted with, “I am going on a trip from LA to New York. How far are these two cities? And what’s the weather like in New York? I want to be prepared for when I get there,” the model will decide which tools to call and what parameters to provide for an effective response. In this scenario, the model suggests two tool calls. Since the model cannot execute the tools, the user must input a sample result to simulate. Suppose you employ a model fine-tuned on the Berkeley Function Calling Leaderboard dataset. When prompted, “How many times has the word ‘freedom’ appeared in the entire works of Shakespeare?” the model will successfully retrieve and return the answer, executing the required tool calls without the user needing to define any input or manage the output format. Such models handle multi-turn interactions adeptly, processing past user messages, managing context, and generating coherent, task-specific outputs. As AI agents evolve to encompass advanced reasoning and problem-solving capabilities, they will become increasingly adept at managing

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Matching Record Check

Matching Record Check

Salesforce Matching Record Check in Flow Create Element: Summer ’24 Update With the Summer ’24 Release, Salesforce introduced a new feature allowing users to check for matching records when using the Create element in Flows. This enhancement provides more control over record creation, especially when dealing with potential duplicates. Single Record Creation with Matching Check When a matching record is identified, you have the following options: If multiple matching records are found, you can choose to: It’s important to note that the definition of a “matching record” in this context is not tied to Salesforce’s traditional matching and duplicate rules. Instead, it is determined by the criteria you set within the Create element. You can specify multiple criteria lines and combine them using AND or OR logic. For example, a match could be identified if both the phone number and last name match the values in the record you’re creating. Use Cases for Single Record Creation and Matching Check This feature can be used to create or update various types of records, such as contacts or leads. It is particularly useful in scenarios where duplicate records need to be avoided, like adding campaign members or public group members. Salesforce typically throws an error if a Flow attempts to add a member who already exists, but this new feature allows you to handle such cases more gracefully. Limitations: Creating Multiple Records with Matching Check: Winter ’25 Update With the Winter ’25 Release, Salesforce extended this functionality to handle collections of records within the Create element. When working with multiple records, you can specify the field to identify existing records: You can also decide what happens if a record creation or update fails: This feature is particularly useful for scenarios like importing leads from an external marketing tool or syncing billing and payment activities from an accounting platform. It mimics the upsert functionality found in other data import tools. Limitations: This enhancement offers more flexibility and control when managing records in Salesforce, ensuring that your data remains clean and accurate while avoiding potential errors in automated processes. 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

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Salesforce Supports Babies and Their Families

Salesforce Supports Babies and Their Families

GE Appliances and Mothers’ Milk Bank Northeast Partner to Support Babies and Families with Cutting-Edge Technology GE Appliances, a leader in home appliances, has announced a transformative partnership with Mothers’ Milk Bank Northeast, a non-profit organization dedicated to providing life-saving pasteurized donor human milk to premature and medically fragile babies. This collaboration is set to revolutionize milk donation tracking and management, providing vital support to vulnerable infants at critical times. “This initiative is a game-changer for our organization,” said Deborah Youngblood, CEO of Mothers’ Milk Bank Northeast. “The new system enables us to track milk donations more efficiently, manage donor information, and ultimately supply more life-saving milk to infants in need across the country.” Research confirms that breast milk significantly reduces the risk of life-threatening diseases in fragile newborns, especially those in NICU care. When a mother’s milk is unavailable, donor milk becomes a critical and often life-saving alternative. However, ensuring safe, timely deliveries and maintaining connections with both donors and beneficiaries has been a persistent challenge. Recognizing the need for a more efficient system, the milk bank partnered with GE Appliances to implement a cloud-based software solution powered by Salesforce. The aim was to streamline donation tracking, improve donor screening, and increase milk availability for families. The collaboration combines GE Appliances’ expertise in digital transformation and AI with Mothers’ Milk Bank Northeast’s commitment to family support. Leveraging Salesforce technology, they developed a more efficient system for managing milk donations, improving donor screenings, and enhancing milk distribution. Since implementing the new system, the non-profit has seen a 15% increase in milk production, allowing it to provide more milk to families in need and safeguard the health of more infants. “GE Appliances is redefining how we engage with communities, focusing on skills-based volunteerism to make a transformational impact,” said Anne Limberg, Senior Principal Digital Technology Program Manager at GE Appliances. “We assembled an all-women, global team with expertise in technology and project management to design a custom Salesforce platform tailored to the specific needs of Mothers’ Milk Bank Northeast. This partnership demonstrates how we can leverage our expertise to support essential causes.” Through Salesforce tools like Experience Cloud, Marketing Cloud, and Sales Cloud, along with GE Appliances’ existing development capabilities, the milk bank’s staff and volunteers are supported throughout the process—from milk collection and pasteurization to distribution in hospitals. “Salesforce is dedicated to using technology for good,” said Lori Freeman, VP and GM of Nonprofit at Salesforce. “We are proud to collaborate with GE Appliances and Mothers’ Milk Bank Northeast to deliver solutions that positively impact families and support the health of vulnerable infants.” In addition to the digital solutions, GE Appliances has provided essential cold storage units to ensure the safe preservation of donor milk during distribution. About GE Appliances GE Appliances, a Haier company, believes in making “good things, for life.” As creators, thinkers, and makers, we strive to find better ways to improve lives. Our diversity strengthens us, allowing us to better understand and serve our customers and meet their needs. About Mothers’ Milk Bank Northeast Mothers’ Milk Bank Northeast is a non-profit organization that collects, pasteurizes, and distributes donor milk to premature and medically fragile babies. Providing over 1.5 million essential feedings annually, the organization serves more than 100 hospitals in the Northeast, playing a critical role in the health of vulnerable infants. 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

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CISA Launches New Services Portal

CISA Launches New Services Portal

CISA Launches New Services Portal to Enhance Incident Reporting and Support In August, the Cybersecurity and Infrastructure Security Agency (CISA) introduced the CISA Services Portal, designed to streamline the process of reporting cybersecurity incidents and enhance information sharing. “The new CISA Services Portal improves the reporting process and offers more features for our voluntary reporters. We ask organizations reporting an incident to provide details such as the impacted entity, contact information, incident description, technical indicators, and mitigation steps,” a CISA spokesperson stated via email. By collecting detailed reports, CISA and its partners can assist victims in mitigating the effects of cyber incidents, prevent attackers from reusing tactics, and gain insights into the broader scope of adversary campaigns. This information-sharing benefits not just the initial victim but also helps protect other organizations from potential attacks. How the Portal Works The CISA Services Portal follows guidelines outlined in the NIST Special Publication 800-61 Revision 2, which defines a cyber incident as: In addition to cyber incidents, users can report malware, software vulnerabilities, threat indicators, and vulnerabilities in government websites. For reporting cyberattacks on critical infrastructure, users are directed to a different link as required by CIRCIA regulations. When using the portal, users are guided through a step-by-step reporting process, which includes identifying the affected organization, providing a detailed description of the incident, and outlining the technical details of the breach. What Makes CISA’s Portal Unique? While many breach reporting portals exist, CISA’s stands out for several reasons. It is a voluntary, stand-alone government portal available to all entities nationwide. It does not replace any breach reporting processes mandated by federal, state, local, or industry-specific regulations, such as those required by the FTC or FCC. The portal allows users to report incidents on behalf of their organization or as individual users. It also offers the option to set up an account for ongoing communication with CISA, where users can save, update, and share reports. What truly differentiates CISA’s portal is its capability to provide direct assistance in incident response and recovery. This is particularly valuable for small and medium-sized businesses that may lack the resources to effectively handle cyber incidents. Although reporting to CISA is not mandatory, the agency strongly encourages organizations to voluntarily report incidents or suspicious activity. CISA has also developed a guide to help prepare organizations for submitting reports, ensuring they have all necessary details related to the breach and their mitigation efforts. “Any organization experiencing a cyberattack or incident should report it—not only for their benefit but to help the broader community. CISA and our government partners have unique tools to assist with response and recovery, but we need to know about the incident to provide support,” said Jeff Greene, CISA Executive Assistant Director for Cybersecurity, in a statement announcing the portal. The new CISA Services Portal aims to strengthen collaboration, offering a more efficient and supportive environment for incident reporting and response. Salesforce comment: SAN FRANCISCO, Sept. 25, 2015—Salesforce (NYSE: CRM), the Customer Success Platform and world’s #1 CRM company, today issued the following statement on the proposed Cybersecurity Information Sharing Act of 2015 (“CISA”): “At Salesforce, trust is our number one value and nothing is more important to our company than the privacy of our customers’ data,” said Burke Norton, chief legal officer, Salesforce. “Contrary to reports, Salesforce does not support CISA and has never supported CISA.” 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

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Winter 25 Release Ready

Winter 25 Release Ready

As summer wraps up and we return from our vacations, it’s time to shift our focus to winter — at least for those of us working in the Salesforce ecosystem. Salesforce’s three major releases each year mean that fall is the only season we skip, jumping straight from summer into the Winter ‘25 Release. Key dates for the Salesforce Winter ‘25 Release include September 6, October 5, and October 12. However, the exact date of the update depends on your Salesforce instance. To find out when your instance will be updated, visit Salesforce Trust, search by instance name or domain, and click “maintenance” to see your specific schedule. As a certified Salesforce professional, I’m excited about the new features rolling out in the coming weeks. Here’s a preview of some of the most important updates in the Winter ‘25 Release: 1. Inline Editing in the Enhanced User List View One long-awaited update is inline editing for user records. While this feature has existed for most objects, it wasn’t available for the user object. With the Winter ‘25 update, inline editing for users will be available through the enhanced user list view, which can be enabled in Setup. 2. View Object Access from Object Manager This update is sure to streamline workflows, offering a read-only object access summary in Object Manager. This summary provides a clear view of access to each object. Although it’s currently read-only, future releases are expected to enable CRUD edits directly from this summary, further enhancing object management. 3. Centralized Management of User Details User information will now be consolidated in the enhanced user access summary page. Both standard and custom user fields will be aligned with the user details section of the assigned profile page layout, simplifying the process of viewing and editing user details. The summary view will be accessible directly from a user’s record in Setup. 4. Insights into User Permission Assignments The Winter ‘25 update introduces a new “user access summary” feature, which provides insights into the profiles, permission sets, and permission set groups that grant specific permissions to users. This new option in Setup will greatly simplify user management, making it easier to track down permissions that were previously harder to locate. 5. Simplified Public Group Membership Management Managing public groups will be more efficient with the new public group summary page. This update improves performance and makes it easier to handle users, roles, and nested groups. The improved member selection experience will allow us to add or remove up to 100 members at once and edit or delete public groups directly from the summary page. 6. Sales Cloud Go: Discover and Set Up Features Easily Sales Cloud Go, a new feature arriving with the Winter ‘25 Release, will help users discover and enable Sales Cloud features with just a click. Located in Sales Setup, Sales Cloud Go offers screenshots, guided tours, videos, and help topics for each feature. If your account app is activated, you’ll even be able to purchase add-on licenses directly from Sales Cloud Go. 7. Conditional Formatting for Record Fields Dynamic forms are getting a boost with the addition of conditional formatting in the Salesforce Lightning App Builder. This feature will enable admins to apply formatting to fields based on rule sets, helping users quickly identify key information on a record page. These updates reflect Salesforce’s continued commitment to improving user experience and streamlining processes. As we look forward to the Winter ‘25 Release, these new features promise to enhance productivity and simplify many aspects of Salesforce management. 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

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Choose Salesforce for SMS

Choose Salesforce for SMS

Why Integrating SMS with Salesforce Transforms Business Communication Effective communication is crucial in today’s fast-paced business environment. A company’s success often hinges on its ability to interact seamlessly with customers—whether through personalized service, timely updates, or the latest product offerings. Choose Salesforce for SMS. Today’s customers demand a seamless, omnichannel experience that goes beyond traditional communication methods like flyers and emails. They expect real-time, two-way interactions, which is where Salesforce SMS apps come into play. These apps, which integrate smoothly with existing CRM systems, are transforming how businesses engage with their customers. 5 Reasons to Integrate SMS with Salesforce Integrating SMS with Salesforce offers numerous benefits, primarily enhancing customer-facing efficiency and effectiveness. Here are five key advantages: SMS for Salesforce enables businesses to provide immediate customer support. For instance, logistics companies can use SMS to notify customers about delivery statuses or appointment updates in real time. SMS boasts an impressive open rate—over 95% within the first three minutes—making it a highly effective medium for increasing marketing engagement compared to email. You can even couple Salesforce SMS with tools like geofencing to send notifications via SMS when they are in the store. Integrating SMS with Salesforce allows for streamlined automation of processes such as order updates and appointment reminders. This reduces the need for manual intervention, boosts productivity, and frees up resources for more strategic tasks. Automated texts can be scheduled based on customer behavior or sales stages, optimizing workflows and enhancing efficiency. With a response rate of approximately 45%, SMS is highly effective for engaging customers. It facilitates prompt replies due to its immediate nature. Sales and marketing teams can leverage SMS for direct interactions, while retailers can use it to distribute discount codes and drive quick responses. Additionally, SMS is ideal for important notifications, enhancing customer service. By integrating SMS with Salesforce, businesses can tailor their messages to address specific customer needs and preferences. This personalization fosters stronger customer relationships and improves conversion rates. For example, a travel agency can send personalized vacation recommendations, while financial advisors can provide client-specific updates and advice. Salesforce’s integration with SMS allows for robust tracking and analysis of customer interactions and campaign effectiveness. Marketing teams can refine their strategies by reviewing metrics such as open rates, click-through rates, and conversion rates from SMS campaigns. Additionally, customer support teams can evaluate response times and resolution rates to improve service efficiency. How to Implement SMS in Salesforce To send and receive texts via Salesforce, you have several options: Salesforce offers two primary SMS solutions: Mobile Studio and Digital Engagement. For more tailored functionality, you can use Salesforce API or another API provider to develop a custom texting solution. While this offers greater flexibility and avoids extra costs, it involves significant development time and expense. Opting for a Salesforce-native SMS app from the Salesforce AppExchange can be advantageous. These apps, designed specifically for SMS within Salesforce, often offer: These native apps also come with dedicated customer support, making them a cost-effective and efficient choice. Best Practices for SMS Communication While SMS boasts high engagement rates, it’s essential to follow best practices to maintain a positive customer experience: Ensure compliance with data privacy regulations like GDPR and CCPA by securing clear consent from customers before sending SMS. Automate re-opt-in processes to maintain compliance. Send messages during the recipient’s regular business hours to avoid disturbing them at inconvenient times. Stay in touch with your audience regularly but avoid overwhelming them with excessive messages. Provide valuable content to keep engagement high. Use the same number for messaging to help customers recognize your communications and build trust. Respond promptly and courteously to customer replies. Provide clear, detailed responses to inquiries. Acknowledge and reward outstanding customer actions with thoughtful messages or gestures, such as donations to their favorite charities. Even a thank you for your purchase message can contain a surprise such as a coupon or a notification that a free gift is included with their order. Use SMS to highlight important announcements, events, or opportunities, tapping into the fear of missing out to drive engagement. SMS is the perfect omnichannel tool to incorporate into all your Salesforce journeys. Balance promotional content with conversational engagement to avoid appearing pushy and to keep the communication enjoyable for customers. People are much happier to get news they can use rather than advertisements. Encourage further engagement by including clear, actionable steps in your SMS messages, such as signing up for a free trial or using a discount code. A call to action must be designed with smaller screen views in mind. Include an easy way for recipients to unsubscribe from future messages to comply with legal requirements and respect customer preferences. By integrating SMS with Salesforce and adhering to these best practices, businesses can enhance their communication strategies, foster better customer relationships, and drive greater engagement. 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

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Introducing Marketing Cloud Advanced

Introducing Marketing Cloud Advanced

Salesforce has unveiled a series of innovations in its Marketing Cloud, (Introducing Marketing Cloud Advanced) designed to empower businesses with AI-driven tools and enhanced data capabilities to elevate customer engagement. These new features aim to deepen customer relationships, improve team productivity, and boost operational efficiency. Introducing Marketing Cloud Advanced One of the standout innovations is Marketing Cloud Advanced, an upcoming edition that integrates advanced automation and AI. This edition is designed to connect marketing journeys with sales, service, and commerce workflows, offering a more personalized experience across multiple customer touchpoints. Additionally, the introduction of Agentforce for Marketing will bring generative and predictive AI into the marketing realm, helping marketers create comprehensive, end-to-end campaign experiences. Steve Hammond, Executive Vice President and General Manager of Marketing Cloud at Salesforce, commented: “Today’s most successful marketers engage customers on their terms and act as value multipliers across the entire customer experience—whether helping sales or service have more personalized conversations or re-engaging inactive customers. Built on Data Cloud, Marketing Cloud is the only solution that unifies data across every department and moment in the customer lifecycle, powered by Agentforce Agents and automation, driving growth, loyalty, and optimizing ROI.” Agentforce for Marketing introduces several capabilities that streamline marketing processes. Marketers can now plan, launch, and optimize campaigns with ease. Agentforce allows marketers to set campaign goals and brand guidelines, after which the AI generates campaign briefs, identifies target audience segments, and drafts initial emails and landing pages. The system continuously monitors performance and provides data-driven optimization suggestions based on key performance indicators (KPIs). A key addition is Einstein Marketing Intelligence (EMI), which helps marketers manage and optimize cross-channel campaign performance. EMI automates the process of data preparation, enrichment, harmonization, and visualization, enabling marketers to measure campaign effectiveness and make informed decisions to improve return on investment. Furthermore, Salesforce introduced Einstein Personalization, an AI-powered decision engine that delivers tailored customer experiences. This tool allows sales, service, and commerce teams to engage customers in real time based on live interactions and data. Using Flow’s A/B split testing feature, marketers can select dynamic email content for different audience segments and track performance to adjust strategies effectively. Sarah Lukins, General Manager of Digital at Fisher & Paykel Appliances, praised the new functionality: “Salesforce enables us to seamlessly access all of our marketing, commerce, service, sales, and external data in one place and leverage AI for more targeted audience engagement. We can now deliver more relevant and consistent personalized experiences across email, ads, web, social, and service engagements.” The Marketing Cloud Advanced Edition will roll out to customers in North America, Europe, and Latin America, while Agentforce Personalization is expected to become generally available by next summer. Additional releases include expanded Einstein multi-language support and unified SMS conversation capabilities. These innovations are part of Salesforce’s ongoing efforts to equip marketers with unified and actionable data, enhancing the performance of marketing teams and fostering deeper integration across organizations. Through AI and automation, Salesforce is helping businesses deliver more personalized, connected, and seamless customer 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

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Agentforce - AI's New Role in Sales and Service

Agentforce – AI’s New Role in Sales and Service

From Science Fiction to Reality: AI’s Game-Changing Role in Service and Sales AI for service and sales has reached a critical tipping point, driving rapid innovation. At Dreamforce in San Francisco, hosted by Salesforce we explored how Salesforce clients are leveraging CRM, Data Cloud, and AI to extract real business value from their Salesforce investments. In previous years, AI features branded under “Einstein” had been met with skepticism. These features, such as lead scoring, next-best-action suggestions for service agents, and cross-sell/upsell recommendations, often required substantial quality data in the CRM and knowledge base to be effective. However, customer data was frequently unreliable, with duplicate records and missing information, and the Salesforce knowledge base was underused. Building self-service capabilities with chatbots was also challenging, requiring accurate predictions of customer queries and well-structured decision trees. This year’s Dreamforce revealed a transformative shift. The advancements in AI, especially for customer service and sales, have become exceptionally powerful. Companies now need to take notice of Salesforce’s capabilities, which have expanded significantly. Agentforce – AI’s New Role in Sales and Service Some standout Salesforce features include: At Dreamforce, we participated in a workshop where they built an AI agent capable of responding to customer cases using product sheets and company knowledge within 90 minutes. This experience demonstrated how accessible AI solutions have become, no longer requiring developers or LLM experts to set up. The key challenge lies in mapping external data sources to a unified data model in Data Cloud, but once achieved, the potential for customer service and sales is immense. How AI and Data Integrate to Transform Service and Sales Businesses can harness the following integrated components to build a comprehensive solution: Real-World Success and AI Implementation OpenTable shared a successful example of building an AI agent for its app in just two months, using a small team of four. This was a marked improvement from the company’s previous chatbot projects, highlighting the efficiency of the latest AI tools. Most CEOs of large enterprises are exploring AI strategies, whether by developing their own LLMs or using pre-existing models. However, many of these efforts are siloed, and engineering costs are high, leading to clunky transitions between AI and human agents. Tectonic is well-positioned to help our clients quickly deploy AI-powered solutions that integrate seamlessly with their existing CRM and ERP systems. By leveraging AI agents to streamline customer interactions, enhance sales opportunities, and provide smooth handoffs to human agents, businesses can significantly improve customer experiences and drive growth. Tectonic is ready to help businesses achieve similar success with AI-driven innovation. Like Related Posts Salesforce OEM AppExchange Expanding its reach beyond CRM, Salesforce.com has launched a new service called AppExchange OEM Edition, aimed at non-CRM service providers. Read more The Salesforce Story In Marc Benioff’s own words How did salesforce.com grow from a start up in a rented apartment into the world’s Read more Salesforce Jigsaw Salesforce.com, a prominent figure in cloud computing, has finalized a deal to acquire Jigsaw, a wiki-style business contact database, for Read more 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

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Key Sales Statistics and Trends

Sales Statistics and Trends

Key Sales Statistics and Trends Sales professionals often face a rapidly evolving industry, with constant changes to navigate. The key to successfully maneuvering through these shifts is a deep understanding of sales data and trends. Here’s a detailed look at the current state of sales, highlighting both opportunities and challenges. Key Sales Statistics and Trends. Revenue Growth Trends Sales Challenges AI and Data Insights Enablement and Training Employee Experience For more insights and detailed statistics, download the full State of Sales Report from Salesforce. 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

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Dreamforce 24 Insights

Dreamforce 24 Insights

Three Key Insights You Might Have Missed from Dreamforce ’24 In today’s digital-driven world, interconnected systems are commonplace and essential, making platform integration and unified operations critical. As AI becomes more central, technologies like Salesforce Agentforce AI are drawing increased attention. At Dreamforce ’24, automation and AI were the event’s stars, particularly Salesforce’s plans for Agentforce AI. Dreamforce 24 Insights. Here are three key insights from Dreamforce ’24 that you might have missed: 1. Salesforce’s Automation Plans Could Reshape Its Future Salesforce has a solid reputation for business automation, but now, with agentic systems entering the picture, the company is looking at a transformative opportunity. John Furrier of theCUBE noted during Dreamforce, “Salesforce is positioned to use generative AI to simplify complexity and reduce the steps required to get things done.” As Salesforce integrates generative AI, the emphasis on securing and utilizing data becomes paramount. Christophe Bertrand of theCUBE pointed out that many organizations are not fully utilizing their data. The introduction of Agentforce AI, which aims to leverage this untapped potential, could bring automation to new heights and fundamentally transform how businesses operate. 2. Salesforce Agentforce AI Aims to Integrate Seamlessly Into Business Workflows A major focus of Dreamforce was Salesforce’s new AI offering—Agentforce. According to Muralidhar Krishnaprasad, Salesforce’s CTO, this represents the next stage of AI for the company. While earlier efforts focused on predictive AI (Einstein) and generative AI copilots, Agentforce moves toward more autonomous AI agents. “Our platform will be one of the most comprehensive for agent development,” Krishnaprasad explained. He highlighted that Agentforce will allow businesses to deploy AI agents across various functions—advertising, sales, service, and analytics—creating a seamless AI-driven ecosystem within the Salesforce platform. David Schmaier, president and CPO of Salesforce, added that Agentforce will transform customer interactions by integrating AI agents with Salesforce Data Cloud to deliver more personalized and efficient experiences. 3. Strategic Partnerships Are Streamlining Business and Enhancing Customer Solutions At Dreamforce, partnerships played a key role in Salesforce’s strategy for the future. A collaboration between Salesforce and AWS is streamlining procurement for joint customers through AWS Marketplace. This partnership allows companies to optimize their spend management and simplify the purchasing process for Salesforce products. IBM is also leveraging Agentforce to drive new outcomes through watsonx Orchestrate, as Nick Otto, IBM’s head of global strategic partnerships, explained. Automation and orchestration have been focal points for both IBM and Salesforce. Another partnership with Canva showcased AI-driven data autofill capabilities that integrate with Salesforce CRM. This allows sales teams to create personalized presentations at scale, automating workflows and increasing efficiency, as noted by Canva’s Chief Customer Officer, Rob Giglio. These insights from Dreamforce ’24 highlight the growing importance of AI, automation, and strategic partnerships in shaping the future of business operations with Salesforce at the forefront. 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

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